Podd: Augmented Ops

Rethinking Governance, eBR, and the CIO Role from the Ground Up with NextPharma’s Joel Fidalgo

23 april 2025 | 29 min

Hannover Messe Unfiltered: The Real State of AI in Manufacturing

9 april 2025 | 24 min

Preview: Hannover Messe Special

2 april 2025 | 1 min

Operational Excellence at Enterprise Scale with Stanley Black & Decker’s Audrey Van de Castle

19 mars 2025 | 42 min

Digital Twins, Industrial AI, and the Future of Manufacturing with TwinThread's Erik Udstuen

5 mars 2025 | 25 min

Augmenting Craftmanship with Digital Technology at a Luxury Jeweler with Nicolette Naya

19 februari 2025 | 33 min

Scaling Open Source in Manufacturing with FlowFuse’s ZJ van de Weg

5 februari 2025 | 27 min

From Sony to Semiconductors: Digital Transformation in the Asia-Pacific with Vijay Chinnasami

22 januari 2025 | 28 min

Interoperability for Industry 4.0 with HighByte’s John Harrington

8 januari 2025 | 32 min

How AI Agents are Closing the Loop with Composabl’s Kence Anderson

18 december 2024 | 34 min

Bringing Open Source to Industrial Automation with Arduino’s Fabio Violante

4 december 2024 | 30 min

Frontline Operations in NASCAR with RFK Racing's Kevin Kidd

20 november 2024 | 29 min

Inside a Next-Gen Medical Device Manufacturer

7 november 2024 | 31 min

Uniting the Frontline Operations Community at Operations Calling

23 oktober 2024 | 21 min

A New Approach to Pharma Manufacturing with Roche’s Daniele Iacovelli

9 oktober 2024 | 34 min

The Past, Present, and Future of Industrial Software with Rick Bullotta

25 september 2024 | 36 min

Episode 141: Reindustrializing America with Liz Reynolds

1 juli 2024 | 28 min

Episode 140: A Frontline Perspective on Industry 4.0 – Reflections on Season 1 of Augmented Ops

26 juni 2024 | 35 min

Episode 139: How to Architect Your Digital Strategy with Jeff Kramer

12 juni 2024 | 30 min

Episode 138: Democratizing Computer Vision with LandingAI’s Kai Yang

29 maj 2024 | 31 min

Episode 137: AI for the Frontline Engineer with Instrumental’s Anna Shedletsky

15 maj 2024 | 33 min

Episode 136: AI Takes Center Stage at Hannover Messe

1 maj 2024 | 24 min

Episode 135: Bringing Spatial Intelligence to Operations with Zerokey's Matt Lowe

10 april 2024 | 29 min

Episode 134: Building Industrial Architectures with MQTT with HiveMQ’s Dominik Obermaier

27 mars 2024 | 37 min

Episode 133: Rethinking Our Approach to AI with Dr. Jay Lee

13 mars 2024 | 31 min

Episode 132: Open Source Software for Manufacturing with UMH's Alex Krüger

28 februari 2024 | 27 min

Episode 131: MQTT, Unified Namespace, and The New Industrial Data Stack with Litmus’s Vatsal Shah

14 februari 2024 | 26 min

Episode 130: Democratization, Gen AI, and the Future of Industrial Analytics with Seeq’s Lisa Graham

31 januari 2024 | 30 min

Episode 129: AI and the Human Element in Industry 4.0 with Jeff Winter

17 januari 2024 | 36 min

Episode 128: From Tailor to Technologist: A Digital Transformation Journey with Joachim Hensch

3 januari 2024 | 24 min

Episode 127: Venture Capital's Role in Digital Transformation with Lior Susan

6 december 2023 | 26 min

Episode 126: Transforming Manufacturers’ Organizational Strategy with Dr. Jörg Gnamm

15 november 2023 | 23 min

Episode 125: Rethinking Quality Control for Pharmaceuticals with Mark Buswell

1 november 2023 | 23 min

Episode 124: Industrial Data Interoperability with Erich Barnstedt

18 oktober 2023 | 20 min

Episode 123: Building a Manufacturing Software Marketplace with Diego Tamburini

4 oktober 2023 | 22 min

Episode 122: Fixing the Failures of Industry 4.0 with Antonio Padovano

20 september 2023 | 23 min

Episode 121: Looking Back and Looking Ahead

23 augusti 2023 | 41 min

Episode 120: Digital Manufacturing in Turkey and Beyond with Efe Erdem

9 augusti 2023 | 30 min

Episode 119: Industrial Design with Kimberly Andersson

26 juli 2023 | 37 min

Episode 118: Digital Manufacturing in the Cloud with Jon Hirschtick (Rebroadcast)

12 juli 2023 | 71 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

The topic is: Digital Manufacturing in the Cloud. Our guest is Jon Hirschtick, Head of SaaS, Onshape and Atlas Platform, PTC.

In this conversation, we talk about the story of SolidWorks, using agile methods, listening to the market, charting the evolution of CAD into SaaS, and its emerging and future iterations in the open source cloud and beyond After listening to this episode, check out PTC, Solidworks, as well as Jon Hirschtick's social media profiles:

PTC (@ptc): https://www.ptc.com/en
Solidworks (@solidworks): https://www.solidworks.com/
Jon Hirschtick (@jhirschtick): https://www.linkedin.com/in/jonhirschtick/

Trond's takeaway: Digital manufacturing is moving to the cloud and that means a whole lot more than office software moving to the cloud. In fact, establishing a real-time digital thread, through next generation low-code and no-code systems, will reshape industry. The notion of factory production, distributed teams, product development, will all evolve significantly, and will enable personalization across industry and across any and eventually all of manufactured goods. The ramifications will be huge, but they won't automatically happen tomorrow, and the benefits will spread unevenly depending on who--be it corporations, nations, startups, or small- and medium enterprises--grabs the gauntlet first.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 43, Digitized Supply Chain, episode 24, Emerging Interfaces for Human Augmentation, or episode 21, The Future of Digital in Manufacturing.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn.

Special Guest: Jon Hirschtick.

Episode 117: Manufacturing with Jessica Yen

28 juni 2023 | 39 min

Episode 116: The Market of Marketplaces with Jason Ray

14 juni 2023 | 33 min

Episode 115: Bridging the Physical-Digital Divide in Industrial Tech with Rony Kubat

31 maj 2023 | 18 min

In episode 115 of the podcast, the topic is: Bridging the Physical-Digital Divide in Industrial Tech. Our guest is Rony Kubat (@kubat), CTO and co-founder, Tulip

In this conversation, we talk about the complexity of the shop floor and programming a physical-digital environment. What does Digital Lean mean to you? What is augmentation? What's next in industrial tech?

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.

Trond's takeaway:
The physical-digital environment is no joke. When you speak with a real technologist who not only has imagined what the future would look like, but who is involved in building it, integrating software and hardware on the factory floor, you realize how difficult it will be to transform industrial work. It is not just about industrial tech, it is about people. It is not just about neat software, or fancy hardware. It all has to work together. And, more importantly, it has to fit into the overall context of what people are already doing.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 44, No-code for IoT in the Cloud, episode 47, Industrial Machine Learning or episode 29, The Automated Microfactory.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn.

Special Guest: Rony Kubat.

Episode 114: The Industry 4.0 Journey with Scott Phillips

17 maj 2023 | 35 min

Episode 113: The Business Model of Lean with Jim Huntzinger

3 maj 2023 | 34 min

Episode 112: Humans, Robots, and the Future of Manufacturing with Anna Waldman-Brown

19 april 2023 | 40 min

Episode 111: Operator 4.0 with David Romero

5 april 2023 | 44 min

Episode 110: Executing on Manufacturing Technology with Jane Arnold

22 mars 2023 | 34 min

Episode 109: Augmenting Workers With Wearables with Andrew Chrostowski

1 mars 2023 | 42 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Augmenting Workers With Wearables." And our guest is Andrew Chrostowski, Chairman and CEO of RealWear. In this conversation, we talk about the brief history of industrial wearables, the state of play, the functionality, current approaches and deployments, use cases, the timelines, and the future.

If you like this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 92: Emerging Interfaces for Human Augmentation. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Industrial wearables have come a long way. There is a big need for assisted reality in many workforce scenarios across industry. There are now companies taking good products to market that are rugged enough, simple enough, and advanced enough to make work simpler for industrial workers. On the other hand, we are far away from the kind of untethered multiverse that many imagine in the future, one step at a time.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind the new era of industrial operations where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Augmenting Workers With Wearables. And our guest is Andrew Chrostowski, Chairman and CEO of RealWear. In this conversation, we talk about the brief history of industrial wearables, the state of play, the functionality, current approaches and deployments, use cases, the timelines, and the future.

Augmented is a podcast for industrial leaders, for process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Andrew, welcome to the show. How are you?

ANDREW: Hi, Trond. Great to be here. I'm doing great.

TROND: You know, you are a poster child entrepreneur engineer, Oregon State, University of Southern California. You are actually an expert on the future of work. There are so many people that say they talk about the future of work. You are implementing and, selling, and evangelizing a true future of work product, not just a story.

We're going to be talking about augmented, assisted all kinds of reality and collaboration, Andrew, because that's, I guess, what it's all about. And you lead the industrial wearable company RealWear. But first, I want to get to the fact that you're a certified firefighter. Now, how does that fit into this?

ANDREW: That's really a great question. And one of the things that's been passionate for me from the beginning is being close to the customer. It was true when I was an Air Force officer designing for systems that would support our warfighters and putting myself in their situations in life and death. Certainly, I think about it in terms of customers, and we were dealing with other lines of business and trying to understand the customers' perspective, and especially the frontline workers that create those products.

And when I took over the Scott Safety business when I was part of Tyco, their particular market was firefighters. They were the leading provider of air tanks, cylinders, respirators, what we call SCBAs, self-contained breathing apparatus for firefighters. Now, I know a lot of things about a lot of areas of technology. But I didn't know anything about firefighting.

And so when I took over that business, the first thing I did was go to Texas A&M and actually get trained and certified as an interior firefighter. So I actually put on all the bunker gear, timed donning just like you do when you're in the fire station, fought real fires that were built, and to understand really the challenges they faced. And I came out of that training really having a greater appreciation for just how challenging that work is.

And I know it's shocking to your listeners, but everything we ever see on TV and movies about firefighting is wrong. Basically, firefighting, besides being terrifying, and difficult, and dangerous, is basically blind. You're in the smoke. You're in the dark. And my background in the Air Force thermal imaging systems and multispectral systems came back to me. And I said, "You know what we need to do is give predator vision to firefighters and give them the chance to see the unseen in the dark." And so, coming out of that training, I initiated an in-mass thermal imaging system for firefighters that went to the market about 14 months later at Scott site.

TROND: Wow, that's some real background there. I'd like to start with that story because it reminds me that what we're about to talk about here, you know, wearables, it's not a joke. These are, you know, in industrial environments, these are not optional technologies once they really, really start working. And you can sort of say that they're first-line technologies. They better work every time. So this is not a case where you could kind of, well, you know, let's install another version and restart and whatnot.

These are eventually going to be hopefully systems that the modern industrial worker really starts to trust to perform their job efficiently. Before we get into the nitty-gritty of all of the different things that RealWear is trying to do, I wanted to just ask you a basic question, what is assisted reality? It's a curious phrase. It's like, why does reality need assistance? [laughs] You know, where does that even come from?

ANDREW: You can deny reality, but you can't deny the effects of denying reality. When we talk about assisted reality, it's a point on the spectrum what we call XR, the extended reality. It starts with reality and ends when that virtual reality, the fully immersive digital environment that we experience and what we talk about a lot in the metaverse. Then coming from reality forward, you have assisted reality, which is a reality-first, digital-second environment, which is what we focus on. It is the idea that this is the technology available now that allows a worker to be productive and work safely in a real-world environment.

When you get into augmented reality, which is something that we think of when we think of products like HoloLens and other similar types of products, that's where this digital environment begins to overlay the actual environment. It imposes a cognitive load on the brain so that you're now having to focus on things that aren't really there while there are things that are really around you that could hurt you.

This is great when you're in a safe environment, in a classroom, in a design area, when you're collaborating in the office to be able to immerse yourselves in these three-dimensional digital objects. It's much different when you're walking on the deck of an oil rig or you're potentially working around a cobot that can hurt you when your attention is distracted.

And then we have sort of that virtual reality game that we started with in the metaverse where people are now kind of transposing themselves into a fully digital atmosphere. We at RealWear have focused on making a difference for the future of work and focusing on those 2 billion frontline workers who could work more safely and more productively if they were connected. And it makes perfect sense to us. If we learned anything from the COVID lockdowns, we learned that this idea of working from anywhere, the idea of the office worker working from home, working from the coffee shop, all of this now has become just a given. We know that we need these digital tools to collaborate remotely.

What we only have begun to just crack the code on is that there are, again, 2 billion people working with their hands on the front line who could work more productively and more safely if they were connected workers, if they had access to information, if they had access to collaborating in a hands-free way with their counterparts across the world. And so RealWear, our focus is this mission of engaging, empowering, and elevating the performance of those frontline workers by giving them an assisted reality solution that is extremely low friction and easy to use.

TROND: I like the distinction there. Even though this podcast is called augmented, I like the distinction between AR and assisted reality. Because there's really, I guess, you can see it more clearly in the consumer space where it sounds so fascinating to enter these virtual worlds. But in industry, the virtual is really subservient and needs to be subservient to the very reality. So I guess assisting reality is the point here. It's not the endpoint that is necessarily the virtual.

You're using the technologies, if I understand it, to strengthen the ability to survive and be very, very efficient in reality as opposed to entering some sort of virtual space where you are simulating more. You're talking about critical applications in the physical industrial reality, so that's now clear to me. Having said that, this is not easy to do, is it, Andrew?

ANDREW: No. I mean, there's a lot that comes into this idea of making technology that's human-centric. And all the things you were just talking about really bring us back to this idea that this kind of assisted reality solution is about helping the human being at that nexus of control operate more safely and effectively in a variety of environmental conditions. It is really important that we think about the technology serving the person and not so much technology that is imposing itself on people, which is oftentimes what we see as we try to roll out different kinds of technical solutions.

The folks who are doing work with their hands who are daily exposing themselves to risk have a very low tolerance for things that waste their time, are difficult to use, or distract them from reality. And so all of those things are factors we took into account as we developed this first head-mounted tablet computer that now is in the market as the Navigator 500.

TROND: Andrew, can you tell me a little bit about the history and evolution of these kinds of technologies? Because there is so much hype out there. And you did a pristine job as to making these concepts fairly distinct. But how long has there even been an industrial product? I guess a lot of us remember the first Google Glass, but partly what we remember is the hype in the consumer market, which then kind of fell flat.

And then they reemerged, I guess, as sort of a light competitor to you guys and then has since somewhat disappeared. But, anyway, there are a lot of attempts in the near history of technology to do this kind of thing. I mean, it corresponds pretty neatly to various sci-fi paradigms as well. But what are the real prototypes that go into the inspiration for the technology as you have it today?

ANDREW: Well, I'm glad you mentioned science fiction because really the way I would start this, otherwise, is, say, a long time ago, in a galaxy far, far away, we had Star Wars. And if you think back to that show, science fiction has been part of how people work in modeling, how people work for decades and more, from Jules Verne all the way through to Star Trek and the like.

And so when you think about these technologies, you go back to processes and technologies that support humans collaborating. And back in Star Wars, we had a character called Boba Fett who famously has, and now you see it in the Mandalorian, a little device that comes down from his helmet in front of his eyes and acts as a rangefinder and computer screen. Actually, one of the founding engineers that were part of the design of the first RealWear device came out of designing Boba Fett's helmet.

And so there is really a connection there about how people have imagined people work and how people actually work. And the actual part really started with Dr. Chris Parkinson and spending over ten years working on what is the right ergonomics. What's the right way to shift the balance, the weight, the size, and manner of the display? How do you control the windows and amount of information displayed? And how do you suppress the outside noise so that you can have a voice control system that makes it truly hands-free?

So it began with this idea of all great things start with a spark of imagination. And then bringing that to a very practical point of view of solving the problem of being able to give someone information and collaboration tools hands-free in an environment where they can work safely but connect to all the value and information that's out there that we enjoy every day working as office knowledge workers with the internet.

TROND: Andrew, what are some of the technical challenges you had to overcome? I can imagine; first, you have to design something that is probably bulkier than you wanted, and then eventually reducing its size is one thing. But I can imagine the algorithms apply to, I mean, there's imaging here, and there's a bunch of design techniques to make this work. And then you said ruggedized, right? I mean, this stuff cannot break.

ANDREW: That's right.

TROND: What are the kinds of things that went into and is going into your next-generation products?

ANDREW: Well, I think that's a great question. And, of course, as new products evolve and we build on the learnings we've had from having one of the largest install base of wearable computers in the world, we can sit there and say, look, it starts with ruggedization. Because, frankly, these frontline workers, when they're wearing these devices on their hard hat, at the end of the day, that hard hat gets tossed into the back of the truck. It gets tossed in the van. It gets dropped on the ground, or in the mud, or out in the rain.

So we knew right away that we had to build a device that was able to hold up to that, things that a lot of similar kinds of products that are out there just can't hold up to. So we started with this idea that it had to be extremely rugged. It had to be lightweight enough to wear all day. And our first version did that very well. The Navigator 500 has come now just as rugged but now 30% lighter. So we've learned how to make that ruggedness, even in a lighter form factor.

You have to trade-off on how you see that display in bright sunlight, in dim settings. You have to think about how you operate in a noisy environment. So you can imagine if you're trying to use a voice-driven assistant, whether it's on your phone or a little microphone device in your home, you use a wake-up word, and then you have to try to talk clearly. And if you don't talk clearly, you end up having it not do what you want. That's very frustrating for a frontline worker, and it's just downright distracting and dangerous at times.

So we chose to have a system and voice control that does not require a wake-up word. It's always listening. And it listens in context to what's on the screen. Literally, what we say is you say what you see. And that's about all the training you need to learn how to use the Navigator 500 effectively. And because it's so easy and intuitive, people get used to it quickly. And they go gravitate towards how it's making their work easier to get to, how it's easy to launch a collaborative meeting in any number of key applications, whether it's Microsoft Teams, Cisco, Webex on demand, whether it's Zoom, whether it's TeamViewer, any number of other partners that we have in terms of the types of collaborations.

TROND: Well, I want to get into some of the use cases in a second, but just briefly, so you were founded as a company in 2016. And you're now, I guess, 140-some employees. I mean, it's fairly recent. This is not something that you've been doing since the '70s here. But on the other hand, this is also very challenging. It's not like you produce something, and all of industry immediately buys into it.

So I just wanted to address that, that this particular market, even though it's always been there as this potential, there doesn't seem to have been kind of a killer application like there is in some other hardware markets. And maybe you're thinking you will be one. But I just wanted you to address this issue. Recently, the IBC the analysts came out with this prediction that they're forecasting a decline actually year over year in units sold. And they're also saying a lot of new vendors are going to come into this market, but the market is not very mature right now. What do you say to that kind of an argument?

ANDREW: There's a lot to unpack there, so forgive me if I miss some of the things you brought up there. But I'd start really with RealWear and how we develop this. The Navigator 500, the product we have on the market today, is highly modular, lightweight, does all these types of things, and that's really the eighth generation. Even though we only have been around since 2016, the thinking behind this form factor has gone on for eight generations.

So we've got a lot more maturity than some of the other folks who might be thinking about entering this market. We've also focused entirely from the beginning on that industrial frontline worker. It's a niche of over 2 billion people but very different from the consumer aspect and what people have gotten used to in terms of dealing with a piece of glass that they might carry in their pocket all day long.

We think that A, we've kind of created this assisted reality space. We've won in so many of these industrial cases because of the way we make work safer and more productive. We've now passed applications where we've had installations over 3,500 units with a single use. We've got, in multiple cases, over 1,000 deployments. We've got 75-80 deployments of over 100 units. So we really have broken through.

And what we see is whenever we talk about the assisted reality market, or we can talk more broadly, we usually only see data on augmented reality. They put all these smart glasses in sort of a category. And we're really only a portion of what they count as smart glasses. So when they start saying there's downward pressure on that market or it's not growing as fast, it goes back to something I just read in a book about builders in terms of how innovation happens.

And the author described augmented reality as a solution looking for a problem. We came at it with a particular problem we were solving, and that's I think the big difference between us and a lot of how people have come into this space. We knew exactly the problem we're trying to solve. We knew that we wanted to make the human the central part of that control Nexus. And we knew that we wanted to be in a space where others would find it difficult to succeed.

And so, as we've been successful here and as we continue to grow and expand these deployments and getting into larger and larger deployments, we know that others will kind of begin to look into this space and try to compete. But most of them are bridging over from that consumer side where a lot of the fundamental design trade-offs they've made do not well-support all shift use in a ruggedized environment and with the ease of use that we've designed into our products.

TROND: Andrew, that makes a lot of sense to me.

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TROND: Let's talk about some of these bigger deployments. So I don't know if you can mention names, but the biggest one, I'm assuming, is in the automotive industry because they are at the forefront of a lot of automation technology. So I'm just going to make that assumption. Tell me a little bit about that deployment. What is it all about? What are they using it for? What can you tell me about what they're using it for?

ANDREW: Thank you, Trond. And I'm super excited about our success in the automotive sector, not only just because of what it represents but because, as an industry, it's so central to economies across the globe. And when we think about the transformation of that industry going to electrification, that change creates opportunity for us as well. So today, with our partner TeamViewer we're in over 3,500 dealerships. Virtually every dealership in America now has a RealWear product in it.

For those technicians, when they're dealing with a particularly tough problem, they're able to put on our device as simple as what I'm doing here, just putting on their Navigator, their HMT-1. And they can call and connect with a technical assistance center in Detroit and have a first-person conversation with an expert who can help walk them through that repair, whether it's pushing diagrams to them to, illustrating over the video that they're getting but helping them solve that problem faster.

And why is this so significant? Well, because from the customer point of view, you're happy that your problem is being solved quicker. You've got your car back. The dealer is happy because now they've been able to invoice the customer or invoice for it in this particular case to get their warranty repair dollars back. And Ford is happy because now they've got a happy customer, and they've got a better reputation and user experience. So it's a very positively reinforced system.

And so when you think about that application alone of just being able to solve problems of existing cars, now think about the introduction of all of these electric vehicles to dealers, not only with Ford but anybody else you can think of is moving into electrification. There are a lot of technicians who know how to work on a gasoline engine, but very few who maybe know how to really solve those electricals. So this is a way that these dealers can bridge the skills gap that exists between what they have and what they need to be able to do in the near future.

And that skills gap, by the way, is recognized not just in the automotive industry, but you and I experience it every day when we deal with restaurant industry, service industries, trucking. You think about any kind of skilled labor situation; we know demographically we've got a big gap. And that's going to be persistent for decades. And so a tool, a knowledge transfer platform that lets people move up that learning curve more rapidly to do more meaningful work, to be more self-actualized as they do that not only helps people but it helps industry serve their customers. And so we see ourselves really at the forefront of transforming work as we know it.

TROND: I'm so glad you went to the skills, and it's so exciting that that's the main application right now because I think there's a lot of discussion, obviously, in the industry across sectors about the skills gap; they say, right? That the gap...we have to train people, or they have to go to school. They have to learn. It's an endless complexity.

But, I mean, you're sort of saying the opposite. You're sort of saying cancel the training, put the headset on. Some of these things, very advanced training, very advanced advice, real-time support, can happen without going aside, looking at a computer, calling someone up, talking to you, you know, see you next week with your car. And then, meanwhile, what you're doing is scratching your head for a while, trying to figure out what's wrong.

But you're saying this creates a much more dynamic scenario both for delivering the service and actually for the human worker who's trying to deliver some sort of service here and is plugged into an information ecosystem. I'm just wondering, is that a very, very typical use case? And do you foresee that that is the use case for assisted reality? Or are there wildly different use cases just depending on, I mean, pick another industry. I was just imagining the medical industry, famously remote surgery, or whatever it is. Some sort of assistance during surgery is obviously the big use case. I could imagine that there's something to be done here also with RealWear.

ANDREW: Yeah, I mean, this is such an exciting area and topic to talk about, education, how people are educated, how that education plays to their employment and their employability, and how they add value and have careers. And we all have talked about whether university work is preparing people for the kinds of careers there are today or whether or not we need to be considering other kinds of applications, going direct to coding or whatever else.

So when you talk about frontline workers, it's absolutely a matter of specific knowledge. It's not just general knowledge that matters. It's very specific things that can happen. And so by connecting people to experts, you do two things: you get the job done right away, but you also mature that worker because they learn from those experiences. And they can use our device to actually, while they're doing the work, film it. It can be curated and then used as training videos for the next generation of work that goes with it. So I think that alone is really exciting.

There are so many use cases, though, beyond this, remote experts see what I see that we've been talking about. That's really...I'd say the predominant deployment today that people think about is how do I collaborate remotely on the front line? And that's super valuable. But what becomes even more interesting is when that device becomes a solution for how you do your daily work.

As an example, if you're a heavy engine manufacturer and you have an end-of-line inspection, and that inspector is using a clipboard and a checklist to look at how the engine is functioning, imagine replacing that. For one of our particular customers, that takes about 30 minutes. When they implemented workflow using hands-free Navigator, they were able to reduce that time to about 12 minutes because now the person is not wasting time going back and forth to a clipboard, or to a table, or writing things down.

They're absolutely hands-free, immersed in the work, being presented the next inspection point in their display, being able to photograph it, work through it, look at a comparison, document it. And the important thing is not just that they're doing it faster; they're finding three times as many defects because they're not distracted. We know there's no such thing as actually dual processing as human beings.

If we think that we can listen to a Zoom call and do emails, we're doing neither very well. We know that we're just quickly switching. And that's the same thing that a lot of frontline workers experience. When you make it immersive and hands-free with workflow, now you begin to expand the value that this technology begins to support so much greater. As we move along, the implementations and the deployments are going to move from sort of this collaboration centric to workflow centric to then being able to be with our partner, IBM.

IBM has actually created something they call Inspector Wearable, where they're giving a superpower inspection to an operator who might be standing at the end of an assembly line watching a car roll by. It stops in front of them. The camera knows, because of machine learning with Watson up in the cloud, that, hey, this is what a good wheel should look like and immediately highlights the operator with a telestration that's the wrong nut. There's a scratch on this rim or whatever defect we might be talking about.

So then you start actually using these technologies that are inherent with the system to be able to augment the capabilities of these workers. And that starts to get really exciting. I'll add one of the points to that is in Q4, we're going to be introducing a thermal imaging camera that can easily be just snapped on on the part of our modular solution for Navigator to be able to then snap on a thermal imaging camera and give that person predator vision to be able to see if they're walking around their plant.

They can see that an electrical panel is overheating or that a motor is hot, or they can use it in any of the hundreds of thermography industrial programs that people use today. So I think part of that transition goes from just being collaboration to how we work and do workflows to actually augmenting the capabilities of the folks who are wearing these wearable computers.

TROND: Yeah, and that's so interesting. And, I guess, correct me if I'm wrong, but that's where it ties into not only IBM but a bunch of your other software partners too where Tulip being one of them, where now that you're providing a device, it actually is the end client that can put that device to use in their own scenarios.

And they can build, I guess, apps around it and find their own use cases that may not be the ones that are super apparent to any of those who deliver it, whether it is you delivering the hardware, IBM, you know, delivering perhaps the machine learning capabilities or some other knowledge, or it is Tulip delivering kind of a frontline software platform that's adaptable. It is actually the end client that sits there and knows exactly how they want to explore it, and then in a second iteration, change that around. Or am I getting this ecosystem wrong here?

ANDREW: No, I think you're onto something there very powerful, Trond. And there are three specific dots we have to connect when we think about a sustainable solution that can be deployed broad-spread across an industrial base, and the first one is the device. The device has to be right. It has to work for the user. It has to meet the requirements of the environmental conditions they're operating in. And so the device is critical. And that's really where RealWear started our journey with that focus on the user and the user experience with our device.

But the next step is really the data that comes with it. That's that part where it's both accessing data and creating data through applications that they use to feed the data lakes above and to feed back into this IoT world where there's information coming up from our equipment and being fed back to us that we can take action on.

And then, ultimately, we have to connect to systems of record. And this is where Tulip, for instance, one of our partners, plays such an important role. It's that connection between all of these things that talk together, the device, the data, and these decision-making systems of record, that now when they talk and connect, it's a very sticky situation. Now you've created more than just a point solution. You've created a system solution where you've changed the way people work, and you reduce friction in interacting with those systems. And I think that that's a real clear case.

I'll give an example that RealWear did in a very simple way. We recently acquired a small company called Genba AI. Their whole purpose in life was to be able to take a CMMS system, which is done for maintenance purposes, and working with eMaint, which is a division of Fortive, and be able to then say, "We can take that currently operating device that requires a worker to print out a work order, go do something, and then put it back into a computer, we can now do that with voice only."

So, again, you take friction out of that interaction and allow them to do things easier but with the systems of record. And so that's why I get so excited about partners like Tulip that are making and connecting the dots between all of these disparate systems that we find in fourth-generation industrial complexes and making them work together seamlessly to give information to make better decisions by the folks who manage that work.

TROND: This makes me think of something that I promise we'll get back to in a second talking about the industrial metaverse, which I think is far more interesting than the consumer metaverse. And we'll get to that because you were starting with this whole ecosystem that starts to develop now. But before we get there, I just wanted you to comment a little bit on COVID, COVID-19. Massive experience; no one is untouched by this. And there clearly was a future of work dimension to it. And people have made a lot out of that and prognosticate that we will never show up in the office again, or hybrid is here forever. What did COVID do to RealWear?

ANDREW: Well, you know, it's an interesting perspective. I've been with RealWear in one capacity or another since almost the beginning, starting off as a Strategic Advisor and Chairman of the Advisory Board to, stepping in as the COO during the series A, and ultimately becoming the CEO and Chairman of the board in 2020 just as COVID was happening.

So a lot of that immediate experience of RealWear was at a time when the whole world was starting to shut down and realize that we had to work differently. So I literally had one meeting with my direct staff as the new CEO before Washington State was shut down. And all the rest of the year was done via remote work. So it's not a dissimilar story to what a lot of people went through in recognizing that, hey, what used to be done in the office and was deemed important to be done in the office had to now be done elsewhere.

And we came quickly with this adoption of digital tools that supported this digital transformation. And what it really did was act as a catalyst because before, you could have a conversation about the value of remote collaboration software, laptop to laptop, and that sort of thing, but nobody was thinking about the front line as much. That was a really tall connection for RealWear to make. We'd go in and talk about the value of a hands-free remote connected worker.

But when you suddenly had millions of displaced workers all contributing, in some cases with productivity increasing, it now said, hey, by the way, do you want to take this great hybrid environment you just created, and do you want to extend it to those important people who don't get to stay home, who don't get to dodge the risk of being exposed to COVID, who have to go out and serve the public or serve your customers?

And now, if we talk about giving those people connectivity and extending that with technology that exists today using familiar platforms...RealWear runs on an Android 11 platform. That means imaginations are limitation, not technology. All those solutions we're talking about can be done in an Android environment, can be imported very quickly, and provide a solution for those users. And so it acted as a catalyst to say that remote experts at smart glasses, as it were, were here, and it was now, and this technology was ready. And the deployments took off.

It probably shortened our deployment cycle. Our sales cycle probably contracted by 70% during COVID as people began to realize this is how we can get work done. This is how we can continue to serve our customers. And so it was a huge change, not only in terms of the demands that we were able to meet thanks to the great teamwork of our whole RealWear ecosystem and supply chain partners, but it also made a difference because it changed the thought processes of leaders who now realized that creating a connected worker not only was feasible, that it had a real, recognizable ROI to it.

TROND: Andrew, you're really speaking to me here because eons ago, in my Ph.D., I was working on this very visionary idea back in 1999, the early internet heydays. Again, the future of work people and tech companies were saying, "We are soon unleashing the situation where no one has to come into the office. We will sit all separately on these islands and work together."

So I would say I guess what has happened now is there's a greater awareness of the need for hybrid solutions meaning some people are physically there, others are not. But the powerful thing that you are enabling and demonstrating visually and physically is that remote is one thing and that it remains challenging, but it can now, in greater extent, be done. Physical presence is still really, really powerful.

But what's truly powerful is the combination of which. It is the combination of physically being there and being amplified or assisted, or eventually perhaps in a fruitful way augmented but without losing touch with reality if it can be done safely. That's really the power. So there's something really interesting about that because you can talk about it all you want. You can say, well, with all the technology in the world, you know, maybe we don't want to meet each other anymore. Yeah, fine.

But there's a powerful argument there that says, well if you combine the world's biggest computer, the human being, with some secondary computers, you know, AIs and RealWears and other things that have other comparative advantages, the combination of that in a factory floor setting or perhaps in other types of knowledge work is really, really hard to beat, especially if you can get it working in a team setting.

I guess as you were thinking more about this as a futuristic solution, Andrew, what kind of changes does this type of technology do to teamwork? Because we've been speaking about the simple, remote expert assistance, which is sort of like one expert calling up another expert at headquarters. And then, you move into workflow, which is powerful product workflow in industry. But what about the group collaboration possible with this kind of thing? Have you seen any scenarios where multiple of these headsets are being used contemporaneously?

ANDREW: Yeah, I mean, I think there's the application of not only people using them broadly in doing their work but also then being connected to a broad number of users. There's a great video that Microsoft put out when they built Microsoft Teams to run specifically on our RealWear platform. And in it, we talk about a plant where, you know, Honeywell was certifying a very large deployment technology in a plant that normally would take 40 workers to go to this facility and physically sign off all the things that need to be done for this large automation system.

But using Microsoft Teams and RealWear devices, Honeywell was able to do that completely remotely. They were able to have the folks who were on site wearing the devices going through. And all of these people who would travel to it are now wherever they happen to be, in the office, at home, somewhere else, being able to see what was happening in the factory and sign off and validate the work remotely. So it's like this world where we've taken away the borders, these artificial borders between the office, not the office, and then the front line.

And I think that the biggest thing that we can take away from this conversation today, Trond, is that we all probably accept that some form of hybrid work is here to stay with office workers. We've just proven over the last two years that you can work extremely productively as a remote team. And we've also validated there are times when we just got to come together from a human point of view to accomplish even more in terms of some of the cultural and emotional intelligence and teaming things that happen.

But what we've also learned is that those frontline workers don't have the luxury of being somewhere other than where the value is being created on the manufacturing line, up on that cell phone tower, or in the street laying asphalt. They all have a job to do, and they have to do it in their presence. And so when we then connect those people and give them access to all of the information that we as connected workers in a hybrid environment accept and the collaboration, we find that that is a place that really brings the dignity of that frontline work up.

It inherently makes them more engaged with their customer, with the job they're doing, with their peers that they can now connect to so seamlessly, and, frankly, with the company. So I think that there's a change here that's happening that's going to be about the right degree of connectivity for the job. And we'll do more of what matters based on the work that has to be accomplished.

And we're just not at a place yet where robots are going to replace carbon-based computing systems that are self-replicating. That's the way NASA described people back, I think, in the '60s is a general-purpose computer that's carbon-based and self-replicating. And really, that's going to be with us for a long time.

And the dignity of those people doing valuable work and helping focus on how do we make them safer and more productive in these very challenging environments? That's changing the future of work. And it's aligning more closely with this idea of, hey, being connected makes us more effective as a company, as a tribe, as a nation, whatever it is. Connectivity becomes extremely valuable.

TROND: It's a big trend. And it's about time there's some justice to it. I mean, you speak with passion about this. It's almost unbelievable to me, and it should be [laughs] unbelievable to a lot of people, that we've invested billions of dollars in office software, in kind of automation for efficiency's sake. But we haven't, until this point almost, invested, certainly not the same amount of dollars and euros and yen, in human-centric technologies that are augmenting people at the same time.

Because there's nothing wrong with these other technologies or if they're benefiting office workers, but as you point out, billions of workers could be enabled, knowledge workers. They just need somewhat different tools, and they're harder to make. This is not like making a desktop software program. These things have to work in a real rugged context.

Andrew, thank you so much for enlightening me on the challenges and the exciting not future anymore. Andrew, it's the exciting presence of this technology in the industrial workplace, and what that bodes for the future when I guess, people see the picture and are willing to truly roll this out to every frontline worker who needs this kind of amplification.

ANDREW: Well, Trond, thank you so much for having me. And I think when your listeners think and hear about AI, I know the first thing that crosses their mind is going to be this artificial intelligence, the compute power that's being built into the cloud to solve all these technical problems. But I'd like them to also begin to think about that as augmented intelligence, the way human-centric technology can make those workers better able to do the work that has to be done by people. And we're so excited to be able to talk about this.

Thank you for the invitation to explore this topic. I really appreciate the chance to share some of the things that RealWear's done in this regard. And I'd love to come back next time and expand our conversation.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Augmenting Workers With Wearables. Our guest was Andrew Chrostowski, Chairman and CEO of RealWear. In this conversation, we talked about industrial wearables now and in the future.

My takeaway is that industrial wearables have come a long way. There is a big need for assisted reality in many workforce scenarios across industry. There are now companies taking good products to market that are rugged enough, simple enough, and advanced enough to make work simpler for industrial workers. On the other hand, we are far away from the kind of untethered multiverse that many imagine in the future, one step at a time.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 92: Emerging Interfaces for Human Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and do let us know if you do so.

The Augmented Podcast is created in association with Tulip, the frontline operation platform connecting people, machines, devices, and systems in a physical location. Tulip is democratizing technology but also, importantly, empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where industrial tech is heading. You can find us on social media; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Episode 108: Lean Operations with John Carrier

15 februari 2023 | 33 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Lean Operations." Our guest is John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talk about the people dynamics that block efficiency in industrial organizations.

If you like this show, subscribe at augmentedpodcast.co. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

The core innovative potential in most organizations remains its people. The people dynamics that block efficiency can be addressed once you know what they are. But there is a hidden factory underneath the factory, which you cannot observe unless you spend time on the floor. And only with this understanding will tech investment and implementation really work. Stabilizing a factory is about simplifying things. That's not always what technology does, although it has the potential if implemented the right way.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. And our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Lean Operations. Our guest is John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talk about the people dynamics that block efficiency in industrial organizations.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

John, welcome to the show. How are you?

JOHN: Trond, I'm great. And thank you for having me today.

TROND: So we're going to talk about lean operations, which is very different from a lot of things that people imagine around factories. John, you're an engineer, right?

JOHN: I am an engineer, a control engineer by training.

TROND: I saw Michigan in there, your way to MIT and chemical engineering, especially focused on systems dynamics and control. And you also got yourself an MBA. So you have a dual, if not a three-part, perspective on this problem. But tell me a little bit about your background. I've encountered several people here on this podcast, and they talk about growing up in Michigan. I don't think that's a coincidence.

JOHN: Okay, it's not. So I was born and raised in the city of Detroit. We moved out of the city, the deal of oil embargo in 1973. I've had a lot of relatives who grow up and work in the auto industry. So if you grew up in that area, you're just immersed in that culture. And you're also aware of the massive quote, unquote, "business cycles" that companies go through.

What I learned after coming to MIT and having the chance to meet the great Jay Forrester a lot of those business cycles are self-inflicted. What I do is I see a lot of the things that went right and went wrong for the auto industry, and I can help bring that perspective to other companies. [laughs]

TROND: And people have a bunch of assumptions about, I guess, assembly lines in factories. One thing is if you grew up in Michigan, it would seem to me, from previous guests, that you actually have a pretty clear idea of what did go on when you grew up in assembly lines because a lot of people, their parents, were working in manufacturing. They had this conception. Could we start just there? What's going on at assembly lines?

JOHN: I'm going to actually go back to 1975 to a Carrier family picnic. My cousin, who's ten years older than I, his summer job he worked at basically Ford Wayne, one of the assembly plants. He was making $12 an hour in 1975, so he paid his whole college tuition in like a month. But the interesting point was he was talking about his job when all the adults were around, and he goes, "Do you know that when they scratch the paint on the car, they let it go all the way to the end, and they don't fix it till it gets to the parking lot?"

And I'll never forget this. All the adults jumped on him. They're like, "Are you an idiot? Do you know how much it costs to shut the line down?" And if you use finance, that's actually the right answer. You don't stop the line because of a scratch; you fix it later. Keep the line running. It's $10,000 a minute. But actually, in the short term, that's the right decision. In the long term, if you keep doing that, you're building a system that simply makes defects at the same rate it makes product. And it's that type of logic and culture that actually was deeply ingrained in the thinking. And it's something that the Japanese car companies got away from.

It's funny how deeply ingrained that concept of don't stop the line is. And if you do that, you'll make defects at the same rate that you make product. And then, if you look at the Detroit newspapers even today, you'll see billion-dollar recalls every three months. And that's a cycle you've got to get yourself out of.

TROND: You know, it's interesting that we went straight there because it's, I guess, such a truism that the manufacturing assembly line kind of began in Detroit, or at least that's where the lore is. And then you're saying there was something kind of wrong with it from the beginning. What is it that caused this particular fix on keeping everything humming as opposed to, I guess, what we're going to talk about, which is fixing the system around it?

JOHN: There's a lot of work on this. There's my own perspective. There's what I've read. I've talked to people. The best I can come up with is it's the metrics that you pick for your company. So if you think about...the American auto industry basically grew up in a boom time, so every car you made, you made profit on. And their competitive metric was for General Motors to be the number one car company in the world.

And so what that means is you never miss a sale, so we don't have time to stop to fix the problem. We're just going to keep cranking out cars, and we'll fix it later. If you look at the Japanese auto industry, when it arose after World War II, they were under extreme parts shortages. So if one thing were broken or missing, they had to stop. So part of what was built into their culture is make it right the first time. Make a profit on every vehicle versus dominant market share.

TROND: Got it. So this, I guess, obsession with system that you have and that you got, I guess, through your education at MIT and other places, what is it that that does to your perspective on the assembly line? But there were obviously reasons why the Ford or the Detroit assembly lines, like you said, looked like they did, and they prioritized perhaps sales over other things.

When you study systems like this, manufacturing systems, to be very specific, how did you even get to your first grasp of that topic? Because a system, you know, by its very nature, you're talking about complexity. How do you even study a system in the abstract? Because that's very different, I guess, from going into an assembly and trying to fix a system.

JOHN: So it's a great question. And just one thing I want to note for the audience is although we talk about assembly lines, most manufacturing work is actually problem-solving and not simply repetitive. So we need to start changing that mindset about what operations really is in the U.S. We can come to that in the end.

TROND: Yeah.

JOHN: I'll tell you, I'm a chemical engineer. Three pieces of advice from a chemical engineer, the first one is never let things stop flowing. And the reason why that's the case in a chemical plant is because if something stops flowing for a minute or two, you'll start to drop things out of solution, and it will gum everything up. You'll reduce the capacity of that system till your next turnaround at least. And what happens you start getting sludge and gunk.

And for every class I was ever in, in chemical engineering, you take classes in heat transfer, thermodynamics, kinetics. I never took a class in sludge, [laughs] or sticky solids, or leftover inventory and blending. And then, when I first went to a real factory after doing my graduate work, I spent four to six years studying Laplace transforms and dynamics. All I saw were people running around. I'm like, that's not in the Laplace table.

And, again, to understand a chemical plant or a refinery, it takes you three to five years. So the question is, how can you actually start making improvement in a week when these systems are so complex? And it's watch the people running around. So that's why I focus a lot on maintenance teams. And I also work with operations when these things called workarounds that grow into hidden factories. So the magic of what I've learned through system dynamics is 80% to 90% of the time, the system's working okay, 10% or 20% it's in this abnormal condition, which is unplanned, unscheduled. I can help with that right away.

TROND: So you mentioned the term hidden factories. Can you enlighten me on how that term came about, what it really means? And in your practical work and consulting work helping people at factories, and operations teams, and maintenance teams, as you said, why is that term relevant, and what does it really do?

JOHN: Great. So I'm going to bring up the origin. So many people on this call recognize the name Armand Feigenbaum because when he was a graduate student at the Sloan School back in the '50s, he was working on a book which has now become like the bible, Total Quality Management or TQM. He's well known for that. He's not as well known for the second concept, which he should be better known for. Right after he graduated, he took a job in Pittsfield, Massachusetts, for one of the GE plastic plants.

Here he comes out of MIT. I'm going to apply linear equations. I'm going to do solving, all these mathematics, operation constraints, all these things. When he gets into that system, he realizes 30% of everything going on is unplanned, unscheduled, chaotic, not repeated. He's like, my mathematical tools just break down here.

So he did something...as important as marketing was as an operational objective, he named these things called hidden factories. And he said, 30% of all that work is in these hidden factories. And it's just dealing with small, little defects that we never ever solve. But over time, they actually erode our productivity of systems that can eat up 10% to 20% of productivity. And then, finally, it's work that I'm doing. It's the precursor to a major accident or disaster. And the good side is if you leave the way the system works alone, the 80%, and just focus on understanding and reducing these hidden factories, you can see a dramatic improvement quickly and only focus on what you need to fix.

TROND: So, for you, you focus on when the system falls apart. So you have the risk angle to this problem.

JOHN: Exactly. And so just two things, I'm like a doctor, and I do diagnosis. So when you go to the doctor, I'm not there to look at your whole system and fix everything. I'm like, here are first three things we got to work at, and, by the way, I use data to do that. And what I realized is if everyone just steps back after this call and thinks about today, right? When you get to the end of the day, what percent of everything in that factory or system happened that was in your schedule?

And you'll start to realize that 30% of the people are chasing symptoms. So you need data to get to that root cause, and that will tell you what data to collect. And second, look for time because what you're doing is these hidden factories are trying to keep the system running because you have a customer. You have your takt time, and so people are scrambling. And if you put that time back into the system, that's going to turn into product.

TROND: John, I'm just curious; when you say data, I mean, there's so much talk of data and big data and all kinds of data. But in manufacturing, apart from the parts that you're producing, I mean, some of this data is hard to come by. When you say data, what data will you even get access to?

JOHN: I come from the Albert Einstein School is. I need a ruler, and I need a stopwatch. Go into any system that you work in, whether it be your factory or your house, and ask the last time someone measured how long something took, and you will find a dearth of that data. And the reason why I love time data is it never lies. Most data I see in databases was collected under some context; I can't use it. So I go right in the floor and start watching 5 or 10 observations and looking at all the variation.

The second point I ask is, what's a minute worth in your system or a second? So if we're in an auto assembly plant, in a chemical plant, if we're in a hospital, in an operating room, those minutes and seconds are hundreds of thousands of dollars. So within about 20 minutes, not only have I measured where there's opportunity, we're already on the way to solving it.

TROND: So, so far, you haven't talked much about the technology aspects. So you work at a business school, but that business school is at MIT. There's a lot of technology there. It strikes me that a lot of times when we talk about improvements, certainly when we talk about efficiencies in factories, people bring up automation machines as the solution to that tool. And I'm sure you're not against machines, but you seem to focus a lot more on time, on organizational factors. How should people think about the technology factor inside of their operations?

JOHN: So, first, you brought up...my nickname is Dr. Don't. And the reason they call me Dr. Don't [laughs] is because they'll go, "Should we invest in this? Can we buy these robots?" I say, "No, you can't do that." And I'm going to tell you why. First is, I was quote, unquote, "fortunate enough" to work in a lot of small and mid-sized machine shops during the 2009 downturn. And I was brought in by the banks because they were in financial trouble.

And the one thing I noticed there was always a million-dollar automation or robot wrapped in plastic. And large companies can get away with overspending on technology, small and mid-sized companies can't. And so what you really want to do is go and watch and see what the problem is, buy just as much technology as you need, and then scale that.

First is, like I just said, I was just in a plant a few weeks ago, and they just implemented several hundred sensors to basically listen to their system. That's all good. It's data we need. Two problems, why'd you put in several hundred and not put in 20? And second, when we inspected it, about 15% were either not plugged in or weren't reading. So what happened was if we would have started with 20 and put the resource in analyzing that data, then when we scaled to the several hundred, we'd have had our systems in place. Instead, we overwhelmed everyone with data, so it really didn't change the way they work. Now we fixed that.

But your question was, why am I skeptical or slow to invest in technology? Technology costs money, and it takes time. If you don't look at the system first and apply the technology to solve the system problem, you're going to end up with a million-dollar piece of equipment wrapped in plastic. If you go the other direction, you will scale successfully. And no one's better at this than Toyota. They only invest in the technology they need. Yet you can argue they're at least as technologically sophisticated as all the rest. And they've never lost money except in 2009 so that is a proof point.

TROND: What are some examples of places you've been in lately, I don't know, individual names of companies? But you said you're working kind of mid-sized companies. Those are...[laughs] the manufacturing sector is mid-sized companies, so that sounds very relevant. But what are some examples in some industries where you have gone in and done this kind of work?

JOHN: I work for large companies and small and mid-sized. And I'm a chemical engineer, but I love machine shops. So I sit on the board of a $25 million machine shop. They make parts for a diesel truck and some military applications. They make flywheels. So one of their big challenges is in the United States and in the world, we're suffering with a problem with castings. We received our castings. Interesting thing is there are void fractions.

One of the things I do want to share is as a systems guy, I'm not an expert in mechanical engineering or any of that, but I can add value by helping look for defects. Let me tell you what their challenge is. So, first of all, more of their castings are bad. Then this surprised me...I learned from asking questions. If you've ever been in a machine shop, one thing I learned about when you're making casting is that there are always bubbles in it. You can't avoid it.

The art of it is can you put the bubbles in the places where they don't hurt? You minimize the bubbles, and you move them to the center. So one is we're getting bad castings, but the second part was when we made some of these castings, and they had a void problem in the center. So that doesn't cause a problem with your flywheel. The customer sent them back because they're becoming oversensitive to the defects that don't count. And it's because they switched out staff.

So I guess what I'm trying to say here is our supply chain is undergoing this new type of stress because we're losing the type of expert system expertise that we've had from people that have worked in this industry 20 to 30 years. That's a really important aspect.

The second is we're in their line balancing all the time. I think a lot of the things you learn in class, you spend one class on load balancing or line balancing, operation and manufacturing, and then you go into a factory, and no one's doing it. So I just wanted to share two points. My one factor is doing that they cut 30% of their time.

Another system I'm working in they have one experienced supervisor managing four new people on four different setups. What I realized is there's not enough of that supervisor to go around. We're like, why don't we shoot videos like the NFL does [laughs] and watch those films of how people do their work? Because when you're an expert, Trond, and you go to do a task, you say, "That has five steps."

But if I sent you or me new, we'd look and go, "There are really about 80 steps in there." And you explained it to me in 15 minutes. How am I going to remember that? So shooting film so people can go back and watch instead of bothering your supervisor all the time, which they won't do. So what I do think, to wrap up on this point, is when you talk about technology, the camera, the video that you have in your pocket, or you can buy for $200, is the best technology you can probably apply in the next three to six months. And I would greatly encourage everyone to do something like that.

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TROND: I wanted to ask you then, derived from this, to what extent can some of these things be taught as skills on a systemic level in a university or in some sort of course, and to what extent? Do you really just have to be working in manufacturing and observing and learning with data on your own? By extension, to what extent can a manager or someone, anyone in the organization, just develop these practices on their own? And to what extent do you need mentorship from the outside to make it happen or see something in the system that is very difficult to see from the inside?

JOHN: So it's interesting you ask that because that's very much the problem I'm dealing with because as good as our universities are, the best place to learn operations in manufacturing is on the factory floor. So how do you simulate that approach? I teach lean operations at MIT Sloan. And what I do with my students is I ask them to pick a routine task, video two minutes of it, and reduce that by 30%. And I've done this two years in a row.

When you look at these projects, the quality of the value streams and the aha moments they had of time that they were losing is stunning. You know what the challenge is? They don't yet always appreciate how valuable that is. And what I want them to realize is if you're washing dishes or running a dishwasher, why is that any different from running a sterilization process for hospital equipment? Why is that any different from when you're actually doing setup so that maintenance can get their work done 30% faster?

I've given them the tools, and hopefully, that will click when they get out into the workspace. But I do have one success point. I had the students...for some classes, they have to run computers and simulations during class. So that means everyone has to have the program set up. They have to have the documentation. So you can imagine 5 to 10 minutes a class, people getting everything working right.

One of my teams basically said we're going to read...it took about five minutes, and they said, we're going to do this in 30 seconds just by writing some automated scripts. They did that for our statistics class, and then they shared it with their other classmates, beautiful value stream, video-d the screens, did it in about four or five hours. The next class they took later I found out they did that for a class project, and they sold the rights to a startup. So first is getting them that example in their own space, and then two, helping them make analogies that improving things in your own house isn't all that much different than the systemic things in a factory.

TROND: Learning by analogy, I love it. I wanted to profit from your experience here on a broader question. It takes a little bit more into the futuristic perspective. But in our pre-conversation, you talked about your notion on industry 4.0, which, to me, it's a very sort of technology, deterministic, certainly tech-heavy perspective anyway.

But you talked about how that for you is related to..., and you used another metaphor and analogy of a global nervous system. What do you think, well, either industry 4.0 or the changes that we're seeing in the industry having to do with new approaches, some of them technology, what is it that we're actually doing with that? And why did you call it a global nervous system?

JOHN: When I graduated from school, and I'm a control systems skilled in the arts, so to speak. And the first thing I did...this is back in the '90s, so we're industry 3.0. When you're in a plant, no one told me I was going to spend most of my time with the I&C or the instrumentation and control techs and engineers. That's because getting a sensor was unbelievably expensive. Two, actually, even harder than getting the budget for it was actually getting the I&C tech's time to actually wire it up. It would take six weeks to get a sensor.

And then three, if it weren't constantly calibrated and taken care of, it would fall apart. And four, you get all those three workings, if no one's collecting or knows how to analyze the data, you're just wasting [laughs] all your money. So what was exciting to me about industry 4.0 was, one, the cost of sensors has dropped precipitously, two, they're wireless with magnets. [laughs] So the time to set it up is literally minutes or hours rather than months and years.

Three, now you can run online algorithms and stuff, so, basically, always check the health of these sensors and also collect the data in the form. So I can go in, and in minutes, I can analyze what happened versus, oh, I got to get to the end of the week. I never looked at that sensor. And four, what excited me most, and this gets to this nervous system, is if you look at the way industries evolved, what always amazes me is we got gigantic boilers and train engines and just massive equipment, physical goods. Yet moving electrons actually turns out to be much more costly in the measurement than actually building the physical device.

So we're just catching up on our nervous system for the factory. If I want to draw an analogy, if you think about leprosy; a lot of people think leprosy is a physical disease; what it is is it's your nerves are damaged, so because your nerves are damaged, you overuse that equipment, and then you wear off your fingers. And if you look at most maintenance problems in factories, it's because they didn't have a good nervous system to realize we're hurting our equipment.

And maintenance people can't go back and say, "Hey, in three months, you're going to ruin this." And the reason I know it is because I have this nervous system because I'm measuring how much you're damaging it rather than just waving it. And now it becomes global because, let's say you and I have three pumps in our plant, and we need to take care of those. They are on the production line, very common. What if we looked at the name of that pump, called the manufacturer who's made tens of thousands of those? There's the global part.

So they can help us interpret that data and help us take care of it. So there's no defect or failure that someone on this planet hasn't seen. It's just we never had the ability to connect with them and send them the data on a platform like we can with a $5,000 pump today. So that's why I look at it, and it's really becoming a global diagnosis.

TROND: It's interesting; I mean, you oscillate between these machine shops, and you had a medical example, but you're in medical settings as well and applying your knowledge there. What is the commonality, I guess, in this activity between machine shops, you know, improving machine shops and improving medical teams' ability to treat disease and operate faster? What is it that is the commonality?

So you've talked about the importance, obviously, of communication and gathering data quicker, so these sensors, obviously, are helping out here. But there's a physical aspect. And, in my head, a machine shop is quite different from an operating room, for example. But I guess the third factor would be human beings, right?

JOHN: I'm going to put an analogy in between the machine shops at the hospital, and that's an F1 pit crew. And the reason I love F1 is it's the only sport where the maintenance people are front and center. So let's now jump to hospitals, so the first thing is if I work in a hospital, I'm talking to doctors or nurses in the medical community. And I start talking about saving time and all that. Hey, we don't make Model Ts. Every scenario we do is different, and we need to put the right amount of time into that surgery, which I completely agree to.

Where we can fix is, did we prepare properly? Are all our toolkits here? Is our staff trained and ready? And you'd think that all those things are worked out. I want to give two examples, one is from the literature, and one is from my own experience. I'd recommend everyone look up California infant mortality rates and crash carts. The state of California basically, by building crash carts for pregnancies and births, cut their infant mortality rate by half just by having that kit ready, complete F1 analogy. I don't want my surgeon walking out to grab a knife [laughs] during surgery.

And then second is, I ran a course with my colleagues at MIT for the local hospitals here in Boston. You know what one of the doctor teams did over the weekend? They built one of these based on our class. They actually built...this is the kit we want. And I was unbelievably surprised how when we used the F1 analogy, the doctors and surgeons loved it, not because we're trying to actually cut their time off. We're trying to put the time into the surgery room by doing better preparations and things like that. So grabbing the right analogy is key, and if you grab the right analogy, these systems lessons work across basically anywhere where time gets extremely valuable.

TROND: As we're rounding off, I wanted to just ask you and come back to the topic of lean. And you, you use the term, and you teach a class on lean operations. Some people, well, I mean, lean means many things. It means something to, you know, in one avenue, I hear this, and then I hear that.

But to what extent would you say that the fundamental aspects of lean that were practiced by Toyota and perhaps still are practiced by Toyota and the focus on waste and efficiency aspects to what extent are those completely still relevant? And what other sort of new complements would you say are perhaps needed to take the factory to the future, to take operational teams in any sector into their most optimal state?

JOHN: As a control engineer, I learned about the Toyota Production System after I was trained as a control system engineer. And I was amazed by the genius of these people because they have fundamentally deep control concepts in what they do. So you hear concepts like, you know, synchronization, observability, continuous improvement. If you have an appreciation for the deep control concepts, you'll realize that those are principles that will never die.

And then you can see, oh, short, fast, negative feedback loops. I want accurate measurements. I always want to be improving my system. With my control background, you can see that this applies to basically any system. So, in fact, I want to make this argument is a lot of people want to go to technology and AI. I think the dominant paradigm for any system is adaptive control. That's a set of timeless principles.

Now, in order to do adaptive control, you need certain technologies that provide you precision analysis, precision measurement, real-time feedback loops. And also, let us include people into the equation, which is how do I train people to do tasks that are highly variable that aren't applying automation is really important. So I think if people understand, start using this paradigm of an adaptive control loop, they'll see that these concepts of lean and the Toyota Production System are not only timeless, but it's easier to explain it to people outside of those industries.

TROND: Are there any lessons finally to learn the way that, I guess, manufacturing and the automotive sector has been called the industry of industries, and people were very inspired by it in other sectors and have been. And then there has been a period where people were saying or have been saying, "Oh, maybe the IT industry is more fascinating," or "The results, you know, certainly the innovations are more exciting there." Are we now at a point where we're coming full circle where there are things to learn again from manufacturing, for example, for knowledge workers?

JOHN: What's driving the whole, whether it be knowledge work or working in a factory...which working in a factory is 50% knowledge work. Just keep that in mind because you're problem-solving. And you know what's driving all this? It is the customer keeps changing their demands. So for a typical shoe, it'll have a few thousand skews for that year. So the reason why manufacturing operations and knowledge work never get stale is the customer needs always keep changing, so that's one.

And I'd like to just end this with a comment from my colleague, Art Byrne. He wrote The Lean Turnaround Action Guide as well as has a history back to the early '80s. And I have him come teach in my course. At his time at Danaher, which was really one of the first U.S. companies to successfully bring in lean and Japanese techniques, they bring in the new students, and the first thing they put them on was six months of operations, then they move to strategy and finance, and all those things.

The first thing that students want to do is let's get through these operations because we want to do strategy and finance and all the marketing, all the important stuff. Then he's basically found that when they come to the end of the six months, those same students are like, "Can we stay another couple of months? We just want to finish this off." I'm just saying I work in the floor because it's the most fun place to work.

And if you have some of these lean skills and know how to use them, you can start contributing to that team quickly. That's what makes it fun. But ultimately, that's why I do it. And I encourage, before people think about it, actually go see what goes on in a factory or system before you start listening to judgments of people who, well, quite frankly, haven't ever done it. So let me just leave it at that. [laughs]

TROND: I got it. I got it. Thank you, John. Spend some time on the floor; that's good advice. Thank you so much. It's been very instructive. I love it. Thank you.

JOHN: My pleasure, Trond, and thanks to everybody.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Lean operations, and our guest was John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talked about the people dynamics that block efficiency in industrial organizations.

My takeaway is that the core innovative potential in most organizations remains its people. The people dynamics that block efficiency can be addressed once you know what they are. But there is a hidden factory underneath the factory, which you cannot observe unless you spend time on the floor. And only with this understanding will tech investment and implementation really work. Stabilizing a factory is about simplifying things. That's not always what technology does, although it has the potential if implemented the right way.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like other episodes on the lean topic. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, and devices, and systems. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where industrial tech is heading. And to find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: John Carrier.

Episode 107: Explainability in AI with Julian Senoner

1 februari 2023 | 30 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Explainability and AI." Our guest is Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talk about how to define explainable AI and its major applications, and its future.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 103: Human-First AI with Christopher Nguyen.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Explainability in AI, meaning knowing exactly what's going on with an algorithm, is very important for industry because its outputs must be understandable to the process engineers using it. The computer has not and will not use the product. Only a domain expert can recognize when the system is wrong, and that will be the case for a very long time in most production environments.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory, and who's leading the change?

In this episode of the podcast, the topic is Explainability and AI. Our guest is Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talk about how to define explainable AI and its major applications, and its future.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Julian, welcome to the show.

JULIAN: Hello, Trond. Thank you for having me.

TROND: I'm excited to have you. You know, you're a fellow runner; that's always good. And you grew up in the ski slopes.; that makes me feel at home as a Norwegian. So you grew up in Austria; that must have been pretty exciting. And then you were something as exciting as a ski instructor in the Alps. That's every man and woman's dream.

JULIAN: Yeah, I think it was very nice to grow up in the mountains. I enjoyed it a lot. But, you know, times have passed, and now I'm happy to be in Zurich.

TROND: You went on to industrial engineering. You studied manufacturing and production at ETH. And you got interested in statistics and machine learning aspects of all of that. How did this happen? You went from ski instruction to statistics.

JULIAN: Yeah, I was always impressed about watching stuff being made. I think it's a very relaxing thing to do. And I always wanted to become an engineer. When I was five years old, I wanted to become a ship engineer. So it was always clear that I wanted to do something with manufacturing and mechanical engineering. So I started actually doing my bachelor's in Vienna at Technische University. And for my master's, I moved to Zurich and studied Industrial Engineering.

ETH has historically been very strong in machine learning research. Every student, no matter if you're interested or not, gets exposed to machine learning, statistics, and AI. It caught my attention. I thought there were very interesting things you can do when you combine both. So that's how I ended up doing research on interface and becoming an entrepreneur in this area.

TROND: Yeah, we'll talk about your entrepreneurship in a moment. But I wanted to go to your dissertation, Artificial Intelligence in Manufacturing: Augmenting Humans at Work. That is very close to our interests here at the podcast. Tell me more about this.

JULIAN: There is a lot of hype about AI. There's a lot of talk about self-aware factories and these kinds of things. These predictions are not new. We had studies in the 1970s that predicted there won't be any people in factories by the 1980s; everything will be run by a centralized computer. I never believed in these kinds of things. During my dissertation, I was interested in looking into how we can develop useful AI tools that can support people doing their jobs more effectively and efficiently.

TROND: Right. But you already were onto this idea of humans at work. Where did you do your case studies? I understand ABB and Siemens were two of them. Give me a little sense of what you discovered there; pick any one of them.

JULIAN: Sure. I'd love to start with the case that we ran with Siemens. I've worked quite a lot with Siemens in different use cases, but one of them was supporting frontline workers in complex assembly tasks on electronic products. So the aim was to help the worker check if the product has been assembled correctly. There are many connectors that could be missing or assembled in the wrong way. So the idea was to have a camera mounted to the workstation, and the worker would put the final product under the camera and get visual feedback if it has been assembled correctly or not.

What we did here is really studying the psychological aspect of that. I would say most of my Ph.D. was really math-heavy and about modeling, but here we were interested in the psychological aspect. Because, in the beginning, we thought perhaps andon lights with green or red signals would be enough. But we got intrigued by the research question of does the worker actually follow these recommendations if it's just the green or red light?

So we did an experiment, which I'm very excited about. So we got 50 workers that volunteered within Siemens to participate, which I'm very grateful for. We basically divided factory workers in two groups. We looked into the effect of explainability in the decisions that the AI makes. So we had one group that got basically just a recommendation to reject or to pass the product. And we had another group that got the exact same recommendations. But in addition to that, we provided visual feedback indicating the area where the AI believes that there could be an error.

And the results of this experiment were perhaps not too surprising, but the effect size clearly was. We found that the people that did not get explanations for these recommendations were more than three times more likely to overrule the AI system, although the AI was correct. And I think this is a really nice finding.

TROND: Well, it's super interesting in terms of trust in AI. And this topic of explainability is so much talked about these days, I guess, not always in manufacturing because people overlook that sometimes, people who are not in the industry. And they think about whether machines will take over and what decisions they're taking over and, certainly, if the machines are part of the decision making, what goes into that decision making?

But as you were discovering more about explainability, what is explainability? And how is it different from even just being able to...it starts, I guess, with the decision of the AI being very clear because if that's not even clear, then you can't even interpret the decision. But then there's a lot of discussion in the industry, I mean, in the AI field, I guess, about interpretability. So can you actually understand the process?

But you did this experiment, and it became very clear, it seems, that just the decision is not enough. Was it the visual example that was helping here? Or what is it that people want to know about a machine decision to make them trust the decision and trust that their processes, you know, remains a good process?

JULIAN: I think I kind of see two answers to this question; one is the aspects of interpretability and explainability; perhaps I start with that. So these terms are often used interchangeably, and academics are still arguing about the differences. But there is now a popular opinion that I also share that these two things are not the same.

So when we talk about interpretable AI, we think about models that have basically an interpretable architecture or functional relationship, so an example would be a linear regression. You have a regression line; it has a slope, it has an intercept. And you know how an X translates into a Y or an input to an output.

Explainable AI is a fairly new research branch, which it's slightly different to that. It looks into more complex AI models like deep neural networks and ensembling techniques, which do not have this inherently interpretable model architecture. So a human, just by looking at it, cannot understand how decisions are formed. And what explainable AI methods do is basically reverse engineer what the model is doing by approximating the inner behavior of the model. So, in essence, we're creating a model of a model.

Coming to your second question, so why might this be important in manufacturing? Basically, what I discovered during my research is that AI is still not trusted in the manufacturing domain, so people often do not understand what AI does in general, and I think explanations are a very powerful tool to simplify that. And a second use case of explainability is also that we can reduce complexity. So we can use more powerful methods to model more complex relationships. And we can use explainability on top of that to, for example, conduct problem-solving.

TROND: Wow, you explain it very easily, but it's not easy to explain an actual AI model. Like, if you were to say, you know, here is the neural network model I used, and it had eight layers, good luck explaining that to a manufacturing worker or to me.

JULIAN: So I think that explaining what a model is is also a different topic, and perhaps it's not even needed. You can still treat this as a mathematical function. I think it's really more about the decisions. We need to understand how decisions are formed, and there are different techniques to that.

So when we talk about vision models, heat maps are a very interesting application. So we cannot really tell how the algorithm came up with a decision, but we can try to visualize the areas of an image where the neural network focused on to inform its decision. And we can, for example, see that certain areas are more highlighted than others, and perhaps that goes with the human intuition and creates more trust.

TROND: You know, this topic is, for me, so fascinating because when we think about frontline workers or, indeed, engineers or quality managers on the shop floor, previously, they didn't have perhaps the tools available to open up the boxes, to open up the machines and look at the decisions that were being made. And, of course, that doesn't lead you to an enormous amount of confidence that what you're doing is good or bad or mediocre. You're not getting enough feedback.

But it does seem to me that as machines and tools and algorithms on the shop floor become more and more complex, this is going to be a big effort. It doesn't sound very easy. And maybe you can characterize, you know, with the process today. These methods are just being applied on the shop floor. Do you have a sense that this general idea that things have to be explainable is a shared commodity on shop floors that are starting to use these techniques? Or would you say that it's enough of a challenge just to start experimenting with them, let alone trying to explain them to anybody around? I guess it's called a black box problem, right?

JULIAN: Sure. I think in any use case where you have some kind of interaction between humans and AI, you've got to have explainability. It's going to be key. And there are also some less obvious use cases around explainable AI that I would also be happy to share. I think everywhere where you're going towards full automation, you perhaps don't need it, perhaps only if it's very high-stakes decisions being made. If you are rejecting products based on an AI that are very expensive, you might want to know why your line scrapped the products. In general, if you're going for automation, I would say explainability is nice to have. When we talk about augmentation, I think it's absolutely key.

TROND: Yeah, it's absolutely clear. But we were talking before, and you were reminding me that in semiconductor production on an average production line, a large percentage of those components tend to be scrapped for quality reasons. And each of those components might be very, very expensive to manufacture. And it's a big problem.

You have to recycle the part again, and they're made out of rare earth metals or whatnot. And it's a complicated thing, so it's not like you're just making wooden parts that you can just do over or plastic, and you can mold it again. These are like you said, they're expensive decisions that you're trusting machines to make.

JULIAN: Yes. Talking about semiconductor industry, we have been also working on a different use case using explainable AI in semiconductors which I'm really excited about, and that is root cause analysis. In semiconductor manufacturing, as you mentioned, it's common that manufacturers throw away 15% to 20% of the chips they produce. We have car production lines that are standing still because of a chip shortage, so, obviously, this is a problem.

What explainable AI can do here is we can try to model relationships in the manufacturing system to try to understand what causes these problems in the first place. So actually, when I was still a researcher back at ETH, I worked together with ABB semiconductors who exactly had this problem, so they had costly yield losses. And process engineers were struggling to find out where these losses were coming from.

Because in semiconductor manufacturing, you often have hundreds of process steps, and each process, you can have even hundreds of process parameters such as temperatures and pressures. And you would like to know which of these process parameters do I need to adjust to avoid my yield losses. And if you have thousands, in this case, we had 3,600 different parameters that could have been suspects for yield losses. It's very hard to kind of track where yield losses are coming from.

And the methods that are still used in industry are often based on linear methods. So we find this big effect, but we can't find the more hidden ones. And I think this is a very neat application of AI because you can use more complex models like neural networks or tree-based methods to model these relationships. So, in essence, we try to imitate the physical processes to learn the physical processes as they are.

But since neural networks are very complex and we cannot really understand what they have been modeling, we need kind of explanations for that. And using these explanations, we can inform process engineers who are domain experts about what the model might have found. We're still acting upon correlations, not causation. But still, we can point towards certain areas that are interesting.

And in the case of ABB, the AI guided the process engineers to suspicious processes, and the domain experts were able to come up with two improvement actions based on that input. Then they were able to reduce the scrap by more than 50% in one of their lines, which was, of course, substantial. And I think it's a very nice example of how humans and AI can collaborate and get more out of it.

TROND: So, Julian, is that what augmentation of workers means to you? The augmented workforce is essentially a collaboration between man and machine in a deeper way than before?

JULIAN: I think it's about expanding capabilities and getting teams of humans and machines that perform better and provide more value than either alone. I think that is what augmentation is about.

MID-ROLL AD:

In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines.

Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution."

Find out more on www.augmentedlean.com, and pick up the book in a bookstore near you.

TROND: You ended up commercializing an idea around this. Tell me more about that and how that came about.

JULIAN: Exactly. So at some point in my Ph.D., I decided I'm not going to become a professor; many PhDs have a similar experience. But I saw that 50% scrap reduction, for example, at one partner company that's quite substantial, and these tools kind of scale. And together with a friend of mine who did a Ph.D. in the same department, we decided let's found a company around this. So we founded a startup called EthonAI.

And we are developing a software platform that helps manufacturers improve their quality management. So we offer five different products in different application areas, three computer vision products that help manufacturers in detecting defects, one product around process monitoring and anomaly detection, and one product for AI-based root cause analysis. So we cover this entire continuous improvement loop that manufacturers need, and we provide tools for that.

And I think one thing that really stands out for us, and this is our key hypothesis, is that all of our products can be used by process engineers without writing a single line of code. We're not building fancy toolboxes for data scientists. We really build the tools for the people in the factory. We want to empower them to do their jobs better or help them do it better because we believe these are the people that drive the improvements on the shop floor. And those people should be the ones that use the tools.

TROND: I'm just curious; in this process, you've talked to a lot of process engineers. How are they reacting to these new opportunities? Are they excited that they get to do some programming or do advanced analysis without getting deeply into software? Or are they a little bit weary that they have to still jump into a new domain? There's so much discussion these days about the need for re-skilling. How have you found them to react to these new changes?

JULIAN: I think the experience is really mixed. Sometimes you see skepticism; sometimes, you see great excitement. I think at the end of the day, you need to solve problems. It's not about bringing AI to the factory. It's rather to solve the problems of the people that have worked in those factories. And if you have a tool that can be used very easily...so anyone that can operate a computer can operate our tools, and it solves problems. People are often happy to use it or mostly happy to use it.

Of course, if you are coming in with this marketing thing of AI is going to change everything, then you experience more skepticism. So this is why we really talk about problems and not about the technology. Technology kind of is in the back end. And people don't care about how fancy your algorithms are; it has to work. What I think is so rewarding working on a startup in the manufacturing space is that the outcomes are so binary; it either works or it doesn't. And that's really cool to see in the physical world. Our entire team is working really hard to bring very useful tools to the market.

TROND: You're a little bit of a hybrid yourself between a manufacturing engineer, basically, and now a little bit of a software engineer or at least an analytics perspective here with statistics and machine learning. Where do you find the expertise that you need at Ethon to move forward?

Because it's a very rare thing still to combine knowledge of shop floor real-world challenges, systems that cannot fail, or at least when they fail, it has a bigger consequence than when a software needs to patch because the whole idea of why your software tentatively is in the shop floor is to reduce these kinds of stops and starts at production lines. How do you find people that really managed to combine the perspective of building software with this reality of how it's going to work in a physical environment?

JULIAN: We have hired three kinds of people, first being brilliant scientists from the area of AI that have never been in a factory before but have been publishing at the highest level, NeurIPS, ICML, all those top outlets in the AI field. And they can really help to push the state of the art.

The second category of people is process engineers. We've hired process engineers from companies that could be potential clients. They know about the problems and have been conducting all this data analysis in a quite manual way. So they're coming into our company to kind of guide us in building the product of their dreams. Then we have some people that have been working on the interface, such as Bernhard and myself. So Bernhard is the CTO in our company, people that have been on the interface between AI and manufacturing.

So I think you really need basically experts and generalists in a company, and then it typically goes well. And then the other thing that you need is really customer-centricity. So you really need to be close to your customers and try to understand their pains. And you also need to bring the machine learning engineers to the factories so that they can see for themselves, and that's usually very helpful.

TROND: What's your experience with frontline operators themselves? In the experiments that you've done with your Ph.D. and with your early products with Ethon, how are they reacting to these new opportunities for augmentation?

JULIAN: I mean, typically, they're quite positive about it because it helps them in their jobs. But when we're talking about augmentation, it's not about replacing people; it's about helping them do things better. And often, they're quite critical about user interfaces, but you learn so much interacting with them, and you improve it. And then they get something that they really need.

And the biggest learnings here have really been around how do you visualize or represent things or content to the workers? This is so crucial because, in factories, you have such a diverse workforce of people in their 60s who will retire soon. And you have young people who just started their first job and basically were raised with iPads. So it's really that's a challenge, but it's a great one to work on.

TROND: Well, I guess that brings me to the important question that a lot of people want to answer about the factory of the future. There's fear about the factory of the future. There's this idea of a 24/7 automated factory, autonomous, working without humans. And then there's this idea that factories will look very different and people will do different things, but there are still people in there.

And then there's anything in between. You could also imagine a world without factories but where industrial production happens somewhat distributed. Because I guess what a factory is, at the heart of it, is it's a mass of things, of people and machines concentrated in one location.

JULIAN: Yes, it's a social, technical system. As I mentioned in the beginning, there is a lot of hype about AI, and these predictions are not new. This lights-out factory has been around for a very long time, and I still haven't seen it, at least on a productive level. You can also over-automate. Companies have seen that Tesla, for example, has cut back on automation. Elon Musk tweeted, "Humans are underrated."

So I think in the future, people will still shape the image of factories. Of course, we will see more automation that is enabled through AI because, with AI, we can also now model stochastic processes. So we don't need deterministic outcomes like a robot that always picks the same things. We can have more self-organized, isolated systems. But I think it's really about isolated systems and not an entire factory that is operated by one single artificial brain.

But I think the most intriguing use cases will be those where we augment humans rather than replacing them. So I think that's really where the magic happens. It's giving process engineers, for example, the tools to conduct more effective problem solving, finding things that were unknown to them previously, and couple that with their domain expertise. I think that's certainly something that we're going to see more.

I don't think that AI will regulate production processes in a fully automated manner. At the end of the day, you always need the process and product expert that has intuition about the processes, creative problem-solving. So I think process engineers will always be around and be integral to any manufacturing system, but I think AI we will see more AI tools that enable these people and empower them. And I think this is an exciting development.

TROND: Yeah, it's so interesting that sometimes I feel like these futuristic discussions fail to take into account innovation. So it's a very basic problem with this discussion because you're assuming that automation and factory production overall is all about squeezing out tiny, little efficiencies, and if that's all it is, then machines might be the better way to go.

But if you're talking about incrementally improving and sometimes radically improving a process or changing even what you are producing based on feedback from a market and stuff like that, it would seem to me that we are quite far into the future before a socio-technical system like that with complete feedback, long supply chain, and understanding what all of these things mean and the decisions that go into it, and costs. And that all can be managed by one network algorithm. It's really a little bit hard to envision how those futurists really have been thinking about it, or maybe they were just considering isolated use of robots that looked very cool.

JULIAN: The thing with AI is that AI is incredibly lazy; that's one of the problems. It always tries to learn shortcuts. And we need to understand the things that it learns. You always have the example of correlation and causation. And I think if you provide outputs from an AI to a human expert who can judge the validity of the results, that's where you can generate value. If an AI is supposed to make fully automated decisions, then I think it would relatively quickly turn out to be a mess because it might just be a correlation and not a causal relationship.

There's always this famous example of shark attacks and ice cream consumption, both have a very high correlation, but it's not because sharks like ice cream. So I think it's very similar in manufacturing. When you produce stuff, you might find a correlation with a certain process parameter that can't causally have an effect on your quality, for example, or downtime. And if you would have a system that operates itself, it very likely would try to tweak that parameter with perhaps a bad outcome. So I think the human in the loop still remains crucial here.

TROND: What excites you about the future of manufacturing? Everyone's always worried about manufacturing because it's such a big part of the economy. A lot of people lose their jobs if things go badly. And, historically, it's gone up and down. And maybe for a while, in some countries, it's gone mostly down, and it hasn't been the most exciting place to be. Are you excited about the future of manufacturing?

JULIAN: Yes, I mean, definitely. I've always been, and I think, I will always be excited about manufacturing. And obviously, at EthonAI, we will also try to leave a mark on the industry. We are helping big companies to improve everything around quality but also helping them improve their CO2 footprint by reducing production waste. And this is something that really excites me to help these companies to provide useful tools and also see that these tools have an impact in the physical world at big corporations like Siemens. That's a very exciting place to be. I think we will see very, very interesting developments over the next couple of years.

TROND: Well, Julian, it's exciting to jump in and hear a little bit about your world here. I certainly wish you best of luck with Ethon, and it was fascinating to hear. Thank you so much for sharing your perspective.

JULIAN: Thank you very much, Trond.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Explainability in AI. Our guest was Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talked about how to define explainable AI, its major applications, and its future.

My takeaway is that explainability in AI, meaning knowing exactly what's going on with an algorithm, is very important for industry because its outputs must be understandable to the process engineers using it. The computer has not and will not use the product. Only a domain expert can recognize when the system is wrong, and that will be the case for a very long time in most production environments.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 103: Human-First AI with Christopher Nguyen. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, devices, and systems. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where industrial tech is heading. You can find us on social media, and we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Julian Senoner.

Episode 106: Post Lean with Frode Odegard

18 januari 2023 | 44 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Post Lean." Our guest is Frode Odegard, Chairman and CEO at the Post-Industrial Institute. In this conversation, we talk about the post-industrial enterprise going beyond digital and higher-order organizations.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 102 on Lean Manufacturing with Michel Baudin.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Lean is a fundamental perspective on human organizations, but clearly, there were things not foreseen in the lean paradigm, both in terms of human and in terms of machine behavior. What are those things? How do they evolve? We have to start speculating now; otherwise, we will be unprepared for the future. One of the true questions is job stability. Will the assumptions made by early factory jobs ever become true again? And if not, how do you retain motivation in a workforce that's transient? Will future organizational forms perfect this task?

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Post Lean. Our guest is Frode Odegard, Chairman and CEO at the Post-Industrial Institute. In this conversation, we talk about the post-industrial enterprise going beyond digital and higher-order organizations.

Augmented is a podcast for industrial leaders, process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Frode, welcome to Augmented. How are you?

FRODE: Pretty good.

TROND: Yeah. Well, look, talking to Norwegians living abroad that's become a sport of mine. You were born in Norway, software design from there, became an entrepreneur, moved to Silicon Valley. I also know you have an Aikido black belt; we talked about this. This could have become its own podcast, right? There's a long story here.

FRODE: [laughs] Absolutely, yeah.

TROND: But you're also the CEO of the Post-Industrial Institute, which I guess used to be called the Post-Lean Institute. But in any case, there's a big connection here to lean, which is a global community for leaders that are driving transition towards something post-lean, post-industrial, post-something. So with that context, tell me a little about your background and how you ended up doing what you're doing.

FRODE: Born in Norway, as you pointed out. My folks had a process control company, so that was kind of the industry I was born into was industrial controls, which included visiting factories as a child and installing process control systems. So I was doing, you know, circuit board assembly at age eight because when you grow up in a family business, that's what you get to do. And I quickly gravitated towards software. I think I was 13 when I was working on my first compiler.

So my first passion was really programming and language, design, implementation, and that sort of got me interested in theoretical computer science. So very far from what I do today, in some ways, but I think theoretical computer science, especially as a software architecture and all that, teaches you how to think and sort of connect the dots, and that's a good life skill.

At 17, I started a software company in high school. And when I was 22, I immigrated to the United States after some trips here. I was on a Standards Committee. I was on the Sun User Group board of directors as a European representative. It was a weird story in itself, how that happened. So yeah, 1990, 1991, I'm in Silicon Valley.

TROND: So you jumped ship, essentially. Because, I mean, I've heard a lot of people who come to the U.S. and are inspired, but you just basically jumped off the airplane.

FRODE: Yeah, I like to say I was here as an entrepreneurial refugee. Things are different now in Norway, but for a long time, they had strange taxation rules, and very difficult to start companies and scale them. But also, they didn't really have the fancy French word. They didn't really have the milieu. They didn't have a community of people trying to build companies in tech. So tech was very much focused on either military applications, that was its own little industry and community, or the energy industry, the oil industry in particular.

TROND: All of that seems to have changed quite a bit. I mean, not that you or I, I guess, are experts on that. As ex-pats, we're outside, so we're looking in, which is a whole other story, I guess. But I'm curious about one more thing in your background so Aikido, which, to me, is endlessly fascinating, perhaps because I only ever attended one Aikido training and, for some reason, decided I wasn't going to do it that year, and then I didn't get back to it.

But the little I understand of Aikido it has this very interesting principle of using the opponent's force instead of attacking. That's at least what some people conceptualize around it. But you told me something different. You said there are several schools of Aikido, and one of them is slightly more aggressive, and you belong to that school. I found that quite interesting.

FRODE: [laughs] Now I'm wondering about my own depiction of this, but the Aikido that I study is known as Iwama-style Aikido, and it's called that because there was an old town in Japan, which has been absorbed by a neighboring city now, but it was called Iwama, and that's where the founder of Aikido moved during the Second World War, and that's where he sort of completed the art. And that's a long technical story, but he included a fairly large weapons curriculum as well. So it's not just unarmed techniques; it's sword-knife stuff.

And it's a really beautiful art in that all of the movements with or without weapons are the same, like, they will follow the same principles. In terms of not attacking, of course, on a philosophical level, it calls itself the art of peace. In a practical sense, you can use it offensively to, for example, if you have someone who is grabbing your child or something like that, this person is not attacking you, but you have to step in and address the situation, and you can use it offensively for sure.

TROND: Very interesting. I was going to jump straight to what you're up to now, then, which is, I guess, charting this path towards a different kind of industrial enterprise. And you said that you earlier called your efforts post-lean, and now you're calling them post-industrial. It's this continuity in industry, Frode. Tell me a little bit more about that.

FRODE: I think a good way to think about approaches to management and understanding the world around us is that various management practices, and philosophies, and ideas, and so on, have been developed in response to circumstances that were there at the time. So if you think about Frederick Taylor and the problems that he was trying to solve, they initially had a lot to do with just getting work organized and standardized.

And then, in 1930s, you start seeing the use of statistical methods. Then you start seeing more of an interest in the psychology of work and so on. And lean kind of melts all of these things together. A great contribution from Toyota is you have a socio-technical system and organizational design where you have a new kind of culture that emphasizes continuous learning, continuous problem solving using some of these ideas and tools that were developed much earlier.

Now, in the post-war years, what we see is information technology making business more scalable, also contributing to complexity, but certainly making large companies more scalable than they would have been otherwise. And what we see in the mid-1990s leading up to the mid-2000s is the commercial internet, and then we get smartphones. That's the beginning of a new kind of industrial landscape. And what we see then is instead of an increasing tendency towards centralization in firms and business models, you start seeing this decoupling and decentralization. And what I discovered was that's actually a new thing for the human species.

Ever since the invention of agriculture 10,000 years ago and then cities in the Bronze Age a little over 5,000 years ago, and then the industrial revolutions, we've seen a culmination of improved mastery of the world, adapting the world to our needs, which is technology and increasing centralization. You had to move to where the work was, and now we're sort of coming out of the pandemic (Let's hope it doesn't come back.) that has accelerated in the pandemic, so you have this decentralization, decoupling.

And this continuity and the way I started using the term post-lean, and we can jump back and forth as you'd like, it was just because a lot of the assumptions behind the lean practices and how those practices were implemented were based on the idea that you had organizations that lasted a long time. You had long employee tenures. You had a certain kind of a...I don't like this term, but a social contract between the firm and workers and managers and workers. And they would come and do their work on-site in person at the factory, and this world is kind of disappearing now.

And so there's all of this work now being done. I think manufacturing labor forces peaked at a third of the workforce some decades ago. But now it's down to about 11%, even though manufacturing as a share of the economy has remained fairly constant since the 1940s. It's gotten more productive. So there are also all these new jobs that have been created with people doing different kinds of work, and much of that work is knowledge work.

And a lot of these industrial-era management practices and ideas have to be changed for knowledge work. And so that was sort of my initial discovery. That happened in the early 2000s. I started a company in 2004, which was called initially Lean Software Institute. I wanted to basically take these ideas and adapt them to software development. And that was generalized for knowledge work in general. And because we have big clients like Lockheed Martin in the aerospace defense sector, we rebranded the company to the Lean Systems Institute.

And so for ten years, myself and a small team, we did organizational redesign work looking at not just workflow but also a bunch of these other factors, which we can talk about, that you have to take into consideration like knowledge management and so on. And then it was about 2014, 2015, when I discovered, hey, even though we kind of extended lean to look at all these other things, there's this decentralization happening. And maybe we should fundamentally revisit what firms should look like and how the external landscape outside the organization changes the way we think about designing companies.

TROND: Yeah. I found it interesting, obviously, that you started from the software angle. And you told me earlier that, in some ways, your kind of Lean efforts are almost in parallel to, I guess, what could be called the lean movement, although there's such a variety of lean practitioners out there. They're obviously not all in the manufacturing industry. That's the whole point. Toyota managed to inspire a whole host of other companies that had nothing to do with automotive and nothing to do even with any kind of basic manufacturing.

And I guess the software industry is no different; you know, the industry as such was inspired by it. And as you said, Lockheed Martin, and perhaps not only for their manufacturing side, were inspired by it when running their software or other types of maybe even office-based knowledge work.

So as you're coming to these realizations, what sorts of things is it that you then start to think about that are the same and that are different in terms of the classic assumptions of lean, as you know, reducing waste or improving a process in a specific way with all the assumptions, so stable labor force like you said.

FRODE: In that initial period from 2004 to 2014, that's when I really worked on adapting lean to knowledge work. And so you could see some people were trying to reduce knowledge work to kind of a simplified version of itself. They were trying...and so I call that the reductionist approach where they then could count documents as inventory, and they could have a Kanban system and all of that. And the agile movement in software became very enthused about doing just that.

And I think what we did was we went the opposite route, so we took an expansionist approach. So we said, well, we got to keep adding practices and models to the original lean to deal with not just the value stream architecture of an organization but also its structure, so organization architecture, how it manages information, and the shape of that information, where it's stored, and how it's designed. And it's also that's information architecture.

And, of course, what we know from wonderful people like Melvin Conway, who discovered that there's a direct relationship between your technology architecture and the shape of the organization, is we really need to also take into consideration what we then called product architecture. Because if your product architecture, and your organization architecture, and your workflow, your value stream architecture is mismatched in product development as well as in manufacturing, that leads to huge misalignment. And that's a cause of massive inventory problems and so on.

And then the last of the five dimensions that we have in this model, which we call the lean systems framework, was a way to look at an organization's culture. So there are values that you explicitly promote, so we call them the organizational ideals. And then you have the actual behaviors that don't always live up to the ideals. And then you have people's beliefs about the past, the present, and the future, so we call all of that social architecture.

And I think the last bit of work we did in this model, which is a pretty rich model or a metamodel of organizations, is we added the way to look at leadership styles and leadership effectiveness as a function of character and competence of perceived effectiveness. So this was used in a bunch of mostly large organizations over a period of 10 years, and Lockheed was able to get a 72, 73 production in lead time, largest subcontractor in the Future Combat Systems. I think that's the biggest defense project in the history of the United States. [laughs] It was canceled by Congress in the end, but yeah, they got some great results.

And a lot of that was because workflow bottlenecks were caused by these other problems in these other four dimensions that had to be addressed, so that was kind of our initial realization. And then there's that big break where we look at decentralization, and how is that causing us to revisit the assumptions about organizational design? So it's not like we get new dimensions of organizational design as much as starting to think about what's the ideal design. And those answers turn out to be very different than they have been up till now.

TROND: So that's interesting. So both...you were kind of discovering some...maybe not weaknesses, just, you know, some social change that was happening that is affecting organizations nowadays, you know, in America or anywhere else trying to implement lean principles.

But also, what you were saying about the agile movement and what's happening in software industrial organizations that it doesn't reflect what needs to be happening in industries across the board and perhaps not even in their own organizations because it is, I guess, if I paraphrase you a little bit, the agile principles they are very valid for achieving a very smooth software development process. But they're not so valid for a lot of other aspects having to do with social and organizational phenomena that you also need to take into account eventually.

So, I mean, if that's correct, it's interesting, right? Because everybody obviously focuses on what they are doing. So the agilists, I guess, they're optimizing a software development process. The lean folks, the classic lean folks, are optimizing a production line. But today's knowledge work is, I guess, over these years also, Frode, it has changed a bit.

FRODE: It has changed, and there is more machine systems, software systems. We have more tools, although we're still in the early stages of what's going to come with the use of AI to make knowledge work more productive and so on. But I think one thing that's important, because I don't want to throw anyone under the bus here, is practitioners. There's a lot to be learned from practitioners.

Often, they're kind of apologetic, "Oh, I'm not doing the pure X, Y, Z method. We have to adapt it a little bit." Well, guess what? That's what Toyota did. And so what happened is a lot of western companies they were just trying to copy what Toyota did without understanding why those things work there. And it's when you can adopt it, so that's also sort of martial arts. --

TROND: That's actually a fantastic point, Frode, because if you're very, very diehard lean, some people would say, "Well, lean is whatever Toyota does." But on the other hand, for Toyota, lean is whatever Toyota does, right? And it seems to have worked for them. That does not even mean that Toyota would tell you to do exactly what they are doing because they will tell you what makes sense for your organization. In a nutshell, that seems to be –

FRODE: And I was there. I mean, I was, you know, I remember one time I was really thinking about standardizing work. And I was reading about the history of all this and reading about Frederick Taylor and the very early days of all of this. And I was coming up with a checklist for housework. I was trying to implement standard work for housework. And guess what? It didn't really work. My girlfriend was upset. [laughter]

TROND: Implementing standards for housework. I like it.

FRODE: Yeah. I mean, if you see something that needs to be cleaned, just clean it. I was like, "No, no, we need a checklist. We need your exit and entry conditions."

[laughter]

TROND: You should work at ISS, you know, the big cleaning professionals company.

FRODE: There you go. And people have done that, right? But I like to tell this joke about how do you know the difference between a terrorist and a methodologist? And the answer is you can negotiate with a terrorist.

TROND: Yeah, that's right.

FRODE: So the methodologist believes that his or her methodology is the answer to all things. And so what we were trying to do with the Lean Systems Framework was not to say, "Ah, you know, all this lean stuff is invalid." We were trying to say, "Well, the methods that they had and the practices that they had that were available to us via the literature...because we never went to visit Toyota. We talked to a bunch of companies that were doing a lot of these things, and we were familiar with the literature.

But we realized there's a whole bunch of other things that are not being addressed, so we have to add those. And that's why I called it the expansionist approach as opposed to the folks taking the reductionist approach, which is we have to shoehorn everything into making it look like manufacturing. But, you know, product development is not manufacturing. And Toyota's product development practices look nothing like their manufacturing processes. It's completely different. And that's a much less well-known area of lean...although the Lean Enterprise Institute has published good stuff on this book. Lean product development is completely different from lean production. And that was not as well-known and certainly not known by the people in the agile world.

Our attitude was always, well, the circumstances change or even from one company to another, the tools might have to change. And so the skill you want to develop in our case as researchers, and advisors, and teachers, or in the case of practitioners, as leaders, or implementers, is keep learning about what other people are doing and what works for them and try to understand what the deeper principles are that you then use to construct a solution that's appropriate for that situation. That's really all it is.

TROND: That's fabulous. So tell me then, apart from Lockheed Martin, what are some of the other organizations that you've worked with? How have they thought about these things? I mean, how does your community work? Is it essentially, I mean, before COVID at least, you met, and you discuss these things, and you sort of reflect on how they show up in your organizations and discuss best practices. Or do you kind of write papers together? How does this knowledge evolve in your approach?

FRODE: It's important to point out here, like in the history of the company, which has been around now for (I'm feeling old.) 18 years, so after the first ten years, there was a big break because that's when we started working on okay, well, what comes after even the expansionist version of lean that we were doing, which was called the Lean Systems Framework? And that's when we started working on all of this post-lean stuff.

And so the companies we worked with in the first decade were the likes of AT&T, and Sony, and Lockheed, and Honeywell, and mostly large companies, a few smaller ones too. But they had a lot of problems with complexity. And often, they were doing a combination of hardware and software. And they were in industries that had a lot of complexity. So in 2014, 2015, there was a big shift where I'd spent about six months to a year reading, talking to a bunch of people, trying to come up with what was going to be the next new thing.

And that was kind of the journey for me as a founder as well because I felt like I'd done all this organizational redesign work, soup to nuts. And it wasn't just Kaizen. We did Kaikaku, which is much less known in the lean world, and that's radical redesign, basically. And we did this working on a board C-level with a lot of companies.

TROND: Tell me more about Kaikaku. Because, like you said, it's not a vernacular that's really well-known outside of the inner circle of lean, I guess.

FRODE: Yeah. So Kaikaku is where you look at an organization, and basically, instead of thinking about how do we put in mechanisms to start improving it incrementally, you say, "Well, there's so much low-hanging fruit here. And there's a breakthrough needed in a very short time. And we're just going to put together a design team, basically, a joint design team, and essentially redesign the whole thing and implement it. So it is a radical redesign. It hasn't been; at least, at the time we were doing it, there were not a lot of details available in the literature.

And you heard stories like Ohno-san would walk into a factory and just say, "Well, this is completely unacceptable. Move this machine over here, and this machine over here. And can't you guys see..." So we didn't do it that way. We didn't tell the clients what the answer should be. We taught them. We had the executive spend a week with us learning about the Lean Systems Framework, and they mapped out the organization they had. And then, basically, we facilitated them through a process that could take sometimes a few weeks designing the organization the way it should be. And then there was an implementation project, and they put it in place, so...

TROND: But Kaikaku basically is a bit more drastic than Kaizen.

FRODE: Very much so.

TROND: Yeah. So it's like a discontinuous sort of break. It's not necessarily that you tell people to do things differently, but you make it clear that things have to be different maybe in your own way. But you're certainly not going for continuous improvement without any kind of disruption. There will be disruption in Kaikaku.

FRODE: I mean, it is disruption. And if you think of the Fremont Factory Toyota took over, that was a reboot. [laughs] And so now --

TROND: Right. So it's almost as if that's where you can use the software analogy because you're essentially rebooting a system. And rebooting, of course, you sometimes you're still stuck with the same system, but you are rebooting it. So you're presumably getting the original characteristics back.

FRODE: So I think of it as sort of a reconfiguration. And in the case of the Fremont factory, of course, there were a bunch of people who were there before who were hired back but also some that weren't that we tend now to avoid just because the knowledge people had was valuable. And in most cases, the issue wasn't that people were malicious or completely incompetent. It was just that the design of the organization was just so wrong in so many ways. [laughs]

And what we had to do, it was more of a gradual reboot in the sense that you had to keep the existing organization running. It had customers. It had obligations. And so it wasn't a shutdown of the factory, the proverbial factory, it wasn't that. But yeah, after I started looking at the effects of decentralization and starting to question these assumptions behind lean practices the way they had appeared in the mainstream, that was around the time, early 2015, I started to use the term post-lean.

It wasn't because I thought I had all the answers yet or certainly, and still, I don't think I do. But it was clear that there was an inheritance from lean thinking in terms of engaging people in the organization to do things better. But the definition of better I thought would change, and the methods I thought would change. And the assumptions behind the methods, such as long-lasting organizations, long employee tenures, tight coupling between people in organizations, organizations taking a long time to grow to a large size, and human problem solving, which already was being eaten by software back then or elevated, I should say, by software, all of these assumptions needed to be revisited so...

TROND: They did. But I have to say, what a gutsy kind of concept to call it post-lean. I mean, I co-wrote a book this year, and we're calling things Augmented Lean for the specific reason maybe that we actually agree with you that there are some things of lean that are really still relevant but also because it takes an enormous confidence, almost a hubris, to announce something post a very, very successful management principle.

FRODE: It was the theoretical computer scientist in me.

TROND: [laughs]

FRODE: So I thought that surely from first principles, we could figure this out and not that it would be the same answer in every situation. But I think it was also, at that point, we had a decade of field experience behind us in doing customized organizational redesign with clients in many different industries. So we knew already that the answer wasn't going to be the same every time. And in a lot of the lean Literature, the assumption was that you weren't really going to dramatically change the organizational structure, for example, which we had a lot of experience with doing.

And we already had experience with teams of teams, and just-in-time changes, and reconfigurations, and so on because we thought of organizations the way software people think of organizations which are, you know, they're computational objects that have humans, and then there are social, technical objects. And they're reconfigurable. And I think if you grew up in a manufacturing world, the shape of the organization is sort of attached to... there are physical buildings and equipment and all of that. So --

TROND: And this is so essential to discuss, Frode, because you're so right. And that's a real thing. And that's something we write about in our book as well. There is a very real sense that I think, honestly, the whole manufacturing sector but certainly the first automation efforts and, indeed, a lot of the digital efforts that have been implemented in manufacturing they took for granted that we cannot change this fact that we have infrastructure. We have people; we have machines; we have factories; we have shop floors. All of these things are fixed. Now we just got to figure out how to fit the humans in between, which is how they then interpreted waste, being let's reduce the physical waste so that humans can move around.

But really, the overall paradigm seems to have been, and you correct me if I'm wrong, but it seems to have been that the machines and the infrastructure was given, and the humans were the ones that had to adapt and reduce all this waste. And no one considered for a second that it could be that the machines were actually wasteful themselves [laughs] or put in the wrong place or in the wrong order or sequence or whatever you have. But with other types of organizations, this is obviously much easier to see it and much easier to change, I mean, also.

FRODE: Yeah, yeah, absolutely. And software is an example of this because now we take for granted that a large percentage of the population works from home and don't want to go back. But if you are part of that 10%, 11% of the population working in a factory and you have to show up at the factory because that's where the machine is that goes ding, that, you know, [laughs] it's not work that requires only a low level of education of course. That hasn't been the case for a while. And these are people with master's degrees. And they're making sure all of this equipment runs. This is fancy equipment.

So what we learned in that 10-year period was this is not just about workflow. It's a five-dimensional model, so there's workflow, organization structure, and knowledge management, the technology, architecture, the product you're making, and the culture. And all of these are five axes if you will, So 5D coordinate system and you can reconfigure. You can make organizations into anything you want.

Now, the right answer might be different in different industries at different lifecycle stages of companies. And basically, our thinking was that we weren't going to just teach our clients or even help our clients. We certainly weren't going to just tell them the answer because I always thought that was a terrible idea. We were going to help them redesign themselves for their emerging landscape, their emerging situation, but also help them think about things, or learn to think about these things in general, so that if their landscape changed again, or if they merged with another company, then they had the thinking skills, and they understood what these different dimensions were to be able to redesign themselves again.

TROND: That makes a lot of sense.

FRODE: That's kind of the whole –

TROND: I just want to insert here one thing that happened throughout, well, I mean, it was before your time, I guess. But remember, in the '70s, there was this concept among futurists, Toffler, and others that, oh, we are moving into a service economy. Manufacturing the real value now is in services. Well, that was a short-lasting fad, right? I mean, turns out we are still producing things. We're making things, and even the decentralization that you're talking about is not the end of the production economy. You produce, and you are, I mean, human beings produce.

FRODE: No, I never thought that we would see the end of manufacturing. And the term post-industrial, he was not the person that coined it, I think. It was coined 10 or 20 years earlier. But there's a book by Daniel Bell, which is called The Coming of Post-industrial Society, where he talks about both the sociological challenges and the changes in the economy moving to a more service-based knowledge-based economy. Of course, what happened is manufacturing itself became more knowledge-based, but that was kind of the whole idea of what Toyota was doing.

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TROND: So, Frode, tell me a little bit about the future outlook. What are we looking at here in the lean post-industrial world? What will factories look like? What is knowledge work going to look like?

FRODE: Yeah, so I think what we're going to see is that companies that do manufacturing are slowly but surely going to start to look like other kinds of companies or companies that do knowledge work. The content of manufacturing work has become more and more filled with knowledge work already. That's a process that's been going on for decades.

As manufacturing technology improves, I think after many, many generations of new technology platforms, we are going to end up in a world where basically any product that you order is going to be either printed atom by atom in your home or in a microfactory, if it's a big bulky thing, in your neighborhood where you can rent capacity in a just-in-time basis.

That's not going to happen overnight. This is going to take a few decades. But you can easily see how this kind of mirrors what happened to old chains like Kinko's and so on where if you needed something to be printed, I mean, I remember there were printers. [laughs] And then you had to go to the equivalent of a Kinko's, and you could, you know, if you wanted to print 100 copies of a manual back in the day when we still did that, you could get that done, and that was surely more efficient than doing it at home.

And in your home office or at your office, you would have a laser printer. And now we have a $99 inkjet printer, or you just might get it included when you order your laptop, or you may not even care anymore because you have a tablet, and you're just looking at it on the tablet. So there's this phenomenon of some of the things getting smaller and almost disappearing.

Now what has happened...this was underway for a while, but the relationship between people and companies has increasingly become more loosely coupled. So a big part of the post-industrial transition is that individuals are empowered, and organizations now become more of a means. They're not institutions that are supposed to last for a long time. I think that ideal is fading. And so they're in a means to an end to produce economic value.

And every investor will agree it's just that they're going to be much more reconfigurable, a lot of management work. There's managing resources, tracking progress, tracking inventory, communicating with customers. A lot of that stuff is going to be eaten by software and powered by AI. That doesn't mean people go away. But I think that a lot of the repetitive management administrative work, much more than we can imagine today, will be eaten by software and AIs.

TROND: But one of the consequences of that surely, Frode, is somewhat risky because there was a certain safety in the bureaucracy of any large organization, whether government or private, because you knew that, yes, they might be somewhat stiflingly and boring, I guess, or predictable, whatever you might want to call it, but at least they were around, and you could count on them being around.

And if you wanted to know what approach was being applied, if you had experienced it once, you knew it. And if you were a government, you knew that this is the GE Way or this is the whatever way, and it was stable. But what you're charting here is something where the only stability might be in the configuration of machines but even that, of course, you know, evolves really rapidly. And even the algorithms and the AIs and whatever is put into the system will evolve. And then, the humans will move around between different organizational units a little quicker than before. So where do you control [laughs] what's happening here?

FRODE: So one of the things to keep in mind...I'll answer this from a technical perspective but also from a sociological perspective. So I'll take the latter first. So we are used to a world of hierarchies. So from the invention of agriculture, that's when silos were invented. The first organizational silos were actually centered around corn silos [laughs] and so a shared resource, right? And we need governance for that, you know, who gets the corn and how much your family's already had enough this week and so on.

And then, in the Bronze Age, you see more specialization of labor and more hierarchies. So the pyramids were built by determined organizations. [laughs] so just like Melvin Conway would tell us. And the same happened with The Industrial Revolution. So you had management; you had oversight. And then as we are thinking about this matured, you know, we developed this notion of organizational values. So that had to do with the day-to-day behavior so people, including managers, and how they should treat their people and what the employee experience should be like.

And then kind of management is about organizing people or organizing people and resources to pursue short or long-term objectives. So, what happens if the AI goes crazy? What happens if there's a bug in the software if there is a flaw? On the technical side of this, what I would say is just like we have people who are concerned about safety with robots, industrial robots in factories, you're going to have people who look at the same kind of thing in organizations. You're also going to have AI watching AIs. So you're going to have a lot of software mechanisms that are there for safety.

People also have the option to leave. The threshold for quitting your job now and you log out from your current employer if you're sitting in your home in the Caribbean somewhere [laughs] because you can live wherever you want and logging in somewhere else and taking a job, that threshold is lower than ever. So organizations have an incentive to treat their people well.

TROND: Well, the interesting thing, though, is that Silicon Valley has been like that for years. I mean, that was the joke about Silicon Valley that you changed your job faster than you changed your parking space.

FRODE: [laughs]

TROND: Because your parking space is like really valued territory. It's like, okay, here's where I park. But you might go into a different part of the office building or in a different office building. So this has been part of some part of high tech for the industry for a while. But now I guess you're saying it's becoming globalized and generalized.

FRODE: Yeah. And part of it it's the nature of those kinds of jobs, you know, of doing knowledge work that's where you're not tied to equipment or location as much. Now, of course, in Silicon Valley, you've had people go back and forth about, and not just here but in other innovation hubs too, about the importance of being together in the room. You're doing brainstorming. You are talking to potential customers. You're prototyping things with Post-it Notes. People have to be there.

And I think there's an added incentive because of the pandemic and people wanting to work from home more to develop better collaboration tools than Post-it Notes on whiteboards. But the last data we have on this is pre-pandemic, so I can't tell you exactly what they are today. But the employee tenures for startups in Silicon Valley when we looked last was 10.8 months average tenure. And for the larger tech companies, you know, the Apples and the Googles and so on, was a little bit more than two years so between two and three years, basically.

And so because more jobs in the economy are moving into that category of job where there's a lower threshold for switching, and there's a high demand for people who can do knowledge work, you're going to see average employee tenders going down just like average organization lifespans have been going down because of innovation.

TROND: Which presumably, Frode, also means that productivity has to go up because you have to ramp up these people really fast. So your incentive is Frode started yesterday. He's already contributing to a sprint today, and on Thursday, he is launching a product with his team. Because otherwise, I mean, these are expensive workers, and they're only going to be around for a year. When is your first innovation?

FRODE: It depends on where the company focuses its innovation. And this will not be the common case, but let's say that you are developing a whole new kind of computing device and a whole new operating system that's going to be very different. You have to learn about everything that's been done so far, and it takes a lot to get started. If what you are doing is more sort of applied, so you're developing apps to be used internally in an insurance company, and you're an app developer, and you know all of the same platforms and tools that they're already using because that was one of the criteria for getting the job, yeah, then you ramp up time is going to be much shorter.

All of these companies they will accept the fact, have had to accept the fact, that people just don't stay as long in their jobs. That also gives some added incentive to get them up and running quickly and to be good to people. And I think that's good. I think it's nice that employers have to compete for talent. They have to have to treat their people well. I think it's a much better solution than unions, where you would basically try to have a stranglehold on employers on behalf of all the workers.

And the less commoditized work is, the less standardized the work is in that sense. The less business models like those of unions, whether they're voluntarily or involuntarily, because the government sort of makes it easier for them to set up that relationship and sort themselves.

The thing that surprised me is that now and as we're coming out of COVID, unions in the United States are making somewhat of a comeback. And I'm sort of scratching my head. Maybe this means that there are a lot of companies where they have scaled because of IT, Amazon being an example. They wouldn't have been able to scale the way they have without information technology. But they haven't yet gotten to the point where they have automated a bunch of these jobs.

So they've hired so many people doing soul-sucking repetitive work, and they're doing their best to treat them well. But the whole mentality of the people who have designed this part of the organization is very Taylorist. And so people are complaining, and they're having mental health problems and so on. And then yeah, then there's going to be room for someone to come and say, "Well, hey, we can do a better job negotiating for you." But gradually, over time, fewer and fewer jobs will be like that.

One of the sort of interesting aspects of the post-industrial transition is that you have industries...well, some industries, like online retail on the historical scales, is still a young industry. But you have industries that when IT was young, you know, I think the oldest software company in the U.S. was started in 1958. So in the aftermath of that, when you started seeing software on mainframes and so on, what software made possible was scaling up management operations for companies. So they made them more scalable. You could open more plants. You could open more offices, whether it was manufacturing or service businesses.

And this happened before people started using software to automate tasks, which is a more advanced use. And the more complex the job is, and the more dexterity is required, physically moving things, the higher the R&D investment is required to automate those jobs. The technology that's involved in that is going to become commoditized. And it's going to spread.

And so what you're going to see is even though more people have been hired to do those kinds of jobs because the management operations have scaled, fewer people are going to be needed in the next 10-20 years because the R&D investment is going to pay off for automating all of those tasks. And so then we're going to get back to eventually...I like to think of Amazon as just like it's a layer in the business stack or technology stack.

So if I need something shipped from A to B or I need to have some sort of a virtual shopping facility, [laughs] I'm not going to reinvent Amazon, but Amazon has to become more efficient. And so the way they become more efficient is drone delivery of packages and then just-in-time production. And then, they take over everything except for the physical specifications for the product to be manufactured.

TROND: It's interesting you say that because I guess if you are Amazon right now, you're thinking of yourself in much wider terms than you just said. But what I'm thinking, Frode is that I'm finding your resident Scandinavian. I'm seeing your Scandinavianhood here. The way you talk about meaningful work, and knowledge work, and how workers should have dignity and companies should treat people well, I found that very interesting.

And I think if that aspect of the Scandinavian workplace was to start to be reflected globally, that would be a good thing. There are some other aspects perhaps in Scandinavia which you left behind, and I left behind, that we perhaps should take more inspiration from many other places in the world that have done far better in terms of either manufacturing, or knowledge work, or innovation, or many other things. But that aspect, you know --

FRODE: It's a big discussion itself. I mean, I was kind of a philosophical refugee from Norway. I was a tech-oriented, free-market person. I didn't like unions. I didn't like the government.

TROND: [laughs]

FRODE: But at the same time, that didn't mean I thought that people should not be treated well that worked into the ground. I thought people should just have healthy voluntary sort of collaborative relationships in business or otherwise. And I've seen technology as a means of making that happen. And I have no sympathy with employers that have trouble with employees because they treat people like crap. I think it's well deserved. But I also have no sympathy with unions that are strong-arming employers.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Post Lean, and our guest was Frode Odegard, Chairman, and CEO at the Post-Industrial Institute. In this conversation, we talked about the post-industrial enterprise.

My takeaway is that lean is a fundamental perspective on human organizations, but clearly, there were things not foreseen in the lean paradigm, both in terms of human and in terms of machine behavior. What are those things? How do they evolve? We have to start speculating now; otherwise, we will be unprepared for the future. One of the true questions is job stability. Will the assumptions made by early factory jobs ever become true again? And if not, how do you retain motivation in a workforce that's transient? Will future organizational forms perfect this task?

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. And if you liked this episode, you might also like Episode 102 on Lean Manufacturing with Michel Baudin. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us; we would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, devices, and systems in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please go ahead and share this show with colleagues who care about where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Frode Odegard.

Episode 105: Product Lifecycle Management's Momentum in Manufacturing with Jim Heppelmann

7 december 2022 | 47 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Product Lifecycle Management's Momentum in Manufacturing." Our guest is Jim Heppelmann, CEO of PTC. In this conversation, we talk about the why and the how of product lifecycle management's momentum in manufacturing.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 93: Industry 4.0 Tools.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

The momentum is clear, and one indication is the trend that PLM is being elevated to an enterprise system. But why is PLM such a hot market right now? One key word is greenhouse gas reduction because companies need a system of record to track their emissions, and this is not easy to do without a system in place.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Product Lifecycle Management's Momentum in Manufacturing. Our guest is Jim Heppelmann, CEO of PTC. In this conversation, we talk about the why and the how of product lifecycle management's momentum in manufacturing.

Augmented serves an audience of executives, industry leaders, investors, founders, educators, technologists, academics, process engineers, and shop floor operators across the emerging field of frontline operation. And it's hosted by futurist Trond Arne Undheim and presented by Tulip.

Jim, welcome to the show. How are you?

JIM: I'm great, Trond. Great to be with you here this morning.

TROND: Yeah, Jim. I thought we would talk a little bit about industrial automation and some specifics. But first of all, I wanted to talk a little bit about you. You grew up in Minnesota, got yourself a mechanical engineering degree, and became an entrepreneur, and sold your company to PTC. You were the CTO, I guess, for a while and now the CEO. It's been quite a journey.

JIM: Yeah, it's fun. And by the way, industrial automation and related topics is my favorite topic. I was born on a dairy farm in Southeastern Minnesota, part of a very large family. It was a tough life. We never quite had enough money. So I was ambitious. I wanted to do something. I wanted to have a better life than I grew up with, not that it was bad, but maybe I wanted to have a little bit more economic security.

I decided to become an engineer because I had spent a lot of time with equipment, machines, using them but also fixing them, taking them apart, putting them back together. I was good at math and science. So I went into mechanical engineering, but right away, I was drawn to software. And so I really got a major in mechanical engineering, a minor in computer science, and focused on how do you use computer science to do engineering? That led me to join a computer-aided design company, a CAD company.

As an intern, I was assigned to a new idea they had which they called product data management. It was not very glamorous compared to the graphics of CAD, where you could twirl models around on the screen and so forth. So it's the kind of thing that you assigned to a new intern. As an intern, I took to it; I mean, it made a lot of sense to me. So basically, that's what I specialized in in my career, especially the early part of my career.

And I became quite an expert at PLM, or at the time; it was called PDM. That led me, ultimately, when I was exposed to the internet, to say, "Wow, if you really leverage web technology with a light client, a web browser, make it easy for people to engage no matter what company they're in, then you could have whole supply chains working together in a very efficient way.

So that led me to create a company called Windchill Technology, kind of a funny name based on a company in Minnesota; that's where the Windchill part comes from. But PTC came to acquire this company, and the business just really took off at PTC. In the ensuing years, I became the Chief Technology Officer across all of PTC, and then, as you said, that led to becoming the Chief Executive Officer a dozen years ago.

It's been a great ride. It's been a lot of fun. We've accomplished a lot. The technology has come so far. Hard to imagine in the early days, it would end up here. But it's been a very exciting career trajectory, for sure.

TROND: So, Jim, before we move into talking about product lifecycle management, I wanted to ask you a more generic question: what is the most challenging part of being a CEO? So you've gone from being an entrepreneur to being a CEO of a much larger structure here. What's exciting, and what's challenging about that?

JIM: Yeah, I mean, I think what is exciting is also challenging, which is so much context-switching. In a single day, I go from worrying about budgets and financial plans to meeting with happy customers, sometimes frustrated customers to meeting with sales teams and R&D teams and R&D projects. And it's just a constant switch from one topic to another, which is exciting because they're all topics I like.

But it puts a lot of pressure on you to very quickly remember where you left this conversation off last time you were involved and how to dive right back in and pick it up. And I think there's some pressure that comes from that, you know, to be on your toes ready to go and just switch from topic to topic to topic. And then, of course, there's the pressure of a public company that every 90 days, we have an earnings call. And our investors want to hear good news. Fortunately, we've had a lot of good news, but there's always a lot of pressure to make sure you keep it going.

TROND: I wanted to jump then to product lifecycle management which is a specialty topic to you; it's not, right? Because you've been involved with this for a while, [laughs] and it's a passion for you. I guess in industrial automation; there are a lot of three-letter acronyms and such. But if you'd give your best way to explain how this software got started, what was the original intention? I mean, this is a while back now. We're talking 1998 when this software suite got created when Windchill started creating this software. What did it do then, and what does it do now?

JIM: Well, PLM is really the system of record for product data. So if you think of salesforce.com, they got started just a couple of years later. They're a system of record for customer information, the 360-degree view of the customer. And in most companies, they have an ERP system, and that's the system of record for the financial data, all the purchase orders, and invoices, and whatnot, and might have a human resource information system, something like Workday, that's the system of record for all your employees.

But if you're an industrial company that makes products, you have a lot of product data. And where is the system you can go to to find and interact with that data in your day-to-day job as part of that product development, or manufacturing, or customer support process? And so PLM really has become that system of record. And for an industrial company that makes products, it's a pretty important system of record. Like a CRM system or an ERP system, you're not just collecting and managing the data; you're also transacting against it, applying change orders, and building configurations of it, and whatnot. So PLM has become recognized in industrial companies as a critical anchor system of record. That's the way I like to think about it.

TROND: Yeah, and we'll get into some of it after a while. But I guess product lifecycle is something that has gone much higher on the agenda for environmental reasons and others. So, I guess, if you think about a product from its ideation and to its disposal, essentially, it's a long chain of events that such a system, theoretically, could help a company with.

JIM: Yeah, for sure. And just to go a little deeper in that, a lot of products are made of mechanical parts, electronic parts, software parts. They come in lots of different configurations. They change from year to year and sometimes month to month, so there are a lot of engineers and product managers involved. And then purchasing gets involved, and supply chain management gets involved because very few companies build everything themselves; they work with a supply chain.

Then you're bringing in the factory and production planners, and then ultimately, the production process. They need this data, and they need the right configurations and versions of it. Then you ship the product to the customer, and you provide, in many cases, service and support. And you can't do that well without understanding the configuration of the product and all the versions of mechanical electronics and software parts in it.

Really what we're talking about is, yeah, following that product throughout its lifecycle. Sometimes I like to use a golf analogy, like the front nine and the back nine on an 18-hole course. The front nine is everything that leads up to the product being manufactured, and the back nine is everything that happens thereafter. And to really do product lifecycle management, you have to think of all 18 holes, and that's kind of the focus we've had here at PTC.

TROND: To what extent is product development kind of a management discipline, and to what extent do you feel like it's a technical discipline? And clearly, the software here is enabling digital records, I guess and tracking a product process. But product development historically it's not among those areas of management that have received the most attention, I guess, arguably. So how do you see this relationship?

JIM: I think it's become more and more of a management methodology over time because you start with innovation. You can't legislate innovation. That sort of just happens naturally, organically, if you will. But every single product has a plan. It has a cost target. It has a launch date target, you know, a time-to-market target if you will. It has a quality target. More and more, it might have regulatory accomplishments or protocols it has to comply with.

So I think that what companies are trying to do is unleash innovation but in a managed process. A lot of companies historically have used management techniques like waterfall management or stage gate. More and more companies are intrigued now about could we use agile, you know, scrum management methodologies to develop hardware like we develop software? Because it really works well for software. Now, hardware is not software, so there are some special concerns there. But definitely, there's a management methodology, and I think PLM really is critical to doing that management methodology well.

You can't manage a process if you don't have access to the right information. You can't even have a dashboard if you don't have the right information. But more important than the dashboard, the people participating in the process can't be expected to do the right things if they're not given the right information to work against. And that's really why PLM is so critical to managing the whole cost, quality, time to market, regulatory, and similar concerns.

TROND: So why, then, is PLM such a hot commodity right now? Because I guess that's what you're arguing, that it's becoming more and more crucial. What are the inflection points since 1998? And what is it now that makes it such a crucial system?

JIM: Yeah, well, I think a lot of industrial companies are really leaning into digital transformation initiatives, a huge amount of spending. And it's because they see themselves potentially being disrupted or losing competitive advantage, at a minimum, if they're not sufficiently digital. And so when they lean into digital transformation, they quickly realize how much could we possibly transform a product company if we're not even managing our digital product data? So PLM quickly becomes a must-have these days in a digital transformation initiative.

And then, of course, COVID has been a huge catalyst because it was hard to share information when everybody came to work every day. But if, on any given day, 40%, 50%, 60% of your employees are working from home, how do you interact with them? You can't walk down the hall and knock on their door anymore because they're not there, and if they're there, you're not there. I think what's happened as a consequence of COVID and the hybrid workforce that we're probably now left with forever; I think PLM is just absolutely critical must-have. So we've gone from nice-to-have and engineering tool to must-have enterprise tool.

TROND: Let's talk about the hybrid workforce for a second. I mean, well, there were two massive predictions, one, this will never happen in industrial companies because we're actually talking about factories, and you can't be away from the factory. And then, of course, there were the future of work people saying, "This should have happened a long time ago. There's no need for any people, and factories are, you know, 24/7. There's technology. You don't really need to come in there." You've said some of these changes, you know, we're stuck with them forever. What does the hybrid workforce mean in an industrial organization like your own, for example, or your largest clients?

JIM: I think if you look at a manufacturing company who has factories and such, you could separate their workforce into knowledge workers; these are people who are paid to think. And frontline workers are people who are basically paid to show up and use their hands, and feet, and so forth. And I think that frontline workers have to be there, and in most manufacturing companies, they are. And they very carefully protected these workers right through COVID because if those workers don't come to work, the factory doesn't run; there are no products.

But the knowledge workers, the engineers, the finance people, the procurement people, supply chain, the planners, the service and support people, they really work on a computer all day. And whether that computer is in the office, or at home, on the dining room table doesn't matter that much in terms of their ability to get their job done so long as they have access to the right information and an ability to participate in the process digitally. So I think we're going to see...the forever state I envision here is hybrid on the knowledge worker side and in the factory on the frontline worker side, or sometimes at the customer side in the frontline worker side of the equation.

TROND: To what extent does a PLM system then actually help frontline workers? So is it more of an enterprise system that helps, I guess, the leadership?

JIM: It's an enterprise system. It is critical for the knowledge workers and informs the frontline workers. The knowledge workers need to participate in the process of creating and evolving this information over time. What's in this product we're going to launch, and how will that change? We have supply chain problems. We have to find a new supplier, okay, that's a change to the product. If we come up with new and better ideas or fix bugs, those are changes to the product. So the product information is changing. And there are a lot of people interacting with it online.

So PLM is the system that they interact with. And they might be in the office interacting with PLM. They might be at home. That's knowledge workers. For frontline workers, when they come to the factory, they're supposed to build something today. What am I supposed to build? And PLM supplies them the information: here's the product you're working on today; here's the configuration, the bill of material, and the work instructions to go build that product. So I'd say think of frontline workers as consumers of this information. And sometimes, they're given feedback because the process isn't sufficiently effective. But the knowledge workers are really the ones developing and evolving this information over time.

TROND: Give me some examples of how a PLM system is used by real customers; you know, what are the biggest use cases when you purchase such a system? And over time, what are the biggest value drivers of such a system in a real organization?

JIM: The main reason all companies buy PLM is cost, quality, time to market associated with the products. A team of engineers and product managers is going to specify an engineer, and simulate, and iterate, and they're going to come up with some product concepts. And they're going to be working with the purchasing department on who will we source these parts from. They might be working with contract manufacturers who are going to actually produce the product if we're not going to produce it ourselves.

If we're going to produce it ourselves, we have to work with the manufacturing engineers and then ultimately the factory. If this is a long-lived asset, we're going to have to figure out how would we service it? What kind of spare parts are we going to need? What kind of technical documentation and service work instructions would be required?

So there are many, many people who have to interact with this product information before that product ever comes to life. Again, if you want to do this quickly, you know cost, quality, time to market. Let's take time to market; if you want to do it quickly, you need everybody working on the right information simultaneously. If you want to have quality, you got to make sure nobody's working on the wrong information because that's the source of quality problems; somebody buys the wrong part or makes the part incorrectly, uses the wrong version of the drawing, or the model, or what have you. That's where quality problems come from.

And then on the cost, if you're trying to hit a cost target, you need to be way up front simulating if we built a product that looked like this and we bought all these parts from the suppliers, and we assembled it like this, what would it cost to do all that? All the decisions made during product development lock in cost. You don't spend so much cost, you know, so much money developing the product, but you make all the decisions that lock in cost later. If you design an expensive product, the factory is not going to make an inexpensive product; they're going to make an expensive product. People really need to collaborate.

But then there are some advanced topics. So cost, quality, time to market, everybody needs that. Some people need regulatory compliance. Some people want to drive greenhouse gas emissions reduction strategies. Some people want to do what I call platform strategies, where they reuse many modules in many different configurations to be efficient. And there's more, and we can probably get into that. But there's a series of more advanced strategies that really go more to the competitive advantage that a company is trying to develop.

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TROND: So, Jim, talk to me a little bit about the future outlook. So there are some very exciting prospects here for more ambitious uses of PLM software. If you are looking into the next, you know, two to five years, what are some of the more advanced use cases for this kind of software? What are customers trying to do? You've been talking a little bit about regulatory requirements and greenhouse gas emissions. What exactly does that use case look like?

JIM: Well, let's take regulatory first. Some products are launched into regulated markets; a good example would be medical devices. That whole product development process and use thereof is regulated by the FDA or similar agencies around the world. Or let's take aircraft; they're regulated by the FAA. Or let's take automobiles; they are regulated by a number of different standards related to safety. So, for example, there are standards around safety critical software to make sure that some supplier doesn't make a late change to the software they contributed to the automobile. And now, suddenly, your anti-lock brakes don't work anymore because they introduced a bug.

So in each case, medical device, automotive, aerospace, and there are others, what the regulators really want is traceability. They want to make sure that all of the changes that were introduced were planned and tested so that no errant change came in that produced some anomalous side effect that could kill people. And so, complying with the standards of the FDA, the FAA, or various automotive bodies is critical. And PLM is the system that gives certainty that those standards have been complied with.

PLM is tracking requirements, changes, test cases to prove we have test cases for all of the changes and all of the changes were driven by legitimate requirements. If you can prove all that, the regulators are going to say, "Great, go ahead and launch the product." So I'm oversimplifying it, perhaps, but that's sort of a way to think about the regulatory use case.

Let me pick a different one, though. Many of our customers have what they call platform strategies, and sometimes I refer to this as diversity with scale. So let me pick a great example of a PTC customer, Volvo, so if you know Volvo, they make trucks, but they also make construction equipment. And they make buses, and they make ship engines, boat engines.

And so across those very different products, they try to reuse the same engines, the same transmissions, the same telematics systems; why? Because if the truck guys develop truck engines and the bus guys develop bus engines, and the boat guys develop boat engines, we'd need a lot more engine factories, and then we'd need a lot more spare parts for all these engines that last decades.

So there's great inefficiency in unbridled innovation. So they actually want to control it a little bit and say, let's agree that the company will have a series of engines. And no matter what bus truck construction equipment or whatever you create, you should try to reuse these engines. What that means, though, is that the engine gets used in many different product configurations, many different buses, many different trucks, many different construction equipments. You get an explosion of configurations.

In fact, just for fun, Volvo says that their products come in 10 to the 84th power hypothetical configurations. Now, very few of those configurations will ever be built, but they could be built. And so, how do you manage that? Just for fun, Caterpillar was meeting with me about a week ago. They were telling us about some of their challenges. And they said that their products, Caterpillar products, come in infinity minus eight configurations. I laughed and said, "That's a funny joke." And they said, "It's not really a joke." I mean, it's not really infinity minus eight, but there are so many configurations.

Now, why is that important? Let's say you're trying to produce manufacturing instructions. You can't hand-author infinity minus eight manufacturing or service instructions. You're going to have to generate them from building blocks. So just like the products have building blocks, the information needs to be constructed in building blocks so that if you assemble a combination of building blocks to create a piece of construction equipment, you could then assemble the information building blocks to create the manufacturing instructions for that same piece of equipment and the service instructions as well.

So the configuration management of the product and all of the information building blocks has to be directly aligned and very, very sophisticated. If you change that engine, you're going to have rippling effects across many different product lines. And so I call this complexity management, sometimes diversity with scale. But how does a company get the ability to create many different products but reuse the same factory and service capabilities to the degree possible?

That's a big challenge for companies. But it's the difference between being competitive, high growth, high margin, and not being competitive. So it's a must-have in certain industries but very much an advanced topic. If you talk to a startup company, they would say, "I don't even understand what you're talking about." But these larger companies, it's absolutely critical to their financial wherewithal.

TROND: So I want to get to green- in a second, but before that, what do you say to people that would claim that industrial automation has taken a long time to get to this fairly advanced stage that you're describing here? I guess, you know, for example, from the perspective of an impatient, young software engineer who's looking at this space, they're saying, "Well, you guys, you're finally coming to cloud, you know, still have some on-premise."

And there are a lot of elements in this software. We talked about software that's been developed since 1998. There's quite some legacy, not just in your product but in every automation company's product. And certainly, your customers must have the legacy challenge as well. This is not a space where systems get changed out every six months. So tell me a little bit about that reality.

JIM: In tech, there's a saying that goes something like this, that many breakthroughs have less impact in the near term than you expected but more impact in the long term than you expected, internet being a perfect example. The first couple of years the internet, you know, it was kind of silly stuff and maybe just publishing papers and whatnot, and today it's the way the whole world exchanges information.

When I look back over my career, the technology has changed a tremendous amount. But when you look at how much is it changing this year, it looks like, well, not that much. But what happens is there are a lot of new concepts, like you mentioned, the cloud. But when I first worked on PLM, it was a mainframe application; then it became a client/server application, then it became a web application. And now it's a SaaS, a cloud application. These changes take time, but then they unleash whole new use cases, whole new value, and the products get better and better and, frankly, less and less expensive over time.

And then you get to that tipping point where it really makes sense. Maybe ERP got to that tipping point, I don't know, 15, 20 years ago, and CRM got to that tipping point 10 years ago. I think right now, PLM is at that tipping point where people really see the value, and the value proposition makes sense. What do I need to put in? What do I get back financially from an investment in PLM? That's starting to make a lot of sense to people. I used the phrase earlier we've gone from nice-to-have to must-have in the last couple of years, thanks in large part to digital transformation and then COVID.

TROND: You used agile and scrum earlier, but even beyond those techniques, there's a demand in the industry for software that can be very easily configured by non-specialists. So here we're talking about perhaps low-code software in and of itself, or at least that the user interfaces are easy to operate. And I guess you can understand that because the training challenge, for example, in manufacturing and, you know, you were referring to frontline workers. And while the training factor there is significant but also, conversely, on the knowledge worker side, to use your definition here and distinction between the two, even engineers have had to contend with a lot of new frameworks.

And they were not trained on the kind of software that you're talking about here. Many of them were industrial engineers and still actually don't receive an enormous amount of IT programming in their curriculum. There are so many other things to focus on. So what do you see there in terms of the low-code space or in terms of the interfaces? Is industrial automation also gradually simplifying? Or are we on this enormous train towards more complexity in all that chain?

JIM: Well, I think what's happening is the systems are becoming more sophisticated behind the curtain. But then we're providing different user communities with role-based views into that information. If you think about a product manager, an engineer, somebody in purchasing, somebody on the factory floor, somebody in the service bay, they all need product information, but their needs are quite different. And then when you go from one company to the next, they might be different again because the companies are different, the products are different.

So yeah, definitely low-code approaches...for example, we have a product called Navigate, which is kind of a low-code overlay onto the basic PLM system. A low-code approach that allows you to tailor what different user communities experience when they log in, I do think is very important because if I'm in purchasing, show me what a purchasing person needs to know and no more.

If I'm on the factory floor, I don't need to know what things cost; I just need to know what the work instructions are. So show me just a limited view that hides all the rest of that complexity. Certainly, there are some power users who need a lot more, but there are a lot of users who really need kind of almost looking at the information through a straw if you will. There's a fairly limited amount of information and functionality that's relevant to them. How can we serve that up to them in the simplest possible way? I do think that's critical. It needs to be tailorable in order to work well.

The introduction of low-code approaches into PLM has certainly helped with the broader adoption to go beyond the engineering department and really make it an enterprise system. It's been a critical enabler.

TROND: I want to benefit from some of your experience to think about, you know, what's going to happen next in the broader field of industrial automation? But perhaps you can kick it off with a little bit more detail on how you see the green challenge working out. Because clearly, more and more industries are starting to take the climate challenge or just even bits and pieces of it, like you were talking about earlier, the product lifecycle tracking of a product, worrying also more about the end state of their products. What are systems then having to adapt to?

JIM: Let me say; first, some companies see climate change and greenhouse gas reduction as an opportunity. And there are a lot of green tech companies launching, startup companies launching to produce next-generation products. On the other hand, there are a lot of larger companies that are under tremendous investor pressure to be more green. If you're a public company right now, you really have to be active on the environmental, social, governance (ESG) front.

You have to have a story, and it can't just be a story. There has to be some reality behind it. So what's happening now is companies are saying, "Okay, well, where does greenhouse gas come from? And, by the way, who really is a great producer of greenhouse gas?" And it turns out manufacturing companies actually have fairly substantial greenhouse gas footprints. The production of their products in their factories and the production of all the materials, you know, raw materials and whatnot, has a lot of energy use associated with it.

And then, some of these products go on to be used by the customers in a way that also consumes a lot of energy use. So manufacturing companies are saying, well, if I wanted to reduce greenhouse gas emissions, I really have to back up and think about the products I make and how could I make them with less greenhouse gas footprint. But how can I also design them so that when operated, they generate less greenhouse gas footprint? But all this stuff starts in engineering. People in factories don't get to make changes. They have to be specified by the engineering department.

So just like the engineering decisions lock in cost, frankly, they lock in greenhouse gas footprint. And the important thing is to bring awareness in analytics upstream so that when an engineer is thinking about how to innovate and solve a particular problem, they say, "Well, this approach would have a high greenhouse gas contribution, and this alternative approach would have a very low greenhouse gas approach. Let's go with this secondary approach for reasons of reducing our greenhouse gas footprint."

Again, if you really want to move the needle in a manufacturing company, you can't get far if you don't open the hood and look at the products, and the system you log in to do that is called PLM. And so PLM will be manufacturing companies' best friend as they think about over time how to consistently reduce their greenhouse gas footprint, and actually, track the progress they're making so that they can publish to their shareholders and whatnot the incremental progress in how well are they advancing toward their goals.

TROND: Well, Jim, what you're talking about now clearly is a big part of the future in the sense that this, you know, it sounds so simple when you're explaining it. But measuring that, obviously, is not something that software in and of itself can help a company in every part of it, right? I'm assuming this means a lot of rethinking inside of these industrial companies.

But if I want to benefit more from your broader view on the industry, what are some of the other things that you think in a longer time frame are happening in the industrial space? I mean, are we looking at more and more innovation from startups? Like, you came yourself from a startup. How do you see the startup innovation in this space versus sort of the giant...PTC now has become more of a giant, but obviously, like every company, you started out in a different position. What are some of the technologies that you're excited about that are going to really change this space as we move into the next decade?

JIM: Let's back up and talk a little bit more about cloud and SaaS because if you look at the PLM industry, it's very much an on-premise industry; you mentioned this earlier. If you look then at business software, in general, this is an important year because this year, more of the entire ecosystem of business software is delivered as a SaaS model than an on-premise model. This is the first year where there are more SaaS in total than on-premise, but within our little corner of the world called PLM, that's not true at all. We're very much an on-premise market.

But customers would have great benefit if we could deliver this to them via the cloud as a service rather than ship them software or let them download software to be more practical. We think, at PTC, this industry is going to the cloud. The automotive industry is going to electrification, and the PLM industry is going to SaaS. It's really that simple. Is it happening today right now? I don't know. I still drive a combustion-engine automobile. But I know at some point, I'm going to be driving an electric vehicle.

And, Trond, here in California, I mean, they just passed a law there that said by 2035, you can't even buy a combustion automobile. So I know you're going to be going to electric if you want to own a car. Again, I'm making an analogy. What's happening in the automotive industry as it relates to electrification is what's happening in the PLM industry as it relates to SaaS.

The industry is in transition. There will be winners and losers in this transition. PTC has tried to position itself to be a winner by being out front, paving the way, and bringing the industry along with us. So I think that's a pretty profound change that's coming, and it brings tremendous benefits, cost of ownership, simplification, real-time collaboration up and down a supply chain, and many others.

TROND: Do you have any advice to would-be entrepreneurs in the industrial space? It's interesting, at least to me, that, yes, we have Tesla now, and a few others, but kind of the poster child examples of startups is usually not an industrial company. Well, there are certainly many, many more of these success stories that seem to come out of the garage-type thing that is perhaps not hardware and certainly not industrial. What is your view of that?

JIM: My advice there is to focus on what's most important, and that is developing your innovation and getting it to market. I'm talking about innovations that involve physical products. But frequently, in the startup world, there are lots, and lots of electronics and software involved these days as well.

But we have several products, like our Onshape CAD product and Arena PLM products, that are pure SaaS. They have never existed in a shippable form and never will. They're extremely popular with startup companies because the startup company says, "I don't have time to hire IT people and set up software systems in my company. I'm trying to get this innovation to market. And I need things like CAD and PLM. I just don't need to own them. I need to use them."

And so products like Onshape and Arena really are popular with startup companies. And plus, in a very unique way, they enable agile product development. And again, when I say agile product development, I mean develop hardware like you develop software. You might remember I said historically; hardware has been developed with a stage gate or waterfall model. Software used to be that way, but software has gone to an agile...almost exclusively gone to agile product development scrum-type methodologies.

Could we bring those scrum methodologies back over to the hardware and develop hardware and software the same way? Yeah, that's very, very interesting to startup companies because it's all about speed. But it's pretty hard to do without SaaS because if you're going to all work on the same data and make new versions of the product every single day, well, then we need to have the data remain collected together. We can't have it distributed out on a whole bunch of desktop computers, or it just doesn't work.

So I think that startup companies need to focus on what's important, the SaaS model. And the ability of the SaaS model to enable an agile scrum approach is absolutely critical to these startup companies, the entrepreneurs that are driving them.

TROND: It's exciting your idea here of developing software, I mean, developing hardware at the speed, I guess, and with the methodology of software. Can you tell me more about what that actually would mean? What sort of differences are we talking about? I mean, for example, in terms of how quickly hardware would evolve or how well it would integrate with other systems.

JIM: Some of the most important principles of agile or scrum product development are daily builds, a highly iterative approach that's not too deterministic upfront. In a waterfall method, by contrast, the first thing you do is determine the customer requirements because that's what's going to guide your whole project.

In an agile world, you say, I'm not sure the customer even knows I'm inventing something new. The customer doesn't even know what I'm doing, but I'll need to show it to them. And they'll be able to react when I show it to them, but I want to show it to them every week or maybe even every day. I want to be able to interact either with the customer or with the product owner, which is a person who has been designated to represent the interest of the customer. And I want to every single day be able to show the progress you've made and test it.

The thing that really burns people in a traditional waterfall process is you're given a set of requirements. You develop a perfect solution. Six months later, you show the perfect solution to the customer, and they say, "That's not what I meant. I know I said that, and you're complying with the words. You're not complying with the intent because the words didn't quite accurately capture the intent." So in this waterfall process, you lose tremendous amounts of time, sometimes by going back and starting over.

In the agile project, you're showing them the digital models of the product every day, or perhaps every week, or even every month, if it makes more sense. But you're showing the customer your progress, and you're getting continuous feedback. And so you're evolving towards an ideal solution very, very quickly. Again, agile software developers have been doing this forever. But we haven't been doing it on the product side, the hardware side, because the tools really weren't set up for that.

When software engineers adopted agile, they adopted a different set of tools. As hardware engineers are adopting agile, they're also saying, "We would need a different set of tools. They'd have to be cloud-based, SaaS-based so that we were always working on the same data, and we always had the latest version of everybody's contribution right there at our fingertips," as opposed to, say, checked out on their laptop, and they're on vacation this week. So it's an interesting time in the industry. And I think there's a real breakthrough coming, which will be enabled by SaaS.

TROND: Is it frustrating sometimes that there's also, I mean, you've been talking now about the inspiration from the software side and innovation side perhaps over to the hardware side and more the industrial side. But isn't it frustrating sometimes that there is obviously a lot of history and experience on the industrial hardware side, and you have to teach new generations that some of these things are...they don't operate as quickly?

So, yes, we can bring some methodologies there, but there are some constants, I guess, around infrastructure and factories that are a little bit harder to change. So as much as we would want all of it to be developed at the speed of software, there are some additional complexities. How do you think about that as, you know, you're running an industrial automation company? There is some value on the other side of this coin, you know, explaining and perhaps working together to smooth out the fact that we're dealing with a material reality here in most factories.

JIM: Yeah, well, I mean, it is frustrating, but it's also what leads to the next generation of companies. Older companies may be entrenched in their working methods and resistant to change. Some little startup company comes along. They're not resistant at all. They're a blank sheet of paper. They can do whatever they want. They have no inertia, if you will, no organizational inertia. So they're very, very flexible.

And these new companies not only have innovative new ideas, they have innovative new approaches, and innovative new processes, and innovative new tools. When we think of all these clean tech companies, startup companies developing electric vertical take-off and landing aircraft, for example, a company I'm thinking of there is Beta Air, or they're maybe producing electric batteries like a customer we have called XING Mobile, or ChargePoint producing chargers for Teslas and other electric automobiles, these companies are saying, "I don't have time to buy systems. I don't have time to build factories.

What I want to do is bring smart people together, use tools that are already running in the cloud, come up with innovative new ideas, and pass them on to contract manufacturers. And I'll have a product in the market with very little capital in very little time. Later, I'll think about how to scale it up to be something much, much bigger."

But, for example, the use of contract manufacturers is a huge breakthrough. It means that you don't have to go build a factory before you can build a product. You just set up a relationship with somebody who already has the factory and knows perfectly well how to build such a product. It's just your ideas in their factory. And so these kinds of disruptive approaches are very, very interesting. It causes pressure on the old companies to say, "Are we really just going to stand here and let them do this to us? Or should we open our mind a little bit and be more flexible to change?"

TROND: Fascinating, Jim. It's certainly...it's a world with a lot of moving parts, the industrial one. So I thank you so much for this discussion. Is there anything you want to leave the listener with in terms of how they should view product lifecycle management as it's kind of moving into the next generation?

JIM: Let me offer up one last idea, kind of a big idea, and that is the role the metaverse will play in the industrial world. When we think of metaverse today, we generally think of gaming or social media. And there are kind of cheesy metaverse ideas, you know, you can go play a game online in some artificial universe, and it's maybe fun, but it's not meaningful.

But what we think we can do, what PTC is working on, is how can we take a setting that's real, could be a factory, could be a customer site, and how could we very quickly virtualize it into a metaverse so that we can then, from a remote place, enter that metaverse and interact with the people in it, the real people in it who have been virtualized but also the equipment and machinery? How can I go debug a problem in a factory by quickly turning the factory into a metaverse and joining the metaverse? How can I go solve a customer product problem by turning that customer site into a metaverse and joining them there?

I mean, I think there are some really interesting ideas that PTC has been working on there. And again, it's not metaverse for gaming and entertainment; it's metaverse for industrial productivity. That's going to be a big thing. We're way ahead of the market there, but wait 5 or 10 years; everybody is going to be talking about this.

TROND: So the industrial metaverse, Jim, that's going to be a real place.

JIM: It's going to be a real place. Let me add we call it a pop-up metaverse because there are so many places in the world. I don't need to virtualize them all because most of them I don't care about. But if I build a certain type of machinery and I ship it to a customer, and it breaks down at the customer site, and I need to service it using product data, well, I can buy an airplane ticket and rental car, and I go to the customer site, and I'll be there in three days.

Or I could ask the customer to whip out their smartphone, convert that situation into a pop-up metaverse and let me join into it. Five minutes later, I'm virtually standing next to the customer observing the problem and suggesting what they should do to try to correct it. It's a big, profound idea. I'm super excited about what it could do for us.

TROND: Well, that's fascinating. I certainly think that the industrial metaverse sounds a lot more useful and perhaps even more exciting than the consumer versions of the metaverse that I've seen so far.

JIM: Yeah, I totally agree with you.

TROND: All right, Jim, it's been a fascinating discussion. Thanks for sharing this and taking the time. I hope you have a wonderful day, and thank you for your time.

JIM: Yeah. Great, Trond. Thank you very much. PLM is obviously an exciting industry to me. You can probably sense that in my voice. It's a world that's really coming to light right now, a lot of growth, a lot of excitement with customers, a lot of big ideas, and I'm happy to have an opportunity to share them with you today.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. Our guest was Jim Heppelmann, CEO of PTC. In this conversation, we talked about Product Lifecycle Management's Momentum in manufacturing.

My takeaway is that the momentum is clear, and one indication is the trend that PLM is being elevated to an enterprise system. But why is PLM such a hot market right now? One key word is greenhouse gas reduction because companies need a system of record to track their emissions, and this is not easy to do without a system in place.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 93: Industry 4.0 Tools. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

Augmented is presented by Tulip.co. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where the industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Jim Heppelmann.

Episode 104: A Scandinavian Perspective on Industrial Operator Independence with Johan Stahre

30 november 2022 | 44 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "A Scandinavian Perspective on Industrial Operator Independence." Our guest is Johan Stahre, Professor and Chair of Production Systems at Chalmers University in Sweden. In this conversation, we talk about how the field of human-centered automation has evolved, the contemporary notion of operator 4.0, Scandinavian worker independence, shop floor innovation at Volvo, factories of the future, modern production systems, robots, and cobots in manufacturing.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Human-centered automation is the only kind of automation that we should be thinking about, and this is becoming more and more clear. Operators are fiercely independent, and so should they be. This is the only way they can spot problems on the shop floor, by combining human skills with automation in new ways augmenting workers. It seems the workforce does not so much need engagement as they need enablement. Fix that, and a lot can happen.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is A Scandinavian Perspective on Industrial Operator Independence. Our guest is Johan Stahre, Professor and Chair of Production Systems at Chalmers University in Sweden. In this conversation, we talk about how the field of human-centered automation has evolved, the contemporary notion of operator 4.0, Scandinavian worker independence, shop floor innovation at Volvo, factories of the future, modern production systems, robots, and cobots in manufacturing.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Johan, Welcome. How are you?

JOHAN: I'm fine, thank you, Trond. It's really nice to see you.

TROND: Yeah, likewise.

JOHAN: Fellow Nordic person.

TROND: Fellow Nordic person. And I apologize for this very American greeting, you know, how are you? As you know, I'm from the Nordic region. I actually mean it, [laughs] you know, it was a question. So I do wonder. [laughs]

JOHAN: I'm actually fine. It's just ending the vacation, so I'm a little bit sad about that because everyone...but it's a very nice time now because the rest of the world seems to be on vacation, so you can get a lot of work done.

TROND: I concur; that is a wonderful time. Johan, I wanted to just briefly talk about your exciting background. You are an engineer, a mechanical engineer from Sweden. And you had your initial degree from Linköping University. Then you went on to do your Ph.D. a while back in manufacturing automation, and this was at Chalmers, the University in Sweden. And that's where you have done your career in manufacturing research.

You are, I think, the first Scandinavian researcher certainly stationed currently in Sweden that we've had on the podcast. So I'm kind of curious, what is manufacturing like in Scandinavia? And what is it that fascinated you about this topic so that you have moved so deeply into it?

JOHAN: Manufacturing in Sweden is the core; it's the backbone of our country in a sense. We have statistically too many large manufacturing companies in Sweden as compared to, I mean, we're only 10 million people, but we have like 10, 12 pretty large companies in the manufacturing area in automotive but also in electronics like Ericsson, you have Volvo, we have SKF. We have a lot of big companies.

Sweden has an industrial structure that we have several small companies and a couple of large companies, not so many in the middle section there. This happened, actually, in the 1800s somewhere. There was a big growth of big companies, and there was a lot of effort from the government to support this, and that has been continued. So the Swedish government has supported the growth of industry in Sweden, and therefore we have a very strong industry and also quite good digital growth and maturity.

TROND: So the Scandinavian background to me when I was there, I remember that one of the things that at least Scandinavian researchers think is distinct about Scandinavia is worker independence. And it's something that I kind of wanted to just tease out a little bit in the beginning of this podcast. Am I wrong in this, or is there something distinct about the relationship between, I guess, workers and managers in Scandinavia, particularly? One speaks about the Scandinavian model. Can you outline a little bit what that means in manufacturing if it still exists? It's an open question.

JOHAN: From my perspective, Sweden usually ranks very high in innovation, also when it comes to international rankings. And I think some of that has to do with the openness and the freedom of thinking in a sense and not so hierarchical, more consensus-oriented, ability to test and check and experiment at work without getting repercussions from top management. And it is much easier.

In fact, if you are at one department in a manufacturing company or in university as such and you want to collaborate with another colleague across the aisle, if you have a two hierarchical system, you need to go three levels up in order to be able to do that. But here, I think it's easier to just walk across the aisle to have this collaboration and establish a cooperative environment. I think that that's part of the reason.

Also, we're not so many; I mean, I think historically, we needed to do a lot of things ourselves in Sweden. We were a country up north with not so many people, and we have harsh environments, and I think it's the same as Norway. I mean, you need to be self-sustainable in that sense, and that creates, I think, environmental collaboration.

TROND: We'll go more deeply into your research on manufacturing and to what extent a question I asked here matters to that. But do you have a sense just at the outset here that this type of worker and operators sort of independence, relative independence, perhaps compared to other regions, is it changing at all? Or is this kind of a feature that is a staple of Scandinavian culture and will be hard to change both for good and for bad?

JOHAN: I think that as everything...digitalization has sort of erased a lot of the cultural differences across the world in that sense. Because when I was a student, there was not this expressed digital environment, of course. The information environment was less complex. But I think now all the young people, as well as my mother, does her banking...she's 90, but she does her banking on her iPad; I mean, it's very well-spread.

And I think that we are all moving towards a similar culture, and the technology is spreading so quick. So you cannot really have cultural differences in that sense. But I think that's still the way that we're using this. And I think that the collaborative sense I think that that is still there. The reason why Sweden is comparatively innovative still is that we still maintain our culture and use the technology to augment that capability.

TROND: So, Johan, we'll talk about a bunch of your experiences because you obviously are based in Sweden. And because of Sweden's industrial situation, you have some examples, you know, Volvo, a world-famous company obviously, and also famous for its management practices, and its factory practices, we'll get into that. But you've also worked, and you're advising entities such as the World Economic Forum, and you are active on the European stage with the European Institute of Technology. Your activity clearly goes way, way beyond these borders.

But why don't we maybe start with some of these Scandinavian experiences and research projects that you've done maybe with Volvo? What is it with Volvo that captured people's attention early on? And what sort of experience and research have you done with Volvo?

JOHAN: I think that Volvo is very innovative, and Volvo today is two types of companies; one is the car company that has now gone fully electric. It was introduced at the stock market, most recently owned by a Chinese company, and before that, it was owned by Ford, and before that, it was also public. But you also have the other part, which is the Volvo Group, which is looking at trucks, and boats, and things like that.

And they both share a high level of innovation, ambition, innovation, and power, I think, using the experiences already from the '60s, where you had a lot of freedom as an employee. And also very good collaboration with the union in investments and in all the changes in the company I think that has been very beneficial. And it's made them...what is now Volvo Cars was very, very early, for example, with digital twins. They were experimenting with digital twins already in the 1990s.

And we work together with Volvo but also with SKF, which is a roller-bearing company here to look at how we can support frontline workers and augment their capabilities because they're very skilled and they're very experienced. But sometimes you need to have sensor input, and you need to have structures, and rules, and procedures, and instructions.

So we worked quite early with them already, maybe in 2009, 2010, to see how can we transform their work situation, provide them with work instructions through wearable devices. It was very popular at that time. MIT was experimenting with cyborgs. And the people that were...I think it was Thad Starner; he was trying to put on a lot of computer equipment. Then he went through the security at the airport and had some problems there. But that's not the case for the operators. But it was a little bit too early, I think.

We tried to experiment with some of the maintenance people at Volvo cars. And they were very interested in the technology, but the use for it was a little bit obscure. And this was at the time when you had the mobile connectivity was 9,600 kilobits through a mobile phone or in the modem, so Wi-Fi more or less did not exist. And the equipment: the batteries weighed two kilos, and the computer weighed one kilo. And then you had a headset that looked like you came from deployment in a war zone. So it was a little bit...it looked a little bit too spacy for them to be actually applicable.

And then some 10 years later, we actually did a similar experiment with SKF, the roller bearing company where we deployed the first iPod touch, I think they were called. That was right before the iPhone. I think it was an experiment by Steve Jobs to see how can we create what then became the iPhone screen. And we put that on the arms of the operators and tried to see how can we give them an overview of the process situation. So they were constantly aware, and they were quite happy about this.

And then, we wanted to finish the experiment. The operators actually said, "Well, we don't want to give the equipment back." And then we said, "Well, we need to have it back. Of course, you can use the software." So they brought their own phones, and they downloaded the software. And they're still using it, actually, not on their own phones anymore. But they use this kind of software that we developed at that time together with them. So that was quite interesting.

TROND: That's fascinating. Extrapolating from some of these early experiences up until now, I wanted to just ask you this from a research perspective, but also, I guess, from a management perspective. So you work on production systems. What is really the goal here, or what has the objective been early on? You talked about these early MIT experiments. And I know control systems is a very old area of research. And from what I understand, in the early days, the use cases weren't just factories; they were also on spacecraft and things.

But to your point, especially earlier, we were working with very, very different technology interfaces. But now, obviously, we are starting to roll out 5G, which gives a whole other type of richness. But does it really matter how rich the technology interface is? Or does it matter more what the objective is with these various types of augmentations that have been attempted really throughout the decades? Can you just give us a little sense of what researchers and yourself what you were trying to augment and how that depends or doesn't depend on the quality of technology?

JOHAN: First, we need to realize that the manufacturing industry has always been a very, very early adopter. The first computers were used for war simulations and for making propellers for submarines to see how you can program the milling machines. This was in the 1950s. And the industrial robots in the '60s in the '70s were also very early applications of digitalization. Before anything else had computers, the manufacturing industry was using it, and that's still the case. That might surprise some people. When they walk out into a shop floor, they see no computers around because all the computers are built into the machines already.

What is still missing is the link, perhaps to the people. So they are still using the screens. And they are the ones...people are the key components of handling complex and unforeseeable situations. So you need to provide them, I think...to be really productive, you need to provide the frontline staff with the equipment for them to avoid and to foresee and to handle unforeseen situations because that's what differs between the man and machine or a human and the machine.

People are much more apt to solve a complex situation that was not programmed before. That's the augmentation part here; how can we augment the human capabilities? And people talk about augmented reality; I mean, I don't think it's the reality that needs to be augmented; it's the human to be handling the reality that needs to be augmented.

TROND: Johan, this is so fascinating because, first of all, it's quite easy to dismiss manufacturing a little bit these days because, to the untrained eye, all the excitement is in the consumer space because that's where the new devices get released, and that's, obviously, where all the attention is these days unless you obviously are in manufacturing.

But can you bring us back to those early days of computing when a lot of the use cases for computing were first explored with manufacturing? So you talked about MIT, and back at MIT and at Stanford, all the way back to the '60s, they were exploring this new and fascinating field of even artificial intelligence, but before that, just regular control systems, electronic interfaces. What fork in the road would you say happened there? Because clearly, the fascination has been with digitalizing everything and software kind of one for 30 years, but in manufacturing, it's more complicated.

You say people, so it's people, and then it's kind of these production systems that you research. That's not the same as the use case of an individual with their phone, and they're sort of talking to people. There are many, many more variables in play here. What is the real difference?

JOHAN: Last year actually the European Commission put forth industry 5.0, which should be the follower after industry 4.0. And they based that on three main challenges. One is sustainability, one is resilience, and the various kinds of resilience towards the shock of the war but also by climate, et cetera. And the third one is actually human-centeredness to see how can we really fully deploy human capabilities in a society and also in industry, of course.

I think what you're referring to is the two guys at Stanford in the '60s; one was John McCarthy. He was the inventor of the artificial intelligence concept. His aim then was to replace human work. That was the ambition with the artificial intelligence because human work is not as productive as computing work, but it still has some drawbacks.

But in the same place not so far away, in another department at Stanford, was a guy called Douglas Engelbart. And he was actually the father of...he called it intelligence augmentation. So it was AI and IA at that time. But his ambition was to augment human work to see how can you have this. And he was the one that invented hypertext and the mouse. And he put up the first hypermedia set in Silicon Valley. So this was a guy that inspired companies like Apple, and Xerox PARC, those kinds of institutions that had a huge bearing.

There was a book by a research colleague at Oxford. He was comparing that over time, from the early industrial days and then forward, technology that replaces people always has more complications when introduced and scaled than technology that augments people. If you look at the acceptance and the adoption of the iPhone, that took months, or weeks, or whatever, seconds for some people, for me, for example.

If you look at what happened in the industrial revolutions in the 1800s and the 1700s, you had a lot of upheaval, and already in the 1960s...I'm starting to sound like a university professor. But in '96, in the U.S., there was a Senate hearing about is automation taking the jobs from people or not? And the conclusion was that it is not, it is actually creating companies that then employ more people because of the productivity gains and the innovation gains. And you allow people to use the automation as augmentation, not only cognitive augmentation.

We think a lot about augmentation as something that you do with your eyes and your brain. But robots are also augmenting people. It lifts heavy objects like cars or big containers, whatever. That's the kind of augmentation that maybe you don't consider when you look at it from an artificial or an augmented reality perspective.

TROND: Well, so many things to pick up here. But the variety of meanings of augmentation are kind of astounding, aren't they? And you've written about this operator 4.0 several times. There's obviously cognitive augmentation, and then there's physical augmentation. Are there other types of augmentation that you can speak of?

JOHAN: I really can't think of any.

TROND: But those are the main ones. So it's either kind of your mentality or sort of your knowledge. So the work instruction parts go to the skills-based, I guess, augmentation, which perhaps is an additional one. Or I'm just thinking if manufacturing wants to make progress in these things, it would perhaps make sense to really verify what workers at any moment actually themselves express that they need.

And I guess that's what I was fishing for a little bit here in this history of all of this, whether the technology developers at all moments really have a clear idea of what it is that the workers are saying themselves they're missing or that they obviously are missing. Because automation and augmentation, I mean, do you find them diametrically opposed, or are they merely complementary when it works well?

JOHAN: I mean, automation traditionally has been the way to scale, and, I mean, in the beginning, you want to see what the machine is doing, right? And then you really don't want to see it. You just want it to work. So it's really helping you to scale up your work. And in that sense, automation, like collaborative robots, for example, which people are talking about robots, are something that is replacing jobs, but if you look at it, it is a very small portion of statistics.

In Singapore, which is the highest user of robots installed, there were 950 maybe robots per 10,000 employees. And the average in the Americas is 100 robots per 10,000 employees, and that's not really a lot. And so there is plenty of space for robots to be the tools for people. So if you don't treat them as something that will replace you but something that will actually augment you, I think it would be much easier.

What could happen, though, and I think that is maybe part of your question, is that, well, these tools are becoming so complex that you cannot use them unless you increase your skill. How do you do that? Because no company would like to end up in a situation where the tools that you have bought and invested a lot of money in are too complex for your employees to use. That's a lost investment.

It's like you're building a big factory out in a very remote place, and you don't have enough electric power to run it. You don't want to end up in that situation. Like you expressed, I think that maybe what's missing and what's trending right now is that the upskilling of the workforce is becoming extremely important.

TROND: And how do you do that, Johan? Because there's obviously...there's now an increased attention on upskilling. But that doesn't mean that everyone has the solution for it. And employers are always asking for other people to pay for it, for example, governments, or the initiative of the worker, perhaps. It seems like Europe has taken this challenge head-on. Germany, at least, is recognized as a leader in workforce training. The U.S. is a latecomer to the game from that perspective. But it typically shows up in a big way. So something is going to happen here in the U.S. when it comes to workforce training.

What is the approach? I mean, there seems to be two approaches to me; one is to simplify the technology, so you need less training. And the other would be obviously an enormous reskilling effort that either is organized, perhaps ideally in the workplace itself, so it's not removed from the tasks. Or some enormous schooling effort that is highly efficient and perhaps online. What do you think are the winning approaches to re-skilling that entire manufacturing workforce continuously? Because it's not like you have to rescale them once, you have to rescale them every time.

JOHAN: Well, I can only guess. I think that you need to do all of these, all of the above. One complicating factor is the demographics of, especially Japan; of course, we know that from a long time that, they have an aging population. But Europe is now becoming the new Japan in that sense. We have a very big problem in terms of aging populations, especially countries like Italy and perhaps Germany but also in northern countries. And we don't have perhaps...there's a lot of discussion on immigration right now. But actually, the workforce would need a lot of immigration to be able to respond to the needs of our industry in the forthcoming situation.

I think that China is maybe 4 or 5 years behind Europe, and the U.S. is maybe 10-12 years behind Europe as well. So that will happen...the only non-affected regions right now are India and Africa. And that means that the European, and Chinese, and U.S. industries will have to compete with a rather young population in Africa and India. And so that will become over time, but it is a long time, so that means that it's not always on the political agenda. Things that take a long time are usually not the things that you speak about when you have election times that we have in Sweden right now. It's mostly what's on the table. So I think that how to do that is really complex.

We had some collaboration within the World Economic Forum. It is a fantastic organization because it spans the whole globe. So that means that the information comes from different parts of the world, and you can see different aspects of this. And a country that has done a lot about this is Singapore, very good experiments, very nice projects, initiatives regarding upskilling. And Europe is now launching an innovation program where they want to go deeper into deep tech to try to...the commissioner for research and education in June launched a big initiative around innovation and how that can be supported by deep technology. So we'll see what comes out of that. It'll be very, very interesting to see.

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TROND: Speaking about the World Economic Forum for a minute, Johan, you have been part of this group project called the Augmented Workforce Initiative. You told me when we spoke earlier that, in your opinion, this initiative couldn't have existed even just five years ago. Can you explain what you mean by that?

Because augmentation, the way that you've been speaking about it now, is a perspective that was nascent, even in the early days of computing and manufacturing control systems. Yet, it seems to have disappeared a little bit, at least from the top end of the political and research agenda. Yet here we are and you said this initiative couldn't have existed five years ago. Can you explain what you meant by that?

JOHAN: That is a very, very nice initiative by the World Economic Forum, and it's run by the forum and Cambridge University, who has a very, very good group on this and some very nice people. And I'm honored to be part of that group together with my colleague from Mexico, David Romero. You may know him as well.

And I think that what they're looking at is the increased understanding. And that was actually one of the sessions at this World Economic Forum, you know, the Davos days that were run this year. And it was actually part of those days as a theme about how to engage, and how to support, and to augment the workforce, which has never happened before on that level. So it's really, really high on the agenda.

The Forum has been running previous projects also on the future of work and how the demographic situation is affecting or how the skill situation is affecting the companies. They have come up with suggestions that more or less half the workforce needs to be upskilled within the next couple of years. And that's a huge undertaking.

TROND: The novelty here is that the world's elite managers, I guess, who are represented at the World Economic Forum are increasingly aware of the complexity of workforce issues generally, and then specifically of upskilling, and maybe even upskilling in this very specific meaning of augmenting a worker which, I guess to my mind, is a little bit different from just generally speaking about robotic automation and hammering these efficiency points.

But obviously, it's a much more challenging debate because it's one thing to find a budget for an automation effort and introduce a lot of computers or introduce a lot of whatever technology, usually hardware, but what we're talking about here is a lot more challenging because you need to tailor it to these workers. And there are many workers, obviously, so it's a complicated phenomenon. How is that going? What would you say are some of the findings of the Augmented Workforce Initiative?

JOHAN: I think that companies like Tulip, companies like Black & Decker, and others have a lot of good use cases actually already, which may or may not before have been labeled augmentation. It might have been labeled as operator support, or decision-making support, or things like that, or upskilling. But I think that the findings are that there is a lot out there, but it's not emphasized as something that is really important for the company's survival in that sense.

TROND: It wasn't so glorified before. A lot of the decision support systems were viewed as lower-level systems that were just kind of more like HR systems or just tinkering with necessary stuff that people had to know kind of a thing. And so you're saying it's been elevated now, yeah, as having a much more essential impact on the quality of work.

JOHAN: It has a leveraging impact for the whole company, I would say, but that's also part of this industry 4.0 approach. And you have the hierarchical integration of companies where the CEO should be aware of what's going on on the shop floor and vice versa, as well as the horizontal integration where you have the companies up and down the supply chain and value chain knowing what's going on early. And that is really something that maybe stopped at mid-management level before, but now it needs to be distributed out to the places where the complexity is higher, and that's the frontline workers.

Maybe...now I'm guessing, but I think that also the understanding that the investments done by this company in complex manufacturing equipment could be at risk if you don't have the right skills to use them is now penetrating, I think, a lot of the companies.

In Europe, in 2019 or something like that, there were almost 30 million people employed in the manufacturing industry. And if you look at the number of...if you say that half of these need to be upskilled somehow over a period of three years...and I actually made a mock calculation that the re-skilling need for in-person months in Europe if we were to fulfill this is 50 million person-months, 50 million person-months, just the time for the people to participate in these trainings. So that's a huge undertaking.

And I think that that scares companies as well as governments because just imagine taking 50 million person-months out of productivity or the production equation. But the alternative might be worse. If you lose your capability to use your equipment, that might even be worse.

TROND: Wow, these are daunting things. I guess that brings me to the last section here and some thoughts from you on the future outlook. When it comes to technology and these tools for human augmentation, what are the timelines for, well, either making the improvements or, as you said, not losing competitiveness because of this skills crisis? What are we looking at here? Is there some imminent challenge and opportunity? Or is this going to play out over 25 years?

JOHAN: I think that in 25 years, the demographic situations will have changed again, so I assume that they will look different. But right now, we have a problem with an aging population. And we have a lot of people going into retirement. A lot of knowledge will disappear unless we can store it somehow.

A lot of people will not go into industry. I mean, when I talk to colleagues, they say, "Well, we need to make the manufacturing industry more sexy. It should be cleaner, or it should be nicer because young people don't go to industry." But if I go to the healthcare section, they will say the same thing, "Oh, we need to make it much better because people are not applying for these educations."

TROND: [laughs] Where are people applying, the tech companies?

JOHAN: No, that's the problem. They don't exist. They were never born.

TROND: [laughs] Right.

JOHAN: So the demographic bomb is that they are actually not there. So you cannot rely on employing young people because they are not existing in Europe and soon not in the U.S. to the extent that they were before. So therefore, you need to focus on the older people. So you need to re-upskill not only the middle-aged people but the people in their 50s and even in their 60s. That adds to the complexity.

In the next 5 to 10 years, there will be a lot of discussions on how to fill the missing places in industry to remain competitive. I also think that you can see the augmentation here as a fantastic tool together with the upskilling because upskilling the new skills together with the augmented tools like collaborative robots, like cognitive support, like whatever you can put in an iPhone, or whatever phone, or tool, or watch, or whatever, you can add the capability to make decisions. And that's the augmentation you will see.

And you will see a lot of digital twins try to foresee problems. You will see a lot of transversal technologies going from different high-tech industry into manufacturing industry to support especially the frontline people and to enable their innovation capabilities.

TROND: Johan, you said earlier that the complexity is higher at the level of frontline workers. Did you mean that, basically, the complexity of frontline work of itself at an individual level is also underestimated? Or were you simply saying that because there are so many frontline workers and the various situations of various types of frontline workers is so different that it's obviously an underappreciated management challenge? Or were you truly saying that frontline work in and of itself is either complicated or becoming more complex?

JOHAN: If a task was not automated, it is inherently complex. So you couldn't automate it, right?

TROND: Right.

JOHAN: Because if you can teach a robot or whatever to do tasks, then it's not difficult, and you can foresee the results. There was a lady called Lisanne Bainbridge. She put out The Paradox of Automation that the more you automate, the more dependent you become on the few people that are still there to handle the situations that are so complex that you could not foresee them.

So everything that is programmed is programmed by a programmer, and the programmer tries to foresee every foreseeable situation, and to that extent, the robots and the automation works. But if these situations go out of hand, if they're too complex, and something happens, then there is no robot that can fix that. Unfortunately, AI is not there yet.

TROND: Well, you said, "Unfortunately, AI is not there yet," but I would also conjecture that, fortunately, AI is not there yet because you're pointing to something missing, I think. And a lot of the AI debate is starting to come back now. And it was there in the '60s because people realized that for lots of different reasons, to have a human oversight over robotic processes is actually a good thing.

And you talked to me earlier about the experiments with imagining a trip to Mars and having to execute robotic actions on Mars in a control system environment where you actually had to foresee the action and plan; it was always a supervised type of situation. So the supervisory control concept has been there from the beginning of computing. If you were to think of a future where AI actually does get more advanced, and a lot of people feel like that's imminent, maybe you and I don't, but in any case, let's imagine that it does become more advanced and becomes sort of a challenge, how do we maintain human control over those kinds of decisions?

I mean, there are researchers that have imagined, you know, famously in Superintelligence, Bostrom imagines this paperclip factory that goes amok and starts to optimize for producing paperclips, and everyone is suddenly producing, you know, and the machine then just reallocates resources to this enormously ridiculous task of producing only paper clips. It's a very memorable example. But a lot of people feel that AI could soon or at some point reach that level. How do we, as a failsafe, avoid that that becomes an issue? Or do you see it as such a far-fetched topic in manufacturing that it would be decades, if not centuries, away?

JOHAN: I think that AI has been seasonal if you allow the expression. There's talk about these AI winters every now and then, and they tend to come every 10 or 15 years, and that matches two Ph.D. lifetimes, Ph.D. development. I mean, people tend to forget the problems, and then they tend to use these Gartner curves. If you look at the Gartner curve, you have the expectation part. I'm not being arrogant towards the AI research. I think that AI is fantastic, but it should be seen, from my perspective, as what it is, as an advanced form of automation that can be used as an augmentation tool.

I think it was Kasparov that started to collaborate with a chess computer maker or developer, and they won every tournament because the combination of the human and the chess computer was astounding. And now I think there are even competitions with chess computers plus chess experts comes with them.

There was, I think, in the 1800s, there was a traveling exhibitionist where they had the Mechanical Turk, I think it was called. It was a chess player that was competing then against the people in the audience. And actually, inside this box, there was a small human that was making all the chess moves. And they were beating all the chess champions. So there was a man inside this. I think that there is still a man inside a lot of the automation.

TROND: A man and a woman. I wanted to just lastly end on a more positive note because you told me earlier that you are more optimistic now than ten years ago on behalf of your industry that you've researched for so many years. Why is that?

JOHAN: I think that the technology, I mean, I'm a techno-optimist. And I think that we have also the full scale, the full attention from the ICT industry on various industrial processes right now. It was a lot of service-oriented. And I think that that is playing out now in the platform wars, the different services, but these different services are actually making a lot of good in the manufacturing and the tougher industries. And so, there is a bigger focus now on creating CO2-less steel. And there's an exploration of different industries that are going across; you look at the electrification of vehicles which is cutting across several sectors in the industry, automotive industry, electronics industry.

And I think that the problems in industry are becoming so complex. So the ICT attention is on industry now more than perhaps on consumers, as it were, and I think that that's promising. I see companies like Ericsson promoting 5G. I see companies doing the Amazon Web Services and such companies looking at services that are useful for industry. And that's also augmenting the people's capability in that sense, so that's why I'm so positive.

I see all the sensors coming. I see all the computing power coming into the hands of the frontline operators. And I see also the use for the upskilling and the skilling technologies that are emerging. How do you do that? What they do in Matrix when the leading lady downloads the instructions for the helicopter or motorcycle or whatever it is. But how do you do that in real life? How do you prepare for something that's coming in the next few minutes? That is something that people are now looking at using technologies, augmenting technologies, digital twins, and things like that in a completely different way than they were five years ago.

TROND: Wow. So these are exciting moments for learning in manufacturing with perhaps wide-ranging consequences if we succeed. Johan, I thank you so much for these reflections. You've spent a career investigating production systems, and manufacturing, and workers. And these are very rich debates. And it seems like they're not over, Johan. So, hopefully, we'll have you back when something happens. And we'll have you comment on some developments. Thank you very much.

JOHAN: Thank you, Trond. Thank you for a very interesting discussion. You always learn a lot by being asked a lot of questions, so thank you so much for this learning experience. Thank you.

TROND: You're very gracious. Thank you, Johan.

You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was a Scandinavian Perspective on Industrial Operator Independence. Our guest was Johan Stahre, Professor and Chair of Production Systems at Chalmers University of Sweden. In this conversation, we talked about how the field of human-centered automation has evolved.

My takeaway is that human-centered automation is the only kind of automation that we should be thinking about, and this is becoming more and more clear. Operators are fiercely independent, and so should they be. This is the only way they can spot problems on the shop floor, by combining human skills with automation in new ways augmenting workers. It seems the workforce does not so much need engagement as they need enablement. Fix that, and a lot can happen. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich. Hopefully, you'll find something awesome in these or in other episodes and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, devices, and systems in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where industry and especially about where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Johan Stahre.

Episode 103: Human-First AI with Christopher Nguyen

23 november 2022 | 43 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations in industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse.

Augmented is a podcast for industrial leaders, process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Christopher, how are you? And welcome.

CHRISTOPHER: Hi, Trond. How are you?

TROND: I'm doing great. I thought we would jump into a pretty important subject here on human-first AI, which seems like a juxtaposition of two contradictory terms, but it might be one of the most important types of conversations that we are having these days.

I wanted to introduce you quickly before we jump into this. So here's what I've understood, and you correct me if I'm wrong, but you are originally from Vietnam. This is back in the late '70s that you then arrived in the U.S. and have spent many years in Silicon Valley mostly. Berkeley, undergrad engineering, computer science, and then Stanford Ph.D. in electrical engineering. You're a sort of a combination, I guess, of a hacker, professor, builder. Fairly typical up until this point of a very successful, accomplished sort of Silicon Valley immigrant entrepreneur, I would say, and technologist.

And then I guess Google Apps is something to point out. You were one of the first engineering directors and were part of Gmail, and Calendar, and a bunch of different apps there. But now you are the CEO and co-founder of Aitomatic. What we are here to talk about is, I guess, what you have learned even in just the last five years, which I'm thrilled to hear about. But let me ask you this first, what is the most formational and formative experience that you've had in these years? So obviously, immigrant background and then a lot of years in Silicon Valley, what does that give us?

CHRISTOPHER: I guess I can draw from a lot of events. I've always had mentors. I can point out phases of my life and one particular name that was my mentor. But I guess in my formative years, I was kind of unlucky to be a refugee but then lucky to then end up in Silicon Valley at the very beginning of the PC revolution. And my first PC was a TI-99/4A that basically the whole household could afford. And I picked it up, and I have not stopped hacking ever since. So I've been at this for a very long time.

TROND: So you've been at this, which is good because actually, good hacking turns out takes a while. But there's more than that, right? So the story of the last five years that's interesting to me because a lot of people learn or at least think they learn most things early. And you're saying you have learned some really fundamental things in the last five years. And this has to do with Silicon Valley and its potential blindness to certain things. Can you line that up for us? What is it that Silicon Valley does really well, and what is it that you have discovered that might be an opportunity to improve upon?

CHRISTOPHER: Well, I learn new things every four or five years. I actually like to say that every four or five years, I look back, and I say, "I was so stupid five years ago." [laughs] So that's been the case.

TROND: That's a very humbling but perhaps a very smart knowledge acquisition strategy, right?

CHRISTOPHER: Yeah. And in the most recent five years...so before co-founding Aitomatic, which is my latest project and really with the same team...and I can talk a lot more about that. We've worked with each other for about ten years now. But in the intervening time, there's a four-and-a-half-year block when we were part of Panasonic. So we had a company called Arimo that was acquired by Panasonic for our machine learning AI skills and software.

And I would say if you look at my entire history, even though I did start with my degree in semiconductor all the way down to device physics and Intel and so on, but in terms of a professional working career, that was the first time we actually faced the physical world as a Silicon Valley team. And anybody who's observed Silicon Valley in the last 15-20 years, certainly ten years, has seen a marked change in terms of the shift from hardware to software. And my friend Marc Andreessen likes to say, "Software is eating the world."

If you look at education, you know, the degrees people are getting, it has shifted entirely from engineering all the way to computer science. And the punch line, I guess, the observation is that we Silicon Valley people do not get physical. We don't understand the manufacturing world. We don't know how to do HVAC and so on. And so when we build software, we tend to go for the digital stuff.

TROND: Christopher, it's almost surprising given the initial thrust of Silicon Valley was, of course, hardware. So it's not surprising to me, I guess because I've been observing it as well. But it is striking more than surprising that a region goes through paradigms.

CHRISTOPHER: Yeah. Yeah. And it's a global trend. It's the offshoring of low-end, shall we say, low-value manufacturing and so on. And we're discovering that we actually went a little too far. So we don't have the skill set, the expertise anymore. And it's become a geopolitical risk.

TROND: Right. Well, a little bit too far, maybe, or not far enough. Or, I mean, tell us what it is that you're losing when you lose the hardware perspective, particularly in this day and age with the opportunities that we're about to talk about.

CHRISTOPHER: Well, I can talk specifically about the things that touch my immediate spheres. Maybe you can think abstractly about the lack of tooling expertise and manufacturing know-how, and so on. But as part of Panasonic, the acquisition was all about taking a Silicon Valley team and injecting AI, machine learning across the enterprise. And so we were part of that global AI team reporting to the CTO office.

And we found out very quickly that a lot of the software techniques, the machine learning, for example, when you think about people saying data is the fuel for machine learning and specifically labeled data, right? In the digital world, the Google place that I came from, it was very easy to launch a digital experiment and collect labels, decisions made by users. You can launch that in the morning, and by evening you're building examples. You can't do that in the physical world. Atoms move a lot more slowly. And so when you try to do something like predictive maintenance, you don't have enough failure examples to train machine learning models from.

So all of the techniques, all of the algorithms that we say we developed from machine learning that seem to work so well, it turns out it worked so well because the problem space that we worked on has been entirely digital, and they all fail when it comes to manufacturing, the things that you can touch and feel, you know, cars that move and so on.

TROND: I want to ask you this, Christopher, because the first company you helped co-found was, in fact, a contract manufacturer. Do you think that reflecting on this long career of yours and these various experiences, what was it that convinced you before others? I mean, you're not the only one now in the Valley that has started to focus on manufacturing and including hardware again, but it is rare still. What does it require to not just think about manufacturing but actually start to do compute for manufacturing? Is it just a matter of coming up with techniques? Or is it a whole kind of awareness that takes longer? So, in your case, you've been aware of manufacturing, acutely aware of it for decades.

CHRISTOPHER: I would say there are two things, one is obvious, and the other was actually surprising to me. The obvious one is, of course, knowledge and experience. When we work on sonar technology that shoots a beam down an echogram that comes back to detect fish in the ocean, it's very necessary, not just convenient, but necessary for the engineers that work on that to understand the physics of sound waves travel underwater, and so on.

So that education, I have long debates, and it's not just recently. When we were trying to structure a syllabus for a new university, I had long debates with my machine-learning friends, and they said, "We don't need physics." And I said, "We need physics." That's one thing. But you can concretely identify you need to know this. You need to know this. So if you're going to do this, learn the following thing.

The thing that was more unexpected for me in the last five years as I sort of sound this bell of saying, hey, we need to modify our approach; we need to optimize our algorithms for this world, is a cultural barrier. It's kind of like the story of if you have a hammer, you want to go look for nails. So Silicon Valley today does not want to look for screwdrivers yet for this world.

TROND: So you're saying Silicon Valley has kind of canceled the physical world? If you want to be really sort of parabolic about this, it's like software is eating the world, meaning software is what counts, and it's so efficient. Why go outside this paradigm, basically? If there's a problem that apparently can't be fixed by software, it's not a valuable problem.

CHRISTOPHER: Or I can't solve that problem with my current approach. I just have to squint at it the right way. I have to tweak the problem this way and so on despite the fact that it's sort of an insurmountable challenge if you tried to do so. And concretely, it is like, just give me enough data, and I'll solve it. And if you don't have enough data, you know what? Go back and get more data. [chuckles] That's what I myself literally said. But people don't have the luxury of going back to get more data. They have to go to market in six months, and so on.

TROND: Right. And so manufacturing...and I can think of many use cases where obviously failure, for example, is not something...you don't really want to go looking for more failure than you have or artificially create failure in order to stress test something unless that's a very safe way of doing so. So predictive maintenance then seems like a, I guess, a little bit of a safer space. But what is it about that particular problem that then lends itself to this other approach to automating labeling? Or what exactly is it that you are advocating one should do to bridge to digital and the physical AIs?

CHRISTOPHER: I actually disagree that it is a safer space.

TROND: Oh, it's not a safer space to you.

CHRISTOPHER: That itself there's a story in that, so let's break that down.

TROND: Let's do it.

CHRISTOPHER: So, again, when I say Silicon Valley, it is a symbol for a larger ecosystem that is primarily software and digital. And when I say we, because I've worn many hats, I have multiple wes, including academia; I've been a professor as well. When we approach the predictive maintenance problem, if you approach it as machine learning, you got to say, "Do this with machine learning," the first thing you ask for...let's say I'm a data scientist; I'm an AI engineer.

You have this physical problem. It doesn't matter what it is; just give me the dataset. And the data set must have rows and columns, and the rows are all the input variables. And then there should be some kind of column label. And in this case, it'll be a history of failures of compressors failing, you know, if the variables are such, then it must be a compressor. If the variables are such, it must be the air filter, and so on.

And it turns out when you ask for that kind of data, you get ten rows. [laughs] That's not enough to do machine learning on. So then people, you know, machine learning folks who say they've done predictive maintenance, they actually have not done predictive maintenance. That's the twist. What they have done is anomaly detection, which machine learning can do because, with anomaly detection, I do not need that failure label. It just gives me all the sensor data.

What anomaly detection really does is it learns the normal patterns. If you give it a year's worth of data, it'll say, okay, now I've seen a year's worth of data. If something comes along that is different from the past patterns; I will tell you that it's different. That's only halfway to predictive maintenance. That is detecting that something is different today. That is very different from, and it isn't predicting, hey, that compressor is likely to fail about a month from now.

And that when we were part of Panasonic, it turns out the first way...and we solved it exactly the way I've described. We did it with the anomaly detection. And then we threw it over the wall to the engineer experts and said, "Well, now that you have this alert, go figure out what may be wrong." And half of the time, they came back and said, "Oh, come on, it was just a maintenance event. Why are you bothering me with this?"

TROND: But, Christopher, leveraging human domain expertise sounds like a great idea. But it can't possibly be as scalable as just leveraging software. So how do you work with that? And what are the gains that you're making?

CHRISTOPHER: I can show you the messenger exchange I had with another machine-learning friend of mine who said exactly the same thing yesterday, less than 24 hours ago.

TROND: [laughs]

CHRISTOPHER: He said, "That's too labor-intensive." And I can show you the screen.

TROND: And how do you disprove this?

CHRISTOPHER: Well, [chuckles] it's not so much disproving, but the assumption that involving humans is labor-intensive is only true if you can't automate it. So the key is to figure out a way, and 10-20 years ago, there was limited technology to automate or extract human knowledge, expert systems, and so on. But today, technologies...the understanding of natural language and so on, machine learning itself has enabled that. That turns out to be the easier problem to solve. So you take that new tool, and you apply it to this harder physical problem.

TROND: So let's go to a hard, physical problem. You and I talked about this earlier, and let's share it with people. So I was out fishing in Norway this summer. And I, unfortunately, didn't get very much fish, which obviously was disappointing on many levels. And I was a little surprised, I guess, of the lack of fish, perhaps. But I was using sonar to at least identify different areas where people had claimed that there were various types of fish. But I wasn't, I guess, using it in a very advanced way, and we weren't trained there in the boat.

So we sort of had some sensors, but we were not approaching it the right way. So that helped me...and I know you work with Furuno, and Garmin is the other obviously player in this. So fish identification and detection through sonar technology is now the game, I guess, in fishery and, as it turns out, even for individuals trying to fish these days. What is that all about? And how can that be automated, and what are the processes that you've been able to put in place there?

CHRISTOPHER: By the way, that's a perfect segue into it. I can give a plug perhaps for this conference that I'm on the organizing committee called Knowledge-First World. And Furuno is going to be presenting their work exactly, talking a lot about what you're talking about. This is kind of coming up in November. It is the first conference of its kind because this is AI Silicon Valley meets the physical world.

I think you're talking about the fish-finding technology from companies like Furuno, and they're the world's largest market share in marine navigation and so on. And the human experts in this are actually not even the engineers that build these instruments; it's the fishermen, right? The fishermen who have been using this for a very long time combine it with their local knowledge, you know, warm water, cold water, time of day, and so on. And then, after a while, they recognize patterns that come back in this echogram that match mackerel, or tuna, or sardines, and so on.

And Furuno wants to capture that knowledge somehow and then put that model into the fish-finding machine that you and I would hold. And then, instead of seeing this jumbled mess of the echogram data, we would actually see a video of fish, for example. It's been transformed by this algorithm.

TROND: So, I mean, I do wish that we lived in a world where there was so much fish that we didn't have to do this. But I'm going to join your experiment here. And so what you're telling me is by working with these experts who are indeed fishermen, they're not experts in sonar, or they're not experts in any kind of engineering technology, those are obviously the labelers, but they are themselves giving the first solutions for how they are thinking about the ocean using these technologies. And then somehow, you are turning that into an automatable, an augmented solution, essentially, that then can find fish in the future without those fishermen somehow being involved the next time around because you're building a model around it.

CHRISTOPHER: I'll give you a concrete explanation, a simplified version of how it works, without talking about the more advanced techniques that are proprietary to Furuno. The conceptual approach is very, very easy to understand, and I'll talk about it from the machine learning perspective.

Let's say if I did have a million echograms, and each echogram, each of these things, even 100,000, is well-labeled. Somebody has painstakingly gone through the task of saying, okay, I'm going to circle this, and that is fish. And that is algae, and that's sand, and that's marble. And by the way, this is a fish, and this is mackerel, and so on. If somebody has gone through the trouble of doing that, then I can, from a human point of view, just run an algorithm and train it. And then it'll work for that particular region, for that particular time. Okay, well, we need to go collect more data, one for Japan, the North Coast, and one for Southwestern.

So that's kind of a lot of work to collect essentially what this pixel data is, this raw data. When you present it to an experienced fisherman, he or she would say, "Well, you see these bubbles here, these circles here with a squiggly line..." So they're describing it in terms of human concepts. And then, if you sit with them for a day or two, you begin to pick up these things. You don't need 100,000-pixel images. You need these conceptual descriptions.

TROND: So you're using the most advanced AI there is, which is the human being, and you're using them working with these sonar-type technologies. And you're able to extract very, very advanced models from it.

CHRISTOPHER: The key technology punch line here is if you have a model that understands the word circle and squiggly line, which we didn't before, but more recently, we begin to have models, you know, there are these advances called large language models. You may have heard of GPT-3 and DALL-E and so on, you know, some amazing demonstrations coming out of OpenAI and Google. In a very simplified way, we have models that understand the world now. They don't need raw pixels. These base models are trained from raw pixels, but then these larger models understand concepts. So then, we can give directions at this conceptual level so that they can train other models. That's sort of the magic trick.

TROND: So it's a magic trick, but it is still a difficult world, the world of manufacturing, because it is physical. Give me some other examples. So you worked with Panasonic. You're working with Furuno in marine navigation there and fishermen's knowledge. How does this work in other fields like robotics, or with car manufacturing, or indeed with Panasonic with kind of, I don't know, battery production or anything that they do with electronics?

CHRISTOPHER: So, to give you an example, you mentioned a few things that we worked on, you know, robotics in manufacturing, robotics arm, sort of the manufacturing side, and the consistency of battery sheets coming off the Panasonic manufacturing line in Sparks, Nevada as well as energy optimization at Westinghouse. They supply into data centers, and buildings, and so on.

And so again, in every one of these examples, you've got human expertise. And, of course, this is much more prevalent in Asia because Asia is still building things, but some of that is coming back to the U.S. There are usually a few experts. And by the way, this is not about thousands of manufacturing line personnel. This is about three or four experts that are available in the entire company. And they would be able to give heuristics. –They will be able to describe at the conceptual level how they make their decisions.

And if you have the technology to capture that in a very efficient way, again, coming back to the idea that if you make them do the work or if you automate their work, but in a very painstaking way like thousands of different rules, that's not a good proposition. But if you have some way to automate the automation, automate the capturing of that knowledge, you've got something that can bridge this physical, digital divide.

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TROND: How stable is that kind of model knowledge? Because I'm just thinking about it in the long run here, are these physical domain experts that are giving up a little bit of their superpower are they still needed then in a future scenario when you do have such a model? Or will it never be as advanced as they are? Or is it actually going to be still kind of an interface that's going to jump between machines and human knowledge kind of in a continuous loop here?

CHRISTOPHER: Yeah, in the near term, it turns out we're not working on replacing experts as much as scaling experts. Almost every case we've worked on, companies are in trouble largely because the experts are very, very few and far between, and they're retiring. They're leaving. And that needs to be scaled somehow. In the case of, for example, the cold chain industry all of Japan servicing the supermarkets, you know, there's 7-ELEVEN, there's FamilyMart, and so on, there are three experts who can read the sensor data and infer what's likely to fail in the next month. So in the near term, it's really we need these humans, and we need more of them.

TROND: I'm glad to hear that even that is a bit of a contrarian message. So you're saying physical infrastructure and the physical world matters. You're saying humans matter. [laughs] It's interesting. Yeah, that's contrarian in Silicon Valley, I'll tell you that.

CHRISTOPHER: It is. And, in fact, related to that problem, Hussmann, which is a refrigeration company, commercial refrigeration supplies to supermarkets. It was a subsidiary of Panasonic. It has a really hard time getting enough service personnel, and they have to set up their own universities, if you will, to train them. And these are jobs that pay very well. But everybody wants to be in software these days.

Coming back to the human element, I think that long-term I'm an optimist, not a blind optimist but a rational one. I think we're still going to need humans to direct machines. The machine learning stuff is data that reflects the past, so patterns of the past, and you try to project that in the future. But we're always trying to effect some change to the status quo. Tomorrow should be a better day than today. So is that human intent that is still, at least at present, lacking in machines? And so we need humans to direct that.

TROND: So what is the tomorrow of manufacturing then? How fast are we going to get there? Because you're saying, well, Silicon Valley has a bit of a learning journey. But there is language model technology or progress in language models that now can be implemented in software and, through humans, can be useful in manufacturing already today. And they're scattered examples, and you're putting on an event to show this. What is the path forward here, and how long is this process? And will it be an exponential kind of situation here where you can truly integrate amazing levels of human insight into these machine models? Or will it take a while of tinkering before you're going to make any breakthroughs?

Because one thing is the breakthrough in understanding human language, but what you're saying here is even if you're working only with a few experts, you have to take domain by domain, I'm assuming, and build these models, like you said, painstakingly with each expert in each domain. And then, yes, you can put that picture together. But the question is, how complex of a picture is it that you need to put together? Is it like mapping the DNA, or is it bigger? Or what kind of a process are we looking at here?

CHRISTOPHER: If we look at it from the dimension of, say, knowledge-based automation, in a sense, it is a continuation. I believe everything is like an s-curve. So there's acceleration, and then there's maturity, and so on. But if you look back in the past, which is sort of instructive for the future, we've always had human knowledge-based automation.

I remember the first SMT, the Surface Mount Technology, SMT wave soldering machine back in the early '90s. That was a company that I helped co-found. It was about programming the positioning of these chips that would just come down onto the solder wave. And that was human knowledge for saying, move it up half a millimeter here and half a millimeter there. But of course, the instructions there are very micro and very specific.

What machine learning is doing...I don't mean to sort of bash machine learning too much. I'm just saying culturally, there's this new tool really that has come along, and we just need to apply the tool the right way. Machine learning itself is contributing to what I described earlier, that is, now, finally, machines can understand us at the conceptual level that they don't have to be so, so dumb as to say, move a millimeter here, and if you give them the wrong instruction, they'll do exactly that. But we can communicate with them in terms of circles and lines, and so on.

So the way I see it is that it's still a continuous line. But what we are able to automate, what we're able to ask our machines to do, is accelerating in terms of their understanding of these instructions. So if you can imagine what would happen when this becomes, let's say, ubiquitous, the ability to do this, and I see this happening over the next...Certainly, the base technology is already there, and the application always takes about a decade.

TROND: Well, the application takes a decade. But you told me earlier that humans should at least have this key role in this knowledge-first application approach until 2100, you said, just to throw out a number out there. That's, to some people, really far away. But the question is, what are you saying comes after that? I know you throw that number out.

But if you are going to make a distinction between a laborious process of painful progress that does progress, you know, in each individual context that you have applied to human and labeled it, and understood a little case, what are we looking at, whether it is 2100, 2075, or 2025? What will happen at that moment? And is it really a moment that you're talking about when machines suddenly will grasp something very, very generic, sort of the good old moment of singularity, or are you talking about something different?

CHRISTOPHER: Yeah, I certainly don't think it's a moment. And, again, the HP-11C has always calculated Pi far faster and with more digits than I have. So in that sense, in that particular narrow sense, it's always been more intelligent than I am.

TROND: Yeah. Well, no one was questioning whether a calculator could do better calculations than a human. For a long time --

CHRISTOPHER: Hang on. There's something more profound to think about because we keep saying, well, the minute we do something, it's okay; that's not intelligence. But what I'm getting to is the word that I would refer to is hyper-evolution. So there's not a replacement of humans by machines. There's always been augmentation, and intelligence is not going to be different. It is a little disturbing to think about for some of us, for a lot of us, but it's not any different from wearing my glasses.

Or I was taking a walk earlier this morning listening to your podcast, and I was thinking how a pair of shoes as an augmented device would seem very, very strange to humans living, say, 500 years ago, the pair of shoes that I was walking with. So I think in terms of augmenting human intelligence, there are companies that are working on plugging in to the degree that that seems natural or disturbing. It is inevitable.

TROND: Well, I mean, if you just think about the internet, which nowadays, it has become a trope to think about the internet. I mean, not enough people think about the internet as a revolutionary technology which it, of course, is and has been, but it is changing. But whether you're thinking about shoes, or the steam engine, or nuclear power, or whatever it is, the moment it's introduced, and people think they understand it, which most people don't, and few of us do, it seems trivial because it's there.

CHRISTOPHER: That's right.

TROND: But your point is until it's there, it's not trivial at all. And so the process that you've been describing might sound trivial, or it might sound complex, but the moment it's solved or is apparently solved to people, we all assume that was easy. So there's something unfair about how knowledge progresses, I guess.

CHRISTOPHER: That's right. That's right. We always think, yeah, this thing that you describe or I describe is very, very strange. And then it happens, and you say, "Of course, that's not that interesting. Tell me about the future."

TROND: Well, I guess the same thing has happened to cell phones. They were kind of a strange thing that some people were using. It was like, okay, well, how useful is it to talk to people without sitting by your desk or in the corner of your house?

CHRISTOPHER: I totally remember when we were saying, "Why the hell would I want to be disturbed every moment of the day?" [laughs] I don't want the phone with me, and now I --

TROND: Right. But then we went through the last decade or so where we were saying, "I can't believe my life before the phone." And then maybe now the last two, three years, I would say a lot of people I talk to or even my kids, they're like, "What's the big deal here? It's just a smartphone," because they live with a smartphone. And they've always had it.

CHRISTOPHER: They say, "How did you get around without Google Maps?" And then somebody says, "We used maps." And I said, "Before Google Maps."

[laughter]

TROND: Yeah. So I guess the future here is an elusive concept. But I just want to challenge you one more time then on manufacturing because manufacturing, for now, is a highly physical exercise. And, of course, there's virtual manufacturing as well, and it builds on a lot of these techniques and machine learning and other things. How do you see manufacturing as an industry evolve? Is it, like you said, for 75 years, it's going to be largely very recognizable? Is it going to look the same? Is it going to feel the same?

Is the management structure the way engineers are approaching it, and the way workers are working? Are we going to recognize all these things? Or is it going to be a little bit like the cell phone, and we're like, well, of course, it's different. But it's not that different, and it's not really a big deal to most people.

CHRISTOPHER: Did you say five years or 50 years?

TROND: Well, I mean, you give me the timeframe.

CHRISTOPHER: Well, in 5 years, we will definitely recognize it, but in 50 years, we will not

TROND: In 50 years, it's going to be completely different, look different, feel different; factories are all going to be different.

CHRISTOPHER: Right, right. I mean, the cliché is that we always overestimate what happens in 5 and underestimate what happens in 50. But the trend, though, is there's this recurring bundling and unbundling of industries; it's a cycle. Some people think it's just, you know, they live ten years, and they say it's a trend, but it actually goes back and forth. But they're sort of increasing specialization of expertise.

So, for example, the supply chain over the last 30 years, we got in trouble because of that because it has become so discrete if you want to use one friendly word, but you can also say fragmented in another word. Like, everybody has been focused on just one specialization, and then something like COVID happens and then oh my God, that was all built very precisely for a particular way of living. And nobody's in the office anymore, and we live at home, and that disrupts the supply chain.

I think if you project 50 years out, we will learn to essentially matrix the whole industry. You talked about the management of these things. The whole supply chain, from branding all the way down to raw materials, is it better to be completely vertically integrated to be part of this whole mesh network? I think the future is going to be far more distributed. But there'll be fits and starts.

TROND: So then my last question is, let's say I buy into that. Okay, let's talk about that for a second; the future is distributed or decentralized, whatever that means. Does that lessen or make globalization even more important and global standardization, I guess, across all geographical territories? I'm just trying to bring us back to where you started with, which was in the U.S., Silicon Valley optimized for software and started thinking that software was eating the world. But then, by outsourcing all of the manufacturing to Asia, it forgot some essential learning, which is that when manufacturing evolves, the next wave looks slightly different. And in order to learn that, you actually need to do it.

So does that lesson tell you anything about how the next wave of matrix or decentralization is going to occur? Is it going to be...so one thought would be that it is physically distributed, but a lot of the insights are still shared. So, in other words, you still need global insight sharing, and all of that is happening. If you don't have that, you're going to have pockets that are...they might be very decentralized and could even be super advanced, but they're not going to be the same. They're going to be different, and they're going to be different paths and trajectories in different parts of the world.

How do you see this? Do you think that our technology paradigms are necessarily converging along the path of some sort of global master technology and manufacturing? Or are we looking at scattered different pictures that are all decentralized, but yet, I don't know, from a bird's eye view, it kind of looks like a matrix?

CHRISTOPHER: I think your question is broader than just manufacturing, although manufacturing is a significant example of that, right?

TROND: It's maybe a key example and certainly under-communicated. And on this podcast, we want to emphasize manufacturing, but you're right, yes.

CHRISTOPHER: The word globalization is very loaded. There's the supposedly positive effect in the long run. But who is it that said...is it Keynes that said, "In the long run, we're all dead?" [laughs] In the short run, the dislocations are very real. A skill set of a single human being can't just shift from hardware to software, from manufacturing to AI, within a few months.

But I think your question is, let's take it seriously on a scale of, say, decades. I think about it in terms of value creation. There will always be some kind of disparity. Nature does not like uniformity. Uniformity is coldness; it is death. There have to be some gradients. You're very good at something; I'm very good at something else. And that happens at the scale of cities and nations as well.

TROND: And that's what triggers trade, too, right?

CHRISTOPHER: Exactly.

TROND: Because if we weren't different, then there would be no incentive to trade.

CHRISTOPHER: So when we think about manufacturing coming back to the U.S., and we can use the word...it is correct in one sense, but it's incorrect in another sense. We're not going back to manufacturing that I did. We're not going back to surface mount technology. In other words, the value creation...if we follow the trajectory of manufacturing alone and try to learn that history, what happens is that manufacturing has gotten better and better. Before, we were outsourcing the cheap stuff. We don't want to do that. But then that cheap stuff, you know, people over there build automation and skills, and so on. And so that becomes actually advanced technology.

So in a sense, what we're really doing is we're saying, hey, let's go advanced at this layer. I think it's going to be that give and take of where value creation takes place, of course, layered with geopolitical issues and so on.

TROND: I guess I'm just throwing in there the wedge that you don't really know beforehand. And it was Keynes, the economist, that said that the only thing that matters is the short term because, in the end, we are all dead eventually. But the point is you don't really know. Ultimately, what China learned from manufacturing pretty pedestrian stuff turned out to be really fundamental in the second wave.

So I'm just wondering, is it possible to preempt that because you say, oh, well, the U.S. is just going to manufacture advanced things, and then you pick a few things, and you start manufacturing them. But if you're missing part of the production process, what if that was the real advancement? I guess that is what happened.

CHRISTOPHER: Okay. So when I say that, I think about the example of my friend who spent, you know, again, we were a Ph.D. group at Stanford together. And whereas I went off to academia and did startups and so on, he stayed at Intel for like 32 years. He's one of the world's foremost experts in semiconductor process optimization. So that's another example where human expertise, even though semiconductor manufacturing is highly automated, you still need these experts to actually optimize these things. He's gone off to TSMC after three decades of being very happy at one place.

So what I'm getting to is it is actually knowable what are the secret recipes, where the choke points are, what matters, and so on. And interestingly, it does reside in the human brain. But when I say manufacturing coming back to the U.S. and advanced manufacturing, we are picking and choosing. We're doing battery manufacturing. We're doing semiconductor, and we're not doing wave soldering.

So I think it is possible to also see this trend that anybody who's done something and going through four or five iterations of that for a long time will become the world's expert at it. I think that is inevitable. You talk of construction, for example; interestingly, this company in Malaysia that is called Renong that is going throughout Southeast Asia; they are the construction company of the region because they've been doing it for so long. I think that is very, very predictable, but it does require the express investment in that direction. And that's something that Asia has done pretty well.

TROND: Well, these are fascinating things. We're not going to solve them all on this podcast. But definitely, becoming an expert in something is important, whether you're an individual, or a company, or a country for sure. What that means keeps changing. So just stay alert, and stay in touch with both AI and humans and manufacturing to boot. It's a mix of those three, I guess. In our conversation, that's the secret to unlocking parts of the future. Thank you, Christopher, for enlightening us on these matters. I appreciate it.

CHRISTOPHER: It's my pleasure.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Human-First AI. Our guest was Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talked about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame.

My takeaway is that physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat as our guest. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

The augmented podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production and logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co.

Please share this show with colleagues who care about where industry and especially about how industrial tech is going. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and on YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Christopher Nguyen.

Episode 102: Lean Manufacturing with Michel Baudin

16 november 2022 | 49 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Lean Manufacturing. Our guest is Michel Baudin, author, and owner of Takt Times Group. In this conversation, we talk about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work and a future where manufacturing is not going away.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Lean manufacturing might mean many things, but industrial work has largely been a consistent practice over several hundred years, which is not necessarily a bad thing. Having said that, if we want to go about improving it, we might want to stay pretty close to the workforce and not sit in statistics labs far removed from it. Efficiency is tied to work practices, and they cannot be optimized beyond what the workforce can handle or want to deal with. As we attempt to be lean, whatever we mean by that, we need to remember that work is a thoroughly human endeavor.

Transcript

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Lean Manufacturing. Our guest is Michel Baudin, author, and owner of Takt Times Group. In this conversation, we talk about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work and a future where manufacturing is not going away.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Michel, welcome. How are you?

MICHEL: Fine, thank you. How about yourself?

TROND: Things are good. Things are looking up. I'm excited to talk about lean manufacturing with you, having had such a rich, professional background. Michel, you're French. You originally, I think, were thinking of becoming a probability researcher, or you were actually, and then you went to Japan and studied Toyota. You have had this career in English, German, Japanese sort of consulting all the way back from 1987 onwards on exciting topics, lean manufacturing, and especially implementing it, right? The real deal.

You've authored at least four technical books that I know about. And I think you listed probably a while back, having written 900 blog posts. You've been very busy. You are the owner of the Takt Times Group, which is a consulting firm on lean manufacturing. And you love math, but you have this very interesting attitude, which we'll talk about, which is math is great, but it's not always the best communication tool. Tell me a little about that to start off. You're a probability researcher that doesn't use math; I think that's fascinating.

MICHEL: I use it, but I don't brag about it with people that it turns off. So I have to be in the closet for this because people who work in manufacturing usually focus on concrete things, things that they can see and touch, and abstraction is not something that they respond well to. So whenever you explain a principle, my approach is to state this principle and then dig into some very specific examples right away; otherwise, I'm losing the people I'm talking to. But anyway, that's what I've had to do.

TROND: So, did I capture your background okay? I mean, you've had a very international life so far. I hope it's been enjoyable and not just professional because you've spent your time in Germany, and Japan, and in the U.S., So you're really enjoying the different kinds of manufacturing environments. Or is it that you just want to be close to where it's all happening?

MICHEL: I've enjoyed living in many different countries. And so you mentioned I'm French. I was born and raised in France, but I'm an American citizen, and I spent most of my life in the U.S. I think of myself as being part French, part American, part German, part Japanese. Because when I'm in a country, I tend to immerse myself in the culture; I don't stay aloof from it.

TROND: Well, I'm curious about that because in the abstract... so if we are in the world of math, then you could maybe say that efficiency techniques are global; that was the idea. Some people have that idea, let's say, that efficiency is a global thing, and there's one thing called efficiency, and everybody should just learn it because then it's all better. It seems to me that because you spent a lot of time in three different places, it shows up differently.

MICHEL: I don't use the word efficiency so much because it's limited. There are techniques to improve manufacturing performance in every aspect of it, efficiency only being one of them, and these techniques are pretty universal. Now, when you're trying to help people in different countries, it's a postulate. You have to postulate what works in one place will work in another. So far, I haven't found any reason to believe otherwise.

I have encountered many people who are saying things like, "This is country X, and these techniques don't work because our people are from country X." It's one of the most common techniques to refuse to implement anything new. The fact is the Toyota Production System wasn't supposed to be applicable to American workers until Toyota applied it with American workers in its joint venture with GM in the early 1980s at NUMMI specifically. It became a showcase.

Later, Toyota opened its own factory in the U.S. in Georgetown, Kentucky, and applied the system there. And then, a few years later, it opened its own factory in France, and it worked with French workers. So it's really the idea that this only works in certain cultures or this only works in Japan. It's just the reality is different. It works pretty much everywhere.

TROND: Well, that's fascinating, though, because, like you said, you have immersed yourself in these different factory and industrial cultures, if you may, and you are implementing lean in all of them or advising on lean methods. Why don't we start with that, then, perhaps? Tell me a little bit, what is lean to you?

MICHEL: Lean to me...and I use the term less and less because I think over the past 30 years, it's lost a lot of its meaning. When it first came out, it was the latest in a number of labels that have been applied to the same thing. In the early 1980s, you talked about just-in-time then there was world-class manufacturing. A number of different terms were used and never really caught on. This one caught on.

And the way I took it, I took it to mean generic versions of the Toyota Production System. There are very good reasons why you can't call what you're proposing to a company that makes frozen foods a Toyota Production System. There are also very strong reasons why you can't even go to a car company and do this. It's very awkward for a car company to openly admit to be using a competitor's system. So you have to have a label that refers to the content but doesn't refer to where it's coming from.

TROND: So for you, at the basic level, if you strip away everything, it still is essentially the Toyota Production System, and lean is just to you, I'm just paraphrasing, it's a convenient wrapping for a way to explain it in a way that's non-threatening. But it is essentially the lessons from the Toyota Production System from a while back.

MICHEL: That's the way I took it. That's why I adopted this label in the early 1990s, but a lot of time has elapsed since then. Because it became popular, very many people started using that label. And the content they were putting under it was pretty much...they were attaching this label to whatever they were doing. It has lost a great deal of its meaning which is why at this point, I rarely refer to it.

TROND: So you're saying a lot of people are attaching lean to whatever they're doing, I mean, understandably so, Michel, right? Because it's become a very successful term. It sells books. It sells consulting. It does refer back to something that you think is real. So can you understand why people would do this if you are in consulting, or even in teaching, or you work in an industry, and you're managing something, why people would resort to this label?

MICHEL: First of all, consultants have to have a brand name for what they're selling. It was useful. As a brand name, you have to call what you're offering by a given name, and clients look for this. It's a keyword they look for, and that's how they find you. So it's really necessary. I'm not criticizing consultants for using that.

TROND: No, no, I understand it. And, I mean, you're also a little bit in a glass box in the sense that you are within the general tent of lean yourself. So I understand that. I fully understand it.

MICHEL: What happens when it's successful is that more and more people jump on this bandwagon and say, okay, I'm going to offer a lean. When you look at what they're saying, it does not reflect the original content. By about 2000s, it had evolved into...what most consultants were offering was drawing value stream maps and organizing Kaizen events. Those two keywords are absent from the Toyota Production System.

TROND: Can you explain...so this is interesting. Because I was going to ask you exactly this, what are the types of elements that you react to the most that you feel is really...because one thing is to say it diverged from the original content, but if it is kind of a valuable extension of something...but you're saying value streams and the Kaizens, the Kaizen practices they have very little to do with the Toyota Production System in your reading.

MICHEL: That's right. The value stream mapping is a new name for a technique that they call; I mean the translation of the original name is, Materials and Information Flow Analysis (MIFA), Mono to Joho no Nagare in Japanese, flow of materials and information. So that's one idea.

And there is a particular graphic convention that has actually evolved from Toyota that became the value stream mapping graphic convention, but it never was in the Toyota context. Mike Rother's own admission (He wrote Learning to See, which promoted this technique.) said it was not an important topic at Toyota. It has some uses, but if you go on factory tours in Japan, you don't see a lot of value stream maps.

And so it's been taken...it was a specific tool for a specific purpose like figuring out how to work with a particular supplier. And then it was made into this supposedly all-powerful analytical tool that is the first thing that you have to do when you go into a factory is map its value streams, so that's taking a very small part of what Toyota does and make it into this big thing.

As for Kaizen Events, it's actually an American invention. It's something that came out of...in the early 1990s; there were a number of executives who were frustrated with the slow pace of lean implementation with other methods. So they came up with this format they called the Kaizen Blitz, that became the Kaizen events. It's also traced back to some Japanese consulting firms, which found this particular format as a convenient way to make good use of a trip from Japan to the U.S. They would organize one-week events at their clients because it was a good way to justify essentially the cost and the trouble of flying over.

TROND: I'm going to go with your story here. So let's say these two are kind of examples for you of things diverting from the original content. Why don't we speak about what the original content then is for a minute? What is the core of the Toyota production method or of lean in its original form for you?

MICHEL: Well, the Toyota Production System is something I'm very interested in and still studying. And it's not a static thing. It's something that, for example, the first publication about it was from the early 1970s, an internal document from Toyota with its suppliers. And then there have been many, many other publications about it through the decades. And it's changed in nature, and the concepts of manufacturing have evolved.

By definition, the Toyota Production System is what Toyota does. They're very good at making cars. And so it's always important to try to keep up with what it is they're doing, knowing that there is a 5 to 10-year gap between the time they come up with new concepts and the time that the rest of the world gets to know about them.

And so, in the early 1990s, there were essentially concepts of how to organize production lines, how to lay out production lines, how to design operator workstations. And there were concepts on how to regulate and manage the flow of materials and the flow of information between stations and lines and between suppliers and customers. And there was also an approach to the management of people and the whole human resource management aspect of hiring people for careers, having career plans for everybody, including shop floor operators, managing to improve the operations based on this infrastructure.

So it's a very rich concept, and it encompasses every aspect of manufacturing, logistics, and production control, all the way to accountability. So it's compared with other things like the Theory of Constraints or TPM that are much more limited in scope. There is an approach to quality that Toyota has. The quality improvement is not all of the Toyota Production System. It's a complete system for making a product covering all the bases.

TROND: Let me just pick up on one thing, so you're saying it's a complete system. So one thing you pointed out was the HR aspect, and hiring people for careers is one thing, but you also said the career plans for shop floor operators. So I took two things from that, and I was going to ask about this because this has been used as one example of why you cannot implement the Toyota Production System in the same way in different countries, namely because that is one aspect of society that a company doesn't fully control because it is regulated.

So, for example, in Europe and in France, which you know, really well, and Germany, you know, employment is regulated in a different way. If a company was going to have the same HR policy in three different factories in three different countries, they would have to have, first of all, obviously, follow the national regulation. But then they would have to add things on top of that that would, you know, specific employee protections that are perhaps not part, for example, of U.S. work culture. So that's one thing I wanted to kind of point to.

But the other thing is interesting. So you said career plans for shop floor operators meaning Toyota has a plan for even the basic level worker meaning the operators, the people who are on the floor. And that seems to me a little bit distinct. Because in the modern workplace, it is at least commonly thought that you spend more time both training and caring about people who are making career progression.

And you don't always start at the bottom. You sort of hope that the smart people or whatever, the people who are doing the best job, are starting to advance, and then you invest in those people. But you're saying...is there something here in the Toyota Production System that cares about everybody?

MICHEL: Yes. But let me be clear about something. The way Toyota manages HR is not something that there are a lot of publications about. There's probably a good reason for this is because they probably consider it to be their crown jewel, and they're not that keen to everybody knowing about it. A lot of the publications about it are quite old. But there's nothing in the regulations and labor laws of any country that prevent you from doing more for your employees than you're required to.

TROND: That's a great point. That's a great point.

MICHEL: So there are laws that forbid you from doing less than certain things, but they're not laws that prevent you from doing more. There is no rule that you have to offer career plans for production operators because there's nothing preventing you from doing it. In a completely different situation, a large company making personal products ranging from soap to frozen foods...I won't name what the company is, but they have a policy of not being committed to their workers. Essentially, if business is good, you hire people. If there's a downturn, you lay people off.

They wanted to migrate from the situation where you have a lot of low-skilled employees that are essentially temps to a situation where they have higher level of qualification and fewer people. So the question is, how do you manage the transition? The way this company eventually did it in this particular plant was to define a new category of employee like, say, technical operator.

And a technical operator will be recruited at higher a level of education than the general population of operators. They will be given more training in both hard skills and soft skills and the specific processes they're going to be running, and some additional training on how to manage the quality of these processes, that sort of thing. But at the level of a production operator, they will be put in charge of these processes. And this small group would be separate job categories than the others. And gradually, this evolves to a situation where you only hire into this group. You don't hire any more of the traditional operators.

And then, you provide a transition path for the other operators to become members of that group so that over a period of time, gradually, the general population of less skilled, less stable operator shrinks. And you end up over a number of years with a situation where all of the operators that you have are these highly trained operators who are there for the duration. So that's one kind of pattern on how you can manage this kind of transition.

TROND: Super interesting. Can I ask you a basic question? So you've been in this consulting part of this venture, you know, of this world for a long time. Where do you typically start? When do you get called, or when do you sign up to help a company, at what stage? What sort of challenge do they have? Do you visit them and tell them they do have a challenge? What is the typical problem a company might have that you can help with or that you choose to help with?

MICHEL: There are a lot of different situations. One particular case was a company in defense electronics in the U.S. had a facility in Indiana, and they were migrating all this work to a new facility in Florida. What they told me...they called me in, and they told me that they wanted to take the opportunity of this move to change the way they were doing production. Generally, my answer to that would be, well, it's really difficult to combine a geographical change of facility with an improvement in the way you do the work. Normally, you improve first where you are. You don't try to combine transformation and migration.

TROND: It's a funny thing, I would say. It seems like the opposite of what you should be doing to try to make one change at a time.

MICHEL: But there were several circumstances that made it work. You can have general principles, and when you're in a real situation, it doesn't always apply. One is the circumstances under which they were doing this migration was such that the people in the old plant were in an environment where there was a labor shortage, so none of them had any problem finding jobs elsewhere if they didn't want to move to Florida. If they wanted to move to Florida, they could, if they didn't want to move to Florida, they had to leave the company, but there were plenty of other companies hiring around.

And so there was not this kind of tension due to people losing their jobs and not having an alternative. And then, the transition was announced way ahead of time, so they had something like a 15-month period to plan for their transfer. And to my great surprise, the operators in the old plan were perfectly...were very helpful in figuring out the design for the new lines and contributed ideas. And there was no resentment of that situation.

TROND: In this particular example and in other examples, to what extent is production, you know, process redesign a technology challenge, and to what extent is it a human workforce challenge? Or do you not separate the two?

MICHEL: I try not to separate the two because you really have to consider them jointly. A technical solution that nobody wants to apply is not going to be helpful. And something everybody wants to apply but that doesn't work, is not going to be helpful either. So you have to consider both. And in this transition, by the way, between these two plants, most of the labor difficulties were in the new plant, not in the old one, because this plant became a section of the new plant. And none of the other lines in that new plant did anything similar, so it stood out as being very different from what all the other lines did.

What all the other lines did is you had a structure that is common in electronics assembly where you have rows of benches at which people sat and did one operation, and then the parts were moved in batches between these rows of benches. And instead of that, we put cells where the parts moved one at a time between different operations. And it was also organized so that it could be expanded from the current volume of work to higher volume of work. And so a lot more went into the design.

I was a consultant there, but I don't claim credit for the final design. It was the design of the people from the company. They actually got a prize within the company for having done something that was exceptionally good. And when I spoke with them a few years later, they had gone from having something like 20% of the space used for production in the new facility to having it completely full because they were able to expand this concept.

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TROND: Michel, I know that you have a consulting life and a consulting hat, but you also have a teaching hat and a teaching passion. Why did you write this recent textbook which is coming out on Routledge this fall, I believe, with Torbjø Netland from ETH? It's an Introduction to Manufacturing but with a very specific kind of industrial engineering perspective.

You told me when we talked earlier that there's a really specific reason why you wrote this textbook, and you have some very, I guess, strong views or worries about how manufacturing education, but perhaps the way it's taught really needs to change. And you feel like some schools are drifting away from the core. What's happening there?

MICHEL: Well, industrial engineering as a discipline is about 100 years old, take or leave a decade or two. It started out as...the way I describe it is the engineering of human work in the manufacturing environment. And it expanded to fields other than manufacturing, even at the time of pioneers like Frank and Lillian Gilbreth.

For example, we know the way operating rooms in hospitals work with the surgeon being assisted by nurses who hand all the tools to the surgeon; that particular form of organization is due to Frank and Lillian Gilbreth, industrial engineers who looked at the way operating rooms worked and figured that you really don't want to leave a patient with his belly open on the table while the surgeon goes to fetch the tool. You got to have some people giving the tools to the surgeon so that the surgeon can keep operating on the patient.

It sounds obvious now, but it wasn't obvious in 1910. And so they were immediately some applications outside of manufacturing, but the bulk of the work was on manufacturing. And the way it's evolved, especially in the past few decades, is that it's gotten away from that focus on human work. And when you look at the research interests of the academics in this field, you find that it's completely dominated by operations research and math.

TROND: So we're back to the math. [chuckles] So I find it fascinating that...well, you obviously have a deep insight into it, so you are sensitized to the challenges of overfocusing on one technical discipline as kind of the mantra and the fodder, I guess, the research data for all kinds of processes. I mean, why is math such a big problem, and what do you mean by human work in industrial manufacturing? Because to many people, the advanced work right now is about digitization, digitalization, and it has to do with machines and computers, and one would assume with big data or at least with data. Are you arguing against that trend?

MICHEL: No. I mean, if you ask the question of what is human work? The classical answer that I would give is what happens when the guy picks up the wrench. That's one answer. But what happens when the operator sees an alarm message on the control screen of a machine, that's a different answer, a more modern answer. So you had people with the torque wrench applying the right torque to a bolt manually, and then the torque wrench would tell him when the torque was achieved. That's one form of human work.

But monitoring and looking after multiple machines that are connected and have a central control system is also human work. You also have people doing it. And they have to feed these machines. They have to make sure that the machines have the right kinds of tools and dyes available to them. They have to maintain these machines. They have to program these machines, and they have to monitor them during production. And one particular problem with automatic systems is micro stoppages. Are you familiar with that term?

TROND: Well, explain it to all of us, micro stoppages. I mean stoppages, obviously, anything that stops the production line, whether it's a minor, major, I mean, that would be what I think you are saying.

MICHEL: Well, if it's a big problem, the operator doesn't solve it. The operator calls maintenance, and maintenance sends somebody to solve it. Micro stoppage is a problem that's small enough for the operator to deal with. And so, in daily life or in any office life, one very common micro stoppage problem is the copier, right? You tell the copier to print 20 collated copies of a document, and you walk away expecting to find these 20 copies ready when you come back. It doesn't happen because there are some paper jams and so you have to clear the paper jam and restart.

You have a lot of things like that in production where parts jam and shoots and stop coming down in automatic system. You have all sorts of issues like this which cause production lines to stop in a way that the operator can resolve in half a minute or a minute and restart. What these things cause is that you have to have an operator there.

And so if you really want to have an automatic system that are fire and forget...when you press a button, you move away to do something else while the machine goes through an automatic cycle. When that automatic cycle is finished, you come back. Micro stoppages prevent you from doing that. And they're very difficult to avoid, but they're a major problem, even today.

TROND: Michel, I wanted to keep talking about the educational part. But before that, I just wanted to benefit from your experience here and ask you a much more basic question which is so you're writing this textbook about the future or introducing prospective students to industrial engineering and manufacturing.

My question is, historically, factories were a very, very big part of manufacturing. Nowadays, meaning in the last few years after the pandemic and other things, a lot of us start to spend a lot more time on an issue, which I'm assuming you have spent a lifetime working on as well, which is supply chain which goes far beyond the factory because it's not located in any one factory, if anything, it's a system of many factories, and it's obviously the supplies of material flows into the factory.

And the reason I'm asking you about this is in thinking about the future, which I'll ask you about in a second, a lot of people are sort of factory of the future, this and that. And there are visions about how this is going to change. But it strikes me that manufacturing is and has always been so much more than the factory. What are the components that you really worry about? So, humans, you worry about humans. And you worry about materials. And then you obviously have to worry about the physical infrastructures that are regulating these things. What else goes into it on the macro level? What is this book about, I guess?

MICHEL: We're talking about supply chains as well because, as you mentioned, they're a very important part of manufacturing. And when you design a manufacturing system to make a product, you have to make decisions about your products, about components of your product, and what you make in-house, and what you buy from the outside.

And there's a major difference between supply chain issues relating to customers, on one hand, the suppliers on the other. It's not just suppliers; it's both sides, incoming supply chain and the outgoing as well. One major difference with what happens in the factory is that you don't control what other people decide, what other organizations decide. So when you manage a supply chain, you have to manage a network of organizations that are independent businesses.

How do you get this network of independent businesses to work with you, to cooperate with you, to make your manufacturing successful? That is a big challenge in supply chain management. Inside a factory, that's an environment you control. It's your organization. What management says is supposed to go; it doesn't always, but it's supposed to go. And you have a lot more control over what happens inside than over what happens in the supply chain.

And how much control you have over what happens in the supply chain depends greatly on your size. For example, if you're a small customer of a special kind of alloy that only has one manufacturer in the world, you're a very small customer to a very large manufacturer, a metals company. You're not in a position of strength to get that supplier to work with you.

If you're a car company making 10 million cars a year and you're dealing with a company that is making forgings for engine parts, you have a lot of control. You have a lot of influence. You represent a large part of their business. They can't afford to lose you. You can't afford to lose them. You can replace them if they don't perform. They can't afford to lose you. They might go out of business if they did. So it's a very different kind of position to be in.

And so when you deal with that sort of thing, you have to think through, what is my position with respect to suppliers and customers? Where is it? Where's the driving influence? And it's not always...power in a supply chain is not always resident with the company that does the final assembly of consumer products. In electronics, for example, semiconductor manufacturers are much more key than people who assemble computers.

TROND: I wanted to ask you a little bit about the trends and how these things are evolving in the next decade and beyond that. And one example you gave me earlier when we talked was pilots and jetliners because manufacturing in...well, the aviation industry is an example of an industry that, yes, it has an enormous amount of high tech. It's a very advanced science-based development that has produced air travel. But yet these pilots...and I experienced it this summer, a pilot strike stops everything.

So the role of people changes as we move into more advanced manufacturing. But people don't always disappear. What do you see as the biggest challenge of manufacturing and the role of manufacturing in the emerging society? What is going to happen here?
MICHEL: What I think is going to happen is that in many countries, the manufacturing sector will remain a large part of the economy, but as economies advance, it will have a shrinking share of the labor market. So it's a distant future, maybe like that of agriculture, where 2% of the population does the work necessary to feed everybody else.

And manufacturing is now about 10% of GDP in the U.S., 20% in Germany and Japan, about 10% in England, France, Italy. In China, we don't really know because they don't separate manufacturing from industry. And industry is a broader category that includes mining, and it includes road construction, et cetera. They don't separate out manufacturing, but really, it's a big sector of the economy.

And so it can remain a big sector, that's not a problem. But you have to think through a transition where the number of people that you employ doing this kind of work goes down, their level of qualifications go up, and the nature of the work they do evolves towards telling machines what to do and maintaining machines. So telling machines what to do can be programming machines when you develop processes, or it can be scheduling what work the machines do.

TROND: Is that incidentally why you have gone into teaching in a kind of an academic setting or at least influencing curriculum in an academic setting so much that you see a role here in the future? Beyond what's happening in factories today, you're quite concerned about what might happen in factories ten years from now, 20 years from now when these students become, I guess, managers, right? Because that's what happens if you get education in management at a good school, reading your hopefully great textbook. It takes a little time because you trickle down and become a manager and a leader in industry.

So I guess my question then is, what is it that you want these people to know ten years from now when they become leaders? What sort of manufacturing processes should they foster? It is something where humans still matter for sure, and machines will have a bigger part of it. But there's things we need to do differently, you think?

MICHEL: The airline pilot metaphor, you know, you have this $300 million piece of equipment. And how much money you make from operating it depends on these two people who are in the pilot's cabin. You have to pay attention to the work of people. And in most factories, the work of people today is an afterthought. So you put in machines. You put in production lines without thinking how will people get from the entrance of the building to where they actually work?

TROND: I was going to say it's a fascinating example you had with the airline industry in the sense that, I mean, honestly, even in the old industrial revolution, these machines were expensive, but I guess even more so. I don't know if you've done any research on this, but the amount of dollars invested per worker presumably has to go up in this future you are talking about here where we're increasingly monitoring machines, even these perhaps in the past viewed as low-skilled jobs or operator jobs.

I mean, you are operating, maybe not airplanes, but you're operating industrial 3D printers that cost hundreds of thousands of dollars with presuming error rates that could be catastrophic, either for you, for the production line, or for the product you're making.

MICHEL: Or photolithography machines that cost millions.

TROND: Right. But then that begs the question for me, Michel, how on earth is it possible? If you are right about this that education has been somewhat neglected and skills has been neglected, how's that even explainable? If you are a responsible factory manager or executive of a large manufacturing firm, how could it have gotten...and I'm obviously paraphrasing here. I don't know if you think it's that bad. But how could it get this bad that you actually had to come out and say it's a massive problem?

MICHEL: What happens is that you hear a lot about systems thinking, which, to me, it's pretty obvious there's more to a factory or more to a manufacturing system, to supply chain than the collection of its components; it's pretty obvious. And when you change the way a supplier delivers parts, it has an impact over what happens at the assembly workstations where these components are being used, for example.

You have to think of the whole as a system. And you have to think about whenever you make any changes to it; you have to think through how these changes affect the whole. What's happening is that there has been a great deal of specialization of skills; I'm not talking about factory workers here. I'm talking about engineers and managers that have been put into silos where they run production control. They become production control manager in the factory. Their next career move is to become production control manager in the factory of a different company.

TROND: So here's my open-ended question to you; you're sort of saying that industrial engineering, in one sense, needs to go back to its roots where it was. But the other side of the coin here is you're also talking about a world that's changing drastically. So my question is, the industrial engineer of the future, what kind of a person is this ideally, and what sort of skill sets and what sort of awareness does this person have?

MICHEL: The skill sets that this person should have are both technical and managerial. It's management and technology considered together. So they may not be able to write code, or they may not be able to design how to cut a piece of metal, or how to tweak the electrical properties of a circuit, but they know the importance of these things. They've been exposed to them through their education and career. And they have an appreciation for what they are.

So, for example, one particular task that has to be done in every manufacturing organization is technical data management. You have to manage the problem definition, the process definitions, which machines you use to do what, down to the process program that these machines run. All of this is data, technical data that has to be managed, put under revision control. And you'd expect someone with training in industrial engineering to understand the importance of revision control on this.

If you change something to the cutting program of a milling machine, you may affect what happens elsewhere. You may affect the mechanical properties of the product and make it difficult to do a subsequent operation later. And that's why before you implement this change in production, you have to have a vetting process that results in revision management. So I would expect an industrial engineer to understand that.

TROND: Well, you would expect an industrial engineer to understand that, but, I mean, some of the challenges that come from these observations that you're making here they impact all operators, not just engineers. And they certainly impact managers because they are about this whole system that you are explaining. So it sounds to me that you're mounting a pretty significant challenge to the future manufacturers, not just in skills development but in evolving the entire industrial system.

Because if we're going to make this wonderful spacecraft, and solve the environmental crisis, and build these new, wonderful machines that everybody expects that are going to come churning out every decade, we certainly need an upskilled workforce, but we need a whole system that works differently, don't we?

MICHEL: Yes. Can I give you a couple of examples?

TROND: Yeah.

MICHEL: One company outsourced the production of a particular component to a supplier then there were technical problems with actually producing this component with the supplier. So the customer company sent a couple of engineers to the supplier, and they found some problems with the drawing that had been provided to the supplier. And they made manual corrections to the drawings, the copies of the drawing in possession of the supplier. And it worked. It solved the immediate problem. But then, at the customer company, they didn't have the exact drawing. The only place with the exact drawings was at the suppliers. And a few years later, they wanted to terminate this supplier.

TROND: Aha.

MICHEL: You can see the situation. You want people to be able to understand that you just don't do that sort of thing.

TROND: Right. So there are so many kinds of multiple dependencies that start to develop in a manufacturing production line, yeah.

MICHEL: And then you find a company that's a subcontractor to the aircraft industry. And you find out they route parts through a process that has about 15 different operations. And the way they route these parts is they print a traveler that is 50 pages long, and it's on paper. And the measurements they make on the parts that they're required to make by their customer they actually record by hand on this paper. What's wrong with this picture?

TROND: So yeah, multiple challenges here.

MICHEL: Yes.

TROND: Are you sensing that these things are fixable? Are you optimistic in terms of this awareness of all aspects of the systems changing both among managers and next-generation industrial engineers, and perhaps even among the operators themselves to realize they're getting a more and more central role in the production system?

MICHEL: I won't try to prophesy what will happen to industry as a whole but what I'm confident about is that the companies that know how to address these problems will be dominant. Those are the sort of basic mistakes that really hurt you and hurt your competitive position. So there will be a selection over time that will eliminate people who do these kinds of mistakes.

TROND: Michel, I don't want to put you on the spot here. And you have spent your career researching and tracking Toyota as an excellent, excellent manufacturer that has graciously taught other manufacturers a lot. And also, people have copied and tried to teach them Toyota methods, even if Toyota wasn't trying to teach everyone.

Are there any other either individual companies or things that you would point to for the eager learner who is trying to stay on top of these things? I mean, so lean, obviously, and the Toyota Production System is still a reference point. But are there any other sources that in your career or as you're looking at the future where there is something to learn here?

MICHEL: Oh yes. Toyota is a great source of information, but it's by far...it's not the only one. One of the key parts of Toyota's management system is Hoshin Planning. Hoshin Planning didn't come from Toyota; it came from Bridgestone tires. And so that's one case where a different company came up with a particular method.

Honda is a remarkable company as well, so there are things to learn from Honda. HP was, under the leadership of its founders, a remarkable company. And they had their own way of doing things which they called The HP Way. Companies have recruited a lot of people...electronic companies have recruited a lot of people out of HP. And you feel when you meet the old timers who have experienced The HP Way, they feel nostalgia for it. And there were a lot of good things in The HP Way. They're worth learning about. So I also believe that it's worth learning about historical examples because history is still with us in a lot of ways.

The Ford Model T plant of 100 years ago was a model for a lot of things at the time. It also had some pretty serious flaws, namely, its flexibility. And you still see people putting up the modern-day equivalent of a Model T plant with new products and new technology but without thinking about the need. That particular plant may have to be converted in the not-too-distant future into making a different product. So it's always worth looking at examples from 100 years ago, even today, not for the sake of history but because, in a lot of ways, history is still with us.

TROND: Well, on that note, history is still with us; I thank you for this, Michel. And I shall remember to forget the right things, right? So history is still with us, but [laughs] you got to know what to remember and what to forget. Thank you so much.

MICHEL: Culture is what remains once you've forgotten everything.

TROND: [laughs] On that note, Michel, thank you so much for your time here and for sharing from your remarkable journey. Thank you.

MICHEL: You're welcome.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Lean Manufacturing. Our guest was Michel Baudin, author, and owner of The Takt Times Group. In this conversation, we talked about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work. And indeed, we are looking at a future where manufacturing is not going away.

My takeaway is that lean manufacturing might mean many things, but industrial work has largely been a consistent practice over several hundred years, which is not necessarily a bad thing. Having said that, if we want to go about improving it, we might want to stay pretty close to the workforce and not sit in statistics labs far removed from it. Efficiency is tied to work practices, and they cannot be optimized beyond what the workforce can handle or want to deal with. As we attempt to be lean, whatever we mean by that, we need to remember that work is a thoroughly human endeavor. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us because we would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform connecting people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Michel Baudin.

Episode 101: How Academia Shapes Manufacturing with John Hart

2 november 2022 | 42 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "How Academia Shapes Manufacturing". Our guest is John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT.

In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 92 on Emerging Interfaces for Human Augmentation.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

There has never been a more interesting time to be in manufacturing or to watch manufacturing. The tremendous breakthroughs that we are about to witness have been made possible by a confluence of emerging technologies and startup innovations, as well as a growing awareness of the importance of building human-centric technologies.

We are indeed at a crossroads with profound challenges in the growing talent shortage, the need for workforce training, an aging industrial base, and the demands for manufacturing competency from the wider innovation ecosystem. We have to make progress fast, and innovations are just maturing to be able to do so at the scale and pace required. It will, again, be amazing to watch the manufacturing industry. Parts of it will perhaps, again, become the industry of industries.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind the new era of industrial operations where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory, and who is leading the change? And what are the skills to learn and how to stay up to date on manufacturing and industry 4.0.

In this episode of the podcast, the topic is How Academia Shapes Manufacturing. Our guest is John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT.

In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history.

Augmented is a podcast for industrial leaders, for process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

John, how are you? Welcome.

JOHN: I'm well, Trond. Great to see you. Thank you for having me.

TROND: Well, I'm excited to have you talking about...well, hopefully, a lot of different things, but how academia gets to shape manufacturing, this fascinating venture that is manufacturing. But you yourself, John, you grew up in Michigan, is that right? You were close to this from an early age.

JOHN: I was close to it. Yeah, I grew up in Royal Oak, Michigan, a suburb north of Detroit. If you know the Detroit Metro area, there are the mile roads, and the Detroit River is sort of Zero Mile. And I grew up between 14 and 15 Mile Roads, so in the hotbed of the good, old U.S. auto industry.

TROND: Well, exactly. Because looking a little bit at your background here, you spent quite a few years as a summer intern at General Motors before you got yourself to...or actually perhaps in the beginning, in your undergrad years from UMichigan, is that right?

JOHN: I did. After my first year at UofM, I worked as a summer intern at GM and went back a few years in a row in different roles in different areas. And honestly, when I decided to pursue a graduate degree and ended up at MIT, I thought I might just get my master's and go back and work in the auto industry, but things changed, and here we are today.

TROND: Well, here we are today. You got yourself an undergrad from UMichigan. And you worked there for a little while, I believe, but then came to MIT with a master's, Ph.D. This is way back. But you won the prize for the best doctoral thesis in micro and nanotechnology. So that set you off on the path to rediscover nanomaterials, I guess.

JOHN: Yeah, well, it's a really maybe exotic combination of topics. My master's thesis was on precision machine design, the design of these large mechanical couplings for industrial robots. And then, for my Ph.D., with the same advisor, I worked on carbon nanotube synthesis. But there you have the dipoles of manufacturing research, materials, processing, and mechanical design that have shaped how I've taken things forward since then.

TROND: Well, but it is in these unique combinations that innovation starts to occur, right?

JOHN: Yeah, exactly, combining different topics. And that's one reason I love manufacturing is that it is the union of materials processing, and automation, and software, and now also getting more interested in the organizational workforce aspects. It's a very rich, multidisciplinary layered topic.

TROND: Yeah. And we'll explore this both from the organizational angle, and, indeed, I'm super interested in this material angle on things because it seems to me like you're exploring the very, very small nanostructures, but then you're then printing them on the very large canvas. So you're exploring materials from one extreme to the other.

JOHN: Yeah. Well, it depends on your objective and what topic you're working on. There are cases in our research where we need to understand the formation of materials, not quite from the atom up but from the nanoscale or microscale up. And there are cases where we more or less abstract or coarse grain those link scales and focus on macroscale properties.

TROND: Well, and then you also focus quite a bit on teaching. I noticed that you actually launched the first massive online course on manufacturing processes, and hopefully, we'll get to this a little bit as well.

JOHN: Sure.

TROND: But teaching and basically working on the next generation of manufacturers, whether they be the engineers or really anybody else, has certainly been one of the big challenges in manufacturing really forever. What is it that fascinates you so much about teaching this to a grander audience than the usual university audience?

JOHN: Well, first, I'll say I believe that the top priority of universities, including in the area of manufacturing, is to educate future leaders and engineers. That said, the number of people we educate on our campus is a small fraction of those who could really benefit from what we teach and the way we teach. And that's not just geographically, but it's also in terms of their role in the workforce. So I believe manufacturing education should address all levels of the workforce.

And to get at your question more directly, when I came to MIT, I was asked to take over our core undergraduate manufacturing class in the Department of Mechanical Engineering. And as I learned to teach the class for myself, I was intrigued by this emerging trend of digital learning, and this was 2015, 2016. And I was able to get some funding from MIT internally to create an online version of the course that would be offered free to the world, and probably 100,000 People have taken it so far. And it's been a great experience and evidence of how there is very broad interest in manufacturing really across the world.

TROND: 100,000 people have taken this course.

JOHN: Yeah. Well, I'll say 100,000 people have signed up for the course. This is the classic trade-off with online courses. It doesn't mean 100,000 people complete the course. It means that number signs up and hopefully took something away from it. It also speaks to the flexibility. You can sign up for a course and maybe just listen to one lecture, but if you take something valuable away from it, that's great.

TROND: So I wanted to talk a little bit about how academia shapes manufacturing. And I know that there are, you know, you and I work at MIT, and you've had experiences obviously at University of Michigan. But there are other manufacturing centers and institutes all around the world. Could you lay out this landscape a little bit for us so that we get a sense of where the excellent centers of manufacturing are located?

I mean, one structure, just to pick that, is manufacturing institutes, and I know that's sort of dear to your heart for a couple of different reasons that we'll get into. But what are some of the centers beyond MIT where there is activity that is organized in a way that really is something to focus on?

JOHN: First, I think of in the U.S., Carnegie Mellon, Georgia Tech, Purdue, Michigan, Stanford, places that have defined manufacturing centers or have a body of work that relates to manufacturing that I would say there's a critical mass of faculty, and students, and affiliation with industry. Also, Penn State in the area of additive manufacturing and product design. It's hard to be comprehensive. I don't want to forget anyone big, but that's a sample of some of the notable ones.

Internationally, a lot of activity in Europe; I admire the University of Cambridge, the Institute for Manufacturing there, where manufacturing is more or less a department, or it's within the Department of Engineering, which is analogous to what we would say is a school or college of engineering here in the U.S. And they have a broad set of activities that have been there for decades focused on manufacturing at the IFM.

TROND: And if you think about the best schools to get educated in this topic, is it necessarily only the top brands? I mean, certainly, they have different roles. So when it comes to undergrads or even shorter, or I guess even community colleges have a really fundamental role in the formation of this sector, can you talk a little bit about that?

JOHN: Oh, for sure. When you think of manufacturing education, we must think of the full stack of institutions that educate the workforce, from vocational institutions to community colleges where the student's goal may just be to complete a vocational program or complete a two-year degree and then exit the workforce, all the way to the four-year degrees, advanced degrees, and executive education. And given how manufacturing is paramount in the workforce and the economy, we need to educate folks at all those levels.

But by far, the largest number of people are at those vocational community college levels and then to the bachelor's level. So I have a Ph.D. I love to mentor Ph.D. students. But that's a small fraction of the manufacturing workforce.

TROND: What about in the U.S. setting? There's something called the Manufacturing USA, and there are these institutes that have sponsorship from various government agencies, most of them through the Department of Defense. But there's also a bunch at the Department of Energy and one, I guess, from the Department of Commerce. What is the role of basically government-sponsored sort of research and innovation activities in this field? It would strike me, I guess, that historically, it's quite important.

JOHN: Certainly. You're alluding to the manufacturing innovation institutes, the MIIs that were started during President Obama's administration. Actually, MIT's work, the Production in the Innovation Economy study, and the Advanced Manufacturing Partnership, which emerged from that, was key in scoping the MIIs, and now there are 16 or so around the country. It's one example of public-private partnership. Public-private partnership is key to cultivating interest in manufacturing and also providing resources for technology translation and commercialization.

I think the MIIs have had a great impact on awareness of manufacturing, on R&D, and really applied research in some critical technology areas. But it's only a small part of what we need to do to regrow and expand our industrial base in the U.S.

TROND: So I want to move us shortly to MIT to discuss both your own research activity and how extensively you are now aiming to take a more organizing role to kind of get more out of all of the exciting work that's happening at MIT. But before that, I just spotted perhaps an older project of yours that I thought was extremely cool. You were once called a nanoartist, and you had this NanoArt Nanobliss gallery with visualizations.

You previously mentioned Obama. I believe you made a NanoArt structure called Nanobama or something of that sort. How did this come about? And, again, I mean, I'm guessing this just sort of testifies to your interest in science communication as much as in the depths of science, which we'll get into in a moment.

JOHN: You got it. The inspiration was how do we communicate what we're doing in the lab to broader audiences just to make them aware of what's happening in new technology, new materials? In that case, it was nanotechnology. If you don't mind, I'll tell you a bit more of the story.

When I was an assistant professor at Michigan, we were doing a lot of work on carbon nanotube manufacturing, which was a follow-on from my graduate work at MIT. And I admired President Obama, or he was a presidential candidate at that time. And without implying a political inclination, I somehow put together the words nano and Obama in my mind.

TROND: [laughs]

JOHN: And I said, wow, it would be cool to have a Nanobama. So one thing led to another, and I actually worked with some students in my group to fabricate these little portraits out of carbon nanotubes representing Shepard Fairey's portrait of Obama that was used widely during that first presidential campaign. And I just posted it online, I think one day after the election, and it took off. It went viral, so to say, and was featured as Nature's Image of the Year.

It was printed on the newspapers you used to get as you walk onto the subway in the morning around the world. There was a company that would syndicate this stuff, and they just sent it around. So it got a lot of attention. And it showed me the power of an image in communicating something. And, of course, President Obama, that was a historic election. The play on words was exciting, and also the fact that it was a little bit intriguing science and technology that was nano was interesting.

And one more thing, a colleague of mine at Michigan then was working in the White House, and he said, "Hey, can you send us a Nanobama?" So I made this frame with a little piece of the real material, and a picture of it from the microscope sent it to Washington. I didn't hear anything about it until I got a call from the White House asking me to declare the value for the President's tax return because he decided to keep it; I kid you not.

And then, after Obama left office, I was with my family at a bookstore in Wellesley, and I saw the book, the retrospective book of Pete Souza, the White House photographer. And I opened up the book, and I see a picture of Obama and John Boehner in the Oval Office in the middle of this book. And right on the doorframe is the Nanobama. So it actually made it to the White House, which was a pretty awesome feeling.

TROND: It must be an awesome feeling, and, again, I think that, especially in this field of manufacturing which is so challenged at times, right? And people are talking about how these factories are greedy, or is this a great job, or whatnot. And there have been all of these historical moments. But then there is also this fascination around the topic of certainly of technologies and the excitement around it. Why don't we continue a little bit on this strand before we get into sort of the overall role of MIT?

I'm really curious about how your research has evolved. So generally, I get that you're combining these nanostructures with manufacturing and materials research, and certainly, you have applied it to additive manufacturing. How would you say that your research has evolved over these years? What are the things that you have been doing? I've picked up on a few things that I definitely wanted to cover. I mean, certainly, you've been working on this industrialization of 3D printing, both as a research area and as a commercial area. Carbon nanotubes must have been kind of where you started. I'm curious where that work is going.

And then I saw that very recently, with a student, you've been doing some work that I'm personally very enthused about, which is a plant-derived composite that might replace, hopefully, plastics with sort of a hardness and stiffness that is somewhere at the boundary between conventional plastics and metals. I mean, for me, I don't quite see how all of these things are intimately connected. Where do you go for, you know, where's my next proposal here, and where's my next patent?

JOHN: They aren't necessarily closely connected. But I like to say that the themes are typically one or more of materials, manufacturing, and mechanical systems or automation. And what I love about manufacturing, especially in the materials domain, is to control a process, to understand a process, and then to do something new, you need to investigate its fundamentals. And sometimes, you need to design a new instrument or machine to get the job done.

So our work is often problem-inspired or opportunity-inspired. Like, the cellulose work that you mentioned recently was actually sponsored by a large consumer products company interested in a more sustainable composite material that could be used in packaging. And we looked at potential routes to formulating different materials, and we landed on cellulose. And then, we developed a formulation, a mixture of cellulose nanocrystals and polymers that ended up having exciting mechanical properties, particularly very high hardness, and toughness, more so than existing polymers.

And another unifying theme is scalability. It's important not to worry too much about scalability in the early stage of research, and there's lots of amazing research that's just for science. But we like to do things that we hope will be scalable one day, so choosing ingredients that would be cost-effective or using techniques that could be industrialized, even if the techniques look very different in the lab. And maybe I've lacked to give a precise definition or focus, but I think it's also indicative of the broad span of manufacturing. And manufacturing has many, many dimensions beyond the ones that we work on in my lab at MIT.

TROND: Well, you kind of answered a question that I was going to ask, too, which is it doesn't seem like you start in a linear fashion, you know, in other words, you start with some sort of basic problem that everybody in their literature has established and then you move to this, that, or the other. Sometimes it comes from a company. The challenge comes from a company, but you formulate the solution completely. It seems to me that students also have lots of ideas and kind of formulate projects.

Talk to me a little bit about this process of where the problem comes from versus where the solution and impact comes from because you seem to...sometimes the output truly is just, you know, like, in this case, art or a physical prototype, and you're sort of happy with that outcome. Other times, you're actually delivering something into, presumably, eventually, an assembly line.

JOHN: Yeah. And we work as hard as we can on technology translation, both in terms of the knowledge that we publish but also in terms of the steps that we take to spin technology out. You're right; the early stage is very important. And I like to often see the early stage as a collaboration between myself and the researchers.

And in many cases, the core idea we end up pursuing comes largely from the research or the research team. In many cases, it might be seeded by the interest of a sponsor or an idea I have, and then we work together on actually figuring out what's the approach, what are the outcomes, and what's the path to success.

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TROND: You have commercialized at least two ventures together with others at MIT and external people as well that I know about for sure. I wanted to just briefly mention both Desktop Metal and VulcanForms. Let's perhaps cover Desktop Metal first, so that's a 3D printing company. Tell me how that got started and what your role was there.

JOHN: So I was very fortunate to be a member of the founding team of Desktop Metal. So there were seven co-founders, and we launched the company in early fall of 2015. And Ric Fulop, who's the lead founder and CEO, approached me at that time, and he heard that I was interested in working on 3D printing and, of course, knew a bit about my background in manufacturing and machine design and asked me to jump on board.

And funny story, how just connections persist over the years; I actually knew Ric when I was a grad student because I was doing my carbon nanotube work using the space of now my colleague, Yet-Ming Chiang. And at that time, Yet and Ric were launching A123 Systems, a successful battery company. So that was a reason why I think Ric knew to get in touch with me when he heard about me. And serendipity was a great experience.

TROND: Serendipity when you are in the right places, right? If you're hanging around Yet-Ming Chang, yeah, that's right, very special serendipity. Tell me a little bit about VulcanForms. Until very recently, you couldn't talk so much about it. Nowadays, you did go out in New York Times. I've read that piece. So there is a little bit more detail around it.

Let me ask a very basic and perhaps dumb question, large-scale metal 3D printing, what's the big deal there? I thought didn't Desktop Metal do 3D printing? So it's kind of a dumb question. Why is there a second company? Is there really such a variety? I think that the regular person just thinks 3D printing is 3D printing.

JOHN: 3D printing is a broad and deep subject. Like, first of all, 3D printing processes exist for polymers, for metals, for many other materials. And there are even several 3D printing technologies for metals. I'll tell the origin story for VulcanForms quickly if that's okay, and then get back to the question.

So when I came to MIT as faculty in 2013, I had been a professor at Michigan for a few years. And I landed, and one of the topics I thought of looking into was 3D printing. I was actually asked by a colleague to teach a class not on 3D printing, but I was able to propose the topic.

And in that class, there were many incredible students. One of them, named Martin, stuck around at MIT after finishing his master's in manufacturing, and we ended up comparing notes and launching VulcanForms in 2015, a little bit before Desktop Metal came to be, but not that long before. And we stayed quiet for seven years. We raised our seed round a couple of years ago.

And the focus of the company is number one, laser-based metal additive manufacturing. And second, while we've built our own additive technology, we're a manufacturing company. So we produce parts at scale, and that is a real need and has been a barrier to growth of the additive industry. There's so much interest and uptake in additive. But the ability to achieve high-quality production using additive as the formative step in the process at scale has largely been untouched. So from the early days, we thought that we could approach the market with that plan to become a manufacturing company.

TROND: Staying quiet for seven years that can't have been [laughs] particularly easy.

JOHN: Yeah, it's not easy, but it's very, very worth it because we got to focus. And also, there are different boundary conditions that allow you to keep your head down and get work done, and one of them is having great and patient investors who believe in your approach and who see the progress behind the curtain. And as a result, we felt we would hold off launch. And we were fortunate to get picked up by the New York Times earlier this summer. And now we're excited to talk about what we do.

TROND: Yeah, that article did hint a little bit at what your printers can print that others cannot and kind of at what scale. Can you give some examples of the kinds of things that you are now contracted to print or are perhaps already printing?

JOHN: So the company is focused on a variety of industries, generally industries where high-value metal parts are difficult to manufacture and where there is a real pent-up need for more agile, high-value manufacturing medical devices such as medical implants, semiconductor components, not microchips but cooling devices for various computer systems. We have a lot of business in the aerospace and defense area, working with several of the defense primes, both on additive parts and on machining, honestly.

The company, as described in the New York Times article, we acquired a machine shop in Newburyport, Massachusetts, earlier this year. And that was twofold, one because in order to deliver finished parts, you need to often integrate additive with machining. So it's not just 3D printing; it's building a stack of software and physical processes to create a finished part. Second, advanced machining is also a digital manufacturing technology, and as a company, we're very interested in applying our capabilities as a digital manufacturing organization to the area of CNC machining as well.

TROND: So, taking that experience then from these two companies and your vast interest and research area plus your interest in communication, what is it that you're now focused on at MIT more largely? That's another kind of secret that's slowly being let out. But you have had this notion and have shared this with me and others, obviously. There was a seminar open to whoever was invited, I think, but not a full public launch.

Manufacturing at MIT has historically been quite important, but you think that there's even more, to be done. You lined up a couple of the projects, but there are many more things that MIT has done. Could you maybe just briefly address the role of MIT historically in influencing manufacturing? And what else is it that you now want to accomplish?

JOHN: Yeah, for sure. And since I came to MIT nine years ago, I've learned of the incredibly rich history that the institute has in manufacturing, both on the technology side, you know, in the mid-1950s, building among the first CNC machines, ultimately transformed commercial aviation in 1980 building one of the first 3D printers in the world, and so on.

But not only that, but also, historic accomplishments in the social sciences, understanding the globalization of manufacturing, you know, what delineated the U.S. versus the Japanese auto industry in the 1980s. What is the intrinsic role of manufacturing in innovation, the production, and innovation economy led by my colleague Suzanne Berger in around 2010. And then broader than manufacturing, though, the work of the future study just a couple of years ago looking at the connection between technology and work.

So looking at all those accomplishments and understanding the present moment that we're in, which I can also reflect on later, I've been exploring how to create a new presence for manufacturing at MIT. And the term manufacturing at MIT is more or less a placeholder representing the community of faculty and students across disciplines, both technology and social sciences, that touch on all the dimensions of manufacturing.

So as we've returned from Zoom life to more in-person life, I've been making my way around campus and building a team of folks, faculty advisors, external advisors, industry partners, and so on to hopefully put forward a new center at MIT that has a focus on manufacturing across the disciplines. And this is not to replace existing activities but just to augment those activities and bring industry together with us to support research, to lean deeply into workforce training programs, to collaborate with public organizations at the state and federal level and internationally, and also hope to cultivate more entrepreneurship.

Because my experience, fortunate experience as an entrepreneur over the past several years tells me that there's opportunity for more new companies that contribute to the future of manufacturing, whether they're manufacturing companies actually making stuff, whether they be software and services companies. Or perhaps the biggest need is hardware companies for whom manufacturing is a route to success. So you may not be manufacturing something yourself, or you may not be manufacturing goods for others, but understanding manufacturing and scaling a process is really key.

And that intellectual DNA of manufacturing is more cross-disciplinary than ever. And I've observed over my nine years at MIT how there's just more engagement in manufacturing as a discipline, as this cross-disciplinary theme. And that's an area where I feel such a center can really play a role by adding something to the intellectual community across the institute.

TROND: There are so many things that come to mind when you produce this narrative because, I guess, on the one hand, manufacturing is a little bit of everything. On the other hand, it is clearly very delineated because it's all about making things and making them at scale. And there's a whole industry, but, of course, every industry almost has a manufacturing arm. How do you delineate the subject of manufacturing? And I'm sort of curious, you know, at MIT, if you use a broad church definition, almost everybody there contributes to manufacturing. So that would be both a challenge and an opportunity, I guess.

JOHN: Yeah, you're exactly right. So, first, within MIT, we have many collaborations with different departments and other research centers. And the nature of the collaboration depends on what the focus is. Second, when it comes to interfacing with industry, I've come to look at industry as kind of a grid where you could say the columns are the end users, say, aviation and space or consumer or construction. And then, the horizontal lines in the grid are technologies, robotics and automation, 3D printing, software and IT, et cetera.

And getting a little bit in the weeds of the organization here, so first, we're working on launching a flagship industry consortium, or we're recruiting flagship industry partners for a new center. And those will be companies, world-leading manufacturing companies across the grid. Second, we will operate consortia in different technology in industry areas that may be located within our center that may be in collaboration with others around MIT to really drive focus.

And when industry comes and interacts with us, I want them to understand how their business fits into the broader spectrum. And we find particularly in the work related to 3D printing that companies appreciate being connected with peers across the value chain. They say 3D printing is materials at the frontend and finished parts at the backend, and there are some machines and software, and so on. When you bring companies together across their value chain, across their supply chain, under the umbrella of an academic organization with this sort of problem-solving mindset, we find that that can be valuable to the companies that we partner with.

TROND: And, John, there's obviously a scale at MIT that's hard to replicate for any university or school just because there are so many people involved in technical innovation. But on the other hand, I would say there has been a sense that other sectors if you could call them that, have always been moving much faster than manufacturing.

And, you know, okay, fine, there are industrial revolutions, but the ones we talk about now as industrial revolutions are more, you know, they are maybe on the software side and stuff, but that the core of manufacturing it may be because of its inherent nature. It's complex; it's about physical infrastructure, at least a lot of it still. So it's hard to innovate in that sector. Would you say that one of the ambitions you have with this manufacturing at MIT initiative is to speed up that innovation? And if so, what are the mechanisms that would bring manufacturing as a whole, I guess, on an even faster sort of clip?

JOHN: First, if I look within MIT, we see the opportunity to combine the physical side, the mechanical engineering, the material science, with the digital side, with software, and controls, and computation. And that's an area where it's clear that new technologies can be de-risked, can be scaled more quickly. And it really requires this symbiosis of the physical processes and the digital intelligence.

Second, I think we can do better research. I can do better research by understanding where the big problems and opportunities are. And by connecting closely with industry, forming networks with various stakeholders, we can define better problems that we can ask our students to solve.

And third, I've noticed, especially over the past year with all the geopolitical discussions and the imperative for sustainability, that we're at a time where there's this alignment between industry and government and the investment community and manufacturing, physical manufacturing, physical industry is vital. We can't do enough there to catch up, to grow. And I think that's a real opportune moment to recognize that while I think the pendulum has swung to the digital world and software over the past 10, 20 years, life has changed for the better in so many ways.

We have to focus on the physical world now, especially to address the climate crisis, and also think of how we can improve economic equality across our communities, how we can provide better job opportunities, how we can deliver education to individuals who don't have the opportunity to go to university or don't have the resources to travel, all those things.

So that's another reason why, one, I see manufacturing as this rich, cross-disciplinary topic that I can file a patent and write some exciting papers and graduate with a Ph.D., but it means so much more to feel technology at scale. And second, you need the intersection of these disciplines to understand not just technology but organizations and human dynamics to create change and create positive impact.

TROND: So I realized that we're going to have to cover... there are so many other questions I have for you is what I'm trying to say here. But my last question in this round, I think, is going to be one on...we briefly mentioned, or you briefly talked about augmentation. And you know that I have a special interest; obviously, the topic of the podcast and the title is augmentation. So there is something here about the tension, perhaps between augmentation and automation.

How do you see that tension or the relationship between working from the human-centric perspective that technologies are in service to perhaps augment people and processes versus this automation perspective which maybe takes, and I'm paraphrasing here, a little bit more of an efficiency approach and tries to go for machine scale first and then just adjust everything later? How do you see those two things now, as perhaps, you know, manufacturing is coming into another kind of growth moment?

JOHN: If I understood you correctly, I don't think they're mutually exclusive, right? Certainly --

TROND: No. Not necessarily. Not necessarily.

JOHN: Certainly, manufacturing will become more automated in places where automation makes sense. Certainly, automation is challenging to implement to scale, to get right. But in some cases, the driver to more efficient technology-first manufacturing is automation. In other cases, and hand in hand with that, human workers and businesses, organizations can only become more effective and efficient, working in synergy with data and automation.

I'll use the example of someone overseeing a 3D printer, a state-of-the-art 3D printer, and watching the screens to make sure everything is going well and doing a better job by being presented with information that shows, hey, this might be a problem, or there are no problems here, but being empowered to make that data-driven decision.

And also, from my work outside of MIT, we find that folks who do best operating that advanced equipment with digital data might have a machining background. They might also have a passion for gaming on the side. So they might be used to sensing and responding to dynamic digital events. And that's another comment on skills evolving in the workforce too.

TROND: Well, I mean, one thing that is for certain is that if MIT gets its act together on manufacturing, things will happen. I trust that we're going to have to come back and talk about a lot of emerging projects here in the coming years if you get people lined up. So very exciting. Thank you for speaking to me.

Is there sort of a challenge that you want out there to the community when it comes to how, you know, not just academics can contribute to shaping manufacturing but how we all should think of these manufacturing challenges? Is it something that we should leave to experts right now because it's so complicated? Or are there ways that the broader interested public can get engaged in this problem? Is it possible to engage, and where should one engage?

JOHN: That's a great question. First, to the general public, I'd say stop and think about what manufacturing means to you, or find one of your favorite things and look up how it’s manufactured. Imagine the life, the journey of the product as it comes to your door.

And second, I'd say the area where most of us can make an impact is in education and learning and contributing to our communities. Perhaps if you're an engineer working somewhere, you might want to teach at a community college one night a week if you have time in a future semester or explore ways that you can bring new knowledge, new technology to your organization if it makes sense.

TROND: Exciting challenges. Thank you so much for sharing a little bit of what you're up to with us, John.

JOHN: Thank you, Trond.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was How Academia Shapes Manufacturing. Our guest was John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT.

In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history.

My takeaway is that there has never been a more interesting time to be in manufacturing or to watch manufacturing. The tremendous breakthroughs that we are about to witness have been made possible by a confluence of emerging technologies and startup innovations, as well as a growing awareness of the importance of building human-centric technologies.

We are indeed at a crossroads with profound challenges in the growing talent shortage, the need for workforce training, an aging industrial base, and the demands for manufacturing competency from the wider innovation ecosystem. We have to make progress fast, and innovations are just maturing to be able to do so at the scale and pace required. It will, again, be amazing to watch the manufacturing industry. Parts of it will perhaps, again, become the industry of industries. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 92 on Emerging Interfaces for Human Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production or a logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: John Hart.

Episode 100: Innovating Across the Manufacturing Supply Chain

19 oktober 2022 | 58 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Innovating Across the Manufacturing Supply Chain. Our guest is Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker.

In this conversation, we talk about lean leadership, productivity, the challenge of digital transformation across operations and supply chains, and how augmented lean means every organization has their own transformation approach.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 94 on Digitized Supply Chain with insights from Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Stanley Black & Decker is a huge organization where any improvements by tweaking their own operations or by adding insight from what happens along the whole supply chain can mean significant productivity gains. I find it interesting that they have their own version of the augmented lean approach tailored to where they are and, most importantly, building on the insight that the workforce is where the innovation comes from. By giving shop floor workers access to insights on big-picture manager deliberations, they are freed up to operate not only more efficiently but also more autonomously. When all of industry works that way, manufacturing will make tremendous advances more rapidly and sustainably than ever before.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Innovating Across the Manufacturing Supply Chain. Our guest is Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker.

In this conversation, we talk about lean leadership, productivity, the challenge of digital transformation across operations and supply chains, and how augmented lean means every organization has their own transformation approach.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Antonio, welcome to the podcast. How are you?

ANTONIO: I'm good. How are you doing?

TROND: I'm doing great. I'm looking forward to thinking and talking about manufacturing supply chains and the rollout of digital technology. So, Antonio, you are actually a business major by origin from North Texas, and then your master's is in HR. And then you're fashioning yourself as a lean leader and an operational expert working on productivity and now much on digital transformation. And you're heading the rollout of digital solutions for Stanley Black & Decker. I'm curious, what was it that brought a business major into the manufacturing field?

ANTONIO: For me personally, businesses is great. I'm a big advocate of free markets. And so for me, the whole time you think of how widgets are created and wanting to understand that aspect in manufacturing, creating widgets. Like you were saying, with a master's in human resource development, my thoughts there were learning that a lot of the cost from any organization is going to be labor and material. So having that understanding was great.

And then transitioning to making widgets and learning under some ultimate awesome leaders in the space along with great engineers that really, really, hand in hand taught me so many things. And then one of the leaders in lean as well having hands-on conversations, walking the site with this person that is known for lean just really, really strengthened my capabilities. But the thought of the digital side is always going to come into our space, in our world. And so to be able to do that for a large fortune 500 company is obviously amazing. I'm like a kid in the candy store.

TROND: [laughs]

ANTONIO: Those concepts really changed the way from an organizational side because business is business no matter how you look at it. We're trying to improve our margins and capture market share just like anyone else. But ultimately, it's just a different way of doing it.

TROND: I wanted to stop a little around lean first because in our pre-conversation you said lean touches everything. I'm just curious, what do you see as the key things in lean that you have learned that you are bringing into this work that we're going to be talking about a little bit?

ANTONIO: I think that it boils down to a way to create continuous improvement by impacting ultimately the lead time. I'm part of the global supply chain so obviously, I'm always looking at a holistic approach. That's why it's all aspects for me from a business standpoint. At the same rate, from a lean perspective, we can find waste in anything. So there are always opportunities to improve in that aspect in every single function.

Every function within the organization can be an aspect of lean. So that's the part for me that I get excited about, and I've touched every single function. So it's really an opportunity for any organization to continuously improve on and removing what they say muda from the origination of the concept in any organization.

TROND: I'm curious; some people would say that lean is or I guess was important early on but that contemporary organizations are somehow different, and digital, which we'll talk about, is one reason, but there are perhaps other things. What are some of the things that you, I mean, I don't know if you agree with this, but what are some of the things that you're incorporating into your thinking here that may be either different or where you have to adjust it to the organization you're actually in at any given moment? I'm just curious.

ANTONIO: You're thinking lean from a digital standpoint or just lean?

TROND: Well, lean was developed in its original form a very long time ago. So I guess the first question I'm asking is how can you be confident that the original insights are still valid? Is that because you're walking around and experiencing it every day, and it resonates with you? I guess, firstly, just curious about what lean generally means today in an organization like yours, and then obviously, we'll talk about the rollout of digital solutions, which you've been doing so much now.

ANTONIO: Right. And that's a great question, and I'm excited to be the person that has to answer that question.

TROND: [laughs] Well, you didn't think I was going to give you easy questions, Antonio. [laughs]

ANTONIO: Lean, the concept, I think, will never go away. And so for those that think that it will, really do not understand engineering from that standpoint because when you think about engineering, an engineer solves problems. And so we know number one, there's always going to be problems. I'm sure that there are a lot of people that say, "Hey, I got something for you to solve. I got a problem for you," so from that perspective, we know.

But then, on top of that, think about innovation from an engineering standpoint, as you see something improved, even if it's making it better, even if it's something like making it better for the customer, ultimately, that transition of change even the slightest or something large, every organization has to do it. They have to embrace it. And so a person that knows those techniques, that are really good and seasoned and experienced, which I would say I do fit in that; I feel mighty confident in that space, and I feel mighty confident in manufacturing, we could see it quickly. You see it immediately.

Like, you see a process, and it just stands out. And I think that you can't wish that away to be able to see the inefficiencies of any system. And if you do not have a system in your approach, then that to me is already folly, you know what I mean? Like, that's an error. If you can't create systems, especially in manufacturing, I think that that's no bueno.

[laughter]

TROND: Got it. I'm then curious, digital. How does digital factor into all of this? So I guess I'm understanding a little bit more of your conception of continuous improvement, lean, whatever you really want to call it, and engineers that are such a crucial part of the kind of organization you represent, Stanley Black & Decker.

So now, clearly, there's been a push in most organizations across fields to go digital and arguably, manufacturing organizations perhaps were resisting it a little bit because there was such an amount of automation in there already, and then now comes digital on top of that. And has it been easy? Has it been difficult? What goes into even the decision to say, "We're going to have a major digital transformation?" Tell me a little bit about the journey that you've gone through with Stanley in that respect.

ANTONIO: So, really great question. And so I'm going to take you down a little bit of a history lesson and introduce how it impacts. So when you think about things of the world, because you always have to relate to what's going on in the real world, you have the introduction of the smartphone. You have to credit that smartphone for that interaction of this interface because it's putting that into a lot of operators' hands to interface with something.

Now, when you think about digital, industry 4.0 touches a lot of things; it's very vast, very broad. But when you think about the insights and paper throughout your organization that's there but being able to in manufacturing...and I'll make this a little bit specific to manufacturers. There are so many points where you actually need data to improve throughout that process, and like I said, it's a system. And so if you can capture it in a digital way, now you can analyze it. Now it's an insight. Now you can take all of this, and you can do predictive analysis. You can add algorithms, AI, whatever you want once it's digital.

And it's transforming your operation to be able to enhance it in this digital way so you can advance and be a little bit more productive and get better, and so it still comes back to lean. [chuckles] Once you've created it digital, now it's like, what am I going to do with the data? Because you can do the wrong things with data. It can give you the wrong insight. And just making those decisions of where you are going to improve, I think that is really huge.

So for me, that transition starts with realizing the digital side, removing some of the paper. I mean, there are so many people that are old school I would say that do everything with paper. And if that paper was digital, then what could be? I'm smiling now because it gets me excited because there are so many processes that are old that people just pull out a paper and they use it even though we're in this digital age.

TROND: So I thought I would then move us a little bit into the aspect of having a digital platform. So digital means a lot of different things to different people. You say having access to digital gives us options basically because then you have data, but you have to do the right thing with it. First off, what kind of a decision and who was involved, I guess, in the decision at Stanley going digital in that sense? Because there are many different echelons of an organization that could potentially use data.

Who was the most excited, I guess, to use new data in your organization? How did that even come about? Was it a leadership decision? Was it mid-level managers that said, "Other organizations, our peers have more data?" Or was it analyzing, you know, Gemba Walks and walking around and saying, "Hey, the operators could be more productive with more data?" Where did the decision point come from?

ANTONIO: To answer your question, short answer would be leadership. We're pushing for the next edge in innovation and pushing forward to create change. And then it's what can be that thought, and I would say the collective. If you were to embrace true employee engagement and start from the shop floor, it's going to be things that they don't know that they're requesting, something digital, so to speak. They're just saying, "Hey, this would be cool. This is what I need in order to do my job effectively."

And then what about the supervisors to the middle managers that are trying to share insight of it's great to say that you hit your numbers or you produced your widget in a successful time or faster than you anticipated, but what about the opposite? What about when you did not meet your numbers? Being able to speak to that with data that's a huge win. Who wouldn't want that? And there are a lot of areas that are little dark areas in a manufacturing facility that you don't have that capability. And that's why you need some type of way to be able to shed light on those areas and capture that in a very effective way.

TROND: Tell us a little bit about the digital rollout process at Stanley. What went into it, and what is the situation? What sort of systems have you opted for, and how are you rolling them out?

ANTONIO: So within our organization, everything comes out with governance so thinking of and a way of controlling exactly what's completed, what's being done, what you are going to put within the facility, and then creating some type of uniformity around that. The interesting thing about our organization is we're a huge conglomerate. We produce many different parts and units. And it's just a lot of complexity and diversity as far as the people are diverse, but I'm just saying end product.

Manufacturing facilities...I'm global, so I'm facing all over the world different processes that we do and so being able to have a very tactic way to roll that out in a uniform way. That's really the strat there, really thinking it out. But then also allowing for those unique scenarios to come about, having what we call citizen developers. It's that employee engagement part, thinking about someone that's really close to the process. They may figure out a way that, hey, we need this type of solution, listening to them.

And then the fact, like I said, I'm global, I'm seeing way more than they are. And I can be like, and our team can look and say, "Hey, this actually could be used at several sites that look just like this one." And so we can get that MVP and create it in a very standard, uniform way so then we can roll it out on an enterprise level. And so all of this together is the way that we go about rolling out digital solutions.

TROND: So, Antonio, I'm curious about this because in classical automation, usually, it's a big sunk cost, and the system is stable, perhaps, but everyone has to learn it and do it one way. Is the current wave of digital transformation that you're talking about here does it allow for both strong governance, which you clearly need in a large organization, but also for those citizen developers to emerge with their more kind of not exactly bottom-up, but they are certainly factory-based, or they are site-based perhaps innovations?

Did you have to choose technologies that allowed for that, or how did that factor in? Because classic solutions of automation is like one size fits all, but you seem to be talking about, yes, the need for governance, but there's also the need for citizen developers. How did you enable those citizen developers?

ANTONIO: So the first thing is that you need to figure out something that's adaptable. And so for us, we use something zero code, so it's really, really easy for them to use. And so the thing is that you don't want to discourage innovation at all. You want to embrace employee engagement all that you can. At the same rate, there's another team that's going to make sure that cybersecurity and all of that that I'm playing within the confines and the rules, and if I do not, then definitely there'll be a discussion about it.

And so understanding that you're really balancing both, and you're controlling that citizen developer as much as you possibly can, being aware of what that individual may do. And at the same rate, watching and being able to take away their permissions if need be if we feel that it goes into...I don't want to say a danger, but it's not good from a governance standpoint of what they're doing due to some federal regulation or law or whatever have you. So it's just the balance of the two of having a platform that can give you that adaptability in order to control.

TROND: Antonio, can you expand a little bit on innovation? Again, in the context of a workplace that is becoming more and more automated, how do you inspire innovation? What does it mean for Stanley, innovation?

ANTONIO: When you think about what can be...let me give you an example of something that we created; I think that it will shed light. Every organization they go through physical inventory. So you have to count all your inventory and make sure that what your books say [laughs] that's what you have. It's just comparing those two from a financial standpoint. So you're going through that process.

And normally, this process is very manual where you're physically going; someone is sending out, making that count, writing on a sheet of paper of what they were able to capture, and then running that sheet of paper to some control room where everyone is conducting...basically calculating where you are now. And so everything's live. So you go, and you audit that area, and they come back.

So basically, someone is running around facilities. And if you look at some of our facilities, they're pretty ginormous, pretty big. So to go to one end to the other it's going to be a hike. And this is all on physical paper for the most part. This is all live, speed. So the thought came up when you say innovation, someone was like, "Is there a way to do this digitally? Why can't we do this digitally?" Just to speed things up, just to figure out, hey, where are we right now? Instead of getting all of these sheets of paper and then typing them again in some system.

And I go back to lean. That's rework. That's overprocessing. Even within this system, rework is someone already wrote it down on a sheet of paper. Now they're going to hand it to someone else to literally type it into another system. That redundancy can be removed. So you see that there is an opportunity there to save time because no one wins when we're doing a physical inventory. The site is shut down, and we're not making widgets. So you don't want that.

So anyway, there was a person that was like, "Hey, can we do this digital? There's an opportunity." So that's the innovation there. It starts with an idea and then sharing that idea saying, "Hey, is this possible? What can be? What is possible?" And then you have a very diverse team look at it along with accepting that idea. And you transform it into an application in order to conduct physical inventory. And we did just that, and it was huge.

And obviously, it's within, like I was saying, you get that MVP. And now we can just copy and paste that across the board to different sites and use it as much as we want from that standpoint with those same winnings, those same gains, and the same objective in order to help the site and use as much waste that is normally committed in a physical inventory.

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TROND: Antonio, you speak of apps. What are those apps that you speak about here, and how do you explain the concept of an app, I guess, to your operators? Because I'm assuming there is a bit of an educational journey there, too, when you're introducing certain new digital processes going, like you said, in a basic sense from paper to digital. And then you said it comes through these apps.

How do you explain the concept of apps, and how do they materialize, I guess, on the shop floor? I mean, they clearly are created. Are they created mostly by the vendors that you contract with, or are they created by your own engineers? Or are they created factory specifically, or how does this app development work? And what is an app?

ANTONIO: So they're created by our engineers. And this is actually pretty funny that you asked me what an app is. And so that thought is really important because this is something that we have to do out there on the floor. And so when approached with someone that you want to use this application, I don't think that I ever even say the word app to an operator as I have physically trained operators on an application. And it's just more so the process of what you would like them to do.

And one of the reasons of perfection, so to speak, is what you strive to do when it comes to the user interface and the user experience. You want to make the least amount of steps. You want to do the least amount to interfere with this individual that has a really, really important job to make widgets. And so the thought here is the explanation of what you're trying to accomplish and then the steps that they need to do to interact.

And like I said, what helps is obviously smartphones, you know, everyone's interacting with it. So, in our times today, I think that it's a little bit easier. If you were to take it maybe 15 years ahead, maybe it'd be a little bit more challenging, but I would say that not everyone is ready for that change. It's still new to them despite smartphones being there saying, "Hey, I have to interface with this iPad or a tablet, or touch screen," whatever have you; however, they're interacting.

So the ideal state is to create it where it's more automated. And so the application is just kind of like, it's a matter of fact. We're capturing all this data, and you're just doing your job. And we're just using triggers to be able to indicate what you're doing. So that's really how I would go about describing an app, never really saying app and just saying, "Hey, this is a process that we would like to use as you do your job really."

TROND: Antonio, would you speak specifically about Tulip as a digital solution? And what is that being used for, and how is that being rolled out? I mean, to the extent you can go into some detail, what is that platform doing for Stanley?

ANTONIO: For us, using Tulip is really, really advantageous because there are a few things that it's really, really great at. You can create pretty much what you want. I don't want to put it too much out there. And the easiest way where you don't...I mean, I have software engineers that work for me. But you don't have to be a software engineer; you could be just anyone. So that part makes it a great deal simple and then what it's capable of connecting to. So it can just easily integrate within your organization in order to achieve some of the things that you want to achieve, so from the standpoint of hey, we just need this very simplistic way of doing this.

And then what's more important? The UI. So it's like, what do you want this interface to look like and do? Because sometimes, I don't want to speak specifically to some organization or tool, but some tools that you can use make it very challenging with the user interface where it's just too much buttons or too difficult to get to what you want to. Versus, you have with Tulip a little bit more autonomy to make it and cater it to what needs to happen, where you've leaned out a lot of it and just say, hey, just come touch this button and do this, and that's it.

Because you want to make it simplistic, but maybe there's something else and another look, another view that you want to use. And so, using the same platform, you can make a view for someone else that will be looking at that data in a different way. And so that's the cool thing is it's all on one platform. So that makes it a little bit more powerful that from an operator standpoint, you've given them what they need, very simplistic, the limited amount of buttons. And then, for a different audience of a managerial role, you've given them the insights that will help to improve productivity within the shop floor.

TROND: What are some of the use cases that you then identified so far and are rolling out in these kinds of apps on that platform? And what are some of the things that one might think of? Or is that more of an iterative process that it's like, can you even map that out a year ahead where it's going to be used? Or is that like it's such an iterative process that it will evolve more organically? But either way, where's the starting point? What kinds of things have you now digitized this way?

ANTONIO: Within every manufacturing facility, they're going to say safety is first, and Stanley Black & Decker is no different. I can tell you what number one is, what 1A and 1B it’s...I can't say the other one is 2. So 1A is going to be safety, 1B will be quality. And so the difference here...and I want to differentiate something really quick because it's very important.

Being able to identify from the factory floor what's going on this is something totally different. From the operator's point of view and the data that they can create, that's different. Looking at other things is interesting, but what actually goes on on the manufacturing facility shop floor that type of data that's where it's important.

And so, to your question, you can, for instance, audit something. You can audit a process. That's something that's very, very easy. And you can do it in both realms. You can audit a process for safety. You can audit a process for quality. Those are two examples there. And obviously, you can advance that even more as you touch the product that you're making. And then once you touch the product that you're making, now you can relate that. That's where my business side comes in. Now I can take this beyond from a holistic approach.

So for me being global supply chain, this one place where it was touch, I can go backwards. So I can go further upstream to the vendor, to the site, to any other buffer in between that, let's say a distribution center, to the customer, back from the customer, and then a thread that goes all the way through. The insights are endless, and the capability and possibilities are endless when you can capture it all at the shop floor.

So that's really what we aim to do, really lighting up those dark spots and getting as much with the operator. And that's why operators, I mean, what's going on in our world and not just Stanley Black & Decker, as automation and digitizing the factory floor, this is going to definitely augment and amplify shop floor workers in a different way. And it's going to be really, really advantageous for you to be alongside that operator and enhance their skills to be able to be within a manufacturing facility to change because it's obviously changing. But you can make it where they're advantageous to the organization of what they do and give them a little bit more skill set.

It's almost like giving them more information, like going to university, so to speak, because they're able to see what they know. But now that cognitive data, we can take it from them digitally, and so now you can do more. You don't have to be thinking about that. It's like, oh yeah, we'll capture all that. Let's put something else on you. Because we'll take that cognitive data and store it for point solutions later on and now if need be. So it's a very interesting time within manufacturing of where we are now and what I foresee in the next 5, 10 years.

TROND: Do you think that manufacturing shop floors have trusted operators enough? Or was it just that the opportunity now of seeing more of the big picture is only now being realized with these digital apps so that this information is there and then you can trust them more? But it was interesting to me. I just want you to talk a little bit more about the new role of shop floor people, basically, that are now perhaps able to take on different things because of this new set of information that's being tracked.

ANTONIO: So when you really think about the frontlines, I would love to say and sit here and talk about how great I am and what I do for the organization. Oh, I think of all of these ideas. But for our organization and probably any organization, it's the people that make the widgets that are the most important people within the organization I would say. They're the workers, and the knowledge that they have of that process is so important.

At the same rate, we would say that the majority of those workers do not have fancy degrees or anything like that. And so we tend to think that possibly...well, I don't want to say that we tend to think that. It talks about the capability of what they're capable of, and so now with this, and if you can do it in a way for a digital transition, you can now look at what those capabilities are, the insight that they have. Okay, you do understand this process, then what's next? How do we improve it from a lean standpoint?

But you also intricately know, let's say, for instance, this machine you work on it every single day. But now we're going to create a way where you don't have to work so much on your, like I was saying, the things that you think about. We'll create something to do that for you. Now we would like for you to do something else. You see how this change comes up. We need you to just do this or that. And I don't want to be specific, but that's really how the change is occurring.

And to be honest with you, it's a huge win because there are many operators that actually enjoy...they want you to know and understand the data of what they do. It changes things because it can be a very technical job within manufacturing where you pull out a drawing. There's a certain specification that you have to hit, and that's going to make a difference if that part is manufacturable or not. And we're talking about sometimes you're pulling out calipers to get it within 2000s where it's got to be exact. It's almost like an exact science. That grace invariant is not that much.

And so, to be able to record that data digitally and view it that way, the operators are all for that because it helps to explain things that maybe they can't put into words, but the data will show it. And it's just like, "You see? You see what I'm saying? Right about this time at 4:00 o'clock, this machine always does this," I'm just giving an example. But you can see that from a data standpoint, and that will help the operator as far as transition into this new manufacturing operator, I believe.

TROND: So, Antonio, I think I'm now understanding a bit more about how this works on a given factory floor. Can you help me understand more about how this works all across the supply chain, which you were talking about earlier? Because now, I'm assuming the use case for you is not just one individual operator or sets of operators and teams doing one product in one location. You're talking about coordinating this across a larger supply chain. Now, how can these apps then come into play? Because now we're talking about different geographies, a lot of different contextual information that would need to be put into place.

How do these apps truly help smooth out the supply chain? It would seem to be a much perhaps more complicated challenge than just simply making an individual worker or team's life easier with safety and quality with precise work instructions. When you're talking supply chain, what do you really mean there? And what are the first, I guess, apps that are coming out that are going to truly impact the full supply chain?

ANTONIO: So know this, [laughs] it's like...I'm going to give an analogy because I want to make sure that you can understand because it can get really advanced when looking at things, so hear this out. So think about those pictures where you have the picture, and everything has a number. And so you go you're number one, and let's say number one is blue. So you fill in all the blue. And then number two is yellow or whatever. At the end, it's going to be a picture that you see, and you can recognize, oh my God, a parrot, when you're at the end.

So the way that the approach here is is that we know that it's a parrot. We understand that. And so the other functions within our organization know that it's a parrot, and maybe they're only focused on the blue, but they know that it's a parrot. And so, having certain datasets will fill in the blanks for them. Something that didn't have color now has more color, so they can make more of an informed decision on what they do because everything is connected. You cannot get away from the other.

So everything really starts where you make the widget, I think. It doesn't necessarily start there because you got to get the supplies to be able to make it. But what I'm saying is is that's the money time. But at the end of the day...and I'm going to go back to what I said earlier of how I summed up lean. Everything is lead time.

So I'll give you another analogy. I love kombuchas. When I go to the store, there's a certain kombucha that I want, and when it's not on the shelf, I'm going to go somewhere and get that kombucha. I'm not going to keep going to that store. And so, at the end of the day, this is the type of data that's needed throughout the whole global supply chain in order to ensure that our customer has that kombucha, so to speak. And all of that data insight is imperative to not only understand it but be able to do magic with it, so to speak, and make changes to continuously improve.

TROND: Interesting. As you're thinking about how these developments are affecting the future outlook in the manufacturing industry, or for your company, or maybe even wider for society, because some of these things, when they're compounded they, could have perhaps larger impact, what are some of the things that you think is going to come out of this in a 3 to 7 or 10-year timeframe? You've talked about shop floor operators becoming something even more special, perhaps. So I'm assuming that's one thing.

And then, if you want to think maybe about the larger workforce, what are some things that this will lead to? And then, finally, we just talked about the supply chain. Thinking ahead, what is likely to change when this has permeated throughout many organizations' supply chains with a lot more information available? What are the potentials here? What are the impacts?

ANTONIO: The main thing I think that will happen, and I think that it's already happening, is there will be a through thread through all the functions. I think that that's imperative. But I think that it will be a little bit easier with data. So the latter of those three that you was talking about from the future standpoint, I think that the through thread with that data as we advance and make even better applications for the shop floor to get even more data, you will be able to take that data to other functions to make changes, to improve, and reduce costs within your organization all across the board. So that's where the future will lead.

The former part of the question, as far as the change of the shop floor worker, I believe that from my perspective, I think that the world is changing. Education is changing. The cost of education is changing. And I think that from the older workforce, not to put an age on it, and what manufacturing was in the past is adapting. And the type of worker that is within a facility is different than it was because the people are different. We think different. We have Twitter, and Instagram, and Snapchat.

And so I'm throwing these things out here just saying, hey, we have a different workforce. They think different. And so I believe that manufacturers are adapting to this different workforce, and with that will come much change and much-needed change. And the capability of what a worker is expected to do, I think, will increase, but it will increase for the better. There are different roles for individuals to have within manufacturing facilities, and I think that we'll see that just come over time because we need data.

Data is going to be very, very important for any organization, and how we obtain that data, how we get that data, it's just better to have that person in the room having a big impact. And I'm saying that person, that operator in the room without having them in the room, so to speak, by getting their data to impact those decisions in their own way, but also using employee engagement with the data that they provide. So I think that's going to be really the change.

I think the number two question I kind of forgot. I apologize. I went from the last to the first.

TROND: No, it's fine. I mean, I was talking about the operators and then the advanced supply chains, which is, I guess, just another layer of complexity, and we have talked about it at length. But I'm just wondering, as these technologies, the digitization really advances and permeates throughout the supply chains, what are some of the cascading changes or not that might occur?

Because I'm assuming, just like you said, shop floor operators will have a different reality. They can do different things because some things are just taken care of or the beans are counted. They can do other things. What are those other things that organizations now can do because their supply chains will become more and more digitized?

ANTONIO: Yeah, those things are really...when you think about the footprint of what a facility needs to be, now that changes. Because one thing that's really, really important in any facility is space, so now this will impact it. Hey, we got this covered; could you go take care of these things? And then also I believe, so this is just going to be my opinion, I think that there's going to be more training. Now we can train up in another skill set to allow someone to have dual if not triple capability within their self to do more.

Let me tell you a little bit more about this machine because what we needed you for we good on that. Let's teach you about this other aspect of this machine in order to make it, you know, the upkeep of it, the PMs and TPMS, you know it. We've automated that and made it digital, but let's advance your knowledge a little bit more so you can understand. And I think that that's what we're about to witness here as we move forward.

To me, it's a really, really beautiful time. And it's going to be really, really interesting here in the next I would say ten would be the keymark, 5, especially with the climate today. And not to speak about the elephant in the room, but it truly is the perfect storm, all of these things happening. Like, going into a supply recession and then possibly having demand to drop, I mean, it's just a perfect storm of all of these things. But you'll see that those that are able to survive this will be better off because of it.

You never wish these things to happen. But you can say that you will improve, and you'll be stronger because it happened. And this also will impact what's needed in the future, especially on an operator level. So it's really interesting where we are today and how digitization will impact our lives and manufacturing from here on out. There won't be a point where it's not there. It will always exist for quite a bit of time unless there's some drastic change or an invention of some sort.

TROND: Antonio, the last question I'm going to just throw at you is, what are the training consequences? And how do you see training going forward in the medium-term future? Because you have pointed out that shop floor operators are going to be asked to do more things, more advanced things. They will get more of a bigger-picture view.

You're going to need a lot of true engineers, and then you might need a lot of engineers, meaning their engineering like they are trained with a mindset of an engineer in the sense that they are trained on improving, and suggesting, and tweaking, and adjusting the way that an engineer did. But surely, all of these people can't go to engineering school.

ANTONIO: [laughs]

TROND: How are you going to do this? Because the way I'm seeing you painting the picture of an emerging manufacturing workforce here, I mean, unless you're not talking about the same people, how are those same people going to adjust to this new reality?

ANTONIO: Right, yeah.

TROND: Is the UI going to be the key here, the UI just has to be simple the way you've explained that apps have to be kept simple so that training is limited? Or are you foreseeing that complexity still will increase so that people are going to have to become trained on still sophisticated piece of equipment? Because it could go two ways here, either you're doing advanced things, but you're keeping it simple still, or you're doing advanced things, and it's complicated. [laughs]

ANTONIO: So this is a great question, and I'm really excited to answer it. So the thought here is is, I'm going to take a CNC, a computerized numeric control machine. That is a very sophisticated piece of equipment, and an operator runs it already. No matter what they do, they're already running it, and so they're capable. And yes, they didn't go and get this advanced engineering, and those that receive those advanced engineering degrees they're worth every penny. It's teaching you on a vast scale.

But in a manufacturing facility, on what you're doing, you're removing some of the noise and saying, hey, I just need you to learn this. This is this process. So just this, just eat what's on your plate. Don't worry about any of this other stuff. And we'll guide you through. We will layer on, and layer on, and layer on the knowledge that we want you to have in order to enhance you on this process. And this process is core to manufacturing. See how that sounds a little bit different?

Because when you go and get your degree, I'm just going to pick engineering, you're learning all types of things, and they're all important. And there's a lot of physics and just a lot of things that you need to understand. At the end of the day, if you were to take an engineer off the streets that just got their degree and throw them in, how different would they be if you had a seasoned, experienced operator that knows this process and you compare the two? That would be an interesting comparison. I actually would like to see a study on that.

I think that, not to get deep, I just think that there would be a point where if you were to graph it where they would intersect, and that person with the advanced engineering would supersede this operator. But how long that would be would be interesting if you've created an environment and a very easy way through applications and digital solutions to improve this operator where they have knowledge and a different way of explaining it to them, all of these things where you've advanced and upped one. Like, you've upped this operator to this process. I think that would be interesting.

I think that that's going to be the future. You're going to have core competencies of manufacturing operators where they can feel proud. Despite that, they would be labeled blue-collar; I believe that their skill set and their knowledge would be probably more than what their label of blue-collar will be because they will be strategically very important to that manufacturing facility because of the knowledge that they know about that core competency of the process. And then just think about this, you learn one, you can learn something else. [chuckles] You know what I mean? And so I think that it just continues. So that's the way that I see it playing out.

TROND: Antonio, I think, to me at least, when I listen to this, it feels inspiring. And it certainly should feel inspiring to whether they are younger or older people who are interested in manufacturing because this spells a day and age where perhaps yet again, this kind of insight of knowing how to work machines and knowing how to coordinate with others on a shop floor or producing something tangible is going to be re-appreciated the way it was in other types of industrial upheavals and revolutions.

It's interesting to me that this is perhaps where we are, this inflection point where the kind of skill sets this will take and perhaps the kind of specialization that now seems perhaps within reach for a different cadre of people. Because clearly, MIT and, Carnegie Mellon, and UCL would have to scale up their training or offer everything they have for free online in order to train 10x, 100x, 1,000x more engineers.

Or these skills are just going to have to be taught in a combination of community colleges; I would assume, and on the shop floor directly by yourselves in these organizations themselves or perhaps a mix of the above. But either way, it would seem to me that it's not all that bleak of a future for manufacturing if what you're saying comes to --

ANTONIO: Fruition.

TROND: Fruition here.

ANTONIO: I agree. And this is really what I see, and that's why I'm excited. I'm happy to be a part of it. And it's one of those things...someone said this to me the other day "Industry 5.0." [laughs] I'm just like, okay. You can hear that concept, but from a societal standpoint and a person that is an advocate of free markets, I think that this is the moment in time in our world because we have to make widgets where we'll define what that is.

And before we talk about this industry 5.0 talk, the human part has to be addressed. And if you do it in the way that we're discussing, it makes for an interesting future. If you do it and bring other things into the discussion room already, I think that it changes basically what's being spoken about and not really discussing, okay, what is really going to move the needle and move us forward as a manufacturing group together? Because we compete against each other in some realms if we're in the same market, but it's all the same game no matter where you are.

And you're taking this from a guy that they would put in the plane and drop in a facility and now have to go through and just figure things out and could actually make change. But one of the things that I recognized everywhere I went in all the facilities that I've been to, all the facilities that I visited, were the people. The people were the important aspect. And you just definitely want to make sure that they're in the equation and in the dialogue of whatever change may happen. And I believe that platforms that allow that will be key for now and the future.

TROND: Antonio, you've been very generous with me, your time. It's been super interesting. Thank you so much.

ANTONIO: Thank you. I appreciate it.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim.

The topic was Innovating Across the Manufacturing Supply Chain. Our guest was Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker. In this conversation, we talked about Lean leadership, productivity, and the challenge of digital transformation across operations and supply chains.

My takeaway is that Stanley Black & Decker is a huge organization where any improvements by tweaking their own operations or by adding insight from what happens along the whole supply chain can mean significant productivity gains. I find it interesting that they have their own version of the augmented lean approach tailored to where they are and, most importantly, building on the insight that the workforce is where the innovation comes from. By giving shop floor workers access to insights on big-picture manager deliberations, they are freed up to operate not only more efficiently but also more autonomously. When all of industry works that way, manufacturing will make tremendous advances more rapidly and sustainably than ever before. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and please rate us with five stars. If you liked this episode, you might also like Episode 94 on Digitized Supply Chain with insights from Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

Special Guest: Antonio Hill.

Episode 99: Augmented Lean: The Book

12 oktober 2022 | 33 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Augmented Lean Prelaunch." Our guest is Natan Linder, in conversation with host, Trond Arne Undheim.

In this conversation, we talk about the background of our co-authored book, Augmented Lean, a human-centric framework for managing frontline operations, why we wrote it, what the process has been like, the essence of the Augmented Lean framework, and the main lessons of this book for C-level executives across industry.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 96 on The People Side of Lean with Professor Jeff Liker.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Industrial revolutions are rarely chronicled as they are happening, but this industrial revolution will be. There is an ongoing shift in the way technology and workforce combine to produce industrial change, and it is happening now. We are lucky to be situated in the middle of it. And I personally feel fortunate that I was brought along for the ride.

It has been a life-changing experience to realize the power and impact of living through a shifting logic of manufacturing and, perhaps more importantly, to realize that as excited as we can be about automation, an augmented workforce represents the best combination of the most important technology we have which is human workers themselves with the second best machines that humans create. The fact that making humans and machines work together is no trivial task has been pointed out before but documenting what happens when it does go well in the biggest industrial companies on the planet feels like a story that deserves to be told.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Augmented Lean Prelaunch. Our guest is Natan Linder, in conversation with myself, Trond Arne Undheim.

In this conversation, we talk about the background of our co-authored book, Augmented Lean, a human-centric framework for managing frontline operations, why we wrote it, what the process has been like, the essence of the Augmented Lean framework, and the main lessons of this book for C-level executives across industry.

Augmented is a podcast for industrial leaders, for process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Natan, good to have you in the studio. How are you today?

NATAN: I'm great. How are you? It's been a minute.

TROND: It's been a little minute for us. It's crazy with book launches. It takes a little out of you. And you are running a company in addition to that, so you had some other things on your plate too.

NATAN: Yep, running a company and having a book coming is an, I don't know if an artifact, but definitely, company is a lot about changing the status quo. And the book tries to capture a movement. So I think they go along nicely.

TROND: Yeah, Natan. And I wanted to bring us in a little bit and converse about why this book was written. Certainly, that's not my benefit. You brought it up to me. But what were we thinking about when writing this book? So I want to bring it back to way before I came into the picture with the book because it was your idea to write a book. What was on your mind? What were the main reasons that you thought I really want to write a book?

NATAN: When I was coming up as an engineer...and my background, I'm not a pure manufacturing production type engineer, but I've been around it my entire career just because of the type of products that I've been involved with whether it's mobile phones, or robots of all sorts, 3D printers. So you get to spend a lot of time in these operational environments, shop floors, machine shops, and the like.

And when we started working on Tulip, it was pretty clear pretty quickly that there's a moment that is emerging in operations that no one has captured the story. And this is back even; I don't know, maybe five or six years ago. We are maybe one or two years old, and I'm already starting to think about this post-lean, or classical lean movement that I'm sure is happening. That really is the genesis of the book in the early, early days.

And fast forward to when we started talking, I think we got Tulip off the ground. But really, that was a platform to meet all those different people who helped operations transform digitally, whether it's all sorts of consultants, or academics who are researching operations, or business leaders, you know, tons of factory managers and the engineers that work with them, and the executive, so a whole bunch of people. And they're all basically talking about the same thing and the deficiencies in lean, the complexity of technology, and how they're trying to change, and it is so difficult.

So I think that's a good description of the landscape before diving in to try and capture what the book attempts to capture.

TROND: Yeah, Natan, I remember some of our early discussions. And we were dancing around various concepts because clearly, lean is a very broad perspective in industrial manufacturing focused on reducing waste and many other things. It's a broad concept that people put a lot of different things into.

But I remember as you and I were thinking about how to describe this new phenomenon that we do describe in the book, we were thinking a little bit that a lot of these new influences come from the digital sphere. So there's also this term agile. There are some people who say, well, you know, let's just replace lean because it's an outdated paradigm. And I remember you were quite adamantly arguing that that's not the case. And this goes a little bit to the message in our book. We are in no way really saying that lean isn't relevant anymore.

NATAN: On the contrary.

TROND: Tell me a little bit about that.

NATAN: A really simple way I think to frame it is that whether you're practicing lean formally or some variant of it, of lean, or Six Sigma, or some program that formalizes continuous improvement in your operation...and we're talking about frontline operations. We're talking about factories, and labs, and warehouses, and places like that. You are practicing lean because this is how the world..., even if you're not doing it formally; otherwise, you're not competitive. Even if you're in a bank or a hospital, you might be practicing lean.

And that's where agile comes to the picture, and it was adopted widely by operations practice in general and pushed into areas that are not pure manufacturing. So, in a way, lean is a reality. Some organizations are more formal about it, some are less, but definitely, they're doing it.

Here's the issue, and this is the main thesis of the book. When lean came about...and we know the catalyzing text. We know the teaching of Taiichi Ohno. We know about The Goal. We know about The Machine That Changed the World. And those are seminal texts that everybody reads. And we know about Juran and lots of great thinkers who thought about operations as a data-driven game, some from the school of thought of quality, some from pure operation research, some from how do you put emphasis on classic just-in-time, Kanban, Kaizen, all those continuous improvement things.

But at the end of the day, all of that thinking, which still holds true, was not done when digital was top of mind, where data is everywhere, where people need to live in such data ecology. It was done, so to speak, in analog times. And it doesn't mean that the principles are wrong, but it doesn't mean they don't need to get augmented. And this is maybe the first time where this idea of augmentation, which, to me, augmentation is always about...I always think about augmentation from a people's perspective or an org perspective. It's just a collective of people. That's where it starts, and that's where we had something to say. So that's one aspect to think about.

The second big one is actually very simple. It's kind of like; we heard ten years of industry 4.0 is going to change everything, and all we got is this lousy OEE graph. And that's kind of like a little tongue-in-cheek on we were promised flying cars, but we only got 140 characters. I mean, come on, stop talking about industry 4.0. It's like, who cares?

If the tools and digital techniques and what have you is not adopted by the people actually doing the work, that then collectively, one engineer, another engineer, another operator, a team lead, the quality lead, and so on come together to transform their org, if that's not happening, then that's not sustainable transformation, and it's not very relevant. Again, augmentation.

TROND: Right. And I think, Natan, that's where maybe some people are surprised when they get into this book. Because it would be almost tempting to dismiss us as traditionalists in the sense that we are not really going whole hog into describing digital as in and of itself, the core of this principle. So there is a little bit of a critique of agile as an idea that agile or using that as a kind of a description for all digital or digital, right? That digital doesn't change everything.

And I guess I wanted to reflect a little bit on that aspect because I know that you, as a business leader now hiring a lot of people, we are spending a lot of energy bringing these two perspectives together, and it's not very obvious. You can't just take a digital person who is completely digital native and say, "Welcome to the factory; just do what you do. And because you do things better than everyone else, we are now going to adapt these factories." How do you think about that?

In factories, you could conceive it as the IT versus OT, so operational technologists versus information technologists and the various infrastructures that are quite different when those two things come into play.

NATAN: So my frame of reference is the most value...and it's a very engineery frame of reference because I'm an engineer at the end of the day. It's like, the most value gets unleashed when people truly change how they work and adopt a tool, and that's true for operations and manufacturing. But, by the way, it's also true for the greater business perspective.

And a lot of people, when I talk to them about Augmented Lean, really take us to the realms of what is the future of work, and I think it's very timely. We're kind of in a post-COVID reality. Working remote has changed many things, working with data. Big ideas like citizen development, you hear them all over the place. And use of advanced platforms like the no-code/low-code that allow people to create software without being software engineers become a reality. So there's a much broader thing here.

But if I focus for a second on what you're asking, the way I see it is when people truly change how they work, it means that they believe, and that belief translates into action, that the tool that they're using is the best way to do something. And they become dependent and empowered by it at the same time because they're not willing to go back to a state where they're not thinking and working with data, or back to the clipboard, or back to being dependent on an IT department or a service provider to give them some technical solution. People have become more self-sufficient.

And it turns out that if you do that, and sometimes people would refer to that as you let people hack or go nuts in the factory floor or in whatever operational environment, that could be a concern to people, and that's a fair observation for sure. And that's where when you look at the book, when we were kind of constructing the framework we call Leader HG where HG stands for hack and govern... We are used to Silicon Valley startups being like, oh yeah, you all just need to hack. And that's a very glorious thing, and everybody understands that.

And they want them to hack when they are a 50,000-person software company. They're still hacking, but they're doing it in a much more structured way, in a much more measured way. So even in hacking, there's governance. And in operational environment, governance is equally important, if not more, because you're making real things. That is something we've observed very empirically.

Talking to a lot of people seeing what they do, it's like, yeah, we want the best ideas from people. How do we get it? What do we do? We tried this approach, that approach. And I think we were sometimes very lucky to be observers to this phenomena and just captured it.

TROND: Yeah. And I wanted to speak to that a little bit. I want to thank you, actually, for bringing me into this project because you and I met at MIT but from different vantage points. I was working at Startup Exchange working with a bunch of very, very excellent MIT startups in all different domains, and you were an entrepreneur of several companies. But my background is more on the science and technology studies but also a management perspective on this.

But I remember one of the things you said early on to me was, "I want to bring you in on this project, but don't just be one of those that stays at the surface of this and just has like a management perspective and writes future of work perspectives but from like a bird's eye view. Come in here and really learn and go into the trenches."

And I want to thank you for that because you're right about many things. This one you were very right about. And this clearly, for me, became a true research project in that I have spent two years on this project, a lot of them in venues and factory floors, and discussing with people really at the ground level.

And for me, it was really a foundational experience. I've read about many things, but my understanding of manufacturing, frankly, was lacking. And you could have told me as much, but I actually, frankly, didn't realize how little I knew about all of the factors that go into manufacturing. I had completely underestimated the field. What do you say to that?

NATAN: It's interesting because I feel like the last two years, everything I think I know [laughs], then I found out that I don't know enough. It just kind of motivates you to do more work to figure out things because it's such a broad field, and it gets very, very specific.

Just listening to your reflection on the past couple of years, the reality is that there is a gap in the popular understanding of what operations and manufacturing is all about. People think that stuff comes from some amorphous factory or machine that just makes the things. And they usually don't see, you know, we have those saying, like, you don't want to see how the sausage is made, which is obviously very graphic.

But you also don't see how the car is made unless you're a nerd of those things and watch those shows like how things are made, but most people just don't. And they don't appreciate the complexity and what goes into it and how much technology and how much operation process it consumes. And as a society and as a set of collective economies and supply chains, it is so paramount to what's actually happening.

Just take things like sustainability or what happens with our planet. If we don't learn to manufacture things better and more efficiently with less people because we don't have enough people in operations, for example, our economies will start to crumble. And if we don't do it in a way that is not just sustainability from the perspective of saving the planet, also that, but if we don't become more efficient in our supply chains, then businesses will crumble because they can't supply their customers with the product that they need.

And this thing is never-ending because products have life cycles. Factories have life cycles. And the human species, that's what we do; we take technology, and then we turn it into products, and we mass produce it. That's part of how we survive. What we need is we increase awareness to this. And I think The Machine That Changed the World and Toyota Production System unveiled those concepts that you need to eliminate waste to build better organizations, to build a better product, to have happier customers; there's something really fundamental there that did not change.

The only thing that changed is that now we're doing it in a reality where the technology is out there; data is out there. And to wield it is difficult, and there is no escape from putting the people who do the work in the center. And to me, if we are capable of doing that, the impact of this is recharging or rebooting lean in the classic sense for the next three decades. And that's my personal hope for this book and the message we're hoping to bring in. We would love people to join that call and fly that flag.

TROND: Yeah. I wanted to take us now, Natan, to this discussion. A lot of people are saying, "Oh, you got to market manufacturing better, and then people will come to this area because there are interesting things to do there." But more broadly, if we think about our book and why people should read that, my first reflection is building on what I said earlier that I didn't realize not just the complexity of manufacturing but how interesting it was.

My take after two years of studying this is actually that there's no need to market it better because it is so interesting and fundamental for the economy that the marketing job, I think, essentially has already been done. And it's just there's a lag in the system for new employees, new talent. And society overall realizes how fundamentally it is shifting and reconfiguring our society.

But I guess I want to ask you more. What is the reason a C-level executive, whether they work in manufacturing, in some industrial company, or really, if they work in any company that is interested in what technology and manufacturing is doing to their business reality...how they can implement some of those ideas in their business. What would you say to them? I mean, is our book relevant to a business leader in any Fortune 500? Or would you say that our messages are kind of confined to an industrial setting?

NATAN: I think it applies to all of them. And the reason is that these types of roles that you're describing, folks will best be served if they learn from other people's experience. And what we tried to do in the book is to bring almost an unfiltered version of the stories of their peers across various industries, from medical devices, to pharmaceuticals, to classic discrete manufacturing, all sorts of industries. And they're all struggling with the same kind of stuff. And so those stories are meaningful and can contextualize the thinking of what those C-levels are actually trying to cope with.

What they're really trying to do, everybody, I'd say, is why do people think about and talk about those big terms of digital transformation? It's really because they want to make sure their companies don't stay behind or, in other words, stay competitive. This stuff is an imperative for organizations that have real operations that span digital and physical, and I don't know many that don't. Of course, there are some service industries that don't have anything but still have operations.

You can't avoid handling the subject and what it entails. It entails training your people differently. It entails defining technology stacks. It entails connecting using various technologies, protocols, what have you, across organizations and finding value in this data so you can make good decisions on how you run your billing cycles, or how you order your stock to build, or how you ship your end product and everything in between.

And I don't think that the book is groundbreaking in the sense that we're the first people who ever thought about it. But I think if we've done anything, is we've observed long and hard. And we've listened very carefully to what people are telling us that they did, and they struggled. And it's a timely book. And maybe in a decade, it's a classic, and, wow, these are good stories. And it's like reading about the first people booting up mainframes or PCs. And if that happens, I'm actually pretty happy.

But you know why I would be happy? Trond, let me tell you something, it's because technology, like, the human needs change much slower than how technology evolves and gets deployed, but still, good technological-driven transformation take a long time.

TROND: That's exactly what I was going to say is that the future is an interesting concept because what's tomorrow to some people is today for others. So you say we're not writing about something that's so new or unique but to industry overall and to some manufacturers, what we're writing about is the future because they haven't implemented it yet.

To some of Tulip customers, to some of the great companies that we have researched in the book, whether they be J&J, Stanley Black & Decker, DMG MORI, a lot of other companies in medical device side, and also smaller and medium-sized companies, even some startups that are implementing some the Augmented Lean principles, to them, this is of course not the future.

And maybe, you know, we're not saying that leaders who try to implement Augmented Lean need to change everything around; we're saying common sense things. It's just that; clearly, all of industry is not human-centric, right? There are parts of industry where you adjust 80% to your machines, and you make economic decisions purely based on the infrastructure efficiency improvements you're trying to make. I guess what we're saying is the innovation argument; people are the most innovative, and you have to restructure around your workforce, even if you are making machine and robot investments.

NATAN: Yeah, automation would always require strong reasons to automate that, you know, some of them are complexity, safety risk, things like that or throughput to like how much product do you need and that kind of stuff. But even if you have the best automation, you typically have people around it, and nothing is just only machine-driven or only human-driven.

The reality is that most stuff gets made through a combination of several manufacturing technologies working in unison with people at the beginning, middle-end doing things from the planning, to running automation setups and machinery, to taking the output, doing assembly, doing tests, audits and checks, and packaging, and logistics, and at the end of the day, human-intensive type of operation in most of the areas we roam, at least.

And as such, to think that in this day and age you don't focus on people is to me nuts when all those people carry a supercomputer called a smartphone in their hand and have uber-connected homes with a million CPUs streaming all this data, and we call that media, whatever. And they're so accustomed to interfacing to their world and their businesses through that.

And you and I are Gen Xers, and let's just think about the generation that comes after us and after us. These are digital natives par excellence. They expect as much, and organizations that don't do that, whether they choose the Augmented Lean approach or any other approach, they're just not going to have employees. That's a little bit of a problem.

TROND: Yeah. But it's important what you're saying in one respect which is there are many reasons to dismiss a book, a management book, a technology book. And one could be like; all these people are just that. And one, I guess, gut reaction when people look at the title or perhaps hear some of the things that you and I are saying is that, oh, these people are Luddites; they're against technology.

But I wanted to, certainly on my end, just to state very clearly there's nothing in our book that's against technology. We're simply saying to optimize for the simplest technology, that is, you know, to our great inspiration here, who was a big inspiration, I know, for you and now for me because you brought her into my sphere. Pattie Maes' perspective from MIT on Fluid Interfaces and the importance, you know, no matter what advanced technology you're going to bring into whatever context, if that context of the technology, the use interface is not a fluid interface, you are simply doing yourself a disservice.

You could have bought a $1 million CNC machine or maybe a $10 million whatever robot, but it has to work in your own organization, and this is just so important. So we're not against technologies. We're just saying these investments will be made. But you have to think about other things as you're making those investments. So I just wanted to make that point and hear your comment to that.

NATAN: Yeah, look, I have a slightly...I guess a complementary angle to this is like when you think about it; I think that technologically democratized organizations in the day and age we living in the future. And what makes, I think, Augmented Lean span beyond the frontline operation perspective is because it tells a story of democratizing operation where fundamentally before lean...and we're talking about the mass production era. Mass production came from a military structure, you know, divisions, and battalions, and commanders, and ranks, and all that kind of stuff.

Enters lean, and democratization starts. Forget technology. It starts because suddenly everybody on the Gemba Walk, you know, the walk where they have an equal voice to find problems on the shop floor, and list them up, and think about a solution, everybody has a voice. So these are fundamental things that shifted things like how you manage your warehouse, or how you do just-in-time, or how you are supposed to do continuous improvement. But you have to collect data to prove that this improvement is actually worthwhile doing.

And this is exactly what agile took, and this is exactly the transition you saw in, well, because the market moves so fast and the internet is here, and clouds are real, why don't we not spend two years in a bunker doing waterfall software development? And, boom, we're now talking sprints and all that kind of stuff. And no one is even questioning that. And that's a lean approach we call agile, lean approach to how you do software development.

And what I'm trying to say is, de facto, when I run a day in a company, like, I talk to my peers, and my leaders, and folks I work with on a daily basis. Everybody talks, yeah, we're on an operation sprint. We are on a marketing sprint. We are on a whatever sprint. What is that? That is a democratized organization with specific leaders owning functions and owning interfaces using tech stacks all over the place: the marketing stack, the sales stack, the HR stack, whatever.

And where we roam also, we're part of the operational or OT stack, and that's what they're doing. And all this book is doing is saying, like, hey, it's actually happening. Let's give this a name. Let's put the beacon on this. Let's try and find what's the commonalities. Let's get the best stories that share the successes and the failures. We have plenty of failures there in the book that teach you something at this moment in time and set up the next decade.

This next decade to me, is seminal. It's not very different to when technologies reached maturity, like clouds and what have you. 10, 15 years ago, you're talking about this thing, cloud, some people will go like, "What cloud? What are you talking about?" That's done. That's the disappearing edge of technology. Now we say AI and all that kind of stuff. And then the problem gets solved and disappearing, you know, it's like, so that's going to happen. I just think we gave it a good name and a good description at this point in time.

TROND: Natan, I love the...personally, I'm a runner. I love the metaphor of a sprint, and for a couple of reasons, not just because I know what a sprint is and what it takes. But I love the fact that a sprint in a management context refers to sprinting partly together because it's a team-based effort. So some people need to sprint a little faster in certain aspects of that team process in order to deliver things that the team needs.

But rounding up and thinking about how people can sprint with us, Natan, how should people think about learning more? So, obviously, reading the book. It's available on every bookstore, and Wiley published it, and it should be everywhere. There's even an e-book.

But beyond that, what are your thoughts about how people can get in touch, join the movement, join the sprint of thinking about Augmented Lean? Which by the way, there is no one Augmented Lean principle. It's a menu of choices. There are ways that you can engage. There are ways you can implement it. It's not like a one, three-step process that everybody has to do. But there are ways that people can connect. We have this Augmented Podcast. What are your thoughts if people are gelling with this message?

NATAN: I can talk about my heart's desire, okay, and my hallucination around this. And this is like, really, kind of living the dream and making sure democratization continues. If we are successful, at the moment, we are starting a movement. And there are millions of people who self-identify as lean Six Sigma quality professionals out there that know exactly what we're talking about viscerally. They spend their days trying to solve problems like that. They pore over data; they train people. They are the people creating the reports and trying to kind of help their organization take another step and another step in the never-ending journey of continuous improvement.

We need to work on a much larger manifesto for Augmented Lean, and this is not for you and me; this is for a greater community to come together. So my recommendation is if you dig this and this is something you want to do, you know where to find us; go to augmentedlean.com. There's a contact email, our contact information. And I guess we can share it for that purpose somewhere in Augmented Podcast or our various other channels. And tell us what you think. And just join us.

We're not sure exactly...we're starting from the excitement around launching the book with our close network of partners, and friends, and customers, and collaborators, and all our network. And it's a very exciting moment for us. But we're going to open it up, and it's going to be in the book tour, and it's going to be in various conferences.

And the first law of creating a movement is show up. So I'm calling everybody to show up if you're okay with lean and the way it's going so far for you and Six Sigma. But if you feel the need to change and observed or experienced some of the stuff we're talking about in Augmented Lean, come tell us about it, and let's shape it up and get people together. The internet is the best tool on the planet to do that, and we'll get it done. Stay safe.

TROND: Right. So, on that note, I want to round us off. I think that it should at least be clear from this conversation that both of us strongly feel that there are greater things ahead for industry and that manufacturing is not just a relevant piece of society, but there are things happening here that are coalescing that we are describing in the book, but that will happen independently of us and the very few examples we were able to put into the book.

And folks that are interested in exploring what that means for them as individuals, as knowledge workers in the factory floor, or as executives who just want to be inspired the way people were inspired by the Toyota lean movement or other movements, they should come and contact us. Natan, thanks for spending the time today.

NATAN: Yeah. Thanks, Trond. Always a pleasure. Will see you very soon.

TROND: You have now just listened to another episode of the Augmented Podcast with host Trond Arne Undheim.

The topic was Augmented Lean Prelaunch. Our guest was Natan Linder, in conversation with myself, Trond Arne Undheim. In this conversation, we talked about why we wrote a book and why C-level executives should read it.

My takeaway is that industrial revolutions are rarely chronicled as they are happening, but this industrial revolution will be. There is an ongoing shift in the way technology and workforce combine to produce industrial change, and it is happening now. We are lucky to be situated in the middle of it. And I personally feel fortunate that I was brought along for the ride.

It has been a life-changing experience to realize the power and impact of living through a shifting logic of manufacturing and, perhaps more importantly, to realize that as excited as we can be about automation, an augmented workforce represents the best combination of the most important technology we have which is human workers themselves with the second best machines that humans create. The fact that making humans and machines work together is no trivial task has been pointed out before but documenting what happens when it does go well in the biggest industrial companies on the planet feels like a story that deserves to be told. Thanks for listening.

If you liked the show, please subscribe at augmentedpodcast.co. And if you liked this episode, you might also like Episode 96 on The People Side of Lean with Professor Jeff Liker, who wrote the best-selling book, The Toyota Way. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us because we would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production and logistics process in a physical location. Tulip is democratizing technology and is empowering those closest to operations to solve problems. You could find Tulip at tulip.co.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Natan Linder.

Episode 98: Decarbonizing Logistics

5 oktober 2022 | 42 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Decarbonizing Logistics. Our guest is Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg.

In this conversation, we talk about the huge tasks of mitigating and adapting to climate change throughout industrial supply chains.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 68: Industrial Supply Chain Optimization.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Decarbonizing logistics without slowing economic growth is a formidable challenge which requires paradigm shifts across many industries, as well as adopting openness principles from the virtual internet onto the physical nature of the supply chain, as well as facilitating new business models, sharing, and standardization, and eventually dematerialization.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Decarbonizing Logistics. Our guest is Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg. In this conversation, we talk about the huge tasks of mitigating and adapting to climate change throughout industrial supply chains.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Alan, welcome. How are you?

ALAN: I'm very well, thank you.

TROND: I'm super excited to have you, Alan, you know, an academic that has transformed and seen the transformation of a field that barely existed when you started. Some 40 years in academia and logistics and now being part of this exciting experiment with creating a whole new university focused on logistics. It's been quite a journey, hasn't it?

ALAN: It certainly has. I think this is my 43rd year as an academic. My colleagues often think maybe it is time to retire, but the subjects in which I specialize, which we'll be talking about in a few moments, like decarbonization, are sort of hot topics at the moment. So I'm very reluctant to phase myself out. So it's been an enjoyable 40-year career, I must confess.

TROND: How did you get to pick this area? It's obviously not; I mean, now, because of the pandemic and other things, logistics or at least supply chains is kind of on everybody's mind because we're not getting whatever product we want or maybe some sort of interest in green practices. And we're starting to realize that transportation is becoming more of an issue. People are worried about that. How did you get into this area?

ALAN: My interests initially were in transport and particularly freight transport. In fact, right at the beginning, it was actually a crime, believe it or not, which got me into this area.

TROND: [laughs]

ALAN: Because I'd done my masters at UBC in Vancouver. I returned to London to do my Ph.D. at the University of London. This was in 1976, a long time ago. And I had spent three or four months reading up on the subject of freight modal split, you know, why so much freight goes by road and so little by rail. And I'd compiled all my notes, and my briefcase was stolen.

[laughter]

So the day before that, I'd been to visit a professor at the London Business School who said to me, "The freight modal split topic has been very much researched." He said, "You're a young man. Why don't you go out and find something new to bring a new perspective to this subject?" And around then, the subject of...it wasn't called logistics back then; it was called physical distribution, right?

TROND: Hmm.

ALAN: Where you saw freight transport in a broader context linking it to inventory management, to production planning, to warehousing, and so forth. And so I began reading up on that subject. And that then became the main theme of my Ph.D., which I think was one of the first PhDs done in the UK on that subject. So you could say that it was the person that stole my briefcase way back in 1996 [laughs] that played a part in me discovering logistics as a field, and that's occupied me for 40 years in my academic career.

TROND: And on that journey, you have entered in and out of different fields. I noticed that you were a lecturer in economic geography in the beginning. So there's a very interesting, I find, physical component to logistics, obviously. How does geography enter into it for you?

ALAN: Well, I see transport and logistics as essentially a spatial subject. My Ph.D. focused on the geographical aspects of logistics, you know, where you locate the warehouses, how you route the vehicles, you know, so much logistics planning has a geographical component.

But the thing about logistics as an academic discipline is that it's drawn together academics from many different disciplines. Many have come from a mathematical background, from engineering, from economics, in my case, as I said, from geography. And that, I think, is one of the strengths of the subject area, that it has got this interesting interdisciplinary mix. And that allows us, in a sense, to deal with a whole range of policy issues, of industrial issues, I mean, from land use planning to environmental issues, which we'll be talking about in a moment. I've really enjoyed engaging with academics really from different disciplines over my career as an academic.

TROND: Well, and we'll talk about these things in a second. But, I mean, it's not just academics, right? Because the subject is so non-academic in a sense, right? [laughs] It's actually very alive, and it affects all of us. So people may not have been super aware of it. But, like you point out, it's very multidisciplinary.

Now, how did this startup University concept come about? You've moved to Hamburg or spent a lot of time in Hamburg with this KLU university for logistics, essentially, which sounds to me like a daunting prospect to create a new university based on a new discipline in Germany of all places.

ALAN: So I'd been 25 years in my previous university here in Edinburgh where I'd set up a master's program in the subject and a research center. And then, in my late 50s, I got the opportunity to go to Hamburg and to join what was a startup University. I mean, when I joined, I think we only had nine academic employees. We only had about 40 or 50 students in total. So it was a challenge.

And a bit of background on the university; it is a legacy project of a very wealthy man, Klaus-Michael Kühne, who is the majority owner of Kuehne+Nagel, which is the world's biggest freight forwarding company. And he also owns about a quarter of Hapag-Lloyd, one of the world's biggest shipping companies. And he, in a sense, wanted to give something back to the industry, and so he founded the university in 2010. So it's now 12 years old, and I think it's been a very successful enterprise.

We're still niche, obviously. We've got, I think, about 27 or 28 professors, about 500 students. But we have this focus on logistics and supply chain management. And there are also quite ambitious plans to globalize the university, to open up satellite KLUs around the world. So I was just very lucky really to get involved in this in the early stages and do my bit to help to shape this institution.

TROND: Well, you're lucky but obviously enormously accomplished. I wanted to talk a little bit about your 2018 book: Decarbonizing Logistics here. So this came out on Kogan Page. I also published on Kogan Page. It's a great UK-based publisher. Tell me a little bit about decarbonization overall and what you see as the main opportunities but also the challenges.

It seems to me there's a lot of talk of decarbonization, but the subject that you are attacking it from is one that points out a lot of the limitations of these visions of changing the world into a decarbonized world. They're very physical limits and very real practices out there in various industries. How can we kick off this discussion on decarbonization? What is the best way to understand the biggest challenge here?

ALAN: If we confine that to logistics, to put that into perspective, I think in my book, I reckoned...I pulled together as many numbers as I could, and I reckoned that logistics worldwide accounted for about between 10% and 11% of energy-related CO2 emissions. I've now revised that upwards, so I think it's probably now closer to 11% to 12%, most of that coming from freight transport but some of it from the buildings, from the warehouses, and the freight terminals. To my knowledge, nobody has yet carbon footprinted the IT and administrative aspects of logistics, but that could maybe be up half a percent or thereabouts.

And there's a general recognition that Logistics is going to be a very hard sector to decarbonize for three reasons: one, because of the forecast growth in the amount of freight movement worldwide over the next few decades. Second thing is because almost all the energy currently used in logistics is fossil fuel, right? So we're going to have to convert from fossil fuel to renewables.

And the third thing is the length of the asset life because ships would typically have an asset life of 25, 30, 35 years; planes, likewise, trucks are a bit shorter, maybe 10 to 15 years. But it's going to take us time to change that asset base away from fossil energy to renewables.

TROND: Well, I believe in the middle of your book, somewhere in chapter three, I read this quote that you had that the only way a restraining future increases in freight movement is basically to slow economic growth. That's not really very exciting of a prospect.

ALAN: Well, that's one of my five decarbonization levers to just reduce the amount of stuff that we have to move.

TROND: You must be a popular guy if you say that to industry leaders.

[laughter]

ALAN: Well, I think the challenge of dealing with a climate problem is so enormous that we really have to think out of the box and think of these radical suggestions. But in this case, a number of things can help us there; I mean, the development for circular economy, increasingly manufacturing and recycling will help to reduce the amount of stuff. A lot of the research suggests that people are prepared now to move to a sharing economy where they're less obsessive about owning things and more willing to share. In some sectors...look at electronics how we have managed to miniaturize products.

There's also 3D printing, which some people think will help us to reduce the amount of stuff that we need to move. It will help us to streamline our supply chains, reduce the amount of wastage in the production process. So it's not all about just people buying less. I mean, there are a number of trends I think we should --

TROND: I get that, but, Alan, I mean, 3D printing, I was just, again, reading from your book. You're not all that bullish on 3D printing, either. It's certainly not on the individual level this vision people might have in their heads that everyone's going to have a 3D printer, or the neighborhood will have a vast 3D printer network, and you can print everything locally. This whole decentralized idea of the world of material goods, essentially, where everything is printed on demand, you don't really see that as a very easy transition, do you?

ALAN: No, I don't. I think it's also a longer-term transition. I mean, there's a debate as to whether this will be truly a game changer. And maybe in the longer term, we will see a lot of consumer products printed in the home, and then we can greatly streamline supply chains. That is a long way off if it ever happens. Where I think it's more likely to reduce, freight demand is further back along the supply chain instead of business applications of 3D printing.

But there's an academic debate on this subject. Some people are quite upbeat about this, thinking 3D printing is going to be an effective decarbonizer. Others are a bit more skeptical. I mean, there are some forecasts being made about the net effect of 3D printing on the amount of air cargo in the future. But there's not necessarily a wide agreement on that. So I think the jury's out on this one, [laughs] on the net contribution 3D printing will make to decarbonization.

TROND: Alan, can you give me some tangible examples of what we're talking about here with logistics? Because, in essence, it's an unfair business to be in to decarbonize logistics in the sense that the subject as a whole is almost a victim of climate change. You're dealing with extractive or heavy industries that are moving about a lot of damaging [laughs] materials that they have extracted.

To turn this into a positive discussion is challenging, but there are a lot of attempts to do so. Maybe we can take trucking perhaps as an example. So transportation, obviously, of goods via air is challenging, and road and by ocean, I guess, is somewhat less climate impactful. But what is the prospect?

If we just take trucks, it's a modal transportation element. People understand truckers, and we see trucks on the road. It's a very visceral kind of element. What has happened there, and what would you see is the prospect there? People talk about electrification of trucks. What are the real prospects for change in trucking, transportation?

ALAN: I think one of the positive things here is that there are many things that can be done, and they're additive. Their net effects will be cumulative. They're going to be implemented over different timescales. So the sort of things that we can do today which yield a significant carbon saving would be to improve the aerodynamics of the vehicles, streamline them.

We can train the truck drivers to drive more fuel efficiently. I mean, I think that's recognized to be one of the most cost-effective ways of cutting carbon emissions and also, of course, reducing fuel costs as well. A lot of this would be self-financing for the trucking businesses.

Then looking to the longer-term, there are technologies that we'll be able to deploy. Here in Europe, there's been a lot of interest in platooning, where it's not just the fuel efficiency of the individual vehicle that you improve but convoys of vehicles that would then be closely coupled, if you like, on the motorway.

But many people see ultimately, the way we decarbonize road freight to get it down to zero emissions is through switching from diesel fuel to low carbon fuels, mainly batteries. I would have thought, certainly for smaller countries where the trucks travel shorter distances, maybe some use of hydrogen though I have to confess that I'm doubtful about the use of hydrogen in the road freight sector. I see we will need the hydrogen to decarbonize other sectors of the freight market, the ones you mentioned, aviation and shipping, because they don't have the same opportunity to electrify the operations that we will have in the road freight sector.

But I mentioned the importance of timescale here because if you look at Europe, I think there are 6.2 million trucks in Europe. We are replacing those trucks at about 200,000 or 300,000 a year. At that replacement rate, it's going to take us probably a couple of decades to entirely replace a diesel fleet with a fleet running on batteries or fuel cells, and therefore there are things we have to do in the interim.

So, in addition to the things I've mentioned, the shorter-term ones, we can fill the vehicles better. Typically in Europe, about 20% of truck kilometers are run empty. In some parts of the world, it's 30% or 40% of truck kilometers run empty. We need better load matching, you know, to get return loads because that would then help us to cut truck kilometers and thereby save energy and CO2.

TROND: You know, it strikes me that a lot of what you're talking about, I guess, resonates with the topic of this podcast because it's not just automating and making things enormously advanced in terms of technology per se. It is optimizing within this idea that you're using your assets differently, perhaps through digital means and organizing people and assets in a system in a better way. How would you say the progress is there?

Because there's, you know, we'll move to this in a second, there are these very high-profile projects, sequestration and such which we'll talk about that require technological leaps. But the kinds of things you're talking about here they are more tweaks, I guess, with better control of where your asset is, what's empty at given moments, and, like you said, platooning and other things, organizing people differently.

ALAN: I think the use of the word tweak may underestimate their contribution. It can be incremental, but it can still be quite significant, I think. So one thing is load matching; you know, if you're a trucking company or a truck driver and your truck is going to be returning empty, how can you find a return load? Or, if your vehicle is only partially loaded, how can you maybe pick up another load that will fill it to a greater extent?

Now, we have heard what we call freight exchanges, online freight exchanges now, for over 20 years where a trucker could go online, and it would be an online market, and they would be finding an available load. But that technology has been greatly upgraded recently with the application...well, moving to cloud computing, for example. But the application of artificial intelligence, machine learning, we can now take that level of transport solution to a new level.

TROND: You know, that's fascinating, Alan. My question, though, is, is the business model of the way that drivers are organized also needing to be optimized for that purpose? For example, if a driver works for a given company, what is the incentive for that company to have that driver take more load? I mean, is there a way that you can take someone else's cargo and then get evenly distributed? I don't know, the driver gets something for the inconvenience of going somewhere, and the company that owns the asset obviously gets part of it. There are business model changes needed too.

ALAN: Yes, again, a very good point. One important feature of the trucking industry, I think virtually everywhere in the world, is it's highly fragmented. Here in Europe, we've got over half a million small and medium-size carriers. I think about 80% of carriers only have one vehicle. So how do you engage that vast community of small operators in this process? Mobile computing has helped the mobile phone.

Now these owner-drivers, of course, have an obvious incentive to keep their vehicle as full as much of the time. For the bigger operators, many of them now operate control towers. So it's no longer the driver's decision to do this. I mean, the driver will be told where to go to pick up a load. But for these bigger companies as well, by deploying this technology, they can improve the efficiency of their operation. And as a cool benefit from all of that, you get the carbon reductions and the energy savings.

And we shouldn't just look at this in terms of Europe and in North America. If we look at this at a global level, these technologies that we've just mentioned are beginning to have a revolutionary effect in countries like India, in Indonesia, in African countries, where small operators with a mobile phone can now tap into these networks to find their next backload.

So it's not so much changing the business model; it's refining the business model and creating new commercial opportunities for these companies. So they're not doing this to decarbonize their operations. They're doing this to fill the vehicles, improve efficiency, and save money, but there will be carbon savings as a consequence.

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TROND: You know, your field is so fascinating for the myriad of different tactics that can be deployed here. Let's move for a second just to the bigger issues around energy, infrastructure, and ideas to change the way that that operates. Sequestration, for example, this idea of removing greenhouse gases, requires an enormous infrastructure. And I know you have written extensively on infrastructure overall. What is really at stake here with this type of process? We're talking about a futuristic, enormous industry that would be, I guess, on top of the existing logistics structure.

ALAN: Yes. It certainly will. I mean, I often flag this up to logistics businesses as the next huge business opportunity for so many of these companies. Because sequestration or carbon dioxide removal, I mean, drawing down the greenhouse gases already in the atmosphere is essentially a logistical process. We're going to be creating new supply chains, moving liquidized CO2 to places where it will either be buried in the ground or maybe used for some other purpose, like to make e-fuels.

But to put this into context, why is this happening? It's because we're almost certainly going to overshoot our carbon budgets. And so, if we want to commit to net zero, it is not simply a matter anymore of reducing emissions. We're also going to have to think about removing greenhouse gases already in the atmosphere. And to put that into perspective, I think last year; there were only about 18 or 19 plants in the world that were engaged in sequestration. And they only withdrew, I think, about 10,000 tons of CO2 from the atmosphere.

They're now projecting that by 2050 we'll, on an annual basis, be removing between 10 and 15 billion tons of CO2 from the atmosphere. And that is going to entail an enormous logistical exercise. But at the moment, thinking as at an early stage, we really haven't worked out where the best place will be to do the sequestration and where we will have to take the stuff to bury it in the ground.

TROND: In one of your presentations. You quoted an article from 2021 that says that the concept itself of net zero is basically a trap that it becomes kind of an excuse to do certain things as an extension of existing industries. These researchers have started to get second thoughts about something that they might even themselves have proposed. Is that the alternative view that you'd like to flag out there, or is this really a serious concern that we're putting too many eggs in one basket here?

ALAN: You're right. I mean, a lot of climate scientists are now seriously worried about the concept of net zero. I read the other day I think if you look at all the countries in the world that have committed to being net zero by 2050 or earlier and all the companies, I think 91% of the global economy is now covered by a net zero commitment. But I suspect a lot of people don't truly understand what net zero entails, I mean, realizing there's a big sequestration side to it, and it's not purely mitigation.

But I sympathize with the views of those who say that if we now get fixated with sequestration, if we realize we don't have to cut our emissions very quickly or dramatically because we can just leave it to future generations to pull down all the CO2 that we have put there. That is highly risky because the technologies we have for doing this are still fairly immature. And we're just not sure how we're going to be able to scale this up to the level I've just mentioned.

But there's an equity and ethical issue here that we should be leaving it to future generations to reverse the climate change processes that we have started. The last thing we want, of course, is for interest in sequestration to deflect attention from cutting emissions now. That's what we really need to do. Because the economic modeling on this suggests, it's an awful lot cheaper to stop emitting today than it will be in the future to remove those greenhouse gases from the atmosphere.

TROND: So let's talk a little bit about the future outlook then because there obviously are technologies on the table, on the books but also in development that do have certainly more renewable potential. There are improvements in renewables. There's the whole switching argument that eventually, once you switch, that is going to take effect.

But are you, I guess, pessimistic or optimistic that this switch or this future, as in 2050, which is kind of the climate future that most people are looking at, what is the prospect that we're anywhere close here? And where are the things where you think we should be putting our energies?

ALAN: One has to be optimistic in this area. I mean, if you're pessimistic, what do you gain? We have to look at the positives. And I think we will ultimately be able to decarbonize logistics. What concerns me is the speed at which we're doing it. Now, as I said, ultimately, we will do this by switching from fossil fuel to zero-carbon energy sources. In most cases, we're going to have to change the vehicles, the locomotives, the ships, the planes to do that, and that's going to be a long-term process.

Another thing which concerns me at the moment is there's a lot of disagreement as to what the dominant low-carbon fuel will be for the various future transport modes. So in the road freight sector, there's a debate as to whether we should be using batteries to do this or hydrogen. In the shipping sector, the main choice is between e-methanol or green ammonia. And some people think we should be using nuclear even. So a disagreement there. And then, on aviation, sustainable aviation fuel will be required in vast quantities to decarbonize aviation.

TROND: How are we going to do that? How are we going to do that, right? Isn't that the question? The vast amounts of forests or whatever agriculture is going to go to these biofuels.

ALAN: Well, I think biofuel will make a contribution. Personally, I think the main fuel we will use for aircrafts in the future is e-kerosene, which is a synthetic fuel which will use green electricity. Once we've decarbonized electricity, we can then use that to make green hydrogen, which we can then combine with other chemicals to make e-kerosene. Now at the moment, that's currently...we can do this currently, but it's two or three times more expensive than fossil kerosene.

But also, until we get the capability to do that, we will rely on biofuels. That's certainly true, not just for aviation but in the road freight sector and possibly to some extent in the shipping sector. But we got to make sure the biofuels are environmentally sustainable. Because, I mean, I was a real enthusiast for biofuels when I began to get involved in the climate change work. I thought it's biofuels that will allow us to decarbonize logistics until we did the lifecycle analysis.

And we discovered that if you make your biofuel with palm oil sourced from, I don't know, Indonesia or Malaysia, on a lifecycle basis, the emissions are three times those of the diesel that we are replacing. It just doesn't make sense at all. So we have to ensure that we're using feedstocks for the biofuels, which are genuinely sustainable. There's a limited quantity of those. So we have to see these as being of limited value short term, as transitional, until we move to the other fuels I've just mentioned.

TROND: But, Alan, it seems to me that as much as you're an enthusiast of various futuristic technologies, you're also saying that in the next ten years, there are a lot of operational things we can do. One idea that has been put forward that you've talked to me about is this idea, which needs to be explained, of the physical internet as a conceptual change in the logistics industry. Can you elucidate that concept? Because at face value, I don't quite understand it, but on the other hand, it's the principle here. It's not recreating the internet.

ALAN: No, yeah. I always have to say that the physical internet is not the Internet of Things because people, I think, often wrongly confuse the two things. The physical internet would be a physical manifestation, if you like, of the digital internet, applying the same principles, the same organizational principles that we have for moving emails to the movement of physical consignments.

So if you think what are the key features of the digital internet, open systems, standardized modules for moving information through the internet, we would be creating an open system. There'd be little proprietary asset-based logistics so that the warehouses, the freight terminals, the vehicles would be available for general access. And we would have to put in place, therefore, IT systems and market mechanisms to make that possible because that would then allow us to use that asset base an awful lot more efficiently.

The other thing which would, if I'd just add something else, is modularization. Because at the moment, we have got some degree of modularization obviously in pallets and containers and so forth, but we may have then to remodularize with a different type of handling equipment that would be nested and compatible to allow us to fill the vehicles better and to manage processes in the warehouses, for example.

TROND: It's surprising, I guess, a little bit to hear this, and maybe you can explain this to me. But at surface value, this whole international container standard and the way that that really changed shipping because there's, after all, one container. It looks the same pretty much everywhere. It was this big battle. And then there is this container, it doesn't quite work for air travel, but it works for freight, ocean-based shipping, and for land transport.

So one would have thought that that perspective is so ingrained in logistics because it was such a success story. But you're telling me that...did one rest too much on the laurels of that one success and then never extended this to other aspects of standardization? Or how do you explain that one element is so standardized and many, many, many other elements remain stuck in kind of that proprietary logic?

ALAN: It's a great point. So containerization was a game changer. I mean, it transformed international trade. And we've always been looking for a similar game changer, [laughs] you know, to be equally transformational. But there were still problems with containerization, you know, so that standardized the boxes and made it easier to transfer them between transport modes and so forth.

But if you look at the internal dimensions of a container, they're not all that compatible with the dimensions of the pallets inside, so you always waste some space. We call this the unit load hierarchy. So at the top end, we got the container, and then we come down to the next level, which would be the pallet load, and then the level below that would be the carton. And then you get down to the individual product. And it's at these lower levels in that hierarchy we don't have sufficient standardization. So there are many different sizes and shapes of pallets and stillages, and so forth. And it would be nice if we could converge on similar standardization at that level.

TROND: Fascinating. Let's move to the policy area in a second. I know that you did some work for Unilever a while back and developed a framework for decarbonization policy essentially or to understand the different factors that that will impact, and you called it the Timber Decarbonization Framework. And I'm just going to quickly recite these factors, and you'll explain why they all are here.

So technology, we've talked about technology, infrastructure, you know, obviously, the physical aspect of all these assets. And then market trends behavior which is interesting because behavior is not the first thing I would think of in logistics, [laughs] and then energy system and regulation. So there are many, many things here in this framework. But what does that mean for a policymaker? Because up until now, we've been talking about private sector optimizing their own portfolios, but there's also a wider concern here for policymakers or indeed for individuals.

ALAN: That's right. So a bit of background then on the project that we did for Unilever. The company had set itself this target to reduce the carbon intensity of its global logistics by 40% between 2010 and 2020, and it obviously had some ideas to how it could do that internally. But I thought over that time period, almost certainly, there'll be development outside Unilever's control, many of them at a national level, a macro level, which will help to decarbonize logistics, which would reinforce anything that the company was doing itself internally.

So they asked us to look at 13 of their main markets in the world and make an assessment as to what extent transport logistics were decarbonizing generally. And it was --

TROND: Only 13 markets. [laughs]

ALAN: Only 13 markets, that's right, I know. [laughter] I can tell you it was hard enough just doing it for 13 markets because that includes big markets like China and Brazil, and so forth. So we came up with the timber framework to say that these macro-level trends would fall basically into those six categories. And what we tried to do then was...this was a desk-based study. We tried to pull together as much data as we could for each of those six subject areas.

TROND: What was the most surprising of them for you, Alan? Technology is perhaps pretty obvious. And then infrastructure, I guess, for you in your field is very obvious. But some of the others, at least for me...and regulation, obviously, this was a regulatory concern as well. But what were some of the surprises, the biggest surprise when you were putting together this and realizing which factors were influential?

ALAN: I think it was the diversity which surprised us. Well, maybe I should qualify that because some of those countries were European countries where there's a lot of similarity. Many of them belong to the EU and therefore were governed by continental-wide regulatory policies.

But when you went into other countries, even countries you might think were similar in their level of development and in the maturity of their logistics industry, there were actually quite different approaches to the way in which they were decarbonizing. Just take one thing, for example, the freight modal split, you know, the division of freight traffic between transport modes can vary a lot between countries, and that can be quite a big determinant of the average carbon intensity of freight movement within that country.

But also, there's a feeling that it's the developed world that are doing the most innovative things in decarbonizing logistics. But we did find examples in less developed countries of quite clever initiatives. One often imagines that the lessons from decarbonizing logistics will transfer from the wealthier countries to the poorer ones. But there could be a scope, I think, for the movement of ideas and practices in the opposite direction as well.

TROND: Alan, let me ask you this. I mean, many times, when you know a lot about an area, you come to the conclusion that if I only ruled this system, things would be better.

ALAN: [laughs]

TROND: And thereby, in French, they say this dirigiste approach where you say government or me, the expert, or whoever it is, we are just going to set this straight. Is that the big wish for you or the experts in this domain that some master planner comes in and just kind of lays down the law? Or is the clue to these very necessary decarbonization strategies a more flexible framework?

ALAN: If I was that global dictator with special powers over logistics, I think the one thing I would prioritize would be pricing using the price mechanism. And things are progressing well in that direction. If you go to the World Bank website, there's a dashboard, and they show the extent to which carbon pricing schemes are developing around the world. And I think currently, almost a quarter of greenhouse gases emitted are in countries that have got some form of emissions trading or carbon taxation. So I think that needs to be extended.

What we're also seeing, of course, is the cost of carbon increasing. So the world's biggest emissions trading market is here in Europe. And I think over the past two years, or so, the price of carbon has rocketed; it's currently, I think, about €100 per ton of CO2. So extending these carbon pricing, carbon taxation schemes, and at the same time raising the cost of carbon will then incorporate carbon pricing into companies' balance sheets and their investment appraisal. And that, I think, will drive a lot of the changes we've been discussing. That includes the managerial, operational things right through to the technological things like switching to lower carbon fuels.

TROND: So at the end of the day then, Alan, you say there's a benefit to being optimistic, and I liked that message. But I do sense that there are some bumps in the road here. It's not going to necessarily be an easy technology fix or even an easy policy fix here. It seems the overall logistics framework it's not one industry; it seems to me. There are the logistics practices, and they are spread around every industry.

ALAN: Yes, you're right. I mean, I don't want to give the impression that any of this is going to be easy. It's going to be tough, but it will have to be done. And just to flag up some of the complexities, I've mentioned how in the trucking industry, we're going to have to shift from diesel trucks to probably battery ones predominantly. And again, almost all the discussion of that relates to Europe and in North America. But we got to do this at a global level.

At the moment, a lot of developing countries buy second-hand trucks from Europe or North America. And one thing that concerns me is that as Europe and North America accelerate the transition to low-carbon vehicles, they will want to dump a lot of their existing diesel vehicles. And the danger is they'll be dumped in less developed countries, where that will then slow their transition to the next generation of battery-powered vehicles.

So this is an area where we really have to take a truly global perspective on how we transform road freight because what's the point of us massively reducing our CO2 emissions in Europe if all we do is inflate emissions from other parts of the world? I mean, climate change is a global problem. We've got one atmosphere, and therefore we have to look at that bigger picture.

TROND: That's fascinating. It would seem to me that the solution would have to be something where you add incentive for everyone regardless of where you are in the pyramid of industrial transition to leapfrog essentially, right?

ALAN: Yes, yes, exactly. I think the key will be transferring technologies best practice from a lot of the more developed countries to the less developed world. I've just written a paper for the World Bank looking at how we tailor logistics, decarbonization to the needs of less developed countries, and that will be coming out in a few months' time. And I think that's going to be really one of our bigger challenges in this field.

TROND: Alan, it's fascinating to hear such an overview of a field and an expanding landscape that is so crucial to something that clearly is one of the bigger challenges of our time. Thank you so much for your time today.

ALAN: You're welcome. Thank you.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Decarbonizing Logistics. Our guest was Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg. In this conversation, we talked about mitigating and adapting to climate change throughout industrial supply chains.

My takeaway is that decarbonizing logistics without slowing economic growth is a formidable challenge which requires paradigm shifts across many industries, as well as adopting openness principles from the virtual internet onto the physical nature of the supply chain, as well as facilitating new business models, sharing, and standardization, and eventually dematerialization. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 68: Industrial Supply Chain Optimization. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us because we would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co.

Please share this show with colleagues who care about where industry and especially where industrial tech is heading.

To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Alan McKinnon.

Episode 97: Industrial AI

21 september 2022 | 48 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

The topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati.

In this conversation, we talk about how AI does many things but to be applicable; the industry needs it to work every time, which puts additional constraints on what can be done by when.

If you liked this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, and the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati.

In this conversation, we talk about how AI does many things but to be applicable, industry needs it to work every time, which puts on additional constraints on what can be done by when.

Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Jay, it's a pleasure to have you here. How are you today?

JAY: Good. Thank you for inviting me to have a good discussion about industrial AI.

TROND: Yeah, I think it will be a good discussion. Look, Jay, you are such an accomplished person, both in terms of your academics and your industrial credentials. I wanted to quickly just go through where you got to where you are because I think, especially in your case, it's really relevant to the kinds of findings and the kinds of exploration that you're now doing.

You started out as an engineer. You have a dual degree. You have a master's in industrial management also. And then you had a career in industry, worked at real factories, GM factories, Otis elevators, and even on Sikorsky helicopters. You had that background, and then you went on to do a bunch of different NSF grants. You got yourself; I don't know, probably before that time, a Ph.D. in mechanical engineering from Columbia.

The rest of your career, and you correct me, but you've been doing this mix of really serious industrial work combined with academics. And you've gone a little bit back and forth. Tell me a little bit about what went into your mind as you were entering the manufacturing topics and you started working in factories. Why have you oscillated so much between industry and practice? And tell me really this journey; give me a little bit of specifics on what brought you on this journey and where you are today.

JAY: Well, thank you for talking about this career because I cut my teeth from the factory early years. And so, I learned a lot of fundamental things in early years of automation. In the early 1980s, in the U.S, it was a tough time trying to compete with the Japanese automotive industry. So, of course, the Big Three in Detroit certainly took a big giant step, tried to implement a very good manufacturing automation system.

So I was working for Robotics Vision System at that time in New York, in Hauppage, New York, Long Island. And shortly, later on, it was invested by General Motors. And in the meantime, I was studying part-time in Columbia for my mechanical engineering, Doctor of Engineering. And, of course, later on, I transferred to George Washington because I had to make a career move. So I finished my Ph.D. Doctor of Science in George Washington later.

But the reason we stopped working on that is because of the shortage of knowledge in making automation work in the factory. So I was working full-time trying to implement the robots automation in a factory. In the meantime, I also found a lack of knowledge on how to make a robot work and not just how to make a robot move. Making it move means you can program; you can do very fancy motion. But that's not what factories want.

What factories really want is a non-stop working system so they can help people to accomplish the job. So the safety, and the certainty, the accuracy, precision, maintenance, all those things combined together become a headache actually. You have to calibrate the robot all the time. You have to reprogram them.

So eventually, I was teaching part-time in Stony Brook also later on how to do the robotic stuff. And I think that was the early part of my career. And most of the time I spent in factory and still in between the part-time study and part-time working.

But later on, I got a chance to move to Washington, D.C. I was working for U.S. Postal Service headquarters as Program Director for automation. In 1988, post service started a big initiative trying to automate a 500 mil facility in the U.S. There are about 115 number one facilities which is like New York handled 8 million mail pieces per day at that time; you're talking about '88. But most are manual process, so packages.

So we started developing the AI pattern recognition, hand-written zip code recognition, robotic postal handling, and things like that. So that was the opportunity that attracted me actually to move away from automotive to service industry. So it was interesting because you are working with top scientists from different universities, different companies to make that work. So that was the early stage of the work.

Later on, of course, I had a chance to work with the National Science Foundation doing content administration in 1991. That gave me the opportunity to work with professors in universities, of course. So then, by working with them, I was working on a lot of centers like engineering research centers and also the Industry-University Cooperative Research Centers Program, and later on, the materials processing manufacturing programs.

So 1990 was a big time for manufacturing in the United States. A lot of government money funded the manufacturer research, of course. And so we see great opportunity, like, for example, over the years, all the rapid prototyping started in 1990s. It took about 15-20 years before additive manufacturing came about. So NSF always looks 20 years ahead, which is a great culture, great intellectual driver. And also, they're open to the public in terms of the knowledge sharing and the talent and the education.

So I think NSF has a good position to provide STEM education also to allow academics, professors to work with industry as well, not just purely academic work. So we support both sides. So that work actually allowed me to understand what is real status in research, in academics, also how far from real implementation.

So in '95, I had the opportunity to work in Japan actually. I had an opportunity...NSF had a collaboration program with the MITI government in Japan. So I took the STA fellowship called science and technology fellow, STA, and to work in Japan for six months and to work with 55 organizations like Toyota, Komatsu, Nissan, FANUC, et cetera.

So by working with them, then you also understand what the real technology level Japan was, Japanese companies were. So then you got calibration in terms of how much U.S. manufacturing? How much Japanese manufacturing? So that was in my head, actually. I had good weighting factors to see; hmm, what's going on here between these two countries? That was the time.

So when I came back, I said, oh, there's something we have to do differently. So I started to get involved in a lot of other things. In 1998, I had the opportunity to work for United Technologies because UTC came to see me and said, "Jay, you should really apply what you know to real companies." So they brought me to work as a Director for Product Environment Manufacturing Department for UTRC, United Technology Research Center, in East Hartford. Obviously, UTC business included Pratt & Whitney jet engines, Sikorsky helicopters, Otis elevators, Carrier Air Conditioning systems, Hamilton Sundstrand, et cetera.

So all the products they're worldwide, but the problem is you want to support global operations. You really need not just the knowledge, what you know, but also the physical usage, what you don't know. So you know, and you don't know. So how much you don't know about a product usage, that's how the data is supposed to be coming back. Unfortunately, back in 1999, I have to tell you; unfortunately, most of the product data never came back. By the time it got back, it is more like a repair overhaul recur every year to a year later. So that's not good.

So in Japan, I was experimenting the first remote machine monitoring system using the internet actually in 1995. So I published a paper in '98 about how to remotely use physical machine and cyber machine together. In fact, I want to say that's the first digital twin but as a cyber-physical model together. That was in my paper in 1998 in Journal of Machine Tools and Manufacture.

TROND: So, in fact, you were a precursor in so many of these fields. And it just strikes me that as you're going through your career here, there are certain pieces that you seem to have learned all along the way because when you are a career changer oscillating between public, private, semi-private, research, business, you obviously run the risk of being a dilettante in every field, but you seem to have picked up just enough to get on top of the next job with some insight that others didn't have. And then, when you feel like you're frustrated in that current role, you jump back or somewhere else to learn something new.

It's fascinating to me because, obviously, your story is longer than this. You have startup companies with your students and others in this business and then, of course, now with the World Economic Forum Lighthouse factories and the work you've been doing for Foxconn as well. So I'm just curious.

And then obviously, we'll get to industrial AI, which is so interesting in your perspective here because it's not just the technology of it; it is the industrial practice of this new domain that you have this very unique, practical experience of how a new technology needs to work. Well, you tell me, how did you get to industrial AI? Because you got there to, you know, over the last 15-20 years, you integrated all of this in a new academic perspective.

JAY: Well, that's where we start. So like I said earlier, I realized industry we did not have data back in the late 1990s. And in 1999, dotcom collapsed, remember?

TROND: Yes, yes.

JAY: Yeah. So all the companies tried to say, "Well, we're e-business, e-business, e-commerce, e-commerce," then in 2000, it collapsed. But the reality is that people were talking about e-business, but in the real world, in industrial setting, there's no data almost. So I was thinking, I mean, it's time I need to think about how to look at data-centric perspectives, how to develop such a platform, and also analytics to support if one-day data comes with a worry-free kind of environment. So that's why I decided to transition to an academic career in the year 2000.

So what I started thinking, in the beginning, was where has the most data? As we all know, the product lifecycle usage is out there. You have lots of data, but we're not collecting it. So eventually, I called a central Intelligent Maintenance System called IMS, not intelligent manufacturing system because maintenance has lots of usage data which most developers of a product don't know. But if we have a way to collect this data to analyze and predict, then we can guarantee the product uptime or the value creation, and then the customer will gain most of the value back.

Now we can use the data feedback to close-loop design. That was the original thinking back in the year 2000, which at that time, no cell phone could connect to the internet. Of course, nobody believed you. So we used a term called near-zero downtime, near-zero downtime, ZDT. Nobody believed us. Intel was my first founding member. So I made a pitch to FANUC in 2001. Of course, they did not believe it either. Of course, FANUC in 2014 adopted ZDT, [laughs] ZDT as a product name.

But as a joke, when I talked to the chairman, the CEO of the company in 2018 in Japan, Inaba-san that "Do you know first we present this ZDT to your company in Michigan? They didn't believe it. Now you guys adopted." "Oh, I didn't know you use it." So when he came to visit in 2019, they brought the gift. [laughs]

So anyway, so what happened is during the year, so we worked with the study of 6 companies, 20 companies and eventually they became over 100 companies. And in 2005, I worked with Procter & Gamble and GE Aircraft Engine. They now became GE Aviation; then, they got a different environment.

So machine learning became a typical thing you use every day, every program, but we don't really emphasize AI at that time. The reason is machine learning is just a tool. It's an algorithm like a support-vector machine, self-organizing map, and logistic regression. All those are just supervised learning or now supervised learning techniques. And people use it. We use it like standard work every day, but we don't talk about AI.

But over the years, when you work with so many companies, then you realize the biggest turning point was Toyota 2005 and P&G in 2006. The reason I'm telling you 2005 is Toyota had big problems in the factory in Georgetown, Kentucky, where the Camry factory is located. So they had big compressor problems. So we implemented using machine learning, the support-vector machine, and also principal component analysis. And we enable that the surge of a compressor predicted and avoided and never happened. So until today --

TROND: So they have achieved zero downtime after that project, essentially.

JAY: Yeah. So that really is the turning point. Of course, at P&G, the diaper line continues moving the high volume. They can predict things, reduce downtime to 1%. There's a lot of money. Diaper business that is like $10 billion per year.

TROND: It's so interesting you focus on downtime, Jay, because obviously, in this hype, which we'll get to as well, people seem to focus so much on fully automated versus what you're saying, which is it doesn't really, you know, we will get to the automation part, but it is the downtime that's where a lot of the savings is obviously. Because whether it's a lights out or lights on, humans are not the real saving here. And the real accomplishment is in zero downtime because that is the industrialization factor. And that is what allows the system to keep operating. Of course, it has to do with automation, but it's not just that.

Can you then walk us through what then became industrial AI for you? Because as I've now understood it, it is a highly specific term to you. It's not just some sort of fluffy idea of very, very advanced algorithms and robots running crazy around autonomously. You have very, very specific system elements. And they kind of have to work together in some architectural way before you're willing to call it an industrial AI because it may be a machine tool here, and a machine tool there, and some data here.

But for you, unless it's put in place in a working architecture, you're not willing to call it, I mean, it may be an AI, but it is not an industrial AI. So how did this thinking then evolve for you? And what are the elements that you think are crucial for something that you even can start to call an industrial AI? Which you now have a book on, so you're the authority on the subject.

JAY: Well, I think the real motivation was after you apply all the machine learning toolkits so long...and a company like National Instruments, NI, in Austin, Texas, they licensed our machine learning toolkits in 2015. And eventually, in 2017, they started using the embedding into LabVIEW version. So we started realizing, actually, the toolkit is very important, not just from the laboratory point of view but also from the production and practitioners' point of view from industry. Of course, researchers use it all the time for homework; I mean, that’s fine.

So eventually, I said...the question came to me about 2016 in one of our industry advisory board meeting. You have so many successes, but the successes that happen can you repeat? Can you repeat? Can you repeatably have the same success in many, many other sites? Repeatable, scalable, sustainable, that's the key three keywords. You cannot just have a one-time success and then just congratulate yourself and forget it, no. So eventually, we said, oh, to make that repeat sustainable, repeatable, you have a systematic discipline.

TROND: I'm so glad you say this because I have taken part in a bunch of best practice schemes and sometimes very optimistically by either an industry association or even a government entity. And they say, "Oh yeah, let's just all go on a bunch of factory visits." Or if it's just an IT system, "Let's just all write down what we did, and then share it with other people." But in fact, it doesn't seem to me like it is that easy.

It's not like if I just explain what I think I have learned; that's not something others can learn from. Can you explain to me what it really takes to make something replicable? Because you have done that or helped Foxconn do that, for example. And now you're obviously writing up case studies that are now shared in the World Economic Forum across companies.

But there's something really granular but also something very systemic and structured about the way things have to be explained in order to actually make it repeatable. What is the sustainability factor that actually is possible to not just blue copy but turn it into something in your own factory?

JAY: Well, I think that there are basically several things. The data is one thing. We call it the data technology, DT, and which means data quality evaluation. How do you understand what to use, what not to use? How do you know which data is useful? And how do you know where the data is usable?

It doesn't mean useful data is usable, just like you have a blood donation donor, but the blood may not be usable if the donor has HIV. I like to use an analogy like food. You got a fish in your hand; wow, great. But you have to ask where the fish comes from. [chuckles] If it comes from polluted water, it's not edible, right? So great fish but not edible.

TROND: So there's a data layer which has to be usable, and it has to be put somewhere and put to use. It actually then has to be used. It can't just be theoretically usable.

JAY: So we have a lot of useful data people collect. The problem is people never realized lots of them are not usable because of a lack of a label. They have no background, and they're not normalized. So eventually, that is a problem. And even if you have a lot of data, it doesn't mean it is usable.

TROND: So then I guess that's how you get to your second layer, which I guess most people just call machine learning, but for you, it's an algorithmic layer, which is where some of the structuring gets done and some of the machines that put an analysis on this, put in place automatic procedures.

JAY: And machine learning to me it's like cooking ware like a kitchen. You got a pan fry; you got a steamer; you got the grill. Those are tools to cook the food, the data. Food is like data. Cooking ware is like AI. But it depends on purpose. For example, you want fish. What do you want to eat first? I want soup. There's a difference. Do you want to grill? Do you want to just deep fry? So depending on how you want to eat it, the cooking ware will be selected differently.

TROND: Well, and that's super interesting because it's so easy to say, well, all these algorithms and stuff they're out there, and all you have to do is pick up some algorithms. But you're saying, especially in a factory, you can't just pick any tool. You have to really know what the effect would be if you start to...for example, on downtime, right?

Because I'm imagining there are very many advanced techniques that could be super advanced, but they are perhaps not the right tool for the job, for the workers that are there. So how does that come into play? Are these sequential steps, by the way? So once you figure out what the data is then, you start to fiddle with your tools.

JAY: Well, there are two perspectives; one perspective is predict and prevent. So you predict something is going to happen. You prevent it from happening, number one. Number two, understand the root causes and potential root causes. So that comes down to the visible and invisible perspective.

So from the visible world, we know what to measure. For example, if you have high blood pressure, you measure blood pressure every day, but that may not be the reason for high blood pressure. It may be because of your DNA, maybe because of the food you eat, because of lack of exercise, because of many other things, right?

TROND: Right.

JAY: So if you keep measuring your blood pressure doesn't mean you have no heart attack. Okay, so if you don't understand the reason, measuring blood pressure is not a problem. So I'm saying that you know what you don't know. So we need to find out what you don't know. So the correlation of invisible, I call, visible-invisible. So I will predict, but you also want to know the invisible reason relationship so you can prevent that relationship from happening. So that is really called deep mining those invisibles.

So we position ourselves very clearly between visible-invisible. A lot of people just say, "Oh, we know what the problem is." The problem is not a purpose. For example, the factory manufacturing there are several very strong purposes, number one quality, right? Worry-free quality.

Number two, your efficiency, how much you produce per dollar. If you say that you have great quality, but I spent $10,000 to make it, it is very expensive. But if you spend $2 to make it, wow, that's great. How did you do it? So quality per dollar is a very different way of judging how good you are. You got A; I spent five days studying. I got A; I spent two hours studying. Now you show the capability difference.

TROND: I agree. And then the third factor in your framework seems to be platform. And that's when I think a lot of companies go wrong as well because platform is...at least historically in manufacturing, you pick someone else's platform. You say I'm going to implement something. What's available on the market, and what can I afford, obviously? Or ideally, what's the state of the art? And I'll just do that because everyone seems to be doing that. What does platform mean to you, and what goes into this choice? If you're going to create this platform for industrial AI, what kind of a decision is that?

JAY: So DT is data, AT is algorithm, and PT is platform, PT platform. Platform means some common things are used in a shared community. For example, kitchen is a platform. You can cook. I can cook. I can cook Chinese food. I can cook Italian food. I can cook Indian food. Same kitchen but different recipe, different seasoning, but same cooking ware.

TROND: Correct. Well, because you have a good kitchen, right?

JAY: Yes.

TROND: So that's --

JAY: [laughs]

TROND: Right?

JAY: On the platform, you have the most frequently used tool, not everything. You don't need 100 cooking ware in your kitchen. You probably have ten or even five most daily used.

TROND: Regardless of how many different cuisines you try to cook.

JAY: Exactly. That's called the AI machine toolkit. So we often work with companies and say, "You don't need a lot of tools, come on. You don't need deep learning. You need a good logistic regression and support-vector machine, and you're done."

TROND: Got it.

JAY: Yeah, you don't need a big chainsaw to cut small bushes. You don't need it.

TROND: Right. And that's a very different perspective from the IT world, where many times you want the biggest tool possible because you want to churn a lot of data fast, and you don't really know what you're looking for sometimes. So I guess the industrial context here really constrains you. It's a constraint-based environment.

JAY: Yes. So industry, like I said, the industry we talked about three Ps like I said: problems, purposes, and processes. So normally, problem comes from...the main thing is logistic problems, machine, and factory problems, workforce problems, the quality problems, energy problem, ignition problem, safety problems. So the problem happens every day. That's why in factory world, we call it firefighting. Typically, you firefight every day.

TROND: And is that your metaphor for the last part of your framework, which is actually operation? So operation sounds really nice and structured, right?

JAY: [chuckles] Yes.

TROND: As if that was like, yeah, that's the real thing, process. We got this. But in reality, it feels sometimes, to many who are operating a factory; it's a firefight.

JAY: Sometimes the reason lean theme work, Six Sigma, you turn a problem into a process, five Ss process, okay? And fishbone diagram, Pareto chart, and Kaizen before and after. So all the process, SOP, so doesn't matter which year workforce comes in, they just repeat, repeat, repeat, repeat, repeat.

So in Toyota, the term used to be called manufacturing is just about the discipline. It's what they said. The Japanese industry manufacturing is about discipline, how you follow a discipline to everyday standard way, sustainable way, consistent way, and then you make good products. This is how the old Toyota was talking about, old one. But today, they don't talk that anymore. Training discipline is only one thing; you need to understand the value of customers.

TROND: Right. So there are some new things that have to be added to the lean practices, right?

JAY: Yes.

TROND: As time goes by. So talk to me then more about the digital element because industrial AI to you, clearly, there's a very clear digital element, but there's so many, many other things there. So I'm trying to summarize your framework. You have these four factors: data, algorithms, platforms, and operations. These four aspects of a system that is the challenge you are dealing with in any factory environment.

And some of them have to do with digital these days, and others, I guess, really have to do more with people. So when that all comes together, do you have some examples? I don't know, we talked about Toyota, but I know you've worked with Foxconn and Komatsu or Siemens. Can you give me an example of how this framework of yours now becomes applied in a context? Where do people pick up these different elements, and how do they use them?

JAY: There's a matrix thinking. So horizontal thinking is a common thing; you need to have good digital thread including DT, data technology, AT, algorithms or analytics, PT, platform, edge cloud, and the things, and OT operation like scheduling, optimizations, stuff like that.

Now, you got verticals, quality vertical, cost vertical, efficiency verticals, safety verticals, emission verticals. So you cannot just talk about general. You got to have focus on verticals. For example, let me give you one example: quality verticals. Quality is I'm the factory manager. I care about quality. Yes, the customer will even care more, so they care. But you have a customer come to your shop once a month to check. You ask them, "Why you come?" "Oh, I need to see how good your production." "How about you don't have to come? You can see my entire quality." "Wow, how do I do that?"

So eventually, we develop a stream of quality code, SOQ, Stream Of Quality. So it's not just about the product is good. I can go back to connect all the processes of the quality segment of each station. Connect them together. Just like you got a fish, oh, okay, the fish is great. But I wonder, when the fish came out of water, when the fish was in the truck, how long was it on the road? And how long was it before reaching my physical distribution center and to my home?

So if I have a sensor, I can tell you all the temperature history inside the box. So when you get your fish, you take a look; oh, from the moment the fish came out of the boat until it reached my home, the temperature remained almost constant. Wow. Now you are worry-free. It's just one thing. So you connect together. So that's why we call SOQ, Stream Of Quality, like a river connected.

So by the time a customer gets a quality product, they can trace back and say, "Wow, good. How about if I let you see it before you come? How about you don't come?" I say, "Oh, you know what? I like it." That's what this type of manufacturing is about. It just doesn't make you happy. You have to make the customer happy, worry-free.

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TROND: So, Jay, you took the words out of my mouth because I wanted to talk about the future. I'm imagining when you say worry-free, I mean, you're talking about a soon-to-be state of manufacturing. Or are you literally saying there are some factories, some of the excellence factories where you've won awards in the World Economic Forum or other places that are working towards this worry-free manufacturing, and to some extent, they have achieved it?

Well, elaborate for me a little bit about the future outlook of manufacturing and especially this people issue because you know that I'm engaged...The podcast is called Augmented Podcast. I'm engaged in this debate about automation. Well, is there a discrepancy between automation and augmentation? And to what extent is this about people running the system? Or is it the machines that we should optimize to run all the system?

For you, it's all about worry-free. First of all, just answer this question, is worry-free a future ideal, or is it actually here today if you just do the right things?

JAY: Well, first of all, worry-free is our mindset where the level of satisfaction should be, right?

TROND: Yep.

JAY: So to make manufacturing happen is not about how to make good quality, how to make people physically have less worry, how to make customers less worry is what is. But the reason we have a problem with workforce today, I mean, we have a hard time to hire not just highly skilled workers but even regular workforce.

Because for some reason, not just U.S., it seems everywhere right now has similar problems. People have more options these days to select other living means. They could be an Uber driver. [laughs] They could be...I don't know. So there are many options. You don't have to just go to the factory to make earnings. They can have a car and drive around Uber and Lyft or whatever. They can deliver the food and whatever. So they can do many other things.

And so today, you want to make workforce work environment more attractive. You have to make sure that they understand, oh, this is something they can learn; they can grow. They are fulfilled because the environment gives them a lot of empowerment. The vibe, the environment gives them a wow, especially young people; when you attract them from college, they'd like a wow kind of environment, not just ooh, okay. [laughs]

TROND: Yeah. Well, it's interesting you're saying this. I mean, we actually have a lack of workers. So it's not just we want to make factories full of machines; it's actually the machines are actually needed just because there are no workers to fill these jobs. But you're looking into a future where you do think that manufacturing is and will be an attractive place going forward. That seems to be that you have a positive vision of the future we're going into. You think this is attractive. It's interesting for workers.

JAY: Yeah. See, I often say that there are some common horizontal we have to use all the day. Vertical is the purpose, quality. I talked about vertical quality first, quality. But what are the horizontal common? I go A, B, C, D, E, F. What's A? AI. B is big data. C is cyber and cloud. D is digital or digital twin, whatever. E is environment ecosystem and emission reduction. What's F? Very important, fun. [laughs] If you miss that piece, who wants to work for a place there's no fun?

You tell me would you work for...you and I, we're talking now because it's fun. You talk to people and different perspectives. I talk to you, and I say, wow, you've built some humongous network here in the physical...the future of digital, not just professional space but also social space but also the physical space. So, again, the fun things inspire people, right?

TROND: They do. So talking about inspiring people then, Jay, if you were to paint a picture of this future, I guess, we have talked just now about workers and how if you do it right, it's going to be really attractive workplaces in manufacturing. How about for, I guess, one type of worker, these knowledge workers more generally? Or, in fact, is there a possibility that you see that not just is it going to be a fun place to be for great, many workers, but it's actually going to be an exciting knowledge workplace again?

Which arguably, industrialization has gone through many stages. And being in a factory wasn't always all that rosy, but it was certainly financially rewarding for many. And it has had an enormous career progression for others who are able to find ways to exploit this system to their benefit. How do you see that going forward?

Is there a scope, is there a world in which factory work can or perhaps in an even new way become truly knowledge work where all of these industrial AI factors, the A to the Fs, produce fun, but they produce lasting progression, and career satisfaction, empowerment, all these buzzwords that everybody in the workplace wants and perhaps deserves?

JAY: That's how we look at the future workforce is not just about the work but also the knowledge force. So basically, the difference is that people come in, and they become seasoned engineers, experienced engineers. And they retire, and the wisdom carries with them. Sometimes you have documentation, Excel sheet, PPT in the server, but nobody even looks at it. That's what today's worry is.

So now what you want is living knowledge, living intelligence. The ownership is very important. For example, I'm a worker. I develop AI, not just the computer software to help the machine but also help me. I can augment the intelligence. I will augment it. When I make the product happen, the inspection station they check and just tell me pass or no pass. They also tell me the quality, 98, 97, but you pass. And then you get your score. You got a 70, 80, 90, but you got an A. 99, you got an A, 91, you got an A, 92. So what exactly does A mean?

So, therefore, I give you a reason, oh, this is something. Then I learn. Okay, I can contribute. I can use voice. I can use my opinion to augment that no, labeled. So next time people work, oh, I got 97. And so the reason is the features need to be maintained, to be changed, and the system needs to be whatever. So eventually, you have a human contribute.

The whole process could be consisting of 5 experts, 7, 10, 20, eventually owned by 20 people. That legacy continues. And you, as a worker, you feel like you're part of the team, leave a legacy for the next generation. So eventually, it's augmented intelligence.

The third level will be actual implementation. So AI is not about artificial intelligence; it is about actual implementation. So people physically can implement things in a way they can make data to decisions. So their decision mean I want to make an adjustment. I want to find out how much I should adjust. Physically, I can see the gap. I can input the adjustment level.

The system will tell me physically how could I improve 5%. Wow, that's good. I made a 5% improvement. Your boss also knows. And your paycheck got the $150 increase this month. Why? Because my contribution to the process quality improved, so I got the bonus. That's real-world feedback.

TROND: Let me ask you one last question about how this is going to play out; I mean, in terms of how the skilling of workers is going to allow this kind of process. A lot of people are telling me about the ambitions that I'm describing...and some of the guests on the podcasts and also the Tulip software platform, the owner of this podcast, that it is sometimes optimistic to think that a lot of the training can just be embedded in the work process. That is obviously an ideal.

But in America, for example, there is this idea that, well, you are either a trained worker or an educated worker, or you are an uneducated worker. And then yes, you can learn some things on the job. But there are limits to how much you can learn directly on the job. You have to be pulled out, and you have to do training and get competencies.

As you're looking into the future, are there these two tracks? So you either get yourself a short or long college degree, and then you move in, and then you move faster. Or you are in the factory, and then if you then start to want to learn things, you have to pull yourself out and take courses, courses, courses and then go in? Or is it possible through these AI-enabled training systems to get so much real-time feedback that a reasonably intelligent person actually never has to be pulled out of work and actually they can learn on the job truly advanced things?

So because there are two really, really different futures here, one, you have to scale up an educational system. And, two, you have to scale up more of a real-time learning system. And it seems to me that they're actually discrepant paths.

JAY: Sure. To me, I have a framework in my book. I call it the four P structure, four P. First P is principle-based. For example, in Six Sigma, in lean manufacturing, there's some basic stuff you have to study, basic stuff like very simple fishbone diagram. You have to understand those things. You can learn by yourself what that is. You can take a very basic introduction course. So we can learn and give you a module. You can learn yourself or by a group, principle-based.

The second thing is practice-based. Basically, we will prepare data for you. We will teach you how to use a tool, and you will do it together as a team or as individual, and you present results by using data I give to you, the tool I give to you. And it's all, yeah, my team A presented. Oh, they look interesting. And group B presented, so we are learning from each other.

Then after the group learning is finished, you go back to your team in the real world. You create a project called project-based learning. You take a tool you learn. You take the knowledge you learn and to find a project like a Six Sigma project you do by yourself. You formulate. And then you come back to the class maybe a few weeks later, present with a real-world project based on the boss' approval.

So after that, you've got maybe a black belt but with the last piece professional. Then you start teaching other people to repeat the first 3ps. You become master black belt. So we're not reinventing a new term. It really is about a similar concept like lean but more digital space. Lean is about personal experience, and digital is about the data experience is what's the big difference.

TROND: But either way, it is a big difference whether you have to rely on technological experts, or you can do a lot of these things through training and can get to a level of aptitude that you can read the signals at least from the system and implement small changes, perhaps not the big changes but you can at least read the system.

And whether they're low-code or no-code, you can at least then through learning frameworks, you can advance, and you can improve in not just your own work day, but you can probably in groups, and feedbacks, and stuff you can bring the whole team and the factory forward perhaps without relying only on these external types of expertise that are actually so costly because they take you away. So per definition, you run into this; I mean, certainly isn't worry-free because there is an interruption in the process.

Well, look, this is fascinating. Any last thoughts? It seems to me that there are so many more ways we can dig deeper on your experience in any of these industrial contexts or even going deeper in each of the frameworks. Is there a short way to encapsulate industrial AI that you can leave us with just so people can really understand?

JAY: Sure.

TROND: It's such a fundamental thing, AI, and people have different ideas about that, and industry people have something in their head. And now you have combined them in a unique way. Just give us one sentence: what is industrial AI? What should people leave this podcast with?

JAY: AI is a cognitive science, but industrial AI is a systematic discipline is one sentence. So that means people have domain knowledge. Now we have to create data to represent our domain then have the discipline to solve the domain problems. Usually, with domain knowledge, we try with our experience, and you and I know; that's it. But we have no data coming out. But if I have domain become data and data become discipline, then other people can repeat our success even our mistake; they understand why. So eventually, domain, data, discipline, 3 Ds together, you can make a good decision, sustainable and long-lasting.

TROND: Jay, this has been so instructive. I thank you for spending this time with me. And it's a little bit of a never-ending process.

JAY: [laughs]

TROND: Industry is not something that you can learn it and then...because also the domain changes and what you're doing and what you're producing changes as well. So it's a lifelong --

JAY: It's rewarding.

TROND: Rewarding but lifelong quest.

JAY: Yeah. Well, thank you for the opportunity to share, to discuss. Thank you.

TROND: It's a great pleasure.

You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Industrial AI. And our guest was Professor Jay Lee from University of Cincinnati. In this conversation, we talked about how AI in industry needs to work every time and what that means.

My takeaway is that industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars.

If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and is empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co.

Please share this show with colleagues who care about where industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Jay Lee.

Episode 96: The People Side of Lean

7 september 2022 | 50 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

The topic is "The People Side of Lean." Our guest is Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talk about how to develop internal organizational capability and problem-solving skills on the frontline.

If you liked this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 84 on The Evolution of Lean.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Lean is about motivating people to succeed in an industrial organization more than it is about a bundle of techniques to avoid waste on a factory production line. The goal is to have workers always asking themselves if there is a better way.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is the People Side of Lean. Our guest is Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talk about how to develop internal organizational capability, problem-solving skills on the frontline.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Jeffrey, how are you? Welcome to the podcast.

JEFFREY: Thank you.

TROND: So I think some people in this audience will have read your book or have heard of your book and your books but especially the one that I mentioned, Toyota. So I think we'll talk about that a little bit. But you started out as an engineering undergrad at Northeastern, and you got yourself a Ph.D. in sociology. And then I've been reading up on you and listening to some of the stuff on the musical side of things. I think we both are guitarists.

JEFFREY: Oh, is that right?

TROND: Yeah, yeah, classical guitar in my case. So I was wondering about that.

JEFFREY: So I play also a classical guitar now. I played folk and rock earlier when I was young. But for the last more than ten years, I've been only studying classical guitar.

TROND: Well, so then we share a bunch of hours practicing the etude, so Fernando Sor, and eventually getting to the Villa-Lobos stuff. So the reason I bring that up, of course, beyond it's wonderful to talk about this kind of stuff with, you know, there aren't that many classical guitarists out there. But you said something that I thought maybe you could comment on later. But this idea of what happened to you during your studies of classical guitar actually plays into what you later brought into your professional life in terms of teaching you something about practicing in particular ways. So I hope you can get into that.

But obviously, you've then become a professor. You are a speaker and an advisor, and an author of this bestseller, The Toyota Way. Now you run some consulting. And I guess I'm curious; this was a very, very brief attempt at summarizing where you got into this. What was it that brought you into manufacturing in the first place? I mean, surely, it wasn't just classical guitar because that's not a linear path. [laughs]

JEFFREY: No. So for undergraduate, I had basically studied industrial engineering because I didn't really know what I wanted to do with my life. And my father was an engineer. And then I literally took a course catalog and just started reading the descriptions of different kinds of engineering. And industrial engineering was the only one that mentioned people. And in theory, industrial engineering is a systems perspective which integrates people, materials, methods, machines, the four Ms.

And in the description from Northeastern University, they said it's as much about human organization as it is about tools and techniques. So that appealed to me. When I got to Northeastern...I was not a particularly good high school student. So I didn't have a lot of choices of what colleges I went to, so Northeastern was pretty easy to get into. But they had a cooperative education program where you go to school, and you work. You go back and forth between school and work and had a pretty elaborate system for setting you up with jobs.

I got one of the better jobs, which was at a company called General Foods Corporation at the time, and they make things like Jell-O, and Gravy Train dog food, and Birds Eye vegetables, and a lot of other household names, Kool-Aid, all automated processes, even at that time in the 1970s. And they had been experimenting with something called socio-technical systems, which is supposed to be what I was interested in, which is bringing together the social and technical, which no one at Northeastern University had any interest in except me.

But I was very interested in this dog food plant where they were written up as a case study pioneer. And the basic essence of it was to give groups of people who are responsible, for example, for some automated processes to make a certain line of Gravy Train dog food, give them responsibility for all their processes, and they called them autonomous workgroups. And what we try to do is as much as possible, give them all the responsibility so they can work autonomously without having to go and find the engineer or deal with other support functions, which takes time and is kind of a waste.

So that fascinated me. I studied it. I wrote papers about it even in courses where it didn't fit. But the closest I could get to the social side was through sociology courses which I took as soon as I was able to take electives, which was about my third year. And I got to know a sociology professor closely and ultimately decided to get a Ph.D. in sociology and did that successfully, published papers in sociology journals at a pretty high level. And then discovered it was really hard to get a job.

TROND: Right. [laughs]

JEFFREY: And there happened to be an advertisement from an industrial engineering department at University of Michigan for someone with a Ph.D. in a social science and an undergraduate degree in industrial engineering. And I was probably the only person in the world that fit the job. And they were so excited to hear from me because they had almost given up. And I ended up getting that job quickly then getting to Michigan excited because it's a great university.

I had a low teaching load. They paid more than sociology departments. So it was like a dream job. Except once I got there, I realized that I had no idea what I was supposed to be doing [chuckles] because it wasn't a sociology department. And I had gotten away from industry. In fact, I was studying family development and life’s course development, and more personal psychology and sociology stuff. So I was as far away as I could be. So I had to kind of figure out what to do next.

And fortunately, being at Michigan and also being unique, a lot of people contacted me and wanted me to be part of their projects. And one of them was a U.S.-Japan auto study comparing the U.S.-Japan auto industry going at the same time as a study at MIT and Harvard that ultimately led to the book The Machine That Changed the World, which defined lean manufacturing. So this was sort of a competitive program. And they asked me to be part of it, and that's what led to my learning about Toyota. I mean, I studied Toyota, Nissan, Mazda mainly and compared them to GM, Ford, and Chrysler. But it was clear that Toyota was different and special.

And ultimately, then I learned about the Toyota Production System. And from my perspective, not from people in Toyota, but from my perspective, what they had done is really solve the problem of socio-technical systems. Because what I was seeing at General Foods was workers who were responsible for technical process and then were given autonomy to run the process, but there was nothing really socio-technical about it. There was a technical system, and then there was social system autonomous work groups and not particularly connected in a certain way.

But the Toyota Production System truly was a system that was designed to integrate people with the technical system, which included things like stamping, and welding, and painting, which were fairly automated as well as assembly, which is purely manual. And Toyota had developed this back in the 1940s when it was a lone company and then continued to evolve it.

And the main pillars are just-in-time and built-in quality. They have a house, and then the foundation is stable and standardized processes. And in the center are people who are continuously improving. Now, the socio-technical part the connection is that just-in-time for Toyota means that we're trying to flow value to the customer without interruption.

So if what they do is turn raw materials into cars that you drive, then anything that's turning material into a component or car physically is value-added, and everything else is waste. And so things like defects where you have to do rework are waste. And machines are shut down, so we have to wait for the machines to get fixed; that's waste. And inventory sitting in piles doing nothing is waste. So the opposite of waste is a perfect process.

And Toyota also was smart enough, and all that they figured out was more like folk learning or craft learning. It was learning from doing and experience and common sense. And they didn't particularly care about linking it to academic theories or learning from academic theories, for that matter.

So their common sense view is that the world is complicated. Humans are really bad at predicting the future. So the best we can do is to get in the ballpark with what we think is a good process and then run it and see how it fails. And then the failures are what lead to then the connection of people who have to solve the problems through creative thinking. So that was the integration that I did not see before that.

TROND: Just one thing that strikes me...because nowadays, comparing the U.S. or Europe and Asia in terms of business practices, it's sort of like, oh, of course, you have to compare them because they are culturally different. But it strikes me that in the automotive industry, was it immediately really clear to you at the outset that there would be such striking differences between the Japanese and the U.S. auto industry? Or is that actually something that had to be studied? Or was it something that was known, but no one really knew exactly what the differences were?

JEFFREY: So it wasn't like the American auto companies figured out that if they get good at using chopsticks, they'll be good at making cars. They weren't looking for something peculiar in Japanese culture. But they were addressing the more general problem, which was that Japanese companies were making small fuel-efficient cars at low cost with high quality. And none of the American companies could do that. The costs were higher. The quality was terrible compared to Japan. They took a long time to do everything, including developing cars.

So somehow, the Japanese were purported, they weren't convinced this was true, but according to the evidence, the Japanese were purported to be better at just about everything. And the Americans wanted to know why particularly. And at that time, there had been an oil crisis, and there was a demand for small cars. The real question they were interested in is how could they make small cars that were competitive with the Japanese? So they had to understand what the Japanese were doing.

Now, they realized that some of what the Japanese were doing were purely technical things that had nothing to do with culture. And then there was also a level of attention to detail and motivation that maybe was, for some reason, peculiar to Japan. But they needed to figure out how to replicate it in the United States.

And then, in addition to that, they had Americans like Dr. Deming, who had gone to Japan and taught the Japanese supposedly quality control methods. And Japanese companies had taken quality control methods that were created in the United States more seriously than the American companies. So part of it was relearning what came from America to Japan and got done better. So it wasn't necessarily this kind of strange place, and how can we emulate this strange culture?

TROND: Right. But that becomes then your challenge then, right? Because what you then discover is that your field is immensely important to this because what you then went on to do is...and I guess part of your consulting work has been developing internal organizational capability. These are skills that particular organizations, namely Toyota, had in Japan. So you're thinking that this then became...it's like a learning process, the Japanese learned some lessons, and then the whole rest of the automotive industry then they were trying to relearn those lessons. Is that sort of what has been happening then in the 30 years after that?

JEFFREY: Yeah, the basic question was, why are they so good? Why are we so bad? And how can we get better in America? Then there were lots of answers to that question coming from different people in different places. My particular answer was that Toyota especially had developed a socio-technical system that was extremely effective, that was centered on people who were developed to have the skills of problem-solving and continuous improvement. And while the study was going on, they were doing a study out of MIT that led to The Machine That Changed the World.

And around that same time, a joint venture between Toyota and General Motors had been formed called NUMMI. It was in California. And in their first year, it was launched in 1983, and in the first year, they had taken what was the worst General Motors plant in the world, with the worst attendance, the worst morale, workers who were fighting against supervisors every day, including physically fighting with them, terrible quality, and General Motors had closed the plant because it was so bad.

And then, in the joint venture, they reopened the plant and took back 80% of the same workers who were like the worst of the worst of American workers. And within a year, Toyota had turned the plant around so that it was the best in North America with the best workers.

TROND: That's crazy, right? Because wouldn't some of the research thesis in either your study or in the MIT study, The Machine That Changed the World, would have to have been around technology or at least some sort of ingenious plan that these people had, you know, some secret sauce that someone had? Would you say that these two research teams were surprised at finding that the people was the key to the difference here or motivating people in a different way?

JEFFREY: Well, frankly, I think I probably had a better grasp that people were really the key than most other researchers because of my background and my interest in human-centered manufacturing. So I was kind of looking for that. And it was what the Toyota people would say...whenever they made a presentation or whenever you interviewed them, they would say, "People are kind of distracted by the tools and methods, but really at the center are people."

And generally, most people listening to them didn't believe it, or it didn't register. Because Toyota did have cool stuff, like, for example, something called a kanban system, which is how do you move material around in the factory? They have thousands of parts that have to all be moved and orchestrated in complicated ways. And Toyota did it with physical cards.

And the concept was a pulse system that the worker; when they see that they're getting low on parts, they take a card and they post it. They put it in a box, and then the material handler picks it up. And they said, okay, they need another bin of these. On my next route, I'll bring a bin of whatever cards I get.

So they were replenishing the line based on a signal from the operator saying, "I need more." So it was a signal from the person who knows best what they need. And it also, from Toyota's point of view, put the employee in the driver's seat because now they're controlling their supply in addition to controlling their work process. And it didn't require that you predict the future all the time because who knows what is happening on the line and where they're backed up, and where they maybe have too many parts, and they don't need more? But the worker knows. He knows when he needs it and when he doesn't.

It was kind of an ingenious system, but the fact that you had these cards moving all over the factory and thousands of parts are moving just to the right place at the right time based on these cards, that was fascinating. So a lot of the consumers were more interested in that than they were in the people aspect, even though Toyota kept talking about the people aspect.

TROND: But so this is my question, then there was more than one element that they were doing right.

JEFFREY: There were multiple elements, yeah.

TROND: There were multiple elements. Some of them were structural or visual, famously.

JEFFREY: Right.

TROND: But you then started focusing, I guess, on not just the people aspect, but you started structuring that thinking because the obvious question must have been, how can we do some of this ourselves? And I guess that's my question is once you and the team started figuring out okay, there are some systematic differences here in the way they motivate people, handle the teams, but also structure, honestly, the organizational incentives minute by minute, how then did you think about transferring this? Or were you, at this point, just really concerned about describing it?

JEFFREY: Like I said, I was kind of unusual in my background, being somewhere between industrial engineering and sociology and being in industrial engineering departments. So maybe I wasn't as constrained by some of the constraints of my academic colleagues. But I never believed this whole model that the university gathers information structures that formulates it, then tells the world what to do. I never thought that made any sense. And certainly, in the case of lean, it didn't, and it wasn't true.

So the way that companies were learning about this stuff was from consultants, largely, and from people who had worked for Toyota. So anybody who had worked for Toyota, even if they were driving a forklift truck, in some cases, suddenly became a hot commodity. I consulted to Ford, and they were developing the Ford Production System.

They were using a consulting firm, and all their consulting firm's business was to poach people from Toyota and then sell them as consultants to other companies. And that company literally had people every day of the week who were in their cars outside the gates of Toyota. And as people came out, they would start talking to them to try to find people that they could hire away from Toyota.

TROND: It's funny to hear you talking about that, Jeff, right? Because in some way, you, of all people, you're a little bit to blame for the fame of Toyota in that sense. I mean, you've sold a million books with The New Toyota --

JEFFREY: Well, that was --

TROND: I'm just saying it's a phenomenon here that people obsess over a company, but you were part of creating this movement and this enormous interest in this. [laughs]

JEFFREY: I didn’t feel that that was...I personally had a policy because I had a consulting company too. So I personally had a policy that I would not hire somebody away from Toyota unless they were leaving anyway. That was my personal policy. But the important point was that there were a lot of really well-trained people coming out of Toyota who really understood the whole system and had lived it. And they could go to any other company and do magic, and suddenly things got better. [laughs]

And what they were doing was setting up the structures and the tools, and they also were engaging the people and coaching the people. They were doing both simultaneously, and that's how they were trained. Toyota had sent an army of Japanese people to America. So every person who was in a leadership position had a one-on-one coach for years, a person whose only reason for being in the United States was to train them. So they got excellent training, and then they were able to use that training.

And then other people once they had worked with a company and then that company got good at lean, then, within that company, you'd spawn more consultants change agents. Like, there was a company that I was studying called Donnelly Mirrors that made exterior mirrors for cars. And one of the persons that was trained by a Toyota person became a plant manager. And he ended up then getting offered a job as the vice president of manufacturing for Merillat Kitchen Cabinets. And now he's the CEO of the parent company that owns Merillat. And he's transformed the entire company.

So little by little, this capability developed where most big companies in the world have hired people with lean experience. Sometimes it's second generation, sometimes third generation. And there are some very well-trained people. So the capability still resides within the people. And if you have someone who doesn't understand the system but they just set up a kanban system or they set up quality systems, and they try to imitate what they read in a book or what they learned in a course; usually, it doesn't work very well.

TROND: Well, that was going to be my next question. Because how scalable is this beyond the initial learnings of Toyota and the fact that it has relied so heavily on consulting? Because there is sort of an alternate discourse in a lot of organizational thinking these days that says, well, not just that the people are the key to it but actually, that as a leader, however much you know or how aware you are of people processes, it is the organization itself that kind of has to find the answers.

So there's perhaps some skepticism that you can come in and change a culture. Aren't there organizations that have such strong organizational practices, whether they are cultural in some meaningful way or they're simply this is the way they've done things that even one person who comes in has a hard time applying a Toyota method? What do you think about that kind of challenge?

JEFFREY: Okay, so, anyway, I think what you said is...how I would interpret it is it’s a gross oversimplification of reality. So first of all, in the second edition of The Toyota Way, because I realized from the first edition, which was fairly early back in the early 2000s, I realized that some people were taking my message as copy Toyota, even though I didn't say that in the book. And I specifically said not to do that, but I said it in the last chapter.

So I put out the second edition a year ago, and I say it in the first page or first few pages. I say, "Don't copy Toyota," and explain why. And then, throughout the book, I say that, and then, in the end, I say, "Develop your own system." So it's probably repeated a dozen times or more with the hope that maybe somebody would then not ask me after reading it, "So, are we supposed to copy Toyota?"

So the reason for that is because, as you said, you have your own culture. And you're in a different situation. You're in a different industry. You're starting in a different place. You're drawing on different labor. You have maybe plants around the world that are in different situations. So the other thing I said in the book, which is kind of interesting and counterintuitive, is I said, "Don't copy Toyota; even Toyota doesn't copy Toyota."

TROND: So what does that mean? Did they really not?

JEFFREY: What it means is that...because Toyota had this dilemma that they had developed this wonderful system in Japan that worked great, but they realized that in auto, you need to be global to survive. So when they set up NUMMI, that was the first experiment they did to try to bring their system to a different culture.

And in reality, if you look at some of the cultural dimensions that make lean work in Japan, the U.S. is almost opposite on every one of them, like, we're the worst case. So if you were a scientist and you said, let’s find the hardest place in the world to make this work and see if we can make it work, it would be the United States, particularly with General Motors workers already disaffected and turned off.

So Toyota's perspective was, let's go in with a blank sheet of paper and pretend we know nothing. We know what the total production system is and what we're trying to achieve with it. But beyond that, we don't know anything about the human resource system and how to set it up. And so they hired Americans, and they coached them. But they relied a lot on Americans, including bringing back the union leader of the most militant union in America. They brought him back.

TROND: Wow.

JEFFREY: And said, "You're a leader for a reason. They chose you. We need your help. We're going to teach you about our system, but you need to help make it work." So that created this sort of new thing, a new organizational entity in California. And then what Toyota learned from that was not a new solution that they then brought to every other plant, whether it was Czechoslovakia, or England, or China. But rather, they realized we need to evolve a cultural system every time we set up a plant, starting with the local culture. And we need to get good at doing that, and they got good at doing it.

So they have, I don't know, how many plants but over 100 plants around the world and in every culture you can imagine. And every one of them becomes the benchmark for that country as one of their best plants. And people come and visit it and are amazed by what they see. The basic principles are what I try to explain in The Toyota Way. The principles don't change. At some level, the principle is we need continuous improvement because we never know how things are going to fail until they fail. So we need to be responding to these problems as a curse. We need people at every level well trained at problem-solving.

And to get people to take on that additional responsibility, we need to treat people with a high level of respect. So their model, The Toyota Way, was simply respect for people and continuous improvement. And that won't change no matter where they go. And their concept of how to teach problem-solving doesn't change. And then their vision of just-in-time one-piece flow that doesn't change, and their vision of building in quality so that you don't allow outflows of poor quality beyond your workstation that doesn't change.

So there are some fundamental principles that don't change, but how exactly they are brought into the plant and what the human resource system looks like, there'll be sort of an amalgam between the Japanese model and the local model. But they, as quickly as possible, try to give local autonomy to people from that culture to become the plant managers, to become the leaders. And they develop those people; often, those people will go to Japan for periods of time.

TROND: So, Jeff, I want to move to...well, you say a lot of things with Toyota don't change because they adapt locally. So my next question is going to be about future outlook. But before we get there, can we pick up on this classical guitar lesson? So you were playing classical guitar. And there was something there that, at least you said that in one interview that I picked up on, something to do with the way that guitar study is meticulous practice, which both you and I know it is. You literally will sit plucking a string sometimes to hear the sound of that string. I believe that was the example.

So can you explain that again? Because, I don't know, maybe it was just me, but it resonated with me. And then you brought it back to how you actually best teach this stuff. Because you were so elaborate, but also you rolled off your tongue all these best practices of Toyota. And unless you either took your course or you are already literate in Toyota, no one can remember all these things, even though it's like six different lessons from Toyota or 14 in your book. It is a lot.

But on the other hand, when you are a worker, and you're super busy with your manager or just in the line here and you're trying to pick up on all these things, you discovered with a colleague, I guess, who was building on some of your work some ways that had something in common with how you best practice classical guitar. What is that all about?

JEFFREY: Well, so, first of all, like I said, the core skill that Toyota believes every person working for Toyota should have is what they call problem-solving. And that's the ability to, when they see a problem, to study what's really happening. Why is this problem occurring? And then try out ideas to close the gap between what should be happening and what is happening. And you can view that as running experiments. So the scientific mindset is one of I don't know. I need to collect the data and get the evidence.

And also, I don't know if my idea works until I test it and look at what happens and study what happens. So that was very much central in Toyota. And they also would talk about on-the-job development, and they were very skeptical of any classroom teaching or any conceptual, theoretical explanations. So the way you would learn something is you'd go to the shop floor and do it with a supervisor.

So the first lesson was to stand in a circle and just observe without preconceptions, kind of like playing one-string guitar. And the instructor would not tell you anything about what you should be looking for. But they would just ask you questions to try to dig deeper into what's really going on with the problems or why the problems are occurring. And the lesson length with guitar, you might be sweating after 20 minutes of intense practice. This lesson length was eight hours.

So for eight hours, you're just on the shop floor taking breaks for lunch and to go to the bathroom and in the same place just watching. So that was just an introductory lesson to open your mind to be able to see what's really happening. And then they would give you a task to, say, double the productivity of an area. And you would keep on trying. They would keep on asking questions, and eventually, you would achieve it. So this on-the-job development was learning by doing.

Now, later, I came to understand that the culture of Japan never really went beyond the craftsman era of the master-apprentice relationship. That's very central throughout Japan, whether you're making dolls, or you're wrapping gifts, or you're in a factory making a car. So the master-apprentice relationship system is similar to you having a guitar teacher. And then, if you start to look at modern psychology leadership books, popular leadership books, there's a fascination these days with the idea of habits, how people form habits and the role of habits in our lives.

So one of my former students, Mike Rother, who had become a lean practitioner, we had worked together at Ford, for example, and was very good at introducing the tools of lean and transforming a plant. He started to observe time after time that they do great work. He would check in a few months later, and everything they had done had fallen apart and wasn't being followed anymore. And his ultimate conclusion was that what they were missing was the habit of scientific thinking that Toyota put so much effort into. But he realized that it would be a bad solution to, say, find a Toyota culture --

TROND: Right. And go study scientific thinking. Yeah, exactly.

JEFFREY: Right. So he developed his own way in companies he was working with who let him experiment. He developed his own way of coaching people and developing coaches inside the company. And his ultimate vision was that every manager becomes a coach. They're a learner first, and they learn scientific thinking, then they coach others, which is what Toyota does.

But he needed more structure than Toyota had because the Toyota leaders just kind of learned this over the last 25 years working in the company. And he started to create this structure of practice routines, like drills we would have in guitar. And he also had studied mastery. There's a lot of research about how do you master any complex skill, and it was 10,000 hours of practice and that idea.

But what he discovered was that the key was deliberate practice, where you always know what you should be doing and comparing it to what you are doing, and then trying to close the gap. And that's what a good instructor will do is ask you to play this piece, realize that you're weak in certain areas, and then give you an exercise. And then you practice for a week and come back, and he listens again to decide whether you've mastered or not or whether he needs to go back, or we can move to the next step.

So whatever complex skill you're learning, whether it's guitar, playing a sport, or learning how to cook, a good teacher will break down the skill into small pieces. And then, you will practice those pieces until you get them right. And the teacher will judge whether you got them right or not. And then when you're ready, then you move on. And then, as you collect these skills, you start to learn to make nice music that sounds good.

So it turns out that Mike was developing this stuff when he came across a book on the martial arts. And they use the term kata, which is used in Japanese martial arts for these small practice routines, what you do repeatedly exactly as the master shows you. And the master won't let you move on until you've mastered that one kata. Then they'll move to the second kata and then third. And if you ask somebody in karate, "How many katas do you have?" They might say, "46," and you say, "Wow, you're really good. You've mastered 46 kata, like playing up through the 35th Sor exercise.

So he developed what he called the improvement kata, which is here is how you practice scientific thinking, breaking it down into pieces, practicing each piece, and then a coaching kata for what the coach does to coach the student. And the purpose of the scientific thinking is not to publish a paper in a journal but to achieve a life goal, which could be something at work, or it could be that I want to lose weight. It could be a personal goal, or I want to get a new job that pays more and is a better job. And it becomes an exploration process of setting the goal.

And then breaking down the goal into little pieces and then taking a step every day continuously toward, say, a weekly target and then setting the next week's target, and next week's target and you work your way up the mountain toward the goal. So that became known as Toyota Kata. He wrote a book called Toyota Kata.

And then, I put into my model in the new Toyota Way; in the center of the model, I put scientific thinking. And I said this is really the heart and soul of The Toyota Way. And you can get this but only by going back to school, but not school where you listen to lectures but school where you have to do something, and then you're getting coached by someone who knows what they're doing, who knows how to be a coach.

TROND: So my question following this, I think, will be interesting to you, or hopefully, because we've sort of gone through our conversation a little bit this way without jumping to the next step too quickly. Because the last question that I really have for you is, what are the implications of all of this? You have studied, you know, Toyota over years and then teaching academically, and in industry, you've taught these lessons. But what are the implications for the future development of, I guess, management practice in organizations, in manufacturing?

Given all that you just said and what you've previously iterated about Toyota's ideas that not a lot of things change or necessarily have to change, how then should leaders go about thinking about the future? And I'm going to put in a couple of more things there into the future. I mean, even just the role of digital, the role of technology, the role of automation, all of these things, that it's not like they are the future, but they are, I guess, they are things that have started to change.

And there are expectations that might have been brought into the company that these are new, very, very efficient improvement tools. But given everything that you just said about katas and the importance of practicing, how do you think and how do you teach preparing for the future of manufacturing?

JEFFREY: And I have been working with a variety of companies that have developed what you might call industry 4.0 technologies, digital technologies, and I teach classes where a lot of the students are executives from companies where in some cases, they have a dual role of lean plus digitalization. So they're right at the center of these two things.

And what I learned going back to my undergraduate industrial engineering days and then to my journey with Toyota, I was always interested in the centrality of people, whatever the tools are. And what I was seeing as an undergraduate was that most of the professors who were industrial engineers really didn't have much of a concept of people. They were just looking at techniques for improving efficiency as if the techniques had the power themselves.

And what I discovered with people in IT, and software development, and the digital movement is often they don't seem to have a conception of people. And people from their point of view are basically bad robots [laughs] that don't do what they're supposed to do repeatedly. So the ultimate view of some of the technologists who are interested in industry 4.0 is to eliminate the people as much as possible and eliminate human judgment by, for example, putting it into artificial intelligence and having the decisions made by computers.

I'm totally convinced from lots of different experiences with lots of different companies that the AI is extremely powerful and it's a breakthrough, but it's very weak compared to the human brain. And what the AI can do is to make some routine decisions, which frees up the person to deal with the bigger problems that aren't routine and can also provide useful data and even some insight that can help the person in improving the process.

So I still see people as the ultimate customer for the insights that come out of this digital stuff, Internet of Things, and all that. But in some cases, they can control a machine tool and make an automatic adjustment without any human intervention, but then the machine breaks down. And then the human has to come in and solve the problem.

So if you're thinking about digitalization as tools to...and sometimes have a closed loop control system without the person involved. But in addition, maybe, more importantly, to provide useful data to the human, suddenly, you have to think about the human and what makes us tick and what we respond to. And for example, it's very clear that we're much better at taking in visual information than text information. And that's one of the things that is part of the Toyota Production System is visual management.

So how can you make the results of what the AI system come up with very clear and simple, and visual so people can respond quickly to the problem? And most of these systems are really not very good. The human user interface is not well designed because they're not starting with the person. And the other thing is that there are physical processes. Sometimes I kind of make a sarcastic remark, like, by the way, the Internet of Things actually includes things.

TROND: [laughs]

JEFFREY: And there's a different skill set for designing machines and making machines work and repairing machines than there is for designing software. There are a lot of physical things that have to go on in a factory, changing over equipment, be it for making different parts. And the vision of the technologists might be we’ll automate all that, which may be true. Maybe 30 years from now, most of what I say about people will be irrelevant in a factory. I doubt it. But maybe it's 100 years from now, but it's going to be a long time.

And there was an interesting study, for example, that looked at the use of robots. And they looked at across the world jobs that could be done by a human or could be done by a robot. And they found that of all the jobs that could be done by a human or a robot, 3% were done by robots, 97%...so this kind of vision of the robots driven by artificial intelligence doing the work of people is really science fiction. It's mostly fiction at this point. At some point, it might become real, but it's got a long way to go.

So we still need to understand how to motivate, develop people. But particularly, the more complex the information becomes and the more information available, the more important it is to train people first of all in problem-solving and scientific thinking to use the data effectively and also to simplify the data because we're actually not very good at using a lot of data. We actually can't handle a lot of bits of data at a time like a computer can. So we need simple inputs that then allow us to use our creativity to solve the problem.

And most of the companies are not doing that very well. They're offering what they call digital solutions, and I hate that term, on the assumption that somehow the digital technology is the solution. And really, what the digital technology is is just information that can be an input to humans coming up with solutions that fit their situation at that time, not generic solutions.

TROND: It's fascinating that you started out with people. You went through all these experiences, and you are directly involved with digital developments. But you're still sticking to the people. We'll see how long that lasts. I think people, from the people I have interviewed, maybe self-selected here on the podcast, people and processes seem enormously important still in manufacturing.

Thank you for your perspective. It's been a very rich discussion. And I hope I can bring you back. And like you said if in X number of years people are somehow less important...well, I'm sure their role will change, will adjust. But you're suspecting that no matter what kind of technology we get, there will be some role, or there should be some role for people because you think the judgment even that comes into play is going to be crucial. Is that what I'm --

JEFFREY: There's one more thing I want to add. If you look at industry 4.0, it'll list these are the elements of industry 4.0, and they're all digital technologies. But there's something that's becoming increasingly popular called industry 5.0, where they're asking what's beyond industry 4.0? Which has barely been implemented. But why not look beyond it? Because we've talked about it enough that it must be real.

Once we kind of talk about something enough, we kind of lose interest in it. We want to go on to the next thing. So none of these things necessarily have been implemented very well and very broadly. But anyway, so industry 5.0 is about putting people back in the center. So I call it a rework loop. Uh-oh, we missed that the first time. Let's add it back in.

TROND: So then what's going to happen if that concludes? Are we going to then go back to some new version of industry 4.0, or will it --

JEFFREY: Well, industry 4.0 is largely a bunch of companies selling stuff and then a bunch of conferences. If you go and actually visit factories, they're still making things in the same way they've always made them. And then there's a monitor that has information on a screen. And the IT person will show you that monitor, and the person on the floor may not even know what it is. But there's a disconnect between a lot of these technologies and what's actually happening on the shop floor to make stuff.

And when they do have a success, they'll show you that success. You know, there's like hundreds of processes in the factory. And they'll show you the three that have industry 4.0 solutions in there. And so it's a long way before we start to see these technologies broadly, not only adopted but used effectively in a powerful way. And I think as that happens, we will notice that the companies that do the best with them have highly developed people.

TROND: Fantastic. That's a good ending there. I thank you so much. I believe you've made a difference here, arguing for the continued and continuing role of people. And thank you so much for these reflections.

JEFFREY: Welcome. Thank you. My pleasure.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was the People Side of Lean. Our guest was Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talked about how to develop internal organizational capability.

My takeaway is that Lean is about motivating people to succeed in an industrial organization more than it is about a bundle of techniques to avoid waste on a factory production line. The goal is to have workers always asking themselves if there is a better way.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 84 on The Evolution of Lean. Hopefully, you will find something awesome in these or in other episodes. And if you do, let us know by messaging us, and we would love to share your thoughts with other listeners.

The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co.

Please share this show with colleagues who care about where industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

Augmented — industrial conversations that matter. See you next time.

Special Guest: Jeffrey Liker.

Episode 95: Smart Manufacturing for All

31 augusti 2022 | 46 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 17 of the podcast (@AugmentedPod), the topic is: Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

After listening to this episode, check out CESMII as well as John Dyck's social profile:

CESMII: (@CESMII_SM) https://www.cesmii.org/
John Dyck: https://www.linkedin.com/in/johnsdyck/

In this conversation, we talked about democratizing smart manufacturing, the history and ambition of CESMII (2016-), bridging the skills gap in small and medium enterprises which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms and controls to radically impact manufacturing performance. We then have the hard discussion of why the US is (arguably) a laggard? John shares the 7 characteristics of future-proofing (interoperability, openness, sustainability, security, etc.). We hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade.

Trond's takeaway: US manufacturing is a bit of a conundrum. How can it both be the driver of the international economy and a laggard in terms of productivity and innovation, all at the same time? Can it all be explained by scale--both scale in multinationals and scale in SMEs? Whatever the case may be, future proofing manufacturing, which CESMII is up to, seems like a great idea. The influx of smart manufacturing technologies will, over time, transform industry as a whole, but it will not happen automatically.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 8 on Work of the Future, episode 5 Plug-and-play Industrial Tech, or episode 9 The Fourth Industrial Revolution post-COVID-19. Augmented--the industry 4.0 podcast.

Transcript:

TROND: Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers.

In Episode 17 of the podcast, the topic is Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

In this conversation, we talked about democratizing smart manufacturing, the history, and ambition of CESMII, bridging the skills gap in small and medium enterprises, which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms, and controls radically impact manufacturing performance. We then have the hard discussion of why the U.S. is, arguably, a laggard. John shares the seven characteristics of future-proofing. And we hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade.

Augmented is a podcast for leaders hosted by futurist, Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday.

Augmented — the Industry 4.0 podcast.

John, how are you today?

JOHN: I'm well, Trond. How are you?

TROND: I'm doing well. I'm looking forward to talking about smart manufacturing. What brought you to this topic, John? We'll get into your background. But I'm just curious.

JOHN: This is my favorite topic, as you probably know. So I appreciate the chance to pontificate a little. I've been at this nexus between IT and OT for the last two decades of my career or more and found over these past two decades that this is one of the most complex pieces of manufacturing period, this sort of unique challenge between the world of operations and the world of IT.

And the work I did at MESA (Manufacturing Enterprise Solutions Association) on the board and as the chairman of the board exposed me to a lot of the great vendors in this ecosystem. And through that work, I found that most of them struggle with the same things. We're all struggling in different ways.

And so the opportunity to take one step back and look at this from a national and a global perspective and try to find ways to address these challenges became a very unique opportunity for me and one that I've enjoyed immensely. And so just the prospect of making a real difference in addressing these challenges as a nation and as an ecosystem has been just a privilege and one that I get really excited about.

TROND: So, John, you mentioned your background. So you've worked in both startups...I think you were raising money for a startup called Activplant, but also, you have worked in large manufacturing for GE and Rockwell, so the big guys, I guess, in a U.S. context for sure. When this institution, C-E-S-M-I-I, CESMII, got started, what was its main objective, and what was the reason why this institution got launched? I guess back in 2016, which is not an enormous amount of time back. Give us a little sense of who took this initiative. And what is the core mission of this organization right now?

JOHN: So Manufacturing USA is the umbrella organization under which these institutes, CESMII being one of them, were created. There are a total of 15 of these institutes, all funded with the exact same business model and funding model, and each of them having a different lens on the specific manufacturing problem that they're addressing. And ours, as the Smart Manufacturing Institute, is directly focused on creating a more competitive manufacturing environment by addressing innovation and research challenges that inhibit manufacturers from doing what they need to do in this fourth industrial revolution.

So our mandate is to cut the cost of implementing smart manufacturing by 50%. Our mandate is to drive energy productivity, energy efficiency. Fundamentally, the agency that funds CESMII is the Department of Energy, which means that our overarching objective is to drive energy productivity as a basic metric.

But we also believe that whether that's a direct challenge meaning addressing energy, performance energy efficiency directly, or an indirect outcome from a more efficient process, or a more effective supply chain, whatever that manufacturing initiative is, that we'll create a better product, a better process that will have direct and indirect impact on energy productivity, which is the connection back to our agency and the source of the funding that we have to accomplish these really important goals.

TROND: And one of the really big identified gaps, also it seems, is this discrepancy between the big and the small industry players. So small and medium enterprises famously in every country is basically...the most of industry is consisting of these smaller players. They're not necessarily startups. They're not necessarily on this growth track to become unicorns. But they are smaller entities, and they have these resource constraints.

Give me a sense of what you're doing to tackle that, to help them out, and to equip them for this new era. And maybe you could also just address...you called smart manufacturing industry 4.0, but I've noticed that that's not a term that one uses much. Smart manufacturing is kind of what you've opted for. So maybe just address that and then get to the small and medium-sized.

JOHN: This is, I think, one of the really important observations that we try to make and the connections that we try to make to say that the status quo, the state of the industry today, Trond, is the result of three or four decades of what we did during the third industrial revolution. We began talking about the fourth industrial revolution many years ago. But we can't just turn that light switch on and assume that overnight everything we do now, despite the cultures we've created, the technologies we've created, the ways of doing things we've created, is now all of a sudden just new and exciting and different, and it's going to create that next wave of productivity.

So when I talk about smart manufacturing and equating it with the fourth industrial revolution, it's truly the characteristics and the behaviors that we anticipate more so than what we're seeing. Because the critical mass of vendors and systems integrators, application and software products in this marketplace still resemble more of industry 3.0 than they do industry 4.0. And it's part of our vision to characterize those two only in the context of trying to accelerate the movement towards industry 4.0 or the fourth industrial revolution. Because it's that that holds out the promise of the value creation that we've been promised for ten decades but really aren't seeing. So that's the way we see the industry 4.0 versus the other concepts that we talk about.

Digital transformation is another important term. All of that happens in the context of some initiative in a manufacturing operation to improve. We've been improving for three or four decades. What's different today? Well, it's not just relabeling [laughs] your portfolio to be industry 4.0 compliant. So anyway, that's a pet topic of ours just to help as a national conversation, as a set of thinking and thought leader organizations and individuals to put the spotlight on that and ensure that we're doing the things that we can to accelerate the adoption, and the behaviors, and the characterizations of what it really means to be industry 4.0. So to your point --

TROND: Yeah, I was just curious. The term revolution anyway is interesting in a U.S. context [laughter] and in any society. So it implies a lot of things, but it also certainly implies a speed that perhaps isn't necessarily happening. So there's all this talk now about how things are speeding up. But as you point out, even if they have some revolutionary characteristics, at the edge, there are some other things that need to happen that aren't necessarily going to happen at the speed of what you might imagine when you use the word revolution. It's not going to turn over like a switch.

JOHN: That's exactly right. Well said, Trond. Manufacturing and bleeding edge never come together in the same sentence, and so it takes time for...and more so on the OT side than the IT side. Right out of the IT world, we have industrial IoT platforms. We have augmented reality. We have powerful AI machine learning tools. But what is the true adoption on the plant floor? Well, that's where the behaviors, and the cultures, and the characteristics of how we've always done things and the reluctance to adopt new things really comes in.

And it's as much a part of the vendor and systems integration ecosystem as it is on the manufacturing side. And that's, again, this whole thing becomes...to drive (I really don't think it's a revolution to your point.) an evolution or accelerate the evolution towards Industry 4.0 requires the ecosystem to get engaged and to recognize these really important things have to change. Does that make sense?

TROND: Yes. A lot of them have to change. And then to these small and medium enterprises, so I've seen a statistic that even in the U.S., it's around 98% of manufacturing. That is an enormous challenge, even for an association like yours. How do you reach that many?

JOHN: Here's an interesting epiphany I had shortly after I came to CESMII and was working through exactly this challenge: how does an organization like ours access and understand the challenges they face and then look at the ecosystem that's there and available to serve them? The epiphany I had was that in my entire career with both big global corporations like Rockwell Automation and General Electric and specifically even the startup organization that I helped raise VC for and venture capital funding for and build and ultimately see acquired; I had never been in a small and medium manufacturing plant environment.

The entire ecosystem is focused on large brands, recognized brands, and enterprises that have the potential for multisite rollouts, multisite implementation. And so the business models, the marketing models, the sales, the go-to-market, the cost of sales, everything in this ecosystem is designed towards the large enterprises called the Fortune 1000 that represent the types of characteristics that any startup, any Global Fortune 500 organization is going to go pursue.

Which then says or leaves us with a really important conversation to say, how can the small and medium manufacturing organizations become part of this dialogue? How can we engage them? What does an ecosystem look like that's there to serve these organizations? And where an implementation organization like a good systems integrator can actually make money engaging in this way.

And so that's where the needs of that ecosystem and our specific capabilities come together. The notion that democratization which is going to help the big manufacturers, and the big vendors, and the big integrators, and the big machine builders, the same things that we can do to cut the cost of deploying smart manufacturing for them, will enormously increase the accessibility of smart manufacturing capabilities for the small and medium manufacturers. And so that's where typically --

TROND: John, let's talk specifics. Let's talk specifics. So smart manufacturing, you said, and I'm assuming it's not just a community effort. You're intervening at the level also of providing a certain set of tools also. So if we talk about sensors, and data, and platforms, and control systems, these are all impacting manufacturing performance.

To what extent can an association like yours actually get involved at that level? Is it purely on the standardization front, sort of recommending different approaches? Or is it even going deeper into layers of technology and providing more than just recommendations?

JOHN: So the short answer is it depends on the domain, and the area of networking, and sensors and controls. Those are areas where longer-term research and investment to drive innovation to reduce the cost of connecting things becomes really important. And that's one of the threads or one of the investment paths that we pursue through what we call roadmap projects where there are longer, larger in terms of financial scope and further out impacts. We're hoping we'll have a dramatic impact on the cost of connecting machines and sensors and variable-frequency drives and motion systems or whatever sort of data source you have in an operation. So that's one track.

The other piece which gets to the actual creation of technologies is more on the data contextualization, data collection, data ingestion side. And you mentioned the word standards. Well, standards are important, and where there are standards that we can embrace and advocate for, we're absolutely doing that.

Part of the OPC Foundation and the standards that they're driving, MQTT and Sparkplug, becomes a really important area as well. And the work that MTConnect is doing to solve many of the same challenges that we believe we need to solve more broadly for a subset of machine classes more in a CNC machine tool side. But this effort, smart manufacturing, is happening today, and it's accelerating today. And we can't wait for standards to be agreed on, created, and achieve critical mass.

So we are investing in a thin but vital layer of technologies that we can drill into if you'd like as a not-for-profit, not to compete in the marketplace but to create a de facto standard for how some of these really important challenges can be addressed, and how as a standard develops and we fund the deployment of these innovations in the marketplace and kind of an innovation environment versus a production environment. Not that they don't turn into production environments, but they start as an innovation project to start and prove out and either fail quickly or scale up into a production environment.

So this idea of a de facto standard is a really important idea for us. That's our objective. And that's what we believe we can build and are building is critical mass adoption for really important ideas. And we're getting support from a lot of the great thought leaders in the space but also from a lot of the great organizations and bodies like, as I mentioned, the OPC Foundation, The Industrial Internet Consortium, the German platform industry 4.0 group responsible in Germany for industry 4.0.

We're working towards and aligning around the same principles and ideas, again, to help create a harmonized view of these foundational technologies that will allow us to accomplish the dramatic reduction of the cost of connecting and extracting information from and contextualizing that information. And then making it available in ways that are far more consistent and compelling for the application vendor.

The bar or the threshold at which an application developer can actually step into the space and do something is in a pretty high space. If you kind of look back, and I know this analogy is probably a little overused, but what it took to build applications for devices and phones, smart devices, and smartphones before Apple and Android became commonplace meant that you had to build the entire stack every single time. And that's where the industry is today. When you sit down in front of a product, you're starting from scratch every time, regardless of the fact that you've created an information model for that paper-converting machine 100 times in 20 different technology stacks.

When I start this project, it's a blank slate. It's a blank sheet of paper every single time. Is that value-add? Is that going to help? No. And yet it requires a tremendous amount of domain expertise to build that. So the notion of standardizing these things, abstracting them from any individual to technology stack, standardizing on them, making them available in the marketplace for others to use that's where democratization begins to happen.

TROND: So what you are about to create is an innovation platform for smart manufacturing. Will that be available then to everybody in the U.S. marketplace? Or is it actually completely open for all of the industry, wherever they reside? And what are the practical steps that you would have to take as a manufacturer if you even just wanted to look into some of the things you were building and maybe plug in with it?

JOHN: So we're not about to build, just a minor detail there. We've been working on this for a couple of years. And we have a growing set of these implementations in the marketplace through the funded projects that we were proud to be able to bring to the marketplace. So the funding, and right now within the scope of what we're doing here as an institute, the funds that we deploy as projects, these grants, essentially mean that we spend these grants, these funds in the U.S. only.

So in the context of what we do here, the smart manufacturing innovation platform, the creation of these profiles, the creation of the apps on top of the platform by our vendor ecosystem and domain experts in this ecosystem those are largely here and exclusively here in the U.S, I should say. So from that perspective, deployments that we have control over in terms of funding are uniquely here in the U.S. What happens beyond that in terms of where they're deployed and how they're deployed, we know we live in a global manufacturing environment. And as our members who want to deploy these capabilities outside of the U.S., those are all absolutely acceptable deployments of these technologies.

TROND: But, John, so all of these deployments are they funded projects so that they're always within involvement of grant money, or is some part of this platform actually literally plug and play?

JOHN: So there are several threads. The projects that we fund are obviously one thread. There's another thread that says any member of ours can use any implementation of our platform or can use our platform and any of the vendors that are here as a proof of concept or pilot, typically lasting 3,4,5,6 months for free of charge. What happens then that leads to the third component is after your pilot, there's one of two things that's going to happen. The system will be decommissioned, and you ideally, well, I shouldn't say ideally...you fail fast, the system is decommissioned, and folks move on.

Ideally, the pilot was a success. And that generates a financial transaction for the parties involved in that. And that organization moves towards a production rollout of these capabilities. So CESMII's role then diminishes and steps away.

But this notion of a pilot actually came from a conversation with one of our great members here at Procter & Gamble. They talk about innovation triage and the complexity of just innovating within a large corporate environment like Procter & Gamble. The fact that just to stand up the infrastructure to invite a vendor, several vendors in to stand up their systems costs hundreds of thousands of dollars and takes months and months and months just to get started.

This notion that we can provision this platform in minutes, bring our vendor partner technologies to bear in minutes allows them to execute what they call innovation triage. And it really accelerates the rate at which they can innovate within their corporation, but it's that same idea that we translate back down to small and medium manufacturing, right? The notion that you don't have to have a server. You don't have to sustain a server. You don't have to buy a server to try smart manufacturing in a small and medium manufacturing environment.

If you've got five sensors from amazon.com and lightly industrialized Raspberry Pi, you have the means to begin the smart manufacturing journey. What do you do with that data? Well, there are great partner organizations like Tulip, like Microsoft Excel, even Microsoft Power BI that represent compelling democratized contemporary low-cost solutions that they can actually sustain.

Because this isn't just about the cost of acquiring and implementing these systems, as you know. This is also about sustaining them. Do I have the staff, the domain expertise as a small and medium manufacturer to sustain the stuff that somebody else may have given me or implemented here for me? And so that's just as an important requirement for these organizations as the original acquisition and implementation challenges.

TROND: It's so important what you're talking about here, John, because there's an additional concept which is not so pleasant called pilot purgatory. And this has been identified in factories worldwide. It's identified in any software development. But with OT, as you pointed out, with more operational technologies, with additional complications, it is so easy to just get started with something and then get stuck and then decide or maybe not decide just sort of it just happens that it never scales up to production value and production operations.

And it seems like some of the approaches you're putting on the table here really help that situation. Because, as you mentioned, hundreds of thousands of dollars, that's not a great investment for a smaller company if it leads to a never-ending kind of stop and start experimenting but never really can be implemented on the true production line.

JOHN: Yeah. Spot on, Trond. The numbers that we're seeing now...I think McKenzie released a report a couple of months ago talking about, I think, somewhere between 70% and 80% of all projects in this domain not succeeding, which means they either failed or only moderately succeeded. And I think that's where the term pilot purgatory comes in.

I talk almost every chance I get about the notion that the first couple of decades of the third industrial revolution resulted in islands of automation. And we began building islands of information as software became a little more commonplace in the late '80s and '90s. And then the OTs here in the last decade, we've been building islands of innovation, this pilot purgatory.

The assumption was...and I get back to the journey between where we thought industry 3.0 or the third industrial revolution became the fourth industrial revolution. The idea was that, man, we're just going to implement some of these great new capabilities and prove them out and scale them up. Well, it gets back to the fact that even these pilots, these great innovative tools, were implemented with these old ideas in these closed data siloed ways and characterizations.

And so yeah, everybody's excited. The CEO has visibility to this new digital transformation pilot that he just authorized or she just authorized. And a lot of smart people are involved, and a lot of domain experts involved. The vendors throw cash at this thing, and the systems integrators, implementers, throw cash at this thing. And even if they're successful, and broadly, as an individual proof of concept, there are points of light that say, we accomplished some really important things.

The success is not there, or the success isn't seeing that scaled out, and those are the really nuanced pieces that we're trying to address through this notion of the innovation platform and profiles. The notion that interoperability and openness is what's going to drive scale, the notion that you don't have the same stovepipe legacy application getting at the same set of data from the same data sources on the shop floor for every unique application, and that there are much more contemporary ways of building standardized data structures that every application can build on and drive interoperability through.

TROND: Yeah, you talk about this as the characteristics of future-proofing. So you mentioned interoperability, and I guess openness which is a far wider concept. Like openness can mean several things. And then sustainability and security were some other of your future-proofing characteristics. Can you line up some of those for us just to give some context to what can be done? If you are a factory owner, if you're a small and medium-sized enterprise, and you want to take this advice right now and implement.

JOHN: Yeah, we've tried as an association, as a consortia, Trond, it's not just CESMII staff like myself who are paid full-time to be here that are focused on identifying and developing strategies for the challenges that we believe will help manufacturing in the U.S. It's organizations that are members here and thought leaders from across the industry that help us identify these really fundamental challenges and opportunities.

And so, as an institute, we've landed on what we call the smart manufacturing first principles. There are seven first principles that we believe characterize the modern contemporary industry 4.0 compliant, if you will, strategy. And just to list them off quickly, because we have definitions and we have content that flushes out these ideas, sort of in order of solve and order of importance for us, interoperability and openness is the first one. Sustainable and energy efficient is the second one, security, scalability, resilient and orchestrated, flat and real-time, and proactive and semi-autonomous.

And so these we believe are the characteristics of solutions, technologies, capabilities that will move us from this world of pilot purgatory and where we've come from as an ecosystem in this third industrial revolution and prepare us for a future-proof strategy whether I'm a small and medium manufacturer that just cares about this one instance of this problem I need to solve, or whether I'm a Fortune 10 manufacturing organization that understands that the mess that we've created over the last 25 years has got to make way for a better future.

That I'm not going to reinvest in a future...not that I can rip and replace anything I've got, but I've got to invest in capabilities moving forward that represent a better, more sustainable, more interoperable future for my organization. That's the only way we're going to create this next wave of productivity that is held out for us as a promise of this new era.

TROND: John, you have alluded to this, and you call it the mess that we've created over the last 25 years. We have talked about the problems of lack of interoperability and other issues. This is not an easy discussion and certainly not in your official capacity. But why is the U.S. a laggard? Because, to be honest, these are not problems that every country has, to a degree, they are but specifically, the U.S. and its manufacturing sector has been lagging. And there is data there, and I think you agree with this. Why is this happening? And are any of these initiatives going to be able to address that short term?

JOHN: So this is probably the most important question that we as a nation need to address, and it's a multifaceted, complex question. And I think the answer is a multifaceted, complex response as well. And we probably don't have time to drill into this in detail, but I'll respond at least at a 30,000 foot-level. Even this morning, I saw a friend of mine sent me a link about China being called out today officially as being a leader in this digital transformation initiative globally, as you've just alluded to.

So, from our perspective, there are a couple of important...and like I said, really understanding why this is the case is the only way we're going to be able to move forward and accelerate the adoption of this initiative. But there are a number of reasons. The reason I think China is ahead is in part cultural, but it's also in part the fact that they don't have much of the legacy that we've built. Most of their manufacturing operations as they've scaled up over the last decade, two decades, really since the World Trade Organization accepted China's entry in this domain, their growth into manufacturing systems has been much, much more recent than ours.

And so they don't have this complex legacy that we do. There are other cultural implications for how the Chinese manufacturing environment adopts technologies. And there's much more of a top-down culture there. Certain leaders drive these activities and invest in these ways. Much of the ecosystem follows. So that's, I'll say, one perspective on how China becomes the leader in this domain very quickly.

Europe is also ahead of the U.S. And I think there are some important reasons why that's the case as well. And a part of it is that they have a very strong cultural connection to the way government funds and is integrated with both the learning and academic ecosystem there in most of Europe as well as with the manufacturing companies themselves. It seems to have become part of their DNA to accept that the federal government can bring these initiatives to the marketplace and then funds the education of every part of their ecosystem to drive these capabilities into their manufacturing marketplace.

We, on the other hand, are a much more American society. We are individualistic. The notion that the government should tell manufacturers what to do is not a well-accepted, [laughs] well-adopted idea here in the U.S. And that's been a strength for many manufacturers, and for many, many years.

The best analogy that I can come up with right now in terms of where we are and where we need to go and CESMII's role in all of this, and the federal government's role in all of this, which I think brings a healthy blend of who we are as a nation and how we work and how we do things here together with a future that's a little more also compatible with these notions of adopting and driving technology forward at scale, is the reality that in 1956, President Eisenhower convinced Congress to fund the U.S. Interstate Highways and Defense Act to build a network of interstate highways, a highway network across this country to facilitate much more efficient flow of people and goods across this country.

Apparently, as a soldier, many decades before, he had to travel from San Diego to Virginia in a military convoy that took him 31 days to cross the country [laughs], which is a slight aside. It was apparently the catalyst that drove the passion he had to solve this problem. And that's the role that I think we can play today, creating a digital highway, if you will, a digital catalyst to bring our supply chains together in a much more contemporary and real-time way and to bring our information systems into a modern industry 4.0 compliant environment.

And that's setting those, creating those definitions, defining those characteristics, and then providing the means whereby we can accelerate this ecosystem to move forward. I think that's the right balance between our sense of individualism and how we do things here in the U.S. versus adopting these capabilities at scale.

TROND: That's such a thoughtful answer to my question, which I was a little afraid of asking because it is a painful question. And it goes to the heart, I guess, of what it means to be an American, to be industrial, and to make changes. And there is something here that is very admirable. But I also do feel that the psychology of this nation also really doesn't deeply recognize that many of the greatest accomplishments that have been happening on U.S. soil have had an infrastructure component and a heavy investment from the government when you think about the creation of the internet, the creation of the highway system. You can go even further back, the railways.

All of those things they had components, at least a regulation, where they had massive infrastructure elements to them whether they were privately financed or publicly financed, which is sort of that's sort of not the point. But the point is there were massive investments that couldn't really be justified in an annual budget.

JOHN: That's right.

TROND: You would have to think much, much wider. So instead of enclosing on that end then, John, if you look to the future, and we have said manufacturing is, of course, a global industry also, what are you seeing over this next decade is going to happen to smart manufacturing?

So on U.S. soil, presumably, some amount of infrastructure investment will be made, and part of it will be digital, part of it will be actually equipment or a hybrid thereof that is somewhat smartly connected together. But where's that going to lead us? Is manufacturing now going to pull us into the future? Or will it remain an industry that historically pulls us into the future but will take a backseat to other industries as we move into the next decade?

JOHN: Yeah, that's another big question. We've been talking about smart manufacturing 2030, the idea that smart manufacturing is manufacturing by 2030. And a decade seems like a long time, and for most functions, for most areas of innovation, it is, but manufacturing does kind of run at its own pace. And there is a timeline around which both standardization and technologies and cultures move on the plant floor. And so that's a certain reality. And we were on a trajectory to get there. But ironically, it took a pandemic to truly underscore the value of digital transformation, digital operations, and digital workers, I can certainly say in the U.S. but even more broadly.

So a couple of important data points to back that up. Gartner just recently announced the outcome of an important survey of, I think, close to 500 manufacturing executives here in the U.S. in terms of their strategic perception of digital transformation, smart manufacturing. And I think they specifically called it smart manufacturing.

And it was as close to unanimous as anything they've ever seen; 86% or 87% of manufacturing executives said that now digital transformation, smart manufacturing is the most strategic thing they can invest in. What was it a year ago? It was probably less than half of that. So that speaks to the experience these organizations have gone through. And the reality that as we talk about resilience, some people talk about reshoring, and some of that will happen.

As we talk about a future environment, that's...I shouldn't say disruption-proof but much more capable of dealing with disruption not just within the four walls of the plant or an enterprise but in the supply chain. These capabilities are the things that will separate those that can withstand these types of disruptions from those that can't. And that has been recognized.

And so, as much as these executives are the same ones that are frustrated by pilot purgatory, it's these executives that are saying, "That's the future. We've got to go there." And we're seeing through this pandemic...we hear CESMII are saying the manufacturing thought leaders understand this and are rallying around these ideas more now than ever before to ensure that what we do in the future is consistent with a more thoughtful, more contemporary, future-proof way of investing in digital transformation or smart manufacturing.

TROND: John, these are fascinating times, and you have a very important role. I thank you so much for taking time to appear on my show here today.

JOHN: Trond, I appreciate that. I appreciate the privilege of sharing these thoughts with you. These are profound questions, and answering the easy ones is fun. Answering the hard questions is important. And I appreciate the chance to have this conversation with you today.

TROND: Thanks. Have a great day.

JOHN: You too.

TROND: You have just listened to Episode 17 of the Augmented Podcast with host Trond Arne Undheim. The topic was Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

In this conversation, we talked about democratizing smart manufacturing and the history and ambition of CESMII, bridging the skills gap in small and medium enterprises, which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms, and controls radically impact manufacturing performance. We then have the hard discussion of why the U.S. arguably is a laggard. We heard about two coming initiatives: the Smart Manufacturing Executive Council & the Smart Manufacturing Innovation Platform. We then turned to the future outlook over the next decade.

My takeaway is that U.S. manufacturing is a bit of a conundrum. How can it both be the driver of the international economy and a laggard in terms of productivity and innovation, all at the same time? Can it all be explained by scale, both scale in multinationals and scale in SMEs? Whatever the case may be, future-proofing manufacturing, which CESMII is up to, seems like a great idea. The influx of smart manufacturing technologies will, over time, transform industry as a whole, but it will not happen automatically.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 8 on Work of the Future, Episode 5 on Plug-and-play Industrial Tech, or Episode 9 on The Fourth Industrial Revolution post-COVID-19.

Augmented — the Industry 4.0 podcast.

Special Guest: John Dyck.

Episode 94: Digitized Supply Chain

24 augusti 2022 | 45 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 43 of the podcast (@AugmentedPod), the topic is: Digitized Supply Chain. Our guest is Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT, Johnson & Johnson.

In this conversation, we talk about why J&J puts operators at the center of its strategy, the empowerment effect of frontline operations apps, the evolution of personalized production, and how supply chain becomes an integral part of product development.

After listening to this episode, check out J&J as well as Arun Kumar Bhaskara-Baba's social medial profile:

J&J (@JNJNews): https://www.jnj.com/
Arun Kumar Bhaskara-Baba: https://www.linkedin.com/in/bhaskarababa/

Trond's takeaway: "Operators are the key to the next phase of industrial evolution, that which involves the deep digitalization of manufacturing, its supply chain, production capacity, personalization, and with that the reinvention of factory production itself.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 21, The Future of Digital in Manufacturing, episode 27, Industry 4.0 Tools or episode 10, A Brief History of Manufacturing Software

Augmented--conversations on industrial tech.

Transcript:

TROND: Augmented reveals the stories behind a new era of industrial operations, where technology will restore the agility of frontline workers.

In Episode 43 of the podcast, the topic is Digitized Supply Chain. Our guest is Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson.

In this conversation, we talk about why J&J puts operators at the center of its strategy, the empowerment effect of frontline operations apps, the evolution of personalized production, and how supply chain becomes an integral part of product development.

Augmented is a podcast for leaders hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday.

Augmented — the industry 4.0 podcast.

TROND: Arun, how are you?

ARUN: I'm doing great. How are you, Trond?

TROND: Oh, it's wonderful to see you and hear you. I'm very excited. This is a big interview. You have really big responsibilities, Arun. We're going to get to that in a second. But global manufacturing that is a wide, wide topic.

ARUN: Yes, indeed. But the bigger responsibility, but more importantly, what we are privileged is how we are impacting the lives of patients and customers around the world with our products. That comes with the privilege to work in the healthcare environment.

TROND: Well, I'm glad you said that because as we're sort of tracing, I want to ask you a little bit about how you got to where you are. And I know from public records, at least, that you have part of your schooling in India. So you grew up in India, my assumption is, and you got your computer degree there. You worked in India for a little while for the Tata system. And then you made your way over to Michigan. You have your MBA from there.

And then, from what I understand, you then had a bit of a career in automotive and then moved on to Dell. And this brings us to J&J. How did you end up in the U.S.? And how was that journey for you? You've come quite a bit of ways.

ARUN: Yes. It's interesting that you asked how I ended up in U.S. For me, it was a choice of either going to Japan or to U.S., And I'm a vegetarian, so for me, U.S. was a better choice. Growing up when you're a kid, you have two years of experience, the decisions that you make, some priorities.

TROND: That's funny, but you told me, Arun, that you came here with a briefcase and a $10 bill.

ARUN: Yes. I was going to go --

TROND: That's, I guess, not an unusual immigrant story, but it is still quite striking.

ARUN: Absolutely. I grew up in a very small middle-class family. So when I landed, I landed with a briefcase and a $20 bill, actually two $10 bills. And out of that, one $10 bill I still have as a reminder of where I started.

TROND: Wow. And I cut your career a little short because you have had the opportunity to work in all of the BRIC countries, essentially. And you now manage teams across, I think, at least 28 countries. And that brings us, I guess, up to present day where I was alluding to this, but you have a very wide responsibility. We're going to talk about some of it. Can you tell me a little bit about your current role?

ARUN: So, my team supports all the manufacturing operations for J&J across the globe. So we have 100-plus manufacturing plants in pharmaceutical, consumer, medical devices, and vision products. As I mentioned earlier, I am privileged to be in healthcare to serve our patients and customers. We are in 28 countries; my team is spread across. And it's a very humbling experience to really work in a global team and continue to support our operations across the world.

TROND: Well, not only that 28 countries, but I understand you operate about 100 manufacturing sites, some obviously state of the art, very big and sprawling, others actually very small or at least mid-size and have all kinds of other issues. And J&J, you know, what is the breadth of products you make? I mean, you make vaccines. You make knees, artificial knees. What else do you guys make?

ARUN: This is amazing. I used to work for Ford Motor Company and Dell. Definitely, they are also very strong in manufacturing. However, the manufacturing processes are very similar. It is either assembly process, marketing and manufacturing at Dell. I come to J&J, and any type of manufacturing, you say we got it. Whether you talk about process manufacturing or discrete manufacturing, we have that.

So in the pharmaceutical area, we produce biological products where we actually grow live cells and make medicine out of it, as you mentioned, the vaccines and biological products. We also have big chemical products where we actually use big chemical reactions to produce the drugs. In medical devices, we have artificial knees and hips, which are more like a foundry operation. You take a mold, you put it in an artificial knee, and make it happen. And we have sutures that we produce.

And in the consumer side, we have different types of liquids, gels, and tablets that we produce. And finally, in vision care is where we produce our lenses in a very high-velocity manufacturing. So if you look at the breadth of the manufacturing processes and products we support, we support almost every aspect of manufacturing.

TROND: Well, this brings us to today's topic because we're going to talk a little bit about digitizing these operations, the supply chains, the whole thing, and think about what digital means to all of it, whether it's in pharma 4.0, or indeed, you know, manufacturing and industry 4.0. Can you maybe just kick us off a little bit and say what does digital mean to your business today? And what is your main take on how to approach it?

ARUN: The first thing is really I see digital as a means to an end. So if you think about it, it's really why digital is the first and then why digital. We need to be very clearly understanding why we want to digitize. We are in the journey to transform our supply chain so that we can put our patients, our customers at the center of the supply chain and how we can get our products to our customers in a fast, nimble way and in an affordable way.

If you think about healthcare, the key is affordability as well as the ability for us to deliver what they need where they need it. And if you think about even the vaccines that we are producing now, we are manufacturing only in some locations, but we have to distribute them everywhere, whether to sophisticated networks like U.S. or developing areas where we don't even have a lot of transportation like Africa.

So how do we put the customer and the patient at the center? And how can we actually serve them in a much more faster way and in an affordable way? So that is the why behind our supply chain journey. And digitization is a very critical component of that transformation. How do we provide that end-to-end connectivity so that we can reach our customers and patients? How do we understand what is happening in the markets and react to those things quickly as well as respond quickly using digital?

And then ensure that we are delighting our customers beyond just our products, that we have world-class products. But how do we make sure that we are delivering the same customer experience to our patients and customers? So for us, the work from the digital side is how do we build that end-to-end connectivity so that we can reach our customers and we can sense and respond very quickly? And finally, how do we make sure that we significantly improve your customer experience?

TROND: I want to pick up on a couple of things, but let me first ask a basic question. I mean, when I think supply chain, I think back to business school where I was teaching for a while, and I think kind of a fairly dry subject that was a specialty subject. You either cared about it, and then you wanted to become an expert and obviously dominate the field.

But now you're speaking of it as if it is a much more integrated part of product development, which I think that was certainly taught as two separate courses, even in the very immediate past. But do you think of the supply chain as completely integrated with what you do, what you produce?

ARUN: Absolutely. If you think about where the healthcare is headed, if you think about personalized healthcare, if I'm taking a knee right now, we ship like six or seven knees to the surgeons so that they pick the right knee during the operation. And we are getting to a place where we take the picture of the knee, get it back, and make the product, and then 3D print it and give it to the surgeon.

Or if you think about how we are personalizing where we are taking the blood from the patient and making the product that is very specific to the patient and shipping it to them. So this whole flow of here is my R&D, and then it goes to supply chain, and then we deliver it versus it is now becoming a connected world where this all comes together.

So it's really a very integrated part of product development and supply chain. So we really look at that end to end. And then digital is the one that is actually accelerating that journey. Because I can now connect all of these things as a digital thread and then really push the envelope forward.

TROND: But producing for a batch of one, I mean, it's enormously challenging at scale, no?

ARUN: Yeah, absolutely. That is the trick, right? How do I produce that batch of one? And if you think about the future, where we can actually get to that and where we can produce batch of one for almost everything that we do is where we are headed. You're right; there are significant investments in terms of our manufacturing operations and the equipment that we need. And there is that balance between the scale that you need to have versus the personalization that is needed.

And the balance is I don't think the pendulum can go either one way or the other. But really, we still have a lot more to move to the personalized level. How do we really become a full supply chain so that we can produce that batch of one wherever possible? And look at that from the customer and patient's angle, right? If you have somebody who has a traumatic surgery going on and they have a bone that we need to fix...and it is not the same from one trauma to another trauma. There you can't come back and say, okay, here is a batch of things that I'm producing, and I'm going to give it to you.

So the customer expectations are also changing. As a patient and as a consumer, their expectations are also changing. And so we are moving to that batch of one. And how do you do it for different products? And how do you do it for different manufacturing processes is going to be tailored to that business model and then the product.

TROND: So another thing that one might assume when we speak about this, because okay, batch of one, but it has to be an advanced system, and it's covering the globe. I mean, historically, if a factory has machinery or systems and digital technologies, it is a very monolithic, massive system. I understand that you have taken at least some care these days to focus on the operators. Why is that so crucial to you? And what does that mean for the kinds of technologies that you're putting into your factories nowadays?

ARUN: So that's a very good question. If you think about where manufacturing is headed so that we can drive that flexibility, that approach so that we can quickly respond, we have to relook at our manufacturing operations. That means they need to be a lot more nimbler and a lot more flexible. And a lot of technologies are emerging, and that's all driving. But for us, at the end of the day, it all comes back to that operator. We are here to serve the operator. We call it #operatorrules.

Because think about this, we can do all these flexible things. We can bring in automation. We can bring in robots and all of it. At the end of the day, there is an operator at the line who is making it happen. So how do we make sure that we put the operator at the center and then create the experience for the operator so that it makes it a lot easier?

If you take any of our plants, the technology is growing very fast. We used to have an ERP system. The operator has to deal with an MES. The operator then has to look at the equipment interface that the equipment provider has given. Now I'm coming from technology and saying, okay, here is the smart glass. Wear the smart glass, and you can look at everything. Think about the operator, how complex we have made the operator's life. So we are trying to take a step back and say, how do we, first of all, make it simple?

Number two is how do we empower them? So far, we all said that, oh, technology is either manufacturing engineering or the OT or IT people. We held the keys for the technology. But how do we really empower the operators so that they can make it flexible and then they can make it nimble? So that gives you the velocity that we need at our manufacturing operations.

TROND: It's striking when you think about at least digital technologies now clearly. There have been machines in factories for centuries. I mean, that was sort of the various industrial revolution. So there have, of course, been machines that could be operated by operators to some degree.

But the kind of control and the detail-level customization that's now becoming possible doesn't come naturally, does it? It takes a lot of attention to create those kinds of platforms. How do you see that evolving? For example, we said you have over 100 different sites, some of them large, others much smaller; what sort of approaches are you taking to experiment with these solutions?

ARUN: So it's purpose-driven experimentation. Because to your point, when we have these large, fully automated factories, the key is how fast I can introduce new capabilities into that operation. Whereas when I go to a middle-tier factory with semi-automated or not as much automated, it is a very target problem-driven. I have an OEE problem. Let me figure out how do I experiment to bring the technology.

But at both the spectrums, the key is to make sure that there is a good, robust architecture principles. There is good, robust security, and then there is a good data architecture. But from a solutions point of view, how do we make sure that these are modular? Think about the mainframe days where you need to know all those to run the application to now you have apps on your device.

So how do we break these monolithic technologies that are running the operations into smaller apps by bite-sized chunks that we can actually deploy very quickly or pull it out? And that gives me the flexibility to say for a large site; I'm going to deploy all these 100 apps so that they can run it as a suite. Whereas when I go to a smaller site, I might only deploy two of those applications for a specific problem. So it's kind of like really breaking down by, number one, by purpose. Number two, having a good consistent architecture. And number three, really breaking these monolithic things into smaller apps and nimble apps that we can drive.

TROND: I know that you've tried some of Tulip's solutions. Tulip is an app system. But clearly, the bar to completely replace any number of advanced technologies that have developed over literally decades is not done overnight. How do you see the journey that app developers on the manufacturing shop floor...what sort of journey are they going to have with you to prove themselves over time to gradually solve many of these very ambitious problems?

I mean, you describe them pretty eloquently, but they're different in each factory, like you pointed out. And we're dealing with operators, some of whom are very advanced and have taken all kinds of industry 4.0 courses and others who have not. So this is a bit of a journey.

ARUN: Yeah, it is a journey, but there are similarities in this journey. If you think about maintenance of the equipment, it used to be a stronghold of those engineers that are sitting somewhere, and they get to the equipment when there is help needed. Look at where we are now. With operator asset care, we are empowering the operators to own that equipment and drive it. So that is the same journey that we have to go through from the digital side.

And the key is, first of all, making sure that we have platforms like Tulip and others that help us to be able to quickly develop those apps, of course, in a very consistent framework. Especially for us when we are in a regulated industry, some of those framework and validation things become extremely critical. How do you set those boundaries?

The second thing is educate the operators so that they feel empowered that they own the work that they are doing, and they can shape it in the way they need to do it and to continue to train them. And then the third level is to really train the rest of the organization. The management and then the operations leaders all need to be digitally savvy to drive that and then see the value. So it is a journey, but you need to be very clear about why we are doing it and putting the operators at the center and helping them.

The thing that is going to help us is this whole COVID pandemic situation. If you think about the digital savvy of almost the entire world, it has significantly improved. Every operator, whether we like it or not, yeah, they might not have a degree, but they know how to order their Uber Eats. They know how to use an app. So we are seeing digital literacy coming up very fast. So this is a great opportunity for us to drive that transformation. But you're right; it is a journey.

TROND: But you also mentioned regulated industry. I mean, to what extent can some of these apps kind of slide in between the cracks and do stuff that was never covered by regulation? And to what extent do you actually need to take very, very good care that you are, I guess, also updating the regulations and knocking on the doors of governments and telling them that "Look, there's an app for this too."? [chuckles] And we need to upgrade the regulatory framework to take that into account. So it seems to be a bit of both.

ARUN: Yes, you absolutely hit the nail on the head. You need to do both. One is, first of all, have a good, robust architecture. That's why the platforms like Tulip will need to ensure that the architecture is robust so that it has enough control so that we can drive this validation and qualification, those things, and giving the parameters of the freedom for the operators within those constraints. And let's not forget cybersecurity, which is a huge thing, especially when we come to the OT cybersecurity as well. And on the other side...sorry.

TROND: No, no, go ahead. On the other side...

ARUN: On the other side, we need to continue with the regulators and work with the regulators to make sure that they understand what we are doing. We are now working with the regulators to educate them on real-time release. How can we actually use the data rather than having to produce these samples and batches as opposed to relying on continuous data that is coming that shows that your process is in compliance?

So working on both sides with the framework so that it is robust as well as regulators to make sure that they understand how the technology is transforming. At the same time, the compliance is improving. Think about it, when you're doing samples, one, you're taking one sample from a batch. But when you're doing continuous sampling, you have the whole sample, whole product batch data you have in your hands. So we'll continue to work with them to make sure that the regulators are also coming with us on that journey.

TROND: How is pharma 4.0 going? I mean, the acronym is the same as industry 4.0. Is 4.0 actually happening, or are we still in 3.0?

ARUN: In pharma-world, I would say we still have 2.0 to 3.12 to 3.33. And there are some great examples where we have the 4.0 when I talk about what we are doing with the personalized solutions when we talk about how we are bringing IoT to the forefront, how we are doing real-time release with digital twins of our whole process. Now we have digital twins, even for bioreactors, which are very difficult to characterize. So yes, the journey is there.

The key is to keep in mind why we are doing it to really make sure that we have the patients that are waiting for our products in mind and then really transform around to support them. So the journey is continuing. Yes, there are very good examples for pharma 4.0. But are we there yet? No. But is everybody working together to get there? Yes.

TROND: Let's talk a little bit about this operator and the training of an operator because training the workforce is something I ask a lot of the people who come on this podcast about just because technology is one thing but training people on the technology to implement it in a fruitful way is a whole other challenge. What approach are you taking at the whole J&J complex when it comes to training your existing future and even training your ecosystem around you?

ARUN: A couple of things there; one is, first of all, making sure that you start with the user experience in mind and design everything from there. So you need to start with the design aspect. The second thing is how do we make it simple? The more simple you make it, the less training. How many people are getting trained on how to use an iPhone? So really, how do we make it simpler?

But actually, in the future, I'm thinking...and this I actually got from one of your podcasts, Trond, is, are we going to get to a point where there is no interface? So can we get our apps to a state where there is no interface, then your training becomes a lot more part of the evolution rather than you have to go; oh, now I need to learn this, and I need...no, it should be so intuitive. It's like gesturing with my hands.

So how do I get to that state? Hopefully, that state comes in soon, as you've been discussing with some of them. But for me, it is really how do we keep on making it so simple that it becomes intuitive? And it starts with the design, where you put the operator at the center and design around the operator.

TROND: Can we talk a little bit more specifically about the digitized supply chain? Because it is such a core to what you're up to. And I know that there are some characteristics that you care about the most one of them I think you mentioned to me was being very responsive. But what are the priorities when you are redesigning a supply chain? What are the kinds of things that are top of mind for you? And where do you start?

ARUN: You start with the customer experience. How do we make sure that that is clear on how it is impacting the customer experience? Now to help with the customer experience, how do we drive that responsiveness in your supply chain so that you can respond very quickly to what is happening at the demand side, the customer side, and then link it back?

Then the next one is really the resiliency. How do we build that resiliency in supply chain so that we can react very quickly? If there is one thing that COVID taught us is that resiliency in our supply chains actually helped the world in one way to survive this pandemic and continue to survive. So how do we drive that resiliency in the supply chain?

TROND: What do you think about these very traditional concepts that have been part of...and, you know, you had the start of your career in automotive. Lean management is something that everybody wanted to copy, and the Toyota processes and a lot from the country you chose not to study in [laughs] essentially because you weren't convinced they were vegetarian enough.

But anyway, what do you think about the heritage from lean and mixed in with some of the agile tradition from software? Is that altogether creating a new paradigm? And what does that look like, and who's describing it? If you would maybe describe where some of your influences come from when you are designing such a large organization around these principles.

ARUN: At the heart, the lean principles and agile principles are still really valid. Like, if you think about lean, what it is saying is think about the floor, eliminate the waste, and continue to improve and zero defects as possible. So that mindset has to be there for us to even look at digital. What digital is doing is actually helping us to implement lean even faster. How do you get there?

Now, from responsiveness, and we talked a lot about the responsiveness, and reacting, and resiliency that requires this agile mindset, this traditional boundaries of I'm going to go from plan, source, make, deliver. This is becoming a network. The only way you can survive in that network is having that agile mindset where we bring people together very quickly, get the problem solved, deliver that MVP, and don't look back and then move on to the next one.

So the agile principles around bringing the teams together very quickly to focus on the key priorities and delivering on the MVP aligned with the lean thinking to make sure that there is no waste and we are really getting the floor done actually is a great combination of these two. And these are the two things that need to come together even for us to roll out the digital solutions very quickly in our operations.

And COVID has been a great example if you think about how we came together to deliver a product for the instruments in a very quick way across the world in a virtual way. It has been a great example that shows that it can be done. So that's where the lean foundations and then the agile mindset are extremely critical, even for us to drive this digital transformation.

TROND: If you think about how this was built, what are some of the best influences that help you along the way? We talked a little bit about startups that bring the app mindset and maybe some of the agile thinking. It doesn't necessarily come from startups, but certainly, it does exist with startups. Where are these industry practices that you are increasingly embodying at J&J? Where do you think they come from?

ARUN: Actually, they come from many places. And for startups, really one of the places where we can actually see how their mindset is there in terms of test and learns, and learning from failure, and more. And even I'm looking at some of the journeys like how companies like Tulip are evolving as well. Especially those companies from a startup to accelerating phase, that's where we are seeing a lot of the learnings that we can learn.

And one of the big things that we at J&J look at is how can we look at our CEO and saying, "Hey, we need to act like a 135-year-old startup."? So how do we actually look at it? And to your point, where we are looking for, we are looking for everywhere; one is really those startups. But more importantly, those startups that got that first phase and are now accelerating, that's where all the processes need to come together.

And then, at the end of the day, we still have to be reliable. And we are in a regulated industry. So how do we make sure that the patient safety, product quality are the top priority and our processes are reliable? That's where the established companies also help us on how we continue to drive that.

TROND: Yeah, because that's what I guess I wanted to drive to because there is an established idea in the established industry to look for industry best practices. And in the manufacturing space, there are these lighthouse projects. Companies on their own might have lighthouse projects that are especially good. And the World Economic Forum has lighthouse factories. In fact, they have designated places around the world where they have tracked and figured out that they are of sufficient quality to put up as inspirational lighthouses for others.

What is your view on how well that works as a practice? For example, you have 100 sites. Is it possible to tell one site to become more like Site A? Because look at site A how well they're doing. Isn't that also a bit of a challenging message to communicate?

ARUN: Yeah.

TROND: No one likes to be like, all right, I understand. [laughs] My golf swing is not up to par, I get it. I need to look at my neighbor over here. It's not always a fantastic message.

ARUN: [laughs] But speaking of that, actually, we have five sites that are lighthouse sites. And we have one that is going to come up with one of the projects that we're working on as well is in one of the sites with Tulip for the lighthouse site. But the thing is, knowledge grows by sharing. The more you share, the more you're going to grow the knowledge and the faster the adoption is going to be. You're absolutely right.

It does not mean that just because this is a lighthouse site, they are at a pedestal, and then everybody else is in another place. I actually look at it the other way around. What did those lighthouse sites do that we can actually copy and paste, so I don't have to reinvent? And then I can focus on something else as well. So the lighthouse sites are helping us to really share that knowledge so that we can learn from one another. We can build on it. And then we eliminate the need for us to redo the things that they have gone through.

But you're absolutely right; that doesn't mean that those are the only sites that are doing everything and everybody else is not. But sometimes, the copycats that are coming behind the lighthouse might be the best of things because they can get lighthouse practices and implement and then really show that they can actually transform their manufacturing operations much more faster.

TROND: Well, and that's true in the history of manufacturing that you can actually leapfrog. It is still a field where if you do many things right, you definitely make a difference. I wanted to shift tact a little bit, Arun, and move to coming years. What are some of the industry developments that you are the most excited about?

So we've talked generally about digital. We've talked about personalization. What are some of the things that are going to be most crucial to get right and even just like in the year ahead? It's been a very...it's been a wild ride in the last 12 to 15 months. What's going to hit us in the next year, and what are you focused on?

ARUN: So let me break it into a few different areas. One is purely from the technology side of it. If we look at how 3D printing is going to evolve and how it is going to help us to change significantly, how the digital twin and digital threads that are coming up fast that we can actually connect. And then, more importantly, how the machine learning and AI models that are coming up that help us to be responding very quickly. So I'm very excited about those areas, how 3D printing is transforming our operations, how we are able to bring digital twins, digital thread, and machine learning to really drive that end-to-end thread all the way to the customer.

The second area is, from a mindset point of view, is how resiliency and responsiveness has become kind of like a norm. If you think about the COVID pandemic, what it has done is how that resiliency and responsiveness has become a norm. So how do we actually drive that and don't lose that as we come out of the pandemic and then go forward?

And the final one is I'm going to go back and harp on the culture side of it. How do we drive that culture where we let operators be empowered and learn from it and let them be the kings? And we also have the operator hashtag #operatorrules. And we support that culture change, the digital change, and which is really going to be accelerated because they are becoming more and more digital savvy. So there is the technology aspect. And there is actually the responsiveness. And finally, how do we drive the digital savvy across the organization?

TROND: So my last question, and I don't know how fair that question is in the context that you're in, because I could imagine that given the amount of factors that are moving at any given moment, very long-term thinking seems perhaps a little farther away from your everyday life. Because there are so many things that could go wrong literally every minute.

But if you permit yourself and me to think a little bit longer term, towards the next decade, are these things on the digital side, you know, digital twins, and AI, and machine learning, and 3D printing, as this decade moves to a close, are there other things on your horizon as well that will even more drastically transform the landscape? I mean, are digital factories going to be really coming into the scene and really transforming the way?

Are we going to recognize a factory even in the next decade? Or am I kind of overblowing this, and things are just fairly complicated, and it's going to take quite a long time to shake out and integrate all these technologies with all of the workforce challenges and cultural challenges that you just pointed out?

ARUN: Imagining the future, first of all, I really love the idea of almost no interface, intuitive use of technology. Can we get to that? That's one. The second thing is, yes, there will still be big manufacturing areas. Some of them are tied to the physics and biology, so we cannot change, but everything else can actually significantly change. And if you think about can we actually do a factory in a box very quickly for vaccine production in a developing world that cannot afford and we deploy it very quickly?

So will we get to a point where it becomes more of Lego blocks that we can assemble very quickly and get it up and running and everything has an equal and digital model that we really don't have to worry about it? It is not about the digital twin of my operations. But if I take the digital twin of my patient's body and the digital twin of operations, think about how easy it is for me to actually respond to that personalized request or personalized medicine.

Since you let me imagine and let my thoughts flow a little bit more broadly, it's really bringing the digital equivalence. So can I actually take my digital equal and to respond to the digital twin to get the personalized product for me either in a batch of 1 or even maybe a batch of 10 if batch of 1 is not possible? So the factories of the future, yes, some of them might not significantly change, but most of them will be that flexible way to bring them together for specific product or specific customer and being able to re-assemble very quickly to do something else.

And then the intelligence, can it move to the equipment so that the equipment itself can rearrange itself based on the customer base? But then, what is the implication to the workforce? And what is the implication to the operators? So this way of getting those operators to be a lot more digital savvy and really helping to manage this complexity will be a great foundation. But at the same time, that is something that we all need to watch. Yes, all of this can happen. But we need to watch for how do we bring our people together?

TROND: Yeah, and I could just imagine putting myself back in my old government days, scratching my head about self-regulating systems in the medical field, right? [laughs]

ARUN: Yes.

TROND: That would seem to be a little bit of a challenge as well. So there are so many interesting challenges. But it seems to me that even if you are occupied every minute with operational challenges and even just digitizing a supply chain without fundamentally changing its logic, it's going to take all men and women on deck. It's a cultural challenge. It is not just a technology challenge.

ARUN: Absolutely. It is. It is a cultural challenge.

TROND: Well, look, it's been fascinating to hear, and I hope I can check back in with you. It seems to me that if we had had this interview just even just 15 months ago, some of these challenges might have looked a little bit less rosy, and we wouldn't have been discussing about the next decade. I'm assuming that a lot of things for you in your business have really, I guess, opened up throughout this pandemic. Is that right?

ARUN: Yeah.

TROND: Some of these opportunities just weren't there before.

ARUN: Absolutely. A lot of the acceleration...first of all, we are privileged to serve our patients. And we have a big part in helping the world get through the pandemic, our vaccine. And even how we have brought in digital twin into our vaccines in a very faster way was enabled by the pandemic situation.

The whole digital acceleration of some of our solutions that were sitting on the shelf for almost six to nine months, the demand for them grew up within the first few months of the pandemic. So the digital acceleration of our operations has happened. The third thing, as I said earlier, is the digital savvy of our day-to-day citizen is helping us to bring these much more faster to our patients and customers around the world.

TROND: That's a very interesting statement. Because when you cannot innovate faster than your end client, then you're really dealing with the total ecosystem here. You actually depend on your end client to be caught up with all of these technologies. It's a fascinating challenge and probably very important too because there isn't a little bit of an insurance policy there, no Arun. Because if you cannot be more advanced than your end user is, at least you have the time to, or you have to take the time to educate the end user and get their real feedback on what needs to happen.

So that leaves me on an optimistic note, and if you have any last statement...I certainly thank you for your time. And if you have a last challenge, you know, there are so many challenges where you could launch, but if you think to your fellow industry executives, what is the one thing maybe you want to leave them with what you think is a shared challenge that people should focus more on in industry these days?

ARUN: Keep the operator at the center #operatorrules. Let's make sure that we empower them. We help them to be as digitally savvy as possible. That will actually help us to move these needles much more faster.

TROND: Arun, I thank you so much. It's been a pleasure. And I hope I can invite you back someday.

ARUN: Definitely. It has been great, Trond.

TROND: You have just listened to Episode 43 of the Augmented Podcast with host Trond Arne Undheim. The topic was Digitized Supply Chain. Our guest was Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson. In this conversation, we talked about why J&J puts operators at the center of its strategy.

My takeaway is that operators are the key to the next phase of industrial evolution that which involves the deep digitalization of manufacturing, its supply chain, the production capacity, personalization, and with that, the reinvention of factory production itself.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 21: The Future of Digital in Manufacturing, Episode 27: Industry 4.0 Tools, or Episode 10: A Brief History of Manufacturing Software.

Augmented — conversations on industrial tech.

Special Guest: Arun Kumar Bhaskara-Baba.

Episode 93: Industry 4.0 Tools

17 augusti 2022 | 46 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 27 of the podcast (@AugmentedPod), the topic is: Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director, Industry 4.0 at Stanley Black & Decker.

In this conversation, we talk about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

After listening to this episode, check out Stanley Black & Decker (@StanleyBlkDeckr): https://www.stanleyblackanddecker.com/ as well as Carl B. March's profile on social media: https://www.linkedin.com/in/carlbmarch/

You may want to also be aware of the 'Israel meets New England' smart manufacturing event on June 9 and its organizers, the Israeli Trade Mission and Amhub New England:

The New England Advanced Manufacturing Hub (AMHUB NE): https://mfg.works/amhub/amhub-new-england/
The Government of Israel’s Economic Mission to North America: https://embassies.gov.il/washington/AboutTheEmbassy/Pages/Economic-Mission.aspx#:
ISRAEL meets NEW ENGLAND: Advanced Manufacturing in Factories and Workplace: https://mfg.works/israel-meets-new-england/

Trond's takeaway: Industry 4.0 requires a mindset shift, not just technology adoption. It's not just about you--whether you in this case is a big company or a top leader--rather, it is about bringing people, partners, SMEs, and the entire ecosystem along. To do so openness to learn, having a strategic roadmap so not chase all shiny objects, and investing in lighthouse factories that can illuminate the possibilities are each important ingredients.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 20, The Digitalization of Körber, episode 14, Bottom up and Deep Digitization of Operations, and episode 9, The Fourth Industrial Revolution post-COVID-19.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Transcript:

TROND: Augmented reveals the stories behind a new era of industrial operations, where technology will restore the agility of frontline workers.

In Episode 27 of the podcast, the topic is Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director Industry 4.0 at Stanley Black & Decker.

In this conversation, we talk about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

Augmented is a podcast for leaders hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday.

Augmented — the industry 4.0 podcast.

Carl, how are you today?

CARL: I'm doing great, Trond. Good to see you.

TROND: Yeah, this is fantastic. We've spent a lot of time together, Carl. We've gotten to know each other. This industry 4.0 is bringing us together.

CARL: Quite a bit. And there's so much going on in this space, especially here in New England. So it's an exciting time.

TROND: Yeah, for sure. Carl, I wanted to talk a little bit about you and your background. You're an engineer. And now you're deeply steeped in industry 4.0. Maybe I'll just ask that question, why did you become an engineer? And how did you end up where you are right now? Was it an obvious path for you? Or did you always want to go into manufacturing?

CARL: I guess from the beginning, I was always a tinkerer, so just growing up and hanging around mechanical equipment, my desire was always to break and fix. [laughs] So eventually, I got wind of a teacher who, in fact, was my music teacher. And he asked me what did I want to do? I said I wanted to break and fix equipment and all of these things. And he said, "Well, you want to be a mechanical engineer." [laughs] So I kept that with me from maybe nine years old, and that's the path I went. Eventually, I did my first degree in mechanical engineering. And then eventually, I did an automotive systems engineering graduate degree.

TROND: Wow. And so then, in the beginning, you were headed for the automotive industry.

CARL: Yeah, yeah. It was always a desire around cars. So my father had all the cars that needed to be fixed. And where I'm from, we're in the Caribbean. I'm from Jamaica originally. It was one of those luxuries that you had where you just dispose of your vehicles once they start giving some problems. So we fixed the cars. [laughs] That's what we had to do.

TROND: [laughs] So you ended up with a bunch of cars then, not just fixing them, but you ended up with a bunch that are not used.

CARL: [laughs] Exactly. And taking parts from one and putting on the other. [laughs]

TROND: That's funny. That's funny. Well, so you did that for a while. And you were in automotive, which is an exciting field in and of itself. And then you went into consulting for a bit as well. So you've done a little bit of that.

CARL: And so the interesting thing is once I did my first degree, which was mechanical engineering, I had the opportunity to start working in the manufacturing environment. And I actually started off in mining and refining. So I was in alumina refining for a while, and then I went back and did the automotive degree. And then, coming out of that, it was the wonderful time in Detroit where everything was a bit uncertain. So though I started off in automotive there, after that degree, I went back to my roots of reliability engineering, which is more along the lines of operational excellence in the manufacturing environment.

TROND: You know, it's kind of fascinating today because automotive has gone full circle.

CARL: Yes, it really has.

TROND: It's like, nobody...who would have guessed [laughs] that automotive was going to go from glory days to, like, it's all over to a renaissance of mobility?

CARL: I've gotten the opportunity to observe that, especially as a consultant, as I eventually went into consulting. More than half of my 20-plus years in manufacturing has been in the consulting space. So, while consulting, that's where I really started to see many sectors, from the very advanced sectors in aerospace and automotive down to what we call base materials, which is going back to the dirt, the mining and refining pieces. And just seeing the range of technology adoption across all fields as it relates to operational excellence was an eye opener for me.

And when I think about this topic of industry 4.0 which really it's not an old topic. It really came about in 2011 or so, which was the mid of my consulting career. And that's when I made a pivot in my consulting, where I started to focus a lot more on the technology enablement within these respective spaces.

TROND: Well, let's dig deeper into it. Because you're indeed, you know, you're with Stanley Black & Decker. You run a lot of their industry 4.0 activities, especially on the analytics and the value stream side. But let's get into the topic more because, as you said, 2011 is not a long time ago. And I hear industry 4.0, by the way, seems to be more of a European term than an American term. Here it’s like smart manufacturing because manufacturing is the main thing. But at Stanley, you guys somehow chose the international term industry 4.0. Why don't you, for the benefit of all of us, just tell us how you define it? What is --

CARL: So industry 4.0 is this terminology referring to the fourth industrial revolution. So it stems back to the first industrial revolution having to do with mass production and steam being used as a driver. Then eventually, it went into the second, where we started to get some computers in the space and started to be able to take advantage of some of those things. The third having to do more with automation. So we started to put a lot more robots and robotics within the manufacturing space.

And interestingly, then we started to do a little bit more sensorization. But in the 2011 or 2010 period of time, that's when we started to make a lot of advances in big data, cyber-physical systems. So that's where those applications started to come into the manufacturing environment, AI, artificial intelligence, anything related to analytics in the manufacturing environment. That's where we're starting to consider the industry 4.0.

And one other thing, there are probably three main elements that differentiate the fourth industrial revolution from its predecessors; one is vertical integration. Vertical integration is what we call from the top floor to the shop floor. You're able to pass data back and forth and get information on what's happening at any given time, at whatever level it is in your production process.

The second is horizontal integration. And that's where you start to look across your value chain. So you're looking at data coming from your supplier, and data coming from your customer, and data within your own manufacturing environment.

And then the third one is integrated product lifecycle. So this is one of the most interesting pieces of industry 4.0 in that you're actually getting feedback, even though the customer doesn't even know you're getting that feedback. And you're getting feedback into your product lifecycle and your product design. And you're designing it to manufacture well, and you're designing it to basically fulfill the purpose of the end consumer, so all of that feedback loop that's taking place there. And what enables it is a part of what we refer to as industry 4.0.

TROND: That's super interesting. And can you comment a little bit on how that translates then into Stanley Black & Decker's digital transformation journey? Because, arguably, and I meant to have it here, I have, you know, I have a bunch of tools in my arsenal. [laughs] I might actually run and go get that. But they weren't always digital; mine happens to be battery operated. And hopefully, I can run and get it in a second; I really wanted it in this tape.

But it has been a journey for you as well, and I guess it's a continuing journey because sensors and all that stuff take quite a bit to transform an entire kind of suite of products into a set of connected arguably industry 4.0 tools. So I'm curious, where would you say you guys are in that transformation process?

And how ready is the world for a fully sensorized reality where everything is connected? I guess the maximal vision of industry 4.0, which is this idea of industrial Internet of Things where everything is starting to connect and yield analytics. Because you took the...these are also difficult things to do, right? The vertical integration, all of these things are difficult. But this full vision, we are a step away from that so far, this full sensorization.

CARL: Yeah, it has not all become a reality as yet. And as you can imagine, the maturity is going to be different depending on the sector, the industry that you're dealing with. But if I was to look back for a second on the journey that we've had at Stanley Black & Decker, I joined the company maybe about three years ago when we made a very interesting pivot in the way that we were approaching industry 4.0. I'll speak on that in a second.

But prior to that point in time, Stanley Black & Decker has always been an innovator in this space. We do make tools, and we're the number one tools company in the world. But we also serve a lot of our other businesses, automotive and aerospace, in particular, in providing fasteners, et cetera. And as a result of this diversity, it made sense for a company like ours with 100-plus sites to be able to start working in smart manufacturing.

And the process was that there were a couple of chosen sites that were given a bit more license to integrate industry 4.0 elements within their four walls, and they were referred to as lighthouse factories. So it was very decentralized, not very organized from the standpoint of having certain standards that would scale well. And this is where we started to see a lot of productivity gains, efficiencies within those sites.

Then in 2017, we did a study internally and determined that let's go after this in the right way, which is to organize ourselves to have a program. And as a result of organizing this program, that's where I came in as one of the first few hires within the program to centralize what we're doing. And then, I ended up leading our analytics value stream. We also had value streams related to connected factory, automation, et cetera. And that's where we started to go after it in the right way.

And I think as a result of that, the gains that we've had and the learnings that we've had over the past three years have been tremendous. And if you compare this to the typical approach, especially that I've seen in my consulting years, is that there's a term that was coined by either McKinsey or the World Economic Forum, I can't remember now, called the pilot's purgatory.

A lot of companies I observed they'll start something. They'll start one use case here, another use case there, nothing linked. And they'll do some form of pilot, but it never scales. It would fizzle out in some way. Somebody would move on from one role to the next. The interest isn't there. So, as a result of that, they will continuously stay in the same place, and there will be no roadmap for movement.

TROND: And how do you avoid that destiny of the pilot purgatory? There are many theories on how to do that. And I would say probably every manager of some seniority would say, "Yeah, yeah, I know about that issue, and we don't have that issue here." [laughter]

CARL: But if we're honest with ourselves, it's very easy to fall into pilot purgatory because, first of all, it is very easy to move after the first shiny object or the next shiny object that catches our eye. That's just the way human nature is. One of the things that we've learned is the value of having a strategic roadmap and especially related to industry 4.0. So one of the things that I'm currently working on with our small to medium size enterprises, small to medium-sized manufacturers is we're trying to enable them with two things, one is to assess yourselves.

And we are currently using a framework from Singapore called SIRI, which is Smart Industry Readiness Index. We're making that available to our small to medium-sized enterprises for us to work with them on assessing where are you with respect to these 16 dimensions of industry 4.0? And you don't need to be at the very top band for any one of these, really. You need to look at where you are with respect to peers, with respect to the best practices, and with respect to where you need to be to meet your business objectives. So once we do the assessment, we are able to filter that out in terms of what should be prioritized on the strategic roadmap.

The second thing that we're offering is given what we've done so far; we have a wealth of experience in this space as well as what we've gathered in terms of partners who have been giving us use cases that can apply to these 16 dimensions. We're then able to work with the manufacturer to specify this is what your roadmap should look over the next three to five years if that's your planning horizon.

You focus on these elements first, these dimensions first, but more specifically, these specific use cases. And these use cases are foundational. These use cases will provide you with some return that will help to fund the rest of your program, et cetera. So I think those two things between the assessment and having a strategic roadmap are critical enablers to avoiding this pilot purgatory.

TROND: That's fantastic. We'll talk a little more about SIRI hopefully later because it relates to the work you and I are doing with the World Economic Forum and our AMHUB network. And we are hoping to bring it in really to play in New England, you know, across the sector. But before we get to that, I wanted to ask you a couple of questions about this physical manufacturing 4.0 facility where I believe you actually work out of sometimes in Hartford, this, I guess, 23,000 square foot center.

So it's a physical kind of advanced manufacturing center like its own little kind of demo factory and training center also, I guess, for your smart factory initiatives. How did that get started? Well, it's the middle of a pandemic. But what do you intend to use it for? And what were you using it for before the pandemic? Because I'm assuming you've had a quiet period like all of us.

CARL: Yes, we have. We've had quite a quiet period over the past year and some. But in 2019, we opened the space, and what we actually did...I'm referring back to when we started to go about this in a different way in 2017. We had one of our...well, our key leader Sudhi Bangalore was, brought in from the outside to lead this program. And he was named the VP of our industry 4.0. Since then, he's been also named as CTO for global operations.

But this was one of Sudhi's visions in that we would not only have the team to do this industry 4.0 enablement in a standardized and centralized way, but we would also have an innovation space that you can physically touch, feel, experience the elements of industry 4.0 all the way from automation. So you'll see the robotics. You'll see the automated mobile robots. You will see the automated conveyors, the machine centers all of these things, as well as data flowing back and forth and analytics being displayed.

All these things were intended to be experienced because within our own factory and network; the expectation was that some of what we'll be trying to get to our sights would be new. And we wanted to make sure that individuals, especially plant leaders, would be able to come in and really feel and experience what good looks like.

At the same time, it was also a vision of our CEO as well as our CFO to use the space within Hartford, and Hartford was chosen as a location for a specific reason because we wanted to work with the city. We wanted to work with the state around making Hartford some central innovation hub for New England and hopefully the nation. So that's where this space came into being. And we had a grand opening in April of 2019. So it was always intended for us internally, but it was always intended for the public in a measured way to be able to come in and experience it.

And then finally, I'd probably say that in terms of what we're thinking going forward, we hope to get back into the space sometime soon. We hope to obviously reopen to manufacturers in the region. But then we also want to be able to utilize more of our partners as well, our technology partners, so that they too can show some of their solutions in the space as well.

TROND: It's so important, I think, to emphasize that technology...well, because of the danger in the shiny objects that you just addressed before that, it is precisely for that reason because when you have this experiential sense of what the technology can accomplish, and on the shop floor, there is so much of that right? Robots. It's very visual and tactile. You can clearly much more easily see how you could adopt it. So it sounds quite important to have a demo factory like that.

CARL: Absolutely

TROND: What do you think is the path forward? So you said you guys are engaging with a bunch of different actors that are not your obvious partners. You're engaging with SMEs in a deeper way than before. You have startup engagements but at a very early stage with the STANLEY+Techstars Accelerator. So you're engaging with organizations that are very different than the mothership. Why do you have such a distributed strategy?

CARL: So, I think a lot of this comes from the innovative culture that we live in. We recognize that innovation comes from many places, disparate sources. And we recognize that we won't know everything. We don't know everything. And especially when we're trying to break new ground, we need to be able to tap into all the resources that we can in order to do so and in a relatively efficient but also agile and quick way.

So a couple of years, probably also coinciding with the 2017 time period, we started working with a group called Techstars. And as some might know, Techstars is an international organization that basically incubates relatively new startups and helps them along the way. And there's some partial investment, generally, with the program. But our first round of investments in Techstars was companies that were focused on additive manufacturing.

The current round, which was just completed maybe a few weeks ago, a couple of weeks ago, had cohorts that were related to artificial intelligence, analytics mostly. And we had a couple of robotics ones in there as well, local robots, which all of this is really to ensure that we're able to keep our pulse on everything that's going on.

So to your earlier question about the shiny object, noticing the shiny object is not a bad thing because you have to keep your pulse on what's going on. And as people innovate and as more and more people enter the space and as more things are democratized and commoditized, you want to make sure that you're able to pull in what's needed at any given time.

So that's what we've been trying to do in different ways within our industry 4.0 program, specifically within our Techstars program. And then, we also have another group called Stanley Ventures, which also directly invests in some startups as well. So we're doing it on multiple fronts.

TROND: That's interesting. I wanted to get into the learning aspect. And maybe the humbling part here is both for you and I, and I'll speak for myself, but we're expected to both be experts on industry developments and then simultaneously be evangelists for the same, which is sort of to intermix roles in industry always. But it's complicated. How do you feel like you are able to stay on top of all these things?

Because it's one thing as a company, as Stanley, to have all these investments to have all these things available, theoretically, that you could pull from. But then, now as an individual, I just wanted to address how you, just to take that as an example, how do you engage? Because you and I are both engaged, and we're supposed to be those leaders. And we are building networks that we'll get into in a second that are helping us do that.

But how do you reflect around your own ability to cut this balance between looking at all the shiny objects, making sure you don't miss any of them, and then advising not only your company and implementing stuff but then also being an advisor to the general ecosystem about what is worth looking at and where are things in the maturity scale to keep everything kind of calibrated?

CARL: Yeah, and it can be difficult. And that's where we have to strike a balance. When we started off our program, we recognized that we couldn't build everything internally. So we had to rely on a robust partner ecosystem, probably having somewhere close to 30-plus different partners doing any one given thing at any one time. And then the learning that we got from that was that as a result of that, we were able to get further quicker. We were able to understand a little bit more about the space and what's truly revolutionary and what isn't.

And then we've recognized over time that we still have to have some portion of our time still spent evaluating what's new and coming out. We're able to do that because we are organized in a way to do that, and we have processes around that. And we have individuals who are more focused on innovation versus deployment. And we're probably able to do that because we're a larger company. And this is just how we're set up. Now, the concern that we have for manufacturing, in general, is that the majority of the space is made up of small to medium size enterprises, which don't have this luxury. They have very few individuals.

TROND: I mean, it's just not possible.

CARL: It's not possible for them to do it, which is why we've made the pivot and said to ourselves if we're trying to uplift the entire system, and as they say, a rising tide lifts all boats, right? If we're to uplift the entire manufacturing sector and manufacturing ecosystem, we need to focus on those who make up the majority of it, which is 95%-plus small to medium-sized enterprise.

And we can filter through some of the noise for them. And how we do that is provide a consolidated technology map against a framework so that they don't have to go through the filtering and figuring out what's good, what's not, how much is this going to be worth to me, et cetera. Because we've actually done some of that on our own. And then we just provide to them that based on where you are and your dimensions that you need to focus on, these are the four or five use cases for that specific dimension.

Now, let's talk through and filter. Let's cut to the chase here; how much will this be worth to you? What will be the return on your investment based on what this costs and based on what it will give back to you in terms of impact value? And I think being able to assist in that way I think is critical to getting everyone else a bit more involved in industry 4.0.

TROND: Yeah, and to that point, you and I are both engaged in...so one of those 30 partners, I'm assuming you would count the World Economic Forum as part of those. And you and I are both engaged in the advanced manufacturing platform there and a bunch of initiatives.

CARL: Absolutely.

TROND: We're not going to cover all of those, but there's one in particular that you and I are responsible for here in New England, which is the Advanced Manufacturing Hub, which is a global network of organizations which were the forum itself, which also started out with a centralized organization of the largest firms. So the likes of Stanley Black & Decker in all fields have realized a version of the same thing that you were saying that if the entire world of industry is going to really take up industry 4.0, they also need to work in a distributed way.

And these networks that we have joined in with...well, maybe you could just give your version. What do you think AMHUB New England is and should be doing? And what are some of the things you are excited about that we are starting to launch here? Because it's very new. It got picked up last year, launched under the worst [laughs] possible conditions during a pandemic. I mean, launch a social network during a pandemic, and you will realize what a tricky task is. But anyway, we're in year two. We're into it. There's still a pandemic, and we're doing some virtual events. What are you excited about? AMHUB New England, what is it to you?

CARL: I think the wonderful thing about the network is that we're not the first ones going at this. This is an ever-expanding network within the World Economic Forum. And everyone knows the World Economic Forum like you said, is a collection of all the leaders of the top companies. And then we're focused on the manufacturing space. So we're talking about the top manufacturers in the world coming together and trying to figure this out.

And the Advanced Manufacturing Hubs, I think we're probably close to 13 or so now in the network. It changes numbers every now and again, but we're not the first, and we've definitely had the opportunity to learn from some of our predecessors. We've had others in the U.S. that have been at this for a couple of years before we have that we're learning how they've integrated with public organizations, so integrated with the county and the state and non-profit institutions in the region to be able to go after their objectives. So that's one of the things that we're obviously trying to do: bring public organizations and get them involved along with the private.

We've also recognized, and I think we've had a passion within our own group here around upskilling. We recognize that this is a critical factor for enabling manufacturing in our region. We need to not only deploy and get new technologies, but we also need to upskill our workforce to meet the demands of these new technologies in our environment. So from my perspective, Trond, we have a lot of work to do.

We, fortunately, have a lot of manufacturers, most of them small, within the region who are interested who are enthusiastic about what the path ahead of us looks like. And I think within the next couple of months, or next few months, as we continue to engage that community, we will be able to provide them with more opportunities to upskill and get to where they need to be with respect to their workforce.

TROND: Yeah, and it's fascinating. I mean, you said the World Economic Forum has a bunch of related activities. But it's also true, and I just interviewed someone (That's a podcast episode that's actually coming out this morning.) who's on the panel that you are on, Michael Tamasi, as well so about manufacturing in New England. Because clearly, there's an established network and ecosystem here already we're building on. And this happens, I think, in all of the New England states and Connecticut, for sure. You and I have been engaging with some of the actors there. There are trade associations. There are state and federally-funded organizations like the MEP system and various other kinds of manufacturing networks.

So from my point of view, it's not substituting for all of this. It's just partnering with all of them and just trying to join the efforts that they're already doing but from the perspective of a global picture. So it's getting, hopefully, if we succeed, the best of breed essentially making sure that all of the activities that we are putting on make local sense here in New England, showcase New England, so there's a showcasing aspect of this, and we have a lot I think to be proud of.

I mean, there's Stanley Black & Decker, clearly a behemoth really in industrial tech and in the manufacturing sector worldwide, but there are a lot of other companies also startups contributing and making headway, and then we have a lot to learn.

I wanted to maybe just discuss for a second this event that we're putting on in June here on Israel meets New England. What do you think is the attraction of having two regions meet? So, in this case, it's Israeli startups. But in other events, we might bring in, like you said, the SIRI folks from Singapore who you're working with to measure progress and benchmark in the field, or we could collaborate with even with Michigan, which is another major, major U.S. manufacturing hub. Or it could be Italy or Spain and many of the other networks that exist worldwide. What do you think the attraction is to gain that kind of regional cohesion?

CARL: I think over time, we've recognized that gone are the days when we think innovation is restricted to a particular country or a region or anything like that. I think we're very much aligned on the fact that technology and innovation in the industry 4.0 space is not restricted. So it makes sense that when we think about sharing of best practices that, we go all over the world, and that's part of the reason why if you think about the World Economic Forum, it has a global network of advanced manufacturing hubs.

Each hub may focus a little bit differently on slightly different topics. Some will overlap, but they are also tapping into the expertise and the ideas from their local regions with the intent that we will go across regions and share with each other.

So this upcoming event, I think, is a wonderful one sponsored by the Advanced Manufacturing Hub here in that it's allowing us to see a couple of...or have a conversation with a couple of innovators from another region, and in this situation, it's Israel. But in the future, we will use other regions as well to bring them in, hear a little bit more about what they've been working on, what has been important in their region, which might be slightly different from us, and then have a bit of discourse between us around what the future holds for technology and innovation in general.

TROND: Well, let me profit from that segue into the future. What is next for you in the digital factory? And what does the next decade look like for you in terms of, I guess, your own business-connected industrial tools, perhaps? You're very, very engaged with the networks and the maker movement. And broadly, your thoughts in industrial tech and where that's heading, and maybe even some comment on this upskilling challenge that you mentioned, I mean, what will happen to all of these things?

It's a mixed bag of challenges that they're all somewhat related. You can't have progress in technology without the skilled labor force and all that stuff, and somewhat dependent on technology development. But what do you see happening here? Are we entering at least at the very least a decade where manufacturing will leap forward somewhat faster than it has done before? Will it start to change this impression that manufacturing is hard and difficult and we're dealing with a slow-moving kind of system? Or do you see that that's going to still be the case?

CARL: I'm quite optimistic. I think based on what I've seen at least in the past three years, I think, the way that manufacturing has moved, it gives me optimism that there will be a significant leap in what we're doing going forward. It took a little bit of time, as I said, from 2011 till about maybe 2016-2017, for people to start to really gain a certain amount of interest and get past a bit of skepticism.

At this point, there are enough proven use cases across the board that individual companies and individuals recognize that this is not just a shiny new object or fly-by-night use case. These are things that are here to stay and will be critical to business going forward. So I think as a result of that, first of all, there will be quite a bit of acceleration of efforts.

The second thing is we decry the pandemic and its effects and everything else. But I have to say that there are certain mindsets that have been shifted as a result of the experience. There's more of a need and interest around being able to monitor your remote operations. So now people are more interested in connectivity than there were before. They're more interested in insights and analytics than they were before. Because now they can't necessarily be by the machine, by the production process, by the production line 24/7 or 24 hours a day. But instead, they can benefit from all of these technologies that will allow them to get the most out of their equipment.

They also recognize how important the workforce is. We always decry automation has taken away jobs, but I'll say no; in fact, the studies that have been done show that those who lead in innovation actually also have an uptick in workforce of some 50% instead of the opposite, which is what the myth would typically tell you. So all of these things coming together, I think, will help us move forward quicker going forward.

And then the third piece that I will mention finally is around upskilling going forward. It's absolutely critical that we upskill our workforce. In the U.S. for many years, and we've seen the charts and the data around the amount of retiring workers in the manufacturing sector, so we have a lot of skills and knowledge that will be leaving manufacturing and have already left.

So to replace those individuals, we need individuals of the younger demographic who will, one, come in with knowledge of processes. But the ones that are coming in they're not interested in our grandfather's factory. They're more interested in what can I do differently in this space with the use of technology and innovation to do twice as much work in half as much time? Which is a good thing. We want them to come in with that mindset. And I think with the advancements in technologies; we will be able to do that.

But what would be critical is to be able to upskill them, give them the right skill sets around these technologies, around the production processes as well as there's going to be a tremendous amount of marketing and PR to get folks interested in manufacturing. Because manufacturing is a very exciting sector. It's buzzing, and it actually has quite a lot of open jobs, frankly, that need to be filled, but we need to upskill individuals to fill those jobs.

TROND: You have just listened to Episode 27 of the Augmented Podcast with host Trond Arne Undheim. The topic was Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director of Industry 4.0 at Stanley Black & Decker. In this conversation, we talked about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

My takeaway is that industry 4.0 requires a mindset shift, not just technology adoption. It's not just about you, whether you, in this case, is a big company or a top leader; rather, it is about bringing people, partners, SMEs, and the entire ecosystem along. To do so, openness to learn, having a strategic roadmap so not chase all shiny objects and investing in lighthouse factories that can illuminate the possibilities are each important ingredients.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 20: The Digitalization of Körber, Episode 14: Bottom-up and Deep Digitization of Operations, and Episode 9: The Fourth Industrial Revolution post-COVID-19.

Augmented — upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Carl B. March.

Episode 92: Emerging Interfaces for Human Augmentation

10 augusti 2022 | 51 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 24 of the podcast (@AugmentedPod), the topic is: Emerging Interfaces for Human Augmentation. Our guest is Pattie Maes, Professor at the MIT Media Lab.

In this conversation, we talk about augmenting people instead of using or making smart machines, AI summers and AI winters, parallels between AI and expert systems and why we didn't learn our lessons, enabling people to perform better through fluid, interactive, immersive and wearable systems that are easy to use, how lab thinks about developing new form factors, and much more.

After listening to this episode, check out MIT Media Lab as well as Pattie Maes's social profile:

MIT Media Lab: @medialab (twitter) https://www.media.mit.edu/ (web)
Pattie Maes: https://www.media.mit.edu/overview

Trond's takeaway: Augmenting people is far more complex than developing a technology or even experimenting with form factors. Instead, there's a whole process to exploring what humans are all about, discovering opportunities for augmentation and tweaking it in dialogue with users. The Media Lab's approach is work intensive, but when new products make it out of there, they tend to extend a human function as opposed to becoming just a new gadget.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 19, Machine Learning in Manufacturing, episode 7, Work of the Future, or episode 13, Get Manufacturing Superpowers.

Augmented--industrial conversations.

Transcript:

TROND: Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. In Episode 24 of the podcast, the topic is Emerging Interfaces for Human Augmentation. Our guest is Pattie Maes, Professor at the MIT Media Lab.

In this conversation, we talk about augmenting people instead of using or making smart machines. We discuss AI summers and AI winters, the parallels between AI and expert systems and why we didn't learn our lessons, enabling people to perform better through fluid, interactive, immersive, and wearable systems that are easy to use, and how the lab thinks about developing new form factors, and much more.

Augmented is a podcast for industry leaders and operators hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday. Augmented — the industry 4.0 podcast.

Pattie, how are you today?

PATTIE: Hi. I'm doing great. Thank you. Thanks for having me.

TROND: Oh, sure. I'm very excited to have you. And in fact, I just feel like the audience should get to know you. I know a lot of them do because you have become an innovator that has a stage on TED. And obviously, a lot of people at MIT know you. But I wanted to just recognize that you were one of the early PhDs in AI, right? 1987 is not a time when --

PATTIE: Yeah. [laughs]

TROND: Is that what we call the second wave of AI? It's certainly not the -- [laughs]

PATTIE: The grandmother of AI, yeah. [laughs]

TROND: You're not a recent convert to this topic. That's for sure.

PATTIE: So yes, I actually studied artificial intelligence long before it was such a big deal or the big deal that it is right now. But actually, soon after doing my Ph.D. in AI, I became more and more interested in a related problem, the problem of not artificial intelligence but intelligence augmentation, or how can we make people more intelligent, more productive, support them in making better decisions? So soon after my Ph.D., I veered more in that direction.

TROND: Well, and that's what we will talk about because you have indeed been on the MIT faculty for 30 years exploring these topics in various kinds of bifurcations. And you have been the advisor to scores of startup founders also. And, of course, people might think that goes through the territory at MIT, but the numbers are really still staggering, and also the performance of some of those startups, including Tulip, which we'll talk about, but also many other startups and many other innovation projects that didn't quite make it to startups. But they still created a lot of attention around the world for the promising demos or the things they suggested about what the future of technology might look like.

So I would like first to just recognize that you've achieved, I guess, the amazing feat of not just innovating a lot yourself, but you must be an amazing innovation mentor. And you certainly have inspired a lot of people that I personally know in AI, and in human augmentation, and beyond. And I wanted, first of all, just to see if I could have you reflect a little bit on your journey, which I imagine...well, first of all, it's a nice wordplay from Belgium to Boston.

PATTIE: Yeah, so I came here after my Ph.D. actually, and of course, wanted to be in the place in the world where the most exciting research was going on in my area. [laughs] And so initially, I ended up at the AI Lab, but I soon after actually accepted a job at the Media Lab. And what really attracted me there was that the lab is very application-driven. We're very interested in really working towards things that can be deployed in the real world, that can make a difference in the real world, that can be through for-profit startups.

But sometimes that is actually in other ways by just freely giving away tools and technologies or maybe starting a not-for-profit to really disseminate something and make something accessible to larger groups of people. So I've always been very attracted to the practical aspect and trying to make a difference really with the work that we do. And as a result, several companies have been created out of my research group.

TROND: Was this something you set out to do? When you were in Belgium, getting your degree at the Vrije Universiteit in Brussels, were you thinking I am going to go to America and become an innovator? Was that in your mind?

PATTIE: No, I think a lot of that sort of happened accidentally, actually. And one reason I think why I'm interested in practical applications and real-world deployment is that I was never really interested in the technology for the sake of the technology. I'm not one of these people who gets really excited about purely just the technology, the algorithms, and so on. I want to make my life easier and other people's lives easier. And that has always been what motivates me and my work.

TROND: And that gets us to intelligence augmentation. Because I guess in some sense, the Media Lab is all about that topic to some extent. And I wanted to also address the fact that not only are you doing the work in your lab, but I think at least for the last few years, you've had the academic responsibility across the lab, and you have shepherded the lab, arguably, through one of its more difficult times.

So surely, you have also experienced innovation and the tricky things that show up with innovation across a plethora of fields. But generally, people at the Media Lab are hired, I guess because they think about application. What is it that is so different when you...so let's just start with that. When you start with a human in mind from the get-go, what is the difference that makes?

PATTIE: So I think; indeed, our philosophy is always to be, like I said, application-driven. And what that means is that we take a closer look at the ultimate target users and their place or where they live or work, and how the technology could make a difference there and could change things there. So rather than starting from the technology and trying to maybe optimize some algorithm that does X, we actually work closely with target users. We really study their lives today to understand what the pain points are, what the opportunities are for technologies to make a difference and support them in being more effective, more productive.

TROND: But you have experienced both sort of AI summers and winters. Is one of the reasons that AI [laughs] tends to get into trouble that it always is very myopic about the technology focus, or is it a more complicated reason why there are these summers and winters? [laughs]

PATTIE: Well, I think that that is indeed a primary problem. So yes, there have been several AI summers and winters. Probably a lot of your listeners are young enough that they don't realize that there was another hype cycle for AI that happened sort of in the '80s and '90s with the emergence of expert systems, so-called expert systems. These were not based on machine learning and neural network techniques but instead were typically based on rule-based systems.

But they were very sophisticated. They had typically a lot of knowledge built in about a particular problem like, say, making a certain diagnosis, or doing some planning, or what have you. So the systems in laboratory settings were very impressive and were often outperforming experts at doing some scheduling problem, or planning problem, or diagnosis, or recognition problem.

But what happened when they were put into the workplace or when people tried to integrate them into the real world was that they basically encountered all sorts of obstacles. One of the obstacles was that people wouldn't necessarily trust the machine, the expert system. They didn't quite know how to work with it or where to fit it into their workflow. They weren't always able to get explanations for why the machine was making a certain decision.

It was very hard to correct the knowledge of the system and give it new information or to update its information if it wasn't correct. So there wasn't really a lot of transparency, a lot of controllability, interpretability. And that ultimately was the downfall of expert systems. And so yeah, at that time, just like now, there were many startups, millions of dollars pumped into all of this. The conferences and exhibits were extremely popular, and all of that died down. And we entered an AI winter where suddenly there was very little interest from the real-world businesses in AI.

Now, of course, we are in another summer, in another hype cycle. And I am actually very worried that we are making exactly the same mistakes because most of the AI systems that are being developed are being developed very much not in the context of where they ultimately will be used or not with the collaboration of the people who ultimately will use these tools. And so we will encounter exactly the same problems of trust and transparency, and controllability, and interpretability.

So, in my work, I've always been emphasizing a different approach. And I like to not call it artificial intelligence but rather maybe augmented human, or augmented intelligence, or maybe human-centric AI because our approach is one where we start out by studying what people are already doing in a certain work environment, whether that is a manufacturing floor or a doctor in the hospital, and so on.

And we actually work together with them or think about how we can support the people that are there to do their work better, to be more effective at their work. And so it's a totally different way of looking at a problem. We try to optimize for the person and the technology together to perform better. We don't try to optimize for the algorithm or the system to become better without thinking about how that system will be integrated into our real lives and real-world scenario.

TROND: Well, this is super interesting. I want to go into a couple of examples of things that you have done with your students and otherwise in a second. But first, why have we not learned collectively this lesson? I mean, what is it? I mean, is this something you think is happening across the board with technology? Or is it even just specific to this machine learning AI environment that we...are we so tempted by the potential impact of the use cases that we’re just getting carried away into the algorithms'depth and then forget the user? Or why haven't people said this is not good enough?

PATTIE: I think that it is actually a broader problem with development of digital technologies. All of the technologies that we use today whether it is maybe AI systems or whether it is social networking services and so on, they mostly have been designed and built by engineers, by teams that just consist of engineers and not people that come from very different backgrounds, for example, more social humanities backgrounds, et cetera.

One of the reasons that I was very excited to join the Media Lab as opposed to a computer science department is that it is very interdisciplinary. And we really recognize and try to emphasize that interdisciplinarity is extremely important in innovation, in creating things that ultimately will be successful and will be able to make a positive difference basically and a positive impact.

So that means involving not just engineers but also designers, people who can really think about making things fluid, seamless about how it integrates into workflow, and so on. But also people from humanities backgrounds, and social scientists, and so on. So I think it's important to have that broader perspective to make or to create technologies that ultimately are desirable and ultimately really improve our lives.

TROND: But, Pattie, take me inside of a week in the Media Lab. Because when you describe it this way, it sounds almost so intuitive and simple that I'm wondering why people need to travel to the Media Lab to learn this. Because if it was just simple to just hire a team with different skills, and it will happen, there surely is some other type of magic ingredient.

What does a week look like in your lab? How do you draw out the kind of creative energy...maybe it's helpful if you take Arnav Kapur's AlterEgo, which most people know as just that video that went viral. And they're like, imagining the future of computing with just this device where he's not even speaking, but he's kind of just basically controlling, it would seem, the computer with his jaw. Now, fantastic video; how does something like this come out of your lab?

PATTIE: So we are a very open laboratory. So, in addition to attracting creative, entrepreneurial people and really cultivating a very interdisciplinary team, we engage a lot in conversations, in discussions with others, with the outside world, which is actually pretty rare still for people in universities. [laughs] So, for example, we have member companies.

We have a consortium of companies that fund the Media Lab, and they, pre-COVID at least, come and visit on a daily basis. Every day we have at least ten different companies visiting to see the work, to engage in discussions, to give us feedback. They don't direct the work, but they can be critical. They can see opportunities for where to take it, and so on. And we engage in a very iterative type of style of work, where we quickly prototype something. Like in the case of AlterEgo, it looked pretty ridiculous the way it was glued together with some cardboard and other things that we could find in the lab. [laughs]

But we create these very early prototypes that are very clunky, don't work very well. But those make a certain future more visible. They envision what is possible or make it more concrete. And then we invite a lot of feedback from all of these visitors, from all of these people with different backgrounds. And they see opportunities for oh, maybe I would use it this way. Or maybe it's really exciting in that application domain, or I see this or that problem with the technology.

So that's really the technique that we pursue, attract a very diverse team of highly creative entrepreneurial people but from very different backgrounds, and engage in a lot of team innovation, and do very iterative types of design, making prototyping, and then getting feedback from really everyone, not just these companies that come and visit but our own families, and of course, the target users of the technologies that we build. So that's the secret sauce, so to speak, [laughs] or the secret to how Media Lab innovation works.

TROND: Take us back maybe to 2012 or something. And in the lab, you have two bright people; one is Rony Kubat, who also had a background from the Computer Science and AI Lab at MIT, but then had already come over to study with you. And then you had Natan Linder, who had industry background and had been already head of a Samsung lab in Israel. Now the two of them show up during their masters, I guess, and then ultimately PhDs but masters, I guess, in this context, and they start developing something.

Can you tell me a little bit about those early days, early conversations you had with them about what each of them were doing, and your reflections on to what extent some of the early work they did with you how that transpired into what now, 2014 I believe, turned into Tulip Interfaces? And now, in 2021 went on the Gartner calendar, essentially, as a manufacturing execution system.

And more broadly, aspirationally, it's a frontline operations platform that can transform the way that workers are working at the frontlines, augmenting them and really changing manufacturing as we know it today with a kind of a no-code system. So this was like fast forward 2012 to 2021. Where were they back then? What was it that you taught them specifically? What were they working on? And how did you work together?

PATTIE: What motivated this work initially was this whole realization, in 2012, that we were living in these two parallel worlds, and it's still very much the case. [laughs] We live in the physical world, and then there's this whole digital world with information about all the things around us in the physical world that we are engaged in and so on, the people we're meeting with, and so on.

And we realized that or we were frustrated really that these two types of experiences were not connected. For example, if I pick up a book, I can look at the pages, the beautiful pictures in the book, read the back cover to see what people have to say about it. But ideally, at that moment, I will also have access to the rating on Amazon and what others have said about that book or not because that's extremely relevant at that moment when I'm considering whether that book may be an interesting book for me to read.

So we were very interested in creating experiences that are more integrated, where our physical lives are more integrated with the digital information that exists about everything around us and all of our actions and experiences. So we experimented with different types of augmented reality systems to bridge that gap and to make the digital information and services available in the physical world.

So that's really where the work that Natan and Rony did and what led to Tulip where that started. They were experimenting with building systems that have an integrated camera and projector so that the machine can see what is happening and can project relevant information onto whatever it is looking at. So that people can get, for example, relevant reviews when they're looking at a product that they want to buy.

So we actually developed all sorts of prototypes to illustrate this vision of this integrated augmented reality. For example, at that time, together with Intel, we built up an example of a store that has the two integrated, that has physical products; I believe it was cameras. And then there was a projector system that would recognize what camera you were looking at or picking up, and it would give you additional information about it. So it would point out the features by actually pointing at the different buttons on the camera and what was so special about them, et cetera.

We also built an augmented desk for a learning context, for an educational context. And in all of these cases, we worked with partners, for example, for the education context to think about how this augmented reality could be used in the context of schools. We worked with Pearson, who's the leading developer of course books and school books, and so on.

We then also worked with Steelcase on how this augmented reality technology could be used on the manufacturing floor. How could it help people in real-time by giving them feedback about what they were doing, maybe giving them real-time instructions projected onto their workspace, or maybe alerting them that something wasn't done right or a step was forgotten, and so on?

And that work with Steelcase ultimately and with some other sponsors as well like GSK, for example, which does drug development, all of that led to the spin-off to Tulip being created as a company that can really realize that whole vision of an augmented manufacturing place where you can have real-time information provided. But you can also track the whole manufacturing floor in real-time and have very detailed data, and analytics, and intelligence about which steps may cause more errors or which steps in the process, say, take a lot of time, and so on. So you have this real-time insight also into the manufacturing floor that we've never had before.

TROND: It's fascinating that you picked this...that they picked this example and that you are kind of explaining it now. Because I want to give people the right sense of what it takes to produce an innovation that turns into a commercial, true product because I saw a version of the product you were explaining now in 2014, in the fall when I was at the Startup Exchange. And I was one of the first in their then Tulip lab with seven employees.

But that demo of something that had a camera and a sensor only this spring turned into what Tulip called their vision product. And it's only now coming to market. So here is arguably some of the brightest people working with you, a very experienced mentor, working from 2012 to a demo in 2014. But then they had to take all kinds of other things to market first, and only now, in 2021, is this coming out. I find that an incredible timeline and path.

PATTIE: Yeah, it's surprising to me as well, although I have seen it happen multiple times. We think that technology moves really fast. But then, in practice, for an invention like this to ultimately make a difference in the real world typically takes ten years or more. I have had that experience with other technologies that we've invented in the past. Actually, an earlier technology that we invented in our lab was recommendation systems that recommend a book to you because you also liked these other books or because people who also liked the books that you buy also bought this book that is being recommended to you.

We invented that technology in '94 [laughs] when browsers were just available. And we were talking a lot to Media Lab member companies about how exciting this would be and how it would personalize the whole online experience if you could get these recommendations from other people like you. And there was excitement among the member companies, but they were at that time saying, "Well, we're not sure that people are ultimately going to feel comfortable giving their credit cards over the internet to buy something. So it seems very exciting, and it's a great vision, but we don't see this happening."

[laughter]

That was companies like Blockbuster [laughs] and other companies that now are bankrupt, maybe because they didn't take this seriously enough. [laughs] But so because these larger companies were a little bit skeptical about this whole vision that we were portraying of online commerce and recommendations and so on, we started a company ourselves called Firefly in '94 and ultimately sold it to Microsoft actually in '98.

But we were just way too far ahead. We were too early. And most people weren't ready to buy things online. Most companies weren't ready to partner with us. And we actually sold a company in '98 at a time when briefly, everybody thought that internet commerce was dead, was not going to take off. A year later, [laughs] our company would have been ten times as much or worth ten times as much as what we sold it for.

So, unfortunately, we sold it at the wrong time when there was a lot of pessimism about...and it's hard to believe that now, [laughs], especially now during COVID, that everybody pretty much buys everything online. But yeah, back then in '98, that was not at all clear. And we were too early, basically. So in my experience, it always takes at least 10 to 15 years, even for a technology that seems ready to be deployed to ultimately make a difference in the real world.

TROND: Well, the digitalization of physical infrastructure like you started with is a different thing, though, and even more complicated than the trust to buy something online, which I guess is vaguely related to you have to trust that something abstract is actually going to have a consequence.

But Rony and Natan told me that they even basically slept over in factories and studied these workers for days and weeks on end, and I guess Tulip is still studying workers. It's not immediately obvious what is the contribution on the factory floor, is it? I mean, it's not as easy as to say, "We have this fancy digital thing that we're going to give you." But why is it so much more complicated?

PATTIE: Yeah, I think it's always complicated. [chuckles] And it is important to really understand the context, the actual context of where some technology is going to have to fit in. I remember very well when Rony and Natan were visiting the factories, and they would come back with amazing stories, to our minds, very primitive ways in which everything [laughs] was being done at that time, still a lot of use of paper records, for example, for collecting information.

So it was a big gap that had to be bridged [chuckles] really between the vision that we had of this totally connected manufacturing place with all of this real-time data, real-time instructions and advice, being able to also modify things and edit this whole digital layer or digital support system in real-time by the people on the floor, and the managers, and so on. There was really a big gap from that reality of paper-based systems in a very low-tech context to that vision that we had of this smart manufacturing floor.

TROND: And how far are we getting with this, and how quickly will it go now? Would you say that this has been a decade of exploration and a lot of these things have been sorted out? Or would you say some quick wins happened, and then some of the slower things they are just slow? Any kind of technology will take the time it takes to fully understand how you can contribute.

I guess I'm asking this in the context of another technology that a lot of people are putting a lot of hope in these days, especially perhaps during COVID, you know, robotics on the manufacturing floor and maybe the merging of AI or machine learning and robotics. How do you see these things?

How disruptive will any kind of digital device, or software system, or augmented system that should benefit workers how disruptive can these devices and systems become? And have we hit some sort of momentum, or is this still going to be kind of case-by-case basis, and the hype is just not going to be true in this domain?

PATTIE: I think we have to accept that progress necessarily is slow. [laughs] I mean, I think the potential is there. But in my experience, really reaching that potential involves learning a lot of hard lessons along the way, but progress is being made. It's just not as quick as we would like it to be. And I think the same will be true for this vision of smart manufacturing, including the use of robotics, which is even more challenging because you have moving parts, [laughs] which means that things break down quicker and that there are also more safety constraints and so on as well.

But yeah, progress will continue to be made. And I think it's very important for companies to engage with all of these new technologies, and to do experiments, and to start integrating some of these new technologies in their workplace, or you end up like the Blockbuster [laughs]example that I gave earlier where they said, "We'll deal with this later or when it becomes more important," and then they were bankrupt.

TROND: Well, it strikes me that you're not going to give me timelines because it depends on so many things. But if you look at the future of, I guess, cognitive enhancement more generally or certainly these immersive and sometimes wearable systems that you have been building for 30 years, you have an interesting role because you are, of course, inspiring a lot of hype just because the products you build are so fascinating, and they seem so simple.

But you are also combining this with being very careful about the predictions that are surrounding it. So tell me a little bit about what the future holds for these things. I mean, are we to expect more of these fascinating devices coming on market, or are you exploring a lot more of those in your lab right now?

PATTIE: Oh yeah.

TROND: Where is it at the moment on the experimental stage?

PATTIE: There's never a shortage of interesting new ideas for us to work on. I always have way too many or more than I have students to work on them. [laughs] But one area that we are exploring in the lab right now is we want to go beyond systems that help people with providing information. The focus on digital technologies, whether it is laptops, or watches, or smartphones, has been primarily on communication and also the system giving you information.

And with the work that we talked about so far today, the focus was on giving them that information integrated into whatever they are doing so that they don't have to try to juggle between the physical and then the digital information that may be relevant to whatever physical stuff somebody is doing. But we're trying now to go beyond systems that give you information and are interested in looking at how digital devices can help people with issues such as attention, motivation, memory, learning, grit even, creativity.

We think that given that all of us are now sort of forever after cyborgs, we always have technology with us. We have our smartphones never far [laughs] away from our body. Many of us wear a smartwatch as well. And so we have this opportunity now to use these systems to help people with a lot more than just giving them access to information.

The systems increasingly have sensors integrated that can sense what the person is doing, where they are, maybe even what their heart rate is, and whether they are maybe a little bit anxious at the moment or not, or maybe the opposite. Maybe they're too sleepy; they're not engaged.

So increasingly, systems will have a better sense like that of the state of a person, the cognitive state of a person, and will help the person with being in the state that they want to be in. For example, we've been building glasses that have built-in sensors for sensing brainwave activity as well as for sensing eye movements. And that pair of glasses it's called the AttentivU project.

It can actually give you feedback about your own attention level. Are you being highly attentive right now? Or are you being distracted? Are you fatigued? And so on. And we use that information to help a person to be aware of the fact maybe that a driver of a truck should be taking a break because they're too fatigued, or it can help a person who's listening to a lecture be more attentive because the system can tell them when their attention is waning.

So we think that this is an exciting new direction to really go beyond just giving a person information about whatever job they're doing, or whatever they're working on, or are thinking about, or doing, but going beyond that and helping them with those skills that are really important for being successful in life that all of us struggle with, and that all of us keep having to work on.

TROND: Fascinating. That's fascinating. I want to ask you what is your goal with all of these activities? Because you are an innovator, but innovators are always motivated. Good innovators are always motivated by something. What is it ultimately that you have been trying to achieve over these years?

PATTIE: I really want to help people. [laughs] I did study computer science and artificial intelligence. But my goal is not to create smarter, more capable machines or algorithms. I ultimately want to help people with machines, with AI. I want to enable them to live their best lives and to grow and learn and ultimately become the person that they would like to be.

TROND: So you have a very optimistic view on a future that a lot of people are scared about right now. Some people might be scared about AI. They might be scared about what they're seeing around them. How do you maintain this very optimistic vision? Is it because you feel like you have agency? You get clever students come in and work on your ideas.

I guess I'm just trying to say that usually, I would ask people what is the best way to stay up to date and kind of model what you're doing? And the obvious thing would be they should try and come and apply and come to your lab. Now, some people will achieve that, not very many, right? It's a small space, so there are limits.

PATTIE: [laughs] [crosstalk 43:43]

TROND: The other advice would be to pay to get to the Media Lab and become a corporate sponsor; that seems to be another avenue. But do you have any other less obvious ways that people can emanate some of this spirit that I think you...because you're sharing an entire approach to how to understand technology, how to develop technology, but also a vision of what technology should be doing for us. You kind of have a philosophy. You told me a philosophy with a small p about technology. How should people try to learn more about it, engage with that kind of philosophy?

PATTIE: Yeah, I do think it is the role of the Media Lab to be optimistic really and to see the potential of emerging technologies in improving people's lives. That is really sort of our unique focus among all university research laboratories. We look at emerging technologies, and we try to be positive thinkers or optimistic thinkers in terms of how those technologies can ultimately empower people to improve their own lives, their communities, and their environment, the natural world around them as well.

We try not to be naive, [laughs] in that quest at the same time. And we are very much aware that all of the powerful technologies that we work on can be abused, can be used in very negative ways as well. But I think that that is ultimately not a reason not to engage in these endeavors. Basically, we try to invent the future that we want to live in, [laughs] or that's really what we are working on.

And we try to be inclusive in that process by, again, not just involving the students and researchers in the lab but really the target communities like people on a manufacturing floor and how do they want to work with AI, and robotics, and augmented reality, et cetera? So we basically involve the target users, companies that are involved in a particular sector, and so on as well. And so yeah, I think that there are many opportunities really for people to be involved.

I would also like to say that, especially now with COVID, all laboratories have become much more open and, for example, lecture series, showcases, virtual open houses, and so on. There are no limits to how many people can attend because it's all [laughs] online anyway these days. So it's actually nice that that has opened up the laboratory more and makes it possible for more people to get involved, to be part of conversations, to listen to talks, see demonstrations, and so on.

TROND: That's fascinating. And I think just in closing, you mentioned this acronym that's typically used in psychological studies, the WEIRD acronym, Western, Educated, Industrialized, Rich, and Democratic. And it seems to me that that is a very, very specific user group, but it is far from the only one. So maybe in closing, my last question would be, how does one, you know, because others might be developing technology on other continents or other places.

How do you avoid this bias of jumping into a lane that other people have created that is this lane? It's maybe demos from Western labs. It's use cases in highly industrialized factories or whatever it is or created for the New York Fifth Avenue consumer market. Those are not the only technologies we should be building. So how do we do it otherwise?

PATTIE: Yes, I fully agree. And meanwhile, today, I talked about my work. And my work is indeed mostly focused on the Western developed world and technologies that might be available here. There's a lot of work happening at the Media Lab with other communities, both within the United States, less fortunate communities, maybe than the ones that many of my technologies are designed for.

There's a lot of work, for example, with people in Africa on use of different technologies. So we try to...maybe we cannot develop technologies for everyone, [laughs] but we try to be explicit about who some technologies are designed for and not assume that they would generally be usable. And we try to work with the target communities that they are designed for. And definitely, we're not exclusively working with or designing technologies for the Western, richer world.

TROND: Well, thank you so much, Pattie. This has been very enlightening. It turns out that advanced technology is complicated and slower, but perhaps more sustainable when it's developed that way. And that's an interesting lesson. Thank you so much.

PATTIE: Thank you. It was a pleasure.

TROND: You have just listened to Episode 24 of the Augmented Podcast with host Trond Arne Undheim. The topic was Emerging Interfaces for Human Augmentation. And our guest was Pattie Maes, Professor at the MIT Media Lab.

In this conversation, we talked about augmenting people instead of using or making smarter machines and enabling people to perform better through fluid, interactive, immersive, and wearable systems that are easy to use, developing new form factors, and much more.

My takeaway is that augmenting people is far more complex than developing a technology or even experimenting with form factors. Instead, there's a whole process to exploring what humans are all about, discovering opportunities for augmentation, and tweaking it in dialogue with users. The Media Lab's approach is work intensive, but when new products make it out of there, they tend to extend a human function as opposed to becoming just a new gadget. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 19: Machine Learning in Manufacturing, Episode 7: Work of the Future, or Episode 13: Get Manufacturing Superpowers.

Augmented — industrial conversations.

Special Guest: Pattie Maes.

Episode 91: Reimagine Training

3 augusti 2022 | 24 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode 3 of the podcast, the topic is: Re-imagining workforce training. Our guest is Sarah Boisvert, Founder and CEO Fab Lab Hub, LLC and the non-profit New Collar Network.

In this conversation, we talk about re-imagining workforce training, industry 4.0., what do you mean by “New Collar” jobs? We discuss the mushrooming of Fab Labs. What skills are needed? How can they be taught? How can the credentials be recognized? .What has the impact been? Where do we go from here.

After listening to this episode, check out Sarah Boisvert's online profile as well as the New Collar Network:

Sarah Boisvert https://www.linkedin.com/in/sarah-boisvert-3a965031/
The New Collar Network (@NewCollarNetwrk): http://newcollarnetwork.com/
Fab Lab Hub (@FabLabHub): http://fablabhub.org/

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by @Art_list_io.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars on Apple Podcasts. To nominate guests, to suggest exciting episode topics or give feedback, follow us on LinkedIn, looking out for live episodes, message us on Twitter @augmentedpod or our website's contact form. If you liked this episode, you might also like episode 3: How to Train Augmented Workers. Augmented--the industry 4.0 podcast.

Transcript:

TROND: Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory? Who's leading the change, and what are the key skills to learn? How to stay up to date on manufacturing and industry 4.0.

Augmented is a podcast for leaders in the manufacturing industry, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, that is M-F-G.works, the open learning community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern, every Wednesday. Augmented — the industry 4.0 podcast.

In episode 3 of the podcast, the topic is Reimagining Workforce Training. Our guest is Sarah Boisvert, Founder and CEO of Fab Lab Hub and the non-profit New Collar Network. In this conversation, we talk about reimagining workforce training, industry 4.0, and what do you mean by new collar jobs? Fab Labs, what skills are needed? How can they be taught? How can the credentials be recognized? What has the impact been, and where do we go from here?

Sarah, how are you doing today?

SARAH: I'm doing well. How are you?

TROND: I'm doing fine. I'm excited to talk about reimagining workforce training, which seems to be an issue on your mind, Sarah. You are a founder yourself. You have been actively involved in advanced manufacturing. I understand part of your story is that your company manufactured and sold the Lasik eye surgery back in 1999. So you've been involved in manufacturing for a while. We're here to talk about something very exciting. You say new-collar jobs is the big focus. I know you didn't invent the term. Can you give me a sense of what new-collar jobs refers to, first of all?

SARAH: Sure. It is a term that was coined by Ginni Rometty, who was then the CEO of IBM. She's now the executive chair. And it refers to blue-collar jobs that have now become digital. And so many of our jobs...if you just think about your UPS man who now everything's not on paper, it's all in a handheld tool that he takes around on his deliveries. And all jobs are becoming digital. And so I thought that Ginny's term encapsulated exactly what's happening, and the technologies that we used to use just in manufacturing are now ubiquitous across industries.

TROND: You have also been instrumental in the MIT spinout project called Fab Labs. Just give us a quick sense, Sarah; what are Fab Labs? Not everybody is aware of this.

SARAH: Fab Labs are workshops and studios that incorporate many different kinds of digital fabrication. So we are taking the ones and zeros, the bits of CAD designs, and turning them into things that you can hold in your hand. And it covers topics like 3D printing, and laser cutting, and CNC machining. But Neil Gershenfeld, who founded the international Fab Lab Network, likes to say the power of digital fabrication is social, not technical.

TROND: You know, this brings me to my next question, what skills are needed? So when we talk about new-collar jobs and the skills and the workforce training, what exact skills is it that we need to now be more aware of? So you talked about some of them. I guess digital fabrication, broadly, is another. Can you go a little bit more into what kind of skills you have been involved in training people for?

SARAH: Well, when I first started this project, I had always been interested in workforce training, obviously, because I had a manufacturing company, and I needed to hire people. And we had worked with the community college near our factory to develop a two-year curriculum for digital manufacturing. But I had in mind exactly what I needed for my own company and the kinds of skills that I was looking for.

And so a lot of Fab Labs, because we have about 2,000 Fab Labs around the world, heard about this program and started asking me, "Could you make a curriculum for us?" And there were so many of them that I thought I needed to come up with something that is going to fit most of the Fab Labs.

And so I interviewed 200 manufacturers in all kinds of industries and from startups to Fortune 10 and so companies like GE, and Boeing, and Apple, and Ford, as well as companies in the medical device space. What they all told me they wanted was...the number one skill they were looking for was problem-solving. And that's even more important today because we're getting all these new technologies, and you haven't got some guy in the back of the machine shop who has done this before. And we're getting machines that are being built that have never been built before. And it's a whole new space.

And the second thing they were looking for was hands-on skills. And I was particularly looking at operators and technicians. They were also looking for technical skills like CAD design, AI. Predictive analytics was probably the number one skill that the international manufacturers' CEOs were looking for. And I got done, and I thought, well, this is all the stuff we do in Fab Labs. This is exactly what we do. We teach people how to solve problems.

And so many of our labs, particularly in places like Asia or Africa where there was tremendous need and not enough resources, necessity is the mother of invention. And so many of our Fab Labs invent amazing things to help their communities. And I thought, well, we don't need a two-year curriculum because the need for the employers was so extreme. I thought we need something more like what we do in Fab Labs.

TROND: And how can these skills be taught? What are the methodologies that you're using to teach these skills that aren't necessarily, you know, you don't need to go to university, as you pointed out, for them? But they have to be taught somehow. What are the methods you're using?

SARAH: Well, I did a lot of research trying to nail that down when I got done figuring out what it was people needed in the factories. And it seemed like digital badges were the fastest, easiest, most affordable way to certify the ability of a badge earner to work with a particular skill set. And they were developed by IBM and Mozilla probably decades ago now and are used by many organizations to verify skills.

And it's a credential that is portable and that you can put on your digital resume and verify. There is an underlying standard that you have to adhere to; an international standards body monitors it. And there's a certain level of certainty that the person who says they have the skill actually has it.

TROND: That's a good point because, in this modern day and age, a lot of people can say that they have gone through some sort of training, and it's hard to verify. So these things are also called micro certifications. How recent is this idea to certify a skill in that digital way?

SARAH: I think that these particular badges have been around for decades, and people like Cisco, and IBM, and Autodesk have been using them for quite a long time, as well as many colleges, including Michigan State, is one that comes to mind that has a big program. And they can be stacked into a credential or into a higher-level course. So we stack our badges, for example, into a master badge.

And that combines a number of skills into something that allows someone to have a job description kind of certification. So, for example, our badges will combine into a master badge for an operator. And so it's not just someone who knows CAD. They know CAD. They know how to run a machine. They know how to troubleshoot a machine.

TROND: So we touched a little bit on how these things can be taught. But is this a very practical type of teaching that you are engaged in? I mean, Fab Labs, so they are physically present, or was that kind of in the old, pre-COVID era?

SARAH: Well, yes, we were typically physically present with COVID. This past summer, I spent a lot of time piloting more online programs. And so, for our design classes, we can still have people online. And our interns 3D-print their designs, and then they can look at them via photography or video, if it's a functional design, and see how the design needs to be iterated to the next step. Because, as you know, it never comes out right the first time; it takes a number of iterations before it works.

And we just recently, this week, actually completed an agreement with MatterHackers, who are a distributor of tabletop 3D printers, to bundle their 3D printers with our badges. And so someone can then have a printer at home. And so, if you have a family and you're trying to educate a number of children, it's actually a pretty economical proposition.

And they offer two printers that are under $1,000 for people who are, for example, wanting to upskill and change careers. They also offer the Ultimaker 3D printer that we use pretty heavily in our lab. And it's a higher level with added expense. But if you're looking at a career change, it's certainly cheaper than going back to college [laughs] instead.

TROND: So I'm curious about the impact. I know that you started out this endeavor interviewing some 200 U.S. manufacturers to see that there was...I think you told me there was like a paradigm shift needed really to bring back well-paying, engaging manufacturing careers back to middle-class Americans. And that's again, I guess, pointing to this new-collar workforce. What has the impact been?

I mean, I'm sitting here, and I see you have the book, too, but you generously gave me this. So I've been browsing some of the impacts and some of the description of what you have been achieving over the past few years. What has the impact been? How many people have you been able to train? And what happened to the people who were trained?

SARAH: We've only been doing it a couple of years. And in our pilot, we probably have trained 2,3,400 people, something on that. And it's been a mix of people who come to us. Because we teach project-based learning, we can have classes that have varying levels of experience. So we have people who are PhDs from the Los Alamos National Lab who drive the 45 minutes over to us, and they're typically upskilling. They're typically engineers who went to school before 3D printing was in the curriculum. And they are adding that to their existing work.

But we get such a wide range of people from artists. We're an artist colony here. And we get jewelers, and sculptors, and a wide range of people who have never done anything technical but are looking to automate their processes. And so my necklace is the Taos Pueblo. And it was designed by a woman...and her story is in the book.

So I should add that the book you're referring to has augmented reality links to the stories of people. And she just was determined. She, I think, has never graduated from high school and is an immigrant to the United States. And she just was determined to learn this. And she worked with us, and now she designs in CAD, and we 3D-print the molds. And her husband has a casting company, and then he has it cast in sterling.

TROND: I find that fascinating, Sarah because you said...so it goes from people who haven't completed high school to kind of not so recent PhDs. That is a fascinating range. And it brings, I guess, this idea of the difficulty level of contemporary technologies isn't necessarily what it was years ago. It's not like these technologies take years to learn, necessarily at the level where you can actually apply them in your hobbies or in the workplace.

Why is that, do you think? Have we gotten better at developing technologies? Or have companies gotten better to tweak them, or have we gotten faster at learning them? Or is the discrepancy...like, this could be surprising for a lot of people that it's not that hard to take a course and apply it right afterwards.

SARAH: Learning anything comes down to are you interested? It comes down to your level of motivation and determination. A couple of things, I think the programs, the technical programs, and the machines have become much easier. When I started in the laser business, every time that I wanted to make a hole, I would have to redesign the optical train. And so I'd have to do all the math, so I'd have to do all the advanced math. I would have to put it together on my bench, and hopefully, it worked, and tweak it until I got the size hole I needed in the material I needed.

Today, there's autofocus. It's just like your camera. You press a button; you dial in the size hole you want, and away you go. And it's interesting because many of the newer employees at our company Potomac Photonics really don't have the technical understanding that I developed because they just press the button. But it moves much faster, and we have more throughput; we have a greater consistency. So the machines have definitely improved tremendously in recent years.

But I also think that people are more used to dealing with technology. It's very rare to run into somebody who doesn't have email or somebody who isn't surfing the web to find information. And for the young people, they're digital natives. So they don't even know what it's like not to have a digital option. I think that a number of things have come together to make that feasible.

TROND: Sarah, let me ask you then this hard question. I mean, it's a big promise to say that you can save the middle class essentially. Is it that easy? Is it just taking one or two courses with this kind of Fab Lab-type approach, and you're all set? Can you literally take someone who feels...or maybe are laid off or feels at least not skilled really for the jobs they had, the jobs they want, and you can really turn them into highly employable in a matter of one course? Has that really happened?

SARAH: In one course or one digital badge, it is possible to get some jobs, but it probably takes a combination of courses in order to have the right skill set because it's typically not one skill you need. It's typically a combination of skills. So to run the 3D printers, for example, you need CAD design. You need to understand design for 3D printing. And then you have to understand how to run the machines and fix them when they break.

So it's probably still a more focused and condensed process. So you could do our master badge, which comprises five or six badges, and get a job in six months for about $2,000. With one class, you could get a job part-time and continue the other badges and be paying for school while you're working in a field that is paying a substantial increase over working at McDonald's.

TROND: So give me a sense. So this is happening, in your case, in Santa Fe, New Mexico. Where do we go from here? Is this going on anywhere else? What are the numbers? How many people are being trained this way? How many people could be trained this way? How easy is the approach you're taking to integrate and scale up? And is it happening anywhere else?

SARAH: Our non-profit, which is the organization that issues the badges, has, right now, I think, 12 or 13 members, and they were part of our pilot, and they are all over the country. So in my team, Lemelson, the Fab Lab in El Paso, the Fab Lab in Tulsa, MakerspaceCT in Hartford, Connecticut.

And so we have a group that just started this year was when I started the scaling after, I was really pretty confident that it was going to work. If it worked in Santa Fe, which is a small town and in a very rural, very poor state, I really thought if I could make it work here, we could make it work anywhere because there are a lot of challenges in our state.

So we started scaling this year, and each of our pilot sites is probably putting through their first cohort of 4, 5, or 6 badges, and they each have about 10 in that first cohort. We have a lot of requests for people to join our group and start issuing the badges. I've really come to see the success of our online program. And so, our online program is instructor-led at this point. And I'm working to create a self-directed program that people could do online with a tabletop printer at home. But we will still continue to scale the New Collar Network that actually disseminates the badges.

And I really see enormous interest. As you know, college enrollment has been declining for the last ten years. There has been an 11% decline in college enrollment. And people are looking for alternatives. And I think that I've had requests from school systems. I had a request from a school system back East that has 45,000 students that they want to get badges. We have had a request from a school system in the Midwest where they get a lot of teachers who are getting 3D printers, and they don't know what to do with them. And they'd like for us to train the teachers.

So I really see a huge opportunity. And these tools that we're using are not just being used in manufacturing. One of the people that we worked with on the HR side in research was Walmart. And their big worry is now they're putting in these janitorial robots. And their big dilemma is who's going to program them, and who is going to fix the robots when they're not working? And it's everywhere. It's not just am I going to get a job at that manufacturing company? It's also your local retail store.

TROND: Fantastic. This is very inspiring. I thank you so much for sharing this with us. And I hope that others are listening to this and either join a course like that or get engaged in the Fab Lab type Network and start training others. So thanks again for sharing this.

SARAH: Oh, it's a pleasure. It's a real mission, I think. [laughs]

TROND: Sounds like it. Have a wonderful rest of your day.

SARAH: Thank you.

TROND: You have just listened to Episode 3 of the Augmented Podcast with host Trond Arne Undheim. The topic was Reimagining Workforce Training. Our guest was Sarah Boisvert, Founder, and CEO of Fab Lab Hub and the non-profit New Collar Network.

In this conversation, we talked about reimagining workforce training, industry 4.0, and what you mean by new-collar jobs and Fab Labs; what skills are needed? How can they be taught, and how can the credentials be recognized? What has the impact been, and where do we go from here?

My takeaway is that reimagining workforce training is more needed than ever before. The good news is that training new generations of workers might be simpler than it seems. Practical skills in robotics, 3D scanning, digital fabrication, even AR and VR can be taught through experiential learning in weeks and months, not in years. Micro certifications can be given out electronically, and the impact on workers' lives can be profound. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. Augmented — the industry 4.0 podcast.

Special Guest: Sarah Boisvert.

Episode 90: A Renaissance in Manufacturing

27 juli 2022 | 38 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode #4 of the podcast, the topic is: A Renaissance of Manufacturing. Our guest is Enno De Boer, Partner, Digital Manufacturing Lead, McKinsey.

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

In this conversation, we talk about What is digital manufacturing? How to transform operations strategy, best practices, specifically the World Economic Forum Global Lighthouse Factories. We also tackle future developments: How to stay up to date in this fast moving field? What’s next?

Trond's takeaway: is that manufacturing is indeed undergoing a renaissance. There should be a tremendous amount of excitement among policy makers, industry professionals, and frontline workers about the changes in play. Technologies are maturing. The digital factory is becoming a reality. For those who already took on board the lessons of lean manufacturing and are exploring the latest opportunities, automation has become augmentation. Yet, there's still a lot to learn. The World Economic Forum's Lighthouse factories is one place to seek inspiration.

After listening to this episode, check out the World Economic Forum Global Lighthouse Network, McKinsey's Operations practice, well as Enno De Boer's social profile.

World Economic Forum Global Lighthouse Network: https://www.weforum.org/projects/global_lighthouse_network
Enno De Boer (bio): https://www.mckinsey.com/our-people/enno-de-boer
McKinsey Manufacturing & Supply Chain practice area (@mckinsey_mfg): https://www.mckinsey.com/business-functions/operations/how-we-help-clients

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by @Art_list_io. The show can be found at http://www.augmentedpodcast.co/

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode #1 on From Automation to Augmentation or Episode #2 on How to Train Augmented Workers. Augmented--the industry 4.0 podcast.

Transcript:

TROND: Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time every Wednesday. Augmented — the industry 4.0 podcast.

In Episode 4 of the podcast, the topic is A Renaissance of Manufacturing. Our guest is Enno De Boer, Partner and Digital Manufacturing Lead at McKinsey & Company. In this conversation, we talk about what is digital manufacturing? How to transform operations strategy, best practices, specifically the World Economic Forum Global Lighthouse Factories. We also tackle future developments: How to stay up to date in this fast-moving field, and what’s next?

TROND: Enno, how are you doing today?

ENNO: Very good.

TROND: I'm excited to have our conversation. First off, Enno, you're an interesting guy. You obviously have a tremendous amount of experience working with a lot of manufacturing factories through your work at McKinsey and also now directly at the World Economic Forum. But what got you into manufacturing? What sparked this interest?

ENNO: I had always kind of a passion for real things, for products and everything. And my dad was an engineer. He was a researcher in the steel industry, and he wanted to get me into steel, but I thought I wanted to have something a little bit more sophisticated. So I got initially into automotive, got really excited about it. And then when you're in automotive, you need to go to the shop floor; that's where the real music is. So that's how I got into it. And then, from there, it took its toll, and I went into any industry, and I'm always passionate about manufacturing.

TROND: Wow, that's where the real music is. Well, you ended up getting your master's and your doctorate in mechanical engineering. I think they're all from Dresden and from Aachen. So you've been around the academic side and now very much on the combination, I guess, of consulting and advising, but you enjoy getting into these factories and hearing the music, basically.

ENNO: Yes, totally. I started my career at BMW, and it was amazing to see what product they are building, et cetera. And then, I moved to McKinsey, and it was always about the products and how can we make the products better? How can we get them better to the consumer, and also, how can we make literally the shop floor a better environment? And I think that's so exciting about what we're seeing at the moment with this digital revolution, and we're getting to that in a moment.

But it's all about augmenting the operator and figuring out how do we take the dull, dirty, and dangerous work out of manufacturing and make it very exciting? I think it's one of the most exciting spots to be. For all the young people, I just say go into manufacturing. That's where all the fun technologies come to bear. Is it augmented reality, virtual reality? Is it digital twins? Is it AI? Is it digitization? Is it 3D printing? All of that is coming there. Tell me any other industry where that's happening.

TROND: You know, I echo what you're saying. And it's almost incredible how long it has...well, how long it has taken, but how long it's taking for the wider world to realize how many things are actually coming to the fore in manufacturing right now. Give us a sense of what this environment looks like. Well, there are many buzzwords, but what is digital manufacturing? Let's start sort of simple here.

ENNO: Yeah, digital manufacturing, actually, it's interesting. It's an interesting term. So when we worked with the World Economic Forum, we defined digital manufacturing as 110 use cases that are spread across...roughly half of them within the factory walls. And then something like predictive maintenance, very apparent, but then half of them also outside of the four walls. So how do you connect to product development, get your products faster developed? Most likely with virtual reality, with digital twins.

How do you connect to the customer? How do you get customer orders in and immediately propagate them down to the shop floor and all the way back to the customer where the product lands? And you want to have this in one digital thread, how we call that. So that's very exciting. So that's digital manufacturing. It's very much about augmenting the operator.

As I said before, it's not so much about this idea that was out there in the '80s and '90s about the lighthouse factory and full automation. Nobody is talking about this. This is really a concert of how we are bringing technologies to allow the operator to bring out better products in higher quality, in higher agility, and more sustainable.

TROND: I know industry 4.0 is a big term. But at the forum, there's also this notion of this fourth industrial revolution, so very specifically calling it a revolution. How do you feel about those things? Are they even sort of perhaps wider terms than just focusing on the worker?

ENNO: I think it's interesting. I think it's partly we have an evolution because the manufacturing sector you cannot change overnight. It's very complex to manufacture products, and you need many technologies. So it feels not like this is happening overnight. Though I would say now, with what we have gone through with this terrible pandemic over the last year, it has almost switched, and it totally accelerated the digital transformation. So I feel now it's becoming much more of a revolution because I'm seeing examples where innovation is not stopping anywhere.

Like we have one lighthouse that we got new on the lighthouse platform from Alibaba. They took an entirely new stand on how do you do apparel manufacture? How do you produce jeans? Now, that sounds very simple and sounds very labor intense. They took a stand at this and fully connected it to the customer to get their ideas on how that would work, but then fully digitized it.

And that allows them to create products, new apparel in only 30% of the time and also bringing it to the customer 70% faster than anything we have seen before. So there's a real revolution going on and a renaissance, I would say, of manufacturing and the art of the possible. I would say the limit is the sky.

TROND: But you said lighthouse. When you say lighthouse, to me, I'm thinking of a navigational tower created out there in the ocean with lights and signals to navigate against. Give me a sense of what this metaphor means and what you have used when you built out this Lighthouse Network at the forum. What does it mean, and what's the purpose? And why this metaphor?

ENNO: Yeah, and I think it's great that you're asking because I got a lot of questions at the beginning. "Is lighthouse the right word, Enno? Lighthouse is where we are driving on rocks. Is that not negative?" And I said, "No." It's like the light. We need always role models. We need examples that we can latch on. We need things that we can learn from, that are lighthouses. Lighthouses are a towering example. They are high. They are shining out, and they're shining the way.

And I'm a sailor, so I love lighthouses. So when I'm coming to the coast and the first thing I see is this light. And it's going up, and it's leading me the way, and then I'm coming nearer. I get the contours. And that's a lighthouse. So what is the lighthouse? The lighthouse is, we said it's not a shiny object. Stop with these shiny objects. It's not about technology forward; that's wrong. It's not about building an ivory tower, and everyone is looking in very different.

Three things we're looking for with WEF Lighthouse; first of all, we want to see impact at scale. Secondly, we want to see that unleashed by several use cases, several technologies that enable that, like really innovation there. And then, we want to see that this is sustainable, that there are the measures and the enablers below that is not only sustainable but also scalable.

That is, for us, a lighthouse, and that's something that is exciting people. And they say, "Well, I want to..." I get every week a call "I want to have a lighthouse in my organization. How do I do this?" And that's exactly what we wanted to create. We wanted to create that everyone gets a feeling of what really is industry 4.0.

TROND: Well, so then here's my question. How did this project get started, and how do you select lighthouses? And what exactly do you collect once you have selected lighthouses, and how is it that then it becomes helpful? Is it kind of a collection of videos from the sites? Is it interviews with the people who have designed the work processes there? What is it exactly?

ENNO: Yeah, it started...as always, the first try is not working. [laughs] So when we started it, the first idea I had I said look, we need these lighthouses. We need examples. It's like in the old lean terms where we had Japan; we had Toyota, we had Honda, as ways to go for the manufacturing community to learn. I said, "We need to create the Japan of digital manufacturing."

And the first answer that I got from a couple of executives where I was on an executive committee, they said, "Hell no. We'll not share our secret sauce. We're ahead, and we don't want that others learn from it." And I said, "I think that's wrong. You need open innovation. You need to share," Because this is such a dynamic environment where you can only stay ahead if you fully open collaborate, and learn from the best, and then stay ahead." And it turned out to be true.

And then I found the World Economic Forum. They loved the idea. We started to build this app jointly. And it's now something that everyone in the manufacturing industry aspires. So that's how it started. It was a lot of work. So we started almost three years ago to build this with the World Economic Forum. And we still feel we're only at the very beginning. We have now 54 lighthouses and more than a dozen, actually, to be announced soon that are coming out. But if you put this into perspective, this is 50 out of 10 million factories, so a lot of work to be done.

TROND: How many lighthouses should there be?

ENNO: I think there should be many, many more. And I think every organization should have at least a handful or a dozen lighthouses. Because what we find is you need different lighthouses in an organization. You need maybe a lighthouse that shows you how you connect your product development to manufacturing. You maybe need a lighthouse on how do you connect to the customer? You need a really sustainable lighthouse. So there are already three.

And then you need to start to use this lighthouse. A lighthouse is not a mean by itself. I think then you need to start that you get the entire organization to kind of moving to transform the entire value chain, the entire production network. So you could almost see that. And that's how I see it.

I think we're very blessed with these lighthouses because, for me, they are a little bit of the window into the future. That should be the standard in three, four years for any manufacturer. So if you ask me, maybe 10 million so all the factories should become lighthouses. Now, every lighthouse will be a little bit different and needs to be built within its context.

TROND: But are you saying that in order to qualify to be a lighthouse, there is an aspect that is better than the average? Because otherwise, you shouldn't be looking at it. Now I'm just trying to figure out, well, one, you how you select it, and on what features you select these things. And on the aspirational side, if I'm a factory owner or an organization and I think I'm inspired by what you're saying, how do I interact with this project? And how do I learn from the lighthouse? How do I build my own lighthouse? What is this thing?

ENNO: So I think you're spot on. We said we wanted to create the Japan of digital manufacturing, that was a vision, and that is still to be true. So what we want to have on the platform is lighthouses that bring learnings to others, that are willing to share those, and that are towering, and these learnings are important and interesting enough that everyone can learn from it. So yes, it should be over the average. It should be better than anything. It should be a best practice. Yes, of course.

We are not looking for someone who has invested a ton of money into technology and has not gotten any returns out of it. There are a lot of examples of that. We are looking for the ones who have smartly invested into technology, also driven the people transformation, also have driven a business transformation with technology and with that created impact at scale. That's the number one we're looking for: impact at scale.

Number two is, is it driven through real technology innovation? And are these use cases there? And then is this sustainable? Is this just kind of a quick blip of a performance? Or is this something where we feel that this company is taking this lighthouse really to fully transform themselves and literally the cluster they are working in?

TROND: Can you give me some concrete examples so some of these lighthouses? There are 54 that have been announced. I mean, that's too much to cover in one quick talk, but give me a sense of what kinds of things you already have in the portfolio.

ENNO: Yeah, so we started initially with factory lighthouses, so the ones that are very factory. We had initially 16, and then we scaled this up. One example is, for example, Procter & Gamble, the Rakona site, really interesting, was about to be closed. They had one last chance, and the factory team was amazing. They said, "We go all in. If you let us do it, we will go in. We take the challenge."

And they turned around the site with digital, with fully digitizing it. It was really on the bottom of the P&G manufacturing sites. It was a brownfield; I think 100 years old, very, very traditional. And they transformed it fully. And they are now one of the top performing sites in the Procter & Gamble network, which says something and which says that anyone who has the ambition and has the leadership and is going full in can do it.

It's not a question of whether you're a greenfield; this is a brownfield. It's not a question of whether you're a new site or an old site. That's one example. Another good example, because we have quite a breadth there, I talked about Alibaba, a digital native company that fully went into apparel manufacturing to innovate apparel manufacturing.

Another example is Henkel. They had very ambitious sustainability goals from the very get-go. They said, "We can only achieve that through digital transformation." They connected over 30 sites with a digital twin. They get really deep into the energy management, into predictive actions. And they were able to reduce their energy consumption by 38% and their water consumption by 25%, very sustainable example.

Another one is Schneider Electric, and I could go on, who reduced their carbon footprint by 78%. So we're not talking about let's do another 10%. If someone comes to me and says, "Look, let's do another 10% of this," I say, "Okay, you most likely don't need [inaudible 18:08]. Think harder. How do you want to hit customer breakpoints? How do you want to do something really spectacular? And then let's build the full stack of digital together to innovate that."

TROND: Well, you've already given out some secrets, I guess, around transforming operations strategy these days. Is a lighthouse strategy the first thing you recommend when you go into a company these days, or what is your approach? Because you are an operation strategy expert in manufacturing. Is that the first thing you suggest, or is it kind of to look inward? Or what is the first thing one should do today?

ENNO: The first question I have is, what business impact do you need to drive? Because that determines everything because a lighthouse is not a lighthouse. So, first of all, I need to know whether you want to drive growth, whether you want to drive agility, mass customization, sustainability, productivity, or speed to market. Let me know that. And that's already a hard question because a lot of CXOs, CEOs, COOs say, "Well, I haven't thought about it. I thought I'm coming to you, and we're building a lighthouse." I say, "No, we're not building a lighthouse just for the lighthouse sakes."

So let's figure out what is really the business impact you need, then let's go from there backwards and say, out of the 110 use cases that we have seen in the lighthouses, what are the ones that will really help you? Typically, it's 20 to 30, maybe 40 use cases that immediately will drive fundamental value. Let's take them.

And then the most important thing is let's figure out how do we scale this? Because that's what has been the biggest challenge, and I would say that is what differentiates the 1% of the lighthouses, or less than 1% of the lighthouses, and the rest of the 99%. It's called pilot purgatory. We've seen thousands of flowers bloom approaches, pilots, over pilots, and they are not scaling.

TROND: Why is there such a purgatory? Why is it so hard? And what did those 1% do that the others don't?

ENNO: I think we are looking at this question for quite long. And I think it's partly; I would say, cultural in the manufacturing sector. The manufacturing sector in the past was the one that would...as a CEO, you're asked, okay, give me another 5% cost reduction and don't interrupt the production. There was no question of, okay, look at this strategically. Tell me about how manufacturing can be a competitive advantage. So really, the thinking and being strategic about manufacturing, I think that's one part.

The other part that I think is cultural is lean has learned us...and lean is really a fundamental and important part of the digital transformation. But lean has learned us to disaggregate, to democratize, and to spread literally everything across all our production network and let everyone do a little bit of something.

Now the problem is that we'll be coming back in the future, and this is great. Democratizing technology is the right thing to do. But at the beginning, to get this started and getting out of pilot purgatory, you need to have some kind of a guided approach that is strategic, that is focused, and that is building certain capabilities that most likely these companies have not in their networks.

TROND: So are there really distillable, small nuggets of best practices in this field of manufacturing? Or is it so complicated that everybody has to....yes, they can look for paragons in the lighthouses. But you have also said one of the reasons you're so fascinated with this is you have to just hear the music. So what is the balance of, I guess, listening to your own music, really just figuring out what is happening in my own work process versus looking at other people's work process? What is the balance between the internal, the external, the inspiration versus the perspiration, I guess?

ENNO: I think it's, like always, you need to start from where you are. And I think I'm glad that you asked this question. This is not about taking the lighthouse, and then that's my blueprint, and then let's just do it and copy it. No, it won't work. You need to start from where you are. So it starts with a diagnostic. It starts with, as I said earlier, it starts with what business goals. Everyone has different business goals. Then it starts with where's your situation? So how do you manufacture? There are thousands of different types of manufacturing.

So what's your starting situation? What's your maturity? What's your capabilities? What's your tech capability? All of that and then build on that. I think there's for anyone a tailored journey on how do you then mobilize your people? How do you build the right capabilities in-house to be then really able to scale something? And there are a lot of learnings from the lighthouses how they have gone about it, how they have mastered to excite the shop floor.

All of these lighthouses the people love it, so they get them excited. But you need to get the middle management also excited because they are sometimes I call them the clay layer or something. They're maybe not so excited about all this change. So you need to get them on board that it's really helping them to do their job better. So that's something you need to figure out.

And then you need to figure out...that's another thing that is big is, in the past in manufacturing, we have already said, okay, the IT guys leave the IT guys where they are, and we're only calling them when we really need them. But you need to closely work with IT because otherwise, you cannot scale it. And then, you will need to work with OT like the operations technology so connecting the sensor. So there's a lot to do. And I think you need to find your own way, and the puzzle pieces are in the Lighthouse Network. You can find them there. And then, you need to put your puzzle together.

TROND: I know you've worked with this for a long time. What are some of the surprises along the way that have shown up in your work? In your experience, what are some of the good and bad surprises that you have learned along the way, things that you didn't expect either when you built out the Lighthouse Network or as you have been spending time listening to this factory music?

ENNO: Yeah, I saw a couple of surprises. So one biggest surprise for me is...so I'm German, but I came over to New York 10 years ago. And so I'm pretty now in the U.S. I'm rooting for the US. I'm also rooting for German engineering. But guess what? The U.S. is behind on adopting these technologies. And it's not behind on developing; it has fabulous startups. It has fabulous technology companies. But the digital transformation is not happening in the U.S., not as much as in China, and also not as much as in Europe. And we should ask all ourselves, why is that?

How do we mobilize the U.S. manufacturing? That's for me, one, and I can tell you I have turned every stone in the U.S. and looked under every stone to find lighthouses here. But the fact is we have many, many more lighthouses in China. And the fact is also, if you look at them, they are freaking exciting. So we can learn from China. Is that a surprise? Yes, that is a surprise. That surprised me.

TROND: Does this make you popular walking around in America when you point this out?

ENNO: No, most likely not. But I want to help U.S. manufacturing. I'm totally excited about U.S. manufacturing. And I think there is all the capabilities. We have the technology here. We have the leadership. We just need to do it, just do it. And as you said, it's about getting the inspiration. I think we should very quickly look at what's out there, and then figure out a way, and then put real effort behind it. And the U.S. has shown that over and over again, once we rally around something, we can really achieve big things.

TROND: But what is the problem here? Is it a technology fix or maybe an overconfidence in, you know, the U.S. has always been innovative, and we're leading everywhere and not looking at the human aspects? Or is it specifically a training challenge? Is it a misunderstanding of how some of these things work? Is it just the old outsourcing thing that people have just said, "Well, all of that stuff is going to happen in foreign factories anyway? It's not important here anymore"? Or how did it start, and how do you think we can get out of it here in the U.S.?

ENNO: I think we have neglected manufacturing. We have neglected manufacturing in the entire Western world. We found an easy way to offshore and bring it to low-cost countries. A couple of decades ago, we have written off manufacturing and have said, okay, there will be a constant decline in manufacturing. Now, I did a study in Germany, I think ten years ago. And honestly, the result of the study was sobering because there was no digital and there were no ideas. We couldn't bring ideas together to innovate manufacturing.

Now, I must say what I've seen now and what is possible is, well, you can be really competitive in the U.S. with manufacturing because the labor differential is not the core thing. But what you need to do is you need to invest, and you need to invest in the people. You need to build and rescale. And you need to augment with the technology, your people, and make sure that they get more productive. That's what you need to do, and then you can be productive. So I think there's something happening now, and I can see that it's really taking off. The conversations I had over the last six months, I would say, are fundamentally different from what I've seen before. So I'm very optimistic.

TROND: That's great to hear. Next for me in my mind is you spend all of your time presumably on this. Where do you go to get your insight? How do you sharpen your teeth? Are there influencers to look at, or are there particular lighthouses? Or do you use yourself a lighthouse strategy? Or how do you digest all of the evolving manufacturing insight that's floating around? I'm just curious.

ENNO: That's a great question. So first of all, I sometimes sneak into some of these factory visits, and I just do a real go see and see what they are doing. And I'm at the source [laughs], so I have the benefit. We have a big team, and they have walked all their shop floors. And I can let them walk first, and then they tell me, "Enno, this is the factory really," or "This is a supply chain that you should really see," and then I can do that. So that's one inspiration.

I think another inspiration is we have an amazing industry 4.0 expert panel that we have created with the WEF that is literally selecting these lighthouses. And it's very independent, so I'm not on there to make this also very independent. But it's a power source. There are 30 individuals around the globe that I would say are the most experienced in industry 4.0, and it's some academics. I think the right portion of academics is important. But then it's also a lot of practitioners. And that's where I'm getting my inspiration.

And then, I get my inspiration typically from client work. I'm spending time with CEOs with COOs. And we are at the moment building something truly amazing in the biotech sector, where we're literally bringing all the best of digital manufacturing to this client. And that's for me always an innovation with young teams, with people who really want to make a difference, and then with people who have really a lot of domain expertise. So I think also these teams of bringing the young, aggressive, technology-minded, and then bring the ones in manufacturing who have the domain expertise, who have seen this for 20-30 years, bringing this together in teams is a true inspiration.

TROND: What about the future? Where are we heading? We've talked a little bit about it. You think it's a very exciting situation. Things are coming together. But we've also spoken about how long things take. Is there a danger now that the story has become one of revolution? And indeed, there are so many exciting things happening, yet they have taken a while. How do you see this? What's next? And how fast is the next going to evolve? We have talked a little bit about the U.S. being somewhat behind, at least from this lighthouse context, other places. How quickly is this entire thing kind of coming together? And what's the outlook really for manufacturing?

ENNO: [laughs] I will give you not a timing answer because I built my first digital manufacturing startup in '99. And it was just 20 years too early, and it failed miserably. Because all the ideas were right and if I would have built it now, it would be maybe very successful but 20 years...so I will not give you an answer on timing. But I would say that we have audacious goals in the world.

So number one, I think we really need to do something in terms of sustainability. The carbon footprint of manufacturing sector is 20%, 54% of the energy consumption worldwide comes out of the factory and out of manufacturing. And we've seen the lighthouse examples. We have maybe a dozen of lighthouses that make truly an impact on how we go to carbon neutral. So how do we scale this up? That's for me, one. And I would say we have the toolset. We have the examples. We have the role models. We need to grab it by the horns and do it. That's number one.

I think number two is with this pandemic which is really bad, is there's a need for rethinking, and there's a need for growth. And there's a need on how do we master through a looming recession? And one thing we're seeing with the lighthouses is they're a true inspiration for growth. So how do you grow with best digital capabilities?

So I think the good news is we have the toolbox. It's ready. We have a real momentum here. Now we need to get everyone on board and everyone doing their work because a lot of work is for the next years ahead of us. [laughs] But there will be also great outcomes out of that. So it's always worthwhile the journey. [laughs]

TROND: So do I take it that for you, there is a true renaissance of manufacturing? I mean, the last Renaissance came after a plague, arguably, right? I mean, if you look at a very long historical perspective, the Renaissance came out of the Black Death; at least that's one version of the story. Without making that entire comparison, taking it too far, the Renaissance of manufacturing, it can happen, you think?

ENNO: It is happening, and not can happen. It is happening. What I've seen is when it hit us in New York in March, my practice, we were doing usually physical shop floor visits, and we switch within the day to virtual. It was possible. We couldn't believe it before that it's possible. We went 100% virtual. I talked to CEOs that entirely managed their shop floor network from the couch in a way that they had their digital tools to really know what's going on because they couldn't go to the factory. So I think it's really happening.

And if this pandemic has one positive, I think it gave us the pause and also the need to really rethink, and that's what is happening now. So, I see Renaissance, yes. And we have also seen how important some products are that we need those products. They are important for not only the well-being, but they are like life critical in part. So having that seen, it was a good wake-up call. And this will foster a lot of innovation in the coming years.

TROND: Fascinating. Enno, thank you so much for this talk. I hope we can stay in touch.

ENNO: Trond, it was a pleasure. Thank you so much.

TROND: You have just listened to Episode 4 of the Augmented podcast with host Trond Arne Undheim. The topic was A Renaissance of Manufacturing. Our guest was Enno de Boer, Partner and Digital Manufacturing Lead at McKinsey & Company.

In this conversation, we talk about what is digital manufacturing? How to transform operations strategy, best practices, specifically the World Economic Forum Global Lighthouse Factories. We also tackle future developments: how to stay up to date in this fast-moving field, and what’s next?

My takeaway is that manufacturing is indeed undergoing a renaissance. There should be a tremendous amount of excitement among policymakers, industry professionals, and frontline workers about the changes in play. Technologies are maturing. The digital factory is becoming a reality. For those who already took on board the lessons of lean manufacturing and are exploring the latest opportunities, automation has become augmentation. Yet, there's still a lot to learn. The World Economic Forum's Lighthouse factories is one place to seek inspiration.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 1 on From Automation to Augmentation or Episode 2 on How to Train Augmented Workers. Augmented — the industry 4.0 podcast.

Special Guest: Enno de Boer.

Episode 89: The Fourth Industrial Revolution post-COVID-19

20 juli 2022 | 35 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 9 of the podcast (@AugmentedPod), the topic is: The Fourth Industrial Revolution post-COVID-19. Our guest is Francisco, Betti, Head of Advanced Manufacturing and Production, World Economic Forum.

In this conversation, we talk about why he got into manufacturing and how the World Economic Forum works. We discuss how the Forum has changed over these past 5 years and how manufacturing has become the lead topic among the global elite. The manufacturing platform is now, arguably, the primary among 17 flagship initiatives at the World Economic Forum. We go deeply into the changing business models of manufacturing and what the next decade holds.

After listening to this episode, check out World Economic Forum as well as Francisco, Betti's social profile.

World Economic Forum: https://www.weforum.org/
Francisco, Betti: https://www.linkedin.com/in/francisco-betti-10074342/?originalSubdomain=ch

Trond's takeaway: Manufacturing has escalated in prominence during COVID-19, and for good reason. What we can produce decides what we can become. The deep digitalization gains society has made over the past few years had to quickly be implemented on the factory floor. Surprisingly, a large part of the industry was ready. But the process now needs to complete and the results will likely be an entirely new production platform for the world.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 4: A Renaissance of Manufacturing or episode 6: Work of the Future. Augmented--the industry 4.0 podcast.

Transcript:

TROND: Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. In Episode 9 of the podcast, the topic is The Fourth Industrial Revolution post-COVID-19. Our guest is Francisco Betti, Head of Advanced Manufacturing and Production, The World Economic Forum.

In this conversation, we talk about why he got into manufacturing and how The World Economic Forum works. We discuss how the forum has changed over these past five years and how manufacturing has become the lead topic among the global elite. The manufacturing platform is now arguably the primary among 17 flagship initiatives at The World Economic Forum. We go deeply into the changing business models of manufacturing and what the next decade holds.

Augmented is a podcast for leaders hosted by futurist, Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the manufacturing upskilling community launched at The World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time every Wednesday. Augmented - the industry 4.0 podcast.

Francisco, it's great to have you here.

FRANCISCO: Excellent. Well, thank you for hosting me, Trond.

TROND: Well, Francisco, you are an international development professional. You are now working for The World Economic Forum. And I meet you regularly because you support an enormous amount of activity there in manufacturing. But I wanted to bring us back a little bit to your origin. I know you went to The Sapienza University in Rome, studied international relations, and then worked a little bit in consulting. What was it that got you interested in manufacturing? Because it's a path that I always have to ask people about. It's not the obvious path today. But perhaps it would be the obvious path tomorrow, which we'll get to. But I'm curious about your way.

FRANCISCO: It's a great question, Trond. And indeed, I have a background in international development relations, economic development as well. And I had a past career in consulting. How did I start focusing in manufacturing? It was at The World Economic Forum back in 2015. Actually, when I joined the forum, I realized there was a need to launch a new piece of work, a new conversation on the future of production on the future of manufacturing. And we started at the time working on a very small concept that was pretty much oriented towards...it was the policy angle, which is why there you get the connection with the international development space.

At that time, we were in conversations with a few governments who were not yet aware of this concept of The Fourth Industrial Revolution but who started to see coming a wave of transformations that they knew were going to disrupt their manufacturing base and have an impact on their manufacturing base. And therefore, we started the conversation with prime ministers, with minister of industry, that then led to put together a product concept that evolved over time.

And today, it's one of the 17 global platforms and initiatives that The World Economic Forum is running that I lead on the future of advanced manufacturing and production. At the very beginning, we started focusing pretty much on the policy angle. Today it's a real public-private platform where both companies and governments advocate for the actions that need to be taken to shape the future of production that works for all.

TROND: Francisco, it's a fascinating journey for you. But it's also an incredibly fascinating journey for the field of manufacturing. And I wanted to address a little bit how manufacturing as a platform became one of the 17 flagship initiatives, and by that, how The World Economic Forum itself has changed over these past few years.

And then lastly, the last year has meant enormous change with preparation around COVID. And again, manufacturing has been escalated into this enormous importance. And I would imagine that your development background has come in handy because the kinds of issues that suddenly became foreground were a little unusual, I guess, for manufacturing as well. So firstly, what has happened at The World Economic Forum over these years that has catapulted manufacturing into this prominence?

FRANCISCO: What I think is that in the conversations with our private sector community but also with the public sector, we realize that there was an urgent need for a public-private dialogue to be brought back on top of the agenda. We all know what happened with COVID. The disruptions that we saw in manufacturing, I think, made us realize that when things go wrong in manufacturing, you run out of essential products, not even talking about the protective equipment, ventilators, and all the things that were most needed because of the public crisis. But even basic supplies such as food or toilet paper became an issue, and people started talking about those.

So let's say that there was a realization that manufacturing still plays a major role in our global economies and society. And that's something that is quite exciting and interesting, but you need to look a couple of years back. And I think if you look at the pre-COVID war, and you start thinking about how different megatrends were shaping the war, you also realize why manufacturing came back and became an important issue on top of the global agenda for both private and public sector communities.

And what I'm talking about is the combination of mainly probably three or four megatrends, but it's The Fourth Industrial Revolution, which is a concept that our Executive Chairman launched back in 2017, mainly referring to the blurring lines in between the OT and IT war that are transforming not just how our companies operate but probably who we are and what we do as human beings.

So it was the concept of The Fourth Industrial Revolution together with climate change, the imperative for sustainability, with the geopolitical trends and tensions that we saw over the past four to five years. And, of course, all things that relate to the geo-economic evolution in which you can add elements such as aging population or the slowing of the global economy. But it's all those trends and combinations that made people realize the importance of manufacturing, and that brought it back onto the global agenda. So it's been a fascinating journey.

There have been plenty of discussions which would trend from the technology angle, and how technology can transform manufacturing for the good, and what's the positive impact it would have in society. And most recently, about the role that manufacturing can play in the context of this concept of stakeholder capitalism and the need to create value, a new value that is delivered not just to shareholders but to shareholders, workers, society, and the environment overall.

TROND: It's fascinating because many of these concepts to some of us are fairly abstract still because like stakeholder capitalism unless you truly are in this battle, or you either were fighting for it to even emerge, or you are at the C-level, and you're basically being tasked with figuring out what this concept is going to translate into...

But it seems to me that what you're saying is that with manufacturing becoming such a forefront of many battles in society, this is not theoretical anymore. And it's something that all people can relate to whether you are on the factory floor implementing your OT, as you said, your operational technology or you are at the leadership level and trying to strategize about what this means for the organization as a whole.

FRANCISCO: And, Trond, you're absolutely right. Manufacturing needs exactly what change is happening today. We are hearing on a daily basis global companies making commitments towards the SDGs. There was the concept of stakeholder capitalists setting very ambitious targets by 2025-2030 getting CO2 emissions. The only place in which we are seeing that happening now it's in manufacturing. Manufacturing is changing the speed.

When you double-click on how digital technologies, digital solutions advance manufacturing, industry for control, it's transforming the world of operations, driving massive efficiencies, productivity revolutions, driving growth, and at the same time, augmenting workers, contributing towards the sustainability targets by cutting energy consumption, water consumption, material consumption, optimizing waste management. I think that's when you realize that it's probably in the real economy space, in the manufacturing space, where all those commitments are starting to come to life, and that we are seeing real change and real action. It's extremely exciting.

TROND: Yeah, and it's also an interesting time because without getting into the politics of this, there clearly is a bit of an outcry because of the success of technology in other fields. Social platforms and other digital advances that we have made have created both an enormous opportunity, which of course, explains why so many things can be achieved in manufacturing. But they were about to run awry a little bit in the sense that they weren't or aren't still contributing to the global good in the direct sense that the manufacturing industry, of course, is primed to do but for so many years was not.

It still boggles my mind, though, that we are having these conversations at the forum. These conversations are happening. But I don't think that everybody is fully aware of how far manufacturing is about to change and emerge on the world stage. Give us a little bit of a sense before we get into the meat. And I want to talk about business model changes in the industry. But what do you think is needed for the greater public? And by that, I mean young people, older people to realize that manufacturing is actually where things are happening today. What does it take to see it?

FRANCISCO: Well, I think that it's a great question, Trond. And I think that one of the...we probably need to start by asking what can manufacturers do to get closer to the general public? And things like open up facilities so that people can see what both a factory, you know, not of the future but the factory of today looks like and how shopfloor operators are interacting with new software technologies, new interfaces, the world of advanced robotics. We are even seeing drones flying within facilities with inspection, right?

The level of technological transformation and the pace in which it's happening within manufacturing facilities it's amazing. And if you link that to the jobs conversation and how that connects with new generations, I think that...I'm sure that you know younger generations will get very excited if they have a chance to see. So I think manufacturing has been traditionally, and because that's the role that it used to play, always behind the scenes. I think that we do have an opportunity for manufacturers to start showing to the general public what it is, and what it really looks like, and how exciting it is, and also the contribution it makes.

A lot of the things that we enjoy today in life could not be enjoyed if there wasn't a factory, if there wasn't a facility, if there wasn't an entire supply chain behind it. So yes, I think that there's something that the manufacturing community can do better, which is to talk more about the things that are happening in manufacturing in a very transparent, open way. And that's one of the things that we are looking at the forum, providing that platform to interact with the general public.

TROND: I just wanted to point out that, again, these are sensitive things, but the forum for a long time was just the way it was set up was a conversation between leaders. But I have seen over the last few years that more and more you realize, or maybe explain it to me, that it's more than a conversation between leaders. Clearly, this conversation is more important than ever. But in addition to a conversation between leaders, you and the industry need to foster a conversation with small and medium-sized enterprises and, as you pointed out, actually, with the greater public because the battle here is kind of a battle of interpretation.

Is this an interesting space to be in? And if so, we all need to innovate within this space. That must be challenging for a network that was basically mostly set up to dialogue with leaders. I wanted you to talk a little and begin this discussion because I know you have written a paper on changing business models. But at the same time, as you're writing this paper, the forum is almost having to change its own business model. So I want you to address that just because business models is such an abstract thing. So I wanted you to personalize it a little bit as you're explaining what business models mean in manufacturing these days.

FRANCISCO: Absolutely. And I think that we are all transforming our self-set speed within the current context. It's either transform, innovate, or die nowadays. And that happened to every organization regarding this space in which they operate. Maybe on the first part of your question, I think that 50 years ago or 51, when the forum was founded by the Executive Chairman, Professor Klaus Schwab, of course, the main role was to convene leaders. But then, over time, we evolved and became an international organization for public-private cooperation in which dialogue is just an important component of what we do.

What really happens at the forum is the formation of communities that are able to drive action and impact over time. Now, of course, in the context of the current pandemic, it’s how do you bring leaders together? And how you orchestrate and incubate those sections that will deliver impact became a challenge. But it forced us to adopt digital technologies at a pace that we didn't think we could. We were doing that before, but COVID played a major accelerator role.

And I'll give you an example to make it tangible. You hear about our initiative, which we call The Global Lighthouse Network, that we are deploying together with McKinsey and other colleagues. And what was fascinating is that we had a process in place through which we were physically assessing the facilities. As soon as the COVID crisis started, we were now forced to change the approach. And we ended up coming up with the process to run virtual remote visits, and we realized that the technology was ready.

By shipping a simple kit in which you have a phone stabilizer, a headset with a canceling noise effect, and a few other very basic technologies, you could almost get to the same product that you could get when visiting the facilities in person. So we reorganized ourselves. And what I'm very proud of is that if you look at how we have been running some of our key gatherings but also some of the initiatives that are having a real impact on the ground, we were able to triple the pace in which we were operating.

And we have seen the same happening in the manufacturing space to connect that with your second part of the question, which is how are business models and operating models evolving? I think that during the crisis, because of the shortages from the supply disruptions on the demand side, every company in the operations, in the manufacturing space was facing new challenges, which forced them to take innovations to new and unprecedented levels.

Now what we saw is, in a very short period of time, new ways of generating and delivering value coming up. When I say generating, I'm referring to the operating model, the operating model in the way in which we defined it together with the professors who wrote for that position paper you referred to. The operating model is a way in which your company generates, creates value. Now the business model is a way in which that value is taken to the customers. It's delivered and then converted into revenues.

And someone could say that the innovations that we started to see accelerating at speed probably were of three or four main types. The first one is that we saw companies finding new innovative ways to make the products they were making before, so a lot of innovations in the way in which they were operating, finding new sources of supply, but also adopting technology to take those products to a new level, and very often finding that those products could be made even better because of the innovations that were injected.

The second relating to that was the emergence of new products. So companies started to repurpose and reposition manufacturing to be able to deliver new goods that were in high demand. The most obvious example there is the companies who started to make masks and any type of protective equipment or ventilator, or hand sanitizer. But what that shows and proves is that quite rapidly, with the right level of technology innovation, you can repurpose, and you can turn into a manufacturer of something different, which can generate new value linking that to the business model.

Now, the third type is this idea of topping up products with services. So this is providing a digital thread across your entire value chain to be able to generate new value and deliver new value to the customers not just by the hardware or the physical products that companies were selling but by setting them up with new services, being able to get data all the way from the customers to continue to improve performance and augment the experience that customers were getting.

And then the fourth one that is a very exciting one is the as a service concept. We have seen many manufacturing companies that were able to rapidly sort out operational and business issues for themselves and became services providers. Now, if you look at those four things, what I believe is exciting is that someone could argue that manufacturing it's again at the age of becoming a new engine of economic growth.

I mean, there is a lot of growth that is going to come from all these innovations that we're seeing accelerating and who are, let's say, experts, emerging from manufacturing. And at the same time, because of what we discussed before, we have the opportunity to make that grow more sustainable, inclusive and make sure it works for a larger number of stakeholders. So that's what makes it very, very exciting.

And maybe the final thought on that is that because of the connection with business models, because of the ability to enable growth, manufacturing today is seeing CEO compensation. We are seeing more and more CEOs getting closer to the operation. Or the other way to look at it, we are seeing more and more chief operating or chief supply chain officers who are either stepping into the CEO role who will likely become the next generation of CEO for their companies.

TROND: Francisco, this is fascinating because it almost brings us historically back to the heydays of manufacturing. And manufacturing has always been at the heart of every Industrial Revolution. And I think when Klaus Schwab first wrote about The Fourth Industrial Revolution, I don't know that everyone really accepted it because it sounds nice, right? And it would be nice if it was happening. And arguably, you're looking at these technologies. You're saying there has to be a revolution.

But back in the day when that was written and the precursors of this discussion, in all honesty, there was perhaps no revolution yet. Because there were platform technologies that were available, but you would still have to implement them. But what you're talking about now is how it is basically over the last very few years becoming implemented into not just large but also smaller organizations and then creating new ones.

But to address the first part of what you were saying about these larger firms, so these kinds of superstar firms, I believe you call them in your report, there is a value there as well in an economy to have superstar firms that are actually creating value. There's, of course, less value to a society to have superstar firms that are only creating value for themselves. So that's an important distinction to make. But I wanted to bring this a little bit into the discussion on education.

I was reading the other day about, you know, in the olden days when Prussia basically recreated the modern education system, it was largely factory owners who were supporting that even economically and in terms of the pedagogical models. And I was reminded that it is almost a similar challenge we're facing today because it's quite an education challenge on all levels. I wanted to ask you how do you see this happening? How is this shift truly going to take place? Because now the technologies are starting to be put in place. How is the world's worker base going to benefit from this fast enough?

How are we going to reshape the education system? Surely we can't put them in four-year colleges. And if we start to reframe the gymnasium, which was the strategy back 250 years ago, that, again, is a nice strategy if you're talking in decades. But this industrial revolution isn't happening over decades; it's happening over years and months.

FRANCISCO: I think it's a very important piece of the bigger puzzle. And you cannot talk about the future of manufacturing without talking about the future of skills and pretty much skills revolution that is needed because we know that today there is a skill shortage in manufacturing. Wherever you go in the world, that is happening and not because there are probably not enough hands available but because the people who are coming out of universities do not have the required skills, or out of college, do not have the required skills that are needed on the shop floor or across your engineering base.

And maybe to break it down, I could share three different avenues that companies and governments are exploring. The first one is rethinking the way in which companies, universities, and colleges, and governments interact. I think that the concept of you get a college degree or a university degree and then you are all set for life is outdated.

If you look at the pace at which technology is evolving and which factories and supply chains are being transformed, we will need to find new mechanisms in which people who work in manufacturing will and should be able to continuously go back and forth in between formation, training, technology updates, and the shop floor and the supply chain. I think that's the very first thing that companies are starting to think about.

And there are interesting pilots that are being deployed in many places with new partnerships forming between a specific university and a specific company in a specific location, or people who are or companies who are investing in training students in a specific college by giving them the chance at the very early stages to get an experience on the shop floor. So that's probably the first basket of change that we need to see happening.

The second one is maybe around the skills needs. I mean, if technology is growing and developing exponentially, and I'm talking about technology not in general, but technology applied to address very specific production or business issues in the manufacturing space and to be able to develop new use cases.

But if that is changing and continuously changing at the pace at which it is changing, we will need to find mechanisms to constantly monitor and identify the skills in which we train people on and how often a college curriculum or university curriculum gets updated; I mean, not often enough. So which should create a new dialogue between those who provide the training and those who are able to identify these key needs to go into a continuous, let's say, update and upgrade process there as well.

And the third one, which is the most exciting, is that we probably need to think about how we democratize training in manufacturing. And the best analogy there, you know, how much training did you get to learn how to use the apps that you have on your mobile phone? None, right? You get a new app. You just download the new application that you need, whether it's for navigating a city or learning a new language, or whatever. And after half an hour, you are ready to go.

So I think that there's a lot that can be democratized when it comes to training in manufacturing is we leverage technology, not just to transform production but also to transform the way in which people interact in a factory. So this concept of new interfaces and also the fact that we need to think about technology not as a given but as something that we can influence, and that we could tailor, and that we can put...the concept of the series that you are running is, you know, the augmented. You used the word augmented.

I think that we can leverage technology today, and we weren't able to do that a few years ago. But we can leverage to augment workers' capabilities. I think that's the secret, and that's the path forward to democratize training in manufacturing and learning. And if we can top up with open source, and we can get companies and in the manufacturing.works platform that you guys are driving; it's amazing how there are more and more companies getting exciting and willing to make their training modules available. So I think that that's going to be probably the fastest way to reach the millions of people who work in manufacturing today.

TROND: It's fascinating. As you're speaking, I'm reminded that there obviously has to be a link back to business models with training because if this was simply a case of saying, we're going to outsource these to existing universities, or we're going to outsource the training to these new online training providers, and then the problem is solved, first of all, the incentive wouldn't be there for industry to completely do that. But also, the value created would be isolated to these other institutions.

But I think what you're speaking of is some sort of a new dialogue. And there's a combination, though, because open sourcing and democratizing learning, I guess you're not saying that it necessarily forever has to be free. There's a business model, or there are multiple business models that have to be formed around training where there obviously must be incentives for those who create that kind of training. Speak to me a little bit more about the new emerging platforms in manufacturing.

And I was fascinated by what you said about technology and interfaces needing to be more intuitive. Historically, that, of course, hasn't been the case (Well, I'm arguing. Let's see what you think.) in manufacturing. If you think about manufacturing technologies, they have been fairly complex, or at least they have been perceived as complex. What is needed, in your mind, to structurally change that aspect? Is it almost like mandating that any technology on the shop floor should be that easy? Or is it just a mindset change among those who buy the technology? Or is it the technology providers that need to themselves take this more seriously? How can this change happen?

FRANCISCO: It's a great question, Trond, and we may be at the very beginning of another little revolution within the manufacturing space itself with this concept of new platform or platform tools that are emerging. And for me, the reason is that when you look at some of the data we have when we consulted about why companies failed when it comes to the deployment of new pilots...or to put it in a different way, many companies have been developing great pilots and were able to develop a lot of new use cases. But they failed in the implementation phase because there was pushback from the shop proprietor because they were not developing the right way, or they were not easy to adopt, or not easy enough to adopt.

So I think that whichever type of platforms and interfaces that can help create that connection between the technological solution that is going to be deployed on the shop floor and the way in which it will be operationalized and managed on a day-to-day basis can bring huge, huge, huge benefits to the workers, to the shop floor. And I think that something that we keep hearing from companies is that whenever you give shop floor operations a technology that makes their life easier, there is no way that you can get that technology back. There's no way that you can get that use case out of their hands.

So I think that it's all about...and it links back with the concept of democratizing the access to training, democratizing the access to tools. But there are some challenges. I think there are things that we need to overcome to take it to the next level so that it connects back to the concept of open source. So, for example, there are many interfaces or tools that can be powered by AI or at least by big data and analytics.

The only way to train those algorithms and make them super performant is by having a large volume of data sources. That's something that is not happening yet because you still have companies who are not willing to share their data with the service provider of a specific platform. But they will reach that level in which everyone is comfortable sharing data in a certain way.

These tools will be taken to a new and unprecedented level, and I think that is going to be a must-have. I think that workers, when you will be hiring or trying to attract talent if you don't have these types of solutions, they may just not be interested in getting the job or may prefer to go to another manufacturing company who does have them. I think that it's a fascinating topic. And we're at the stage in which we have seen those solutions being deployed for the first time at scale. So I think that very soon we'll see the results of that, and it's going to be very exciting.

TROND: Francisco, these are fascinating developments. I hope that I can tap into your expertise several times and on a regular basis on this podcast. This has been a fascinating discussion. And it seems like the gains even in just a short year during...or I guess we cannot say post-COVID, but it is during COVID that a lot of these changes have been enacted. And you have had a central place in coordinating the global response, I must say. Fascinating developments, thank you so much.

FRANCISCO: My pleasure. And I think, Trond, the next challenge for the manufacturing community, for the overall manufacturing community, is to see how we can keep the high pace of innovation that was great over the past couple of months. I think that's going to be the greatest next challenge. But thank you very much for hosting me; a real pleasure.

TROND: You're welcome. You have just listened to Episode 9 of the Augmented Podcast with host Trond Arne Undheim. The topic was The Fourth Industrial Revolution post-COVID-19. Our guest was Francisco Betti, Head of Advanced Manufacturing and Production at The World Economic Forum.

In this conversation, we talked about why he got into manufacturing and how The World Economic Forum works. We discussed how the forum has changed over these past years and how manufacturing has become the lead topic among the global elite. The manufacturing platform is now, arguably, the primary among 17 flagship initiatives at The World Economic Forum. We go deeply into the changing business models of manufacturing and what the next decade holds.

My takeaway is that manufacturing has escalated in prominence during COVID-19, and for good reason. What we can produce decides what we can become. The deep digitalization gains that society has made over the past few years had to quickly be implemented on the factory floor. Surprisingly, a large part of the industry was ready. But the process now needs to complete, and the results will likely be an entirely new production platform for the world.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 4: A Renaissance in Manufacturing or Episode 6: Work of the Future. Augmented - the industry 4.0 podcast.

Special Guest: Francisco Betti.

Episode 88: The Future of Digital in Manufacturing

13 juli 2022 | 44 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode 21 of the podcast @AugmentedPod, the topic is: "The Future of Digital in Manufacturing." Our guest is Çağlayan Arkan, VP of Manufacturing Industry at Microsoft (@Caglayan_Arkan).

In this conversation, we talk about where manufacturing has been in the past, why manufacturing has been lacking a sense of urgency in the sense of industry 4.0 but how everything we know about manufacturing has changed. We also discuss workforce transformation, democratizing operational technology, and the future of industrial innovation.

After listening to this episode, check out Microsoft's manufacturing approach as well as Çağlayan Arkan's social media profile:

Microsoft Cloud for Manufacturing: https://www.microsoft.com/en-us/industry/manufacturing/microsoft-cloud-for-manufacturing
Çağlayan Arkan: LinkedIn,
Blog: https://aka.ms/CaglayanArkanBlog

Trond's takeaway: The future of digital in manufacturing is enormously impactful. Yet, even deep digitalization will not make workers obsolete. Rather, the challenge seems to be achieving a dramatic workforce transformation which also entails empowerment, upskilling, and autonomy through augmentation of frontline operations.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 9, The Fourth Industrial Revolution post-COVID-19, episode 4, A Renaissance in Manufacturing or Episode 20, The Digitalization of Körber.

Augmented--industrial conversations.

Transcript:

Augmented reveals the stories behind a new era of industrial operations, where technology will restore the agility of frontline workers. In Episode 21 of the podcast, the topic is The Future of Digital in Manufacturing. Our guest is Çağlayan Arkan, VP of Manufacturing Industry at Microsoft.

In this conversation, we talk about where manufacturing has been in the past, why manufacturing has been lacking a sense of urgency in the sense of industry 4.0, but how everything we know about manufacturing has changed. We also discuss workforce transformation, democratizing operational technology, and the future of industrial innovation.

Augmented is a podcast for leaders hosted by futurist, Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time every Wednesday. Augmented - the industry 4.0 podcast.

Çağlayan, how are you today?

ÇAĞLAYAN: I am very well. Great to be here. Thank you.

TROND: So I am alerted to the fact that you're an outdoor person. And I wanted to cover that just because a technology discussion in manufacturing is not complete without a little bit of personality. And I think you said you are a backcountry skier. I was curious about this.

ÇAĞLAYAN: I am. Skiing is my passion, one of them, but probably the one that makes me happiest, the one that I love the most. I like ski touring, and I like skiing the backcountry, the off-piste. I like climbing. I'm a very physical person. And on a similar note, I also am a cyclist. I'm a sailor, a windsurfer. I just love being out, and I love the wind on my face.

TROND: So at some point in the future, when the pandemic is over and behind us, I think my next podcast with you we will simply go for a hike.

ÇAĞLAYAN: Well, let's do it. But doing it with me has the following potential downside for you. The other piece of my outdoors work, or kind of world, if you will, is that I love miserable weather. I'm a winter person. I love my rain, my cold, my wind. [laughs] And people typically, even if they categorize themselves as outsiders, like outdoor people, they will just love fair weather, I don't. I'm not that person. I don't like the sun on my face. I don't like a lot of people out. I like trails to myself. I like mountains to myself. [laughs] If you're up for it, we'll do it together.

TROND: Well, this is probably something you didn't realize. But I grew up in Norway, and there are no people. And we have plenty of bad weather. So admittedly, I don't live in Norway, so that could give you a clue. [laughter] But there is something there.

ÇAĞLAYAN: All right, we're on. We're on.

TROND: Yeah, we're on. Okay, so having settled that, I wanted to ask you this question. So we're going to talk about, I guess, the future and the current state, present state of manufacturing. But where has manufacturing been in the past? And by the way, when you think past, how far do you go back? I mean, is this just pre-COVID? Because I've heard you talk a little bit about manufacturing traditionally, and I want you to just give us a quick sense of where you think the industry was just a few moments ago.

ÇAĞLAYAN: You started personally. Let me personalize manufacturing for me. I'm an industrial engineer with an MBA. And so, my whole education was in plants in the manufacturing environment. And I studied from operations research to metallurgical engineering, to electrical engineering, to construction, to electrical. You just name it. And so that has been something that I really really liked, the system's thinking, the optimization. I've done a lot in OR back in the day, linear and multiple. So maybe too much detail for now.

But where is manufacturing? Manufacturing has been mostly manual siloed with a separation between information technology and the data estate that that brings to the table and operations technology that that brings to the table. Technology has never really been, particularly from an IT standpoint, top of mind. Digital transformation has not really been a sense of urgency in manufacturing because things worked. Yet people at the shop floor and things were working until the pandemic hit.

So pandemic question, slap on the face for manufacturers. Business continuity none. You can't send people to the shop floor. You cannot operate. You don't see your inventory. You can't see your suppliers. You don't even know whether they're surviving or not, financially or otherwise. So it was a huge, huge, huge problem. But the silver lining of all of this is now there's acceleration into the transformation of manufacturing.

Look, why is manufacturing important? Let's spend a minute on that. Manufacturing, unlike many other industries (And I kind of make fun of my peer industry leaders at Microsoft as well.), manufacturing is very real. Manufacturing creates employment. Manufacturing creates growth, builds the economy, builds capacity. Manufacturing is about innovation. Manufacturing is about competitiveness. So it is core to populations, countries. It's core to politicians, to business leaders, and it's just phenomenal.

And so if you do things right in manufacturing, things work, including climate change, and sustainability, and a lot of other stuff. And if you do things wrong, you could see a lot of damage done. It collapses economies. It collapses, grids and stops, and creates a lot of disruption. So it is very real. And so I'm sorry I'm providing a long answer, but you can tell I'm passionate about it. It's very personal for me.

But by and large, I'm actually excited about where we are. We are at an inflection point. And we'll see a lot of acceleration coming out of the pandemic, the crisis. And stuff we're working on is actually to ensure business continuity and resiliency. Those are the things that are the conversations going forward.

TROND: Çağlayan, you took me in an interesting direction. I was just thinking as you were speaking, right before we go to the inflection, it's actually not just a little bit surprising but actually quite surprising that there haven't been any reported massive disruptions due to the pandemic. If you think about all of these mission-critical systems that we have around the world, in every manufacturing-related industry, how do you explain because, as you were saying, historically...and some of these silos are sort of still there, although obviously, we are at this inflection point so somehow already transitioned.

But how do you explain that we haven't had more horror stories? And by horror stories, I guess I mean operations completely collapsing, or I guess grids falling apart, or that one manual worker couldn't go in. So X happened that they had never, never thought about. Why haven't we heard anything like that? Are those stories going to come out, do you think, or did nothing seriously happen?

ÇAĞLAYAN: Well, it happened. I know for a fact because once this started, I started calling down on my customers, like, "How are you doing? What do you need?" For one, I think that from a table stakes standpoint, we've seen massive teams deployment because people wanted to communicate. They wanted continuity in terms of being able to talk to one another, being able to work, and then work from home, of course, because they couldn't go to their plants or to their offices. So there was a lot of pain. There was a lot of disruption.

I talked to some of my customers, and they were like, billions of dollars are tied in inventory, and we have no idea where that sits. Again, they're disconnected from suppliers as well as their customers, and so there was disruption. But luckily, we've had some leaders actually having foreseen what is to come, or they were disruptors or at least early adopters. And they have taken pre-COVID pre-crisis steps for digital transformation.

And I love my examples and partnerships where Erickson had started work pre-COVID in terms of digital manufacturing, Outokumpu, a leader in steel manufacturing, significant progress including during the pandemic, Airbus, Unilever. I mean, those are leading examples, only some of them. But you look at the World Economic Forum Global Lighthouse Network; there are so many lighthouse factories that are just like literally lighthouses for people to look at and look up to. That work started years ago.

So there are some extremely encouraging examples. There are some very, very dark stories in terms of complete stoppage and horror stories. But by large, we are at a good place in terms of we understand the issues and we understand how to deal with them. And I think most importantly, that notion of time to value is accelerated in manufacturing. And we're coming from prohibitively expensive, I mean, we're talking hundreds of millions of dollars of IT projects that never end to now negligible cost and like 10-12 weeks, a couple of months, and then you stand up a digital factory capability. You have visibility into your supply chain by standing up a control tower.

And then, in the case of Airbus or Alstom, you can have your 2,000 engineers still keep doing design and engineering work from home; examples go on. But we understand the issues. We have a very quick ability to build capability, to show that stuff works and you can operate remotely, et cetera, et cetera.

TROND: But would you say that this is the definite end to, I guess what you were alluding to is kind of this pilot purgatory? Is COVID the definite end to pilot purgatory? Or is it just that this particular situation was so serious that everybody kind of scrambled, and most of them got it right? Or would you say that...I guess possibly because once you have made this transition, that is the hard work. Do you think that these pilots that everyone was waiting for will that problem disappear because people have learned that this is not the way to introduce technology? You sort of learned it the hard way.

ÇAĞLAYAN: Oh, well, my view is if you take a step back, Trond, here's how I see it. One hundred years ago, we were by and large an agricultural society, and we had like 50% of the workforce in agriculture. Today we are by and large an industrial society. And we have like 2% of the workforce in agriculture, and we brought everyone along in terms of The Industrial Age. Today we are at the next junction; some call it industry 4.0, some call it other names. But we as a society assume...like humanity, we're moving from industrial to digital. So that's the higher order.

Now, what's the role of the pandemic in this? I think it's that of acceleration. So in any major shift, there are behaviors and categories of actors or players. There are the disruptors. There are those who go and make a market, build a trend. And we have seen those, and we're still seeing them. They are the early adopters. We talked about some of them as well. And then there's going to be the slower adopters and the laggards. And then some of the laggards will not see the light of day or will not maybe exist after we transition to the new reality, new realm, or that notion of digital society.

So what I'm saying is it was going to happen, those pilots or people's way, like, slow adopters' way of touching it, putting their toes in the water. For some, it's proving value and acceleration. Pandemic, again, that kind of disruption is going to accelerate and bring more to the table. But it certainly has a role to play. But the higher-level order is we are moving to a very, very different reality for manufacturers and supply chains and even as a society.

TROND: Super interesting. Çağlayan, I've heard you talk earlier. And I guess we talked a little bit in the prep about whether this is a different wave of technology because I know you have some views on the democratization of basically operational technology because there are different waves of technology in manufacturing. And traditionally, like you said, the industry has been siloed. But one of the reasons the industry was siloed is that the technology then also turned into silos, arguably.

And what is it about the technology these days? Is it getting simpler? Are you, for instance, in Microsoft spending more time on user interfaces than you were before? Or I guess even the introduction of your company so deeply into manufacturing is in and of itself a bit of a novelty. The tech players that weren't specialists are now going deep, deep into industry segments. Give me a sense of why this is happening. And what exactly is this democratization? Gartner calls it citizen developers.

ÇAĞLAYAN: Yeah, that's one aspect of it. The way I see it is, very shortly, technology now works.

TROND: [laughs]

ÇAĞLAYAN: Honestly, I don't know, like five years ago, it just didn't. It was so hard for implementations, for integration, et cetera. It now works. There's virtually nothing technology cannot deliver today. It's up to the leader's vision, leader's ability to execute, and magic happens. There's so much at play right now, that's one.

Secondly, technology is the business right now. I mean, technology was isolated. Trond, you will remember those days not too distant past. We had our own language. The CIO, it's like they were from Mars in the organization. [laughter] And they were not mainstream as an executive in the company. Company did their work, and CIO did stuff that nobody really understood.

Now, technology is the business. I mean, if you look at any research, you will see that the mainstream business leader, whether it's the CMO, the Chief, Marketing Officer, Chief Digital Officer, Chief Financial Officer, whatever those may be, they're making more technology decisions and have bigger technology budgets than the technology people themselves. So that's the other piece that business is technology. Technology is business.

The third piece is that the siloed nature of not only manufacturing, so many different industries, was because it was an application-led view into enterprises or into business. Now, it's data-driven work. And so data dictates everything, and data is actually end to end. So to the extent that you have a data architecture, enterprise-level data architecture, and a system-level approach to things, it's a completely different world.

And to bring those three together as a business, you have to forget more than you remember. And then you have to reinvent yourself. And if you do that, everybody knows cliché examples here, but then you find yourself as a completely different company or services company or actually at the risk of being disrupted by competition in ways that were not thought of or unprecedented. So that's what's happening.

So what we like to approach this whole kind of...I like to call this opportunity. It's a major opportunity. It's a huge inflection point. It's all about reinventing your business. None of that is about technology. Technology is a tool. It's a powerful tool. It's a tool that works. It's very capable. But it's about the business outcomes. Because we said, you have to reinvent your entire enterprise, starting from your culture, how you operate, your value proposition, all of that.

It is where you start should be dictated by which outcome is most important for you, or the highest value for you, or the most burning for you. Whatever your drivers are, focus on the outcome. Go back to work to find the relevant data for it and get to that in weeks, literally seriously in weeks and get to the next outcome, the next outcome. And don't forget the people and culture. It's all about the people piece, and we can talk about that later. I think we should. But those are the things that I will say to your technology question.

TROND: That's great.

ÇAĞLAYAN: Focus on data, lead with culture, and always major prioritizations on the outcomes you want to drive.

TROND: You said lead with culture, but it's not just company culture, I guess. It's the whole nature of the skills that are now needed in this new workplace. A lot of people are saying that that is changing and that the workforce needs are changing. So you initially said well, technology now works. So that's true, but what are the skills that then are needed?

So okay, technology is easier. But what are some of the tasks that are, I guess, less relevant because of this influx of call it industry 4.0 type technologies? And what are some of the skills that are more relevant? And the frontline worker of the future, what should they be focused on? And your clients, what are they starting to teach their workforce?

ÇAĞLAYAN: Great question. I will say at the highest level, Trond, it is a data-driven culture. I mean, in manufacturing, maybe other businesses and industries as well, we operate on the basis of past successes, habits. This has been delivering for me. This has been working for me, et cetera, or experience. You kind of listen to stuff. You kind of watch stuff. You anticipate stuff. And you're like, I've been doing this for 25 years. None of this has anything to do with data because, again, we established we were using less than 1% of our data, at least in manufacturing. Now the biggest cultural change is data-driven.

And then once you go to data telling you what to do, data giving you predictions, data giving you systems of intelligence like the insights in terms of what to do, and when to do it, and how to do it, et cetera, then that dictates actually two things. Again, I'm trying to come down to it in terms of a hierarchy. Manufacturing had a skills gap, has a bigger skills gap in the face of digital. And we're not an attractive industry. The young generation does not see career opportunities in manufacturing. Actually, manufacturing is fantastic. It's real; it's innovative. So we have to change that, and so we're working on it.

And secondly, the existing jobs, even if they may still be the most important jobs in manufacturing, those people have to learn new skills in terms of doing their jobs using technology. Let's see now a couple of examples. You talked about the frontline workers, first-line workers, or just shopfloor, the very people who get the job done. They typically did not use any technology. They were all mostly manual, what we called HMI, like Human-Machine interfaces, old, very, very, antique equipment, if you will, blue screens. I think anyone who's close to manufacturing will know that we used a lot of paper, et cetera.

Today's frontline worker is actually acting on data, acting on predictions, double-clicking under the modern interface, and responding to traffic lights, responding to alerts. You got to be able to do those, wearing augmented or virtual reality devices. We call it mixed reality with the unique technology that we have in terms of HoloLens in our entire mixed reality platform.

But you come to a job, and then you don't need to learn to do the job. You just wear your HoloLens. And the mixed reality platform will actually teach you how to do it with your two hands-free. If you're in the field service, someone at the back office, remote connections, or remote assist capabilities can actually guide you through as to how to deal with that; I don't know, grid asset, extruder, or packaging line because they know how to and you don't need to. And then this is the way you learn how to do stuff.

So I guess the gist of it is some jobs will no longer exist. Most of the repetitive low-value-added jobs can be automated, robots, artificial intelligence, and other means in terms of process automation, et cetera. Most of the jobs, if not all of the jobs, will be rescaled in terms of technology. And at the highest level, probably 75 million jobs will go away. Again, this is a World Economic Forum study. One hundred thirty-five million new jobs will be created. What are those jobs? Data jobs, software jobs.

And then how you do your design and engineering, you have to be able to understand AI-led generative design, additive manufacturing, 3D printing to be able to be successful. And so, all of that is a call to action for universities, policymakers, corporate learning officers, for all of us, and calls for partnerships to lean in. And again, I used agricultural example. Bring everyone along from the Industrial Age to the digital age.

TROND: It's a fascinating challenge, and it's a big one. I was just curious; there's a lot of talk about middle jobs meaning jobs that are somewhere between more than high school but less than traditional college. But then you also have an echelon above that, of course, which traditionally certainly Microsoft was hiring into, which is more high-level cognitive jobs which required bachelors, and masters, and PhDs traditionally in computer programming. But I'm guessing now certainly in your field in sort of hybrid engineering studies where engineering plus IT.

The middle jobs is a big challenge, even just from an operational point of view. It's hard to educate a billion people worldwide or whatever it is that we have to do continuously to keep the lights up. How is all that going to happen? And what sort of effort does this require? Can we use the existing institutions we have to do this? Or do you foresee that it's going to be a lot more on-the-job type of training in digital training?

ÇAĞLAYAN: I'll say all of the above in the following ways; for one, we're already working with Purdue, University of Wisconsin, and many, many universities and education institutions. So for one, manufacturing-related jobs were kind of graduate jobs. We're trying to bring the curriculum to undergrad, if not high school, so there's that. So vocational training, et cetera, all of this is important.

Secondly, we partner with the National Association of Manufacturers, MxD, Sesame, obviously Tulip, and many others in terms of call to action and doing institutionalizing, programatize, very, very important for all of that. Thirdly, I deliberately talked about corporate learning officers because a lot of people, tens if not hundreds of thousands of people in large corporations, actually had to learn new skills. And it is happening as we speak in multiple ways in many, many, many, many leading enterprises. But it's a huge part of the whole equation.

And then, I talked about the World Economic Forum and the Global Lighthouse Network. Programs like that actually bring it to everyone's attention in terms of what is possible, and how it works, and how some leading institutions deal with it, which brings me to this notion of what I like to call art of possible. I think leadership at large, political leaders, enterprise leaders, any institution, education, leadership at large has to understand what I call the art of possible, and that is how technology has already transformed everyone's lives.

And what is that leaders need to do differently? Starting from communication, setting new standards, to building the new curriculum, to encouraging everyone, bringing everyone along, and all the rest of it from cultural change to change management and defining the new normal. But by and large, just bringing everyone along. And so that is really, really important that we start that education and understanding with the leadership because it's all about leadership. It's all about them having the right vision and being able to execute to that.

TROND: What is the role of actors such as startups? You mentioned Tulip. What are startups' role in the emerging manufacturing and frontline operations ecosystem? Tulip thinks of itself a little wider than manufacturing. But what is it that startups can do? Because clearly, this is a game, technology overall, and also industries. It's an industrial game. Industrial companies are massive traditionally.

So the juxtaposition traditionally in the old world would be between the industrial conglomerates and then the SMEs. And the game was to get the SMEs to be useful providers and suppliers into the supply chain ecosystem was an educational challenge. But you now have startups somewhere in this picture as well. Can you address how you think these startups function in the ecosystem going forward?

ÇAĞLAYAN: Yeah, I think the example that I would use is startups are like Tesla for automotive, Airbnb for hospitality. They're the disruptors. They have zero legacy. And so we're talking major change, major transformation. What happens in change? Lots of the legacy will drag their feet. They will want to protect status quo. They'll be slower. What startups do is they teach you the new normal. They teach you the art of possible, and they go on and do it. This is how you carry from years of implementation time to weeks. This is how you go from hundreds of millions of dollars to pennies and cents.

And so Tulip and many, many others that I'm so excited to work together with, define the new normal. They make it happen. They go and make stuff. And actually, they are the ones who bring what I call art of possible to life. Let's take Tulip's example. Again, they go into the shop floor. And they look at that low-code/no-code citizen developers, a term that you used in this very conversation. And then they bring it to life in the context of manufacturing operations.

And so suddenly, the human-machine interfaces are modernized. The legacy-heavy applications that do not necessarily connect the enterprise have changed, and there's a new workflow in place. And people just act on data and intelligence. The job is much easier to do, et cetera, and then you can build on it. And so what they do is just extremely important, actually much bigger than their sizes or the number of people that they employ. The role that they play is actually what's going to change economies. And this is one reason why we embrace and work very, very closely with the likes of Tulip at Microsoft through multiple, multiple tools and investments that we have from Microsoft for Startups to M12 and to many others.

TROND: Yes, I understand. That's fantastic. However, it does remain the case that right now, you are a gorilla in the big space, and you do have a privileged position to analyze what you think is happening. So if you use that futurist hat that you have from your vantage point of a large player that does work with everyone, I guess, where is this now heading?

You said it's a disruptive time. It's an inflection point. You were using big, revolutionary words. We're talking about industry revolutions. There's also some uncertainty, and we have been dealing with resilience issues. But you pointed out simplicity has improved. Where is all this taking us, all of these bits and pieces altogether? Where is the manufacturing industry heading?

ÇAĞLAYAN: Manufacturing is very complex, and it's actually not one industry. So many industries are manufacturers. So let's kind of break it down and simplify to maybe customer-facing systems, sales, services, et cetera, design, and engineering making stuff which is really manufacturing, supply chains, and then maybe you look at people.

In the customer systems, particularly the pandemic, taught us that online sales and delivery, omnichannel strategies, profit optimization, pricing, contact lifecycle management, all of that is here to stay. Connected field services or field services at large is going to be changed forever. Again, we talked about mixed reality, remote assistant, remote capabilities, all of that. So that is where that is headed.

In terms of the design and engineering piece, we talked about AI-led generative design, where AI engines actually design stuff like mother nature. They don't have corners. They're not straight lines. So the existing manufacturing paradigms like welding, and bending, and et cetera, can go away, and 3D printing actually is very revolutionary in that it's the only way to actually make the stuff that is designed by AI engines which is faster, stronger, lighter, cheaper, et cetera. But again, you can only build them with the new 3D or additive paradigms, and so there's that.

And obviously, from design and engineering, that whole design supply chain is moving to a virtual environment so that you do not have to send designs in paper when it comes to like...You look at Boeing, and they have like six million suppliers. You look at Rolls Royce, the same deal. And then what they do now is they send electronic drawings. You can validate. You can verify the source is correct. You can just keep building in the virtual environment, and you can run simulations and tests. I can go on and on, but that is completely disrupted and changed forever.

Manufacturing as we know it is moving to...some call it lights-out manufacturing. But this whole remote capability being able to...business continuity, people at the shop floor being able to remotely operate, manage and monitor your assets, get predictions on them, actually have predictions visibility into your suppliers and be connected to their environment. Digital twins and digital threads are actually huge enablers from that perspective. So this whole kind of lights-out manufacturing conversation can happen. Again, technology is capable of delivering it. You have to optimize or rationalize for your own enterprise. Supply chains, completely moving to an autonomous and sustainable fashion.

And then finally, at the highest level, what we're seeing perhaps the largest opportunity is go from your...even your own enterprise was siloed. Let alone your enterprise, go and reinvent the whole value chain that you operate in. We tend to think about industries, but actually, value chains are made up of multiple adjacent industries. Look at food; it starts with perhaps the farmer, but the farm equipment manufacturer, the likes of John Deere, Mahindra, et cetera, do play a huge role. There's a lot of data there.

Then you look at warehouses, then you look at mills, and processors, and packagers, and shippers, and then you go all the way to retail. I've talked about seven different industries. The notion of I call it lead with opportunity as opposed to leading with risks. Share your data for the greater good. New value creation at the value chain level we haven't even begun starting that journey, really. And so, just some of the examples of how everything we know is already disrupted. Again, do all the leaders know, the world leaders know how to deal with it or where to take their enterprises, their people, their cultures, their businesses? And so that's kind of the conversation.

TROND: Indeed it is. Disruption at the value chain level that seems to be at the core. And then I guess my last question for you really is to take this back to the human being because I know you think that fundamentally, this is not really about the technologies or even just the various industries at the center, and maintaining and constructing is the human being.

The augmented human capabilities that these new structures and technologies enable, what does that look like? I mean, if you think augmented reality and mixed reality, HoloLens is like a beginning of that vision. But it seems like we're arguably going from a day where the idea was automate, but you have a vision of more augmenting, meaning you're supplementing the human as opposed to replacing them. How do you see the human being in this picture? What is going to be the role of the human worker?

ÇAĞLAYAN: Well, it's going to be a combination of vision and maybe aspiration. But I'll say augmented society first because of diversity and inclusion. Let's start there. Let's bring everyone along. Let's not leave one person behind, wherever they may be, whatever background. Let's bring everyone along. And as a society, let's elevate everyone. Let's make everything accessible, technology, and data, and education, and health, and water, and safe food, all of that accessible to everyone.

The new set of paradigms actually might create value at such a level in which we can give people more free time, more fulfillment, provide better work-life balance, provide other means of seeking reason and purpose in life and communicate and work together at very, very different levels. And so all of that is just, again, I think this whole kind of leader, art of possible, and what technology is capable of today. If we put the people in the center and go from there, I think we can remember these days as some of the best kind of inflection points in history.

TROND: Wow, that's a great way to end. I thank you so much. This was a whirlwind of observations. [laughs] Thank you very much.

ÇAĞLAYAN: Thank you.

TROND: You have just listened to Episode 21 of the Augmented Podcast with host Trond Arne Undheim. The topic was The Future of Digital in Manufacturing, and our guest was Çağlayan Arkan, VP of Manufacturing Industry at Microsoft. In this conversation, we talked about where manufacturing has been in the past, workforce transformation, democratizing operational technology, and the future of industrial innovation.

My takeaway is that the future of digital in manufacturing is enormously impactful, yet even deep digitalization will not make workers obsolete. Rather, the challenge seems to be achieving a dramatic workforce transformation which also entails empowerment, upskilling, and autonomy through augmentation of frontline operations.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player. View our YouTube channel and rate us with five stars. If you liked this episode, you might also like Episode 9: The Fourth Industrial Revolution, post-COVID-19, Episode 4: A Renaissance in Manufacturing, or Episode 20: The Digitalization of Körber.

Augmented - upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Çağlayan Arkan.

Episode 87: A Brief History of Manufacturing Software

6 juli 2022 | 54 min

In episode 10 of the podcast (@AugmentedPod), the topic is “A Brief History of Manufacturing Software.” Our guest is Rick Bullotta, Partner, TwinThread, and co-founder, ThingWorx.

In this conversation, we talk about how Rick has shaped manufacturing software history and the lessons learned from being an early employee of Wonderware, the famous precursor to manufacturing automation, back in 1993, a company first sold to British engineering giant Siebe in 1998, which merged with BTR to form Invensys, which, in turn, merged with French multinational Schneider Electric, and later the CTO. Rick Bullotta was also the co-founder of Lighthammer Software which was later acquired by SAP, then in 2009 founding ThingWorx, the first complete, end-to-end technology platform designed for the industrial Internet of Things (IIoT) which was acquired by PTC in 2003. We also touch on his current advice to founders in the industrial space, his board role at Tulip, and what he sees lie ahead for the industry.

After listening to this episode, check out Thingworx as well as Rick Bullotta's social profile.

Trond's takeaway: Wonderware, Lighthammer, and ThingWorx are prominent parts of manufacturing software history, and there's a chance that the 4th company he now is involved with, Tulip, also will be. I do things with things is Rick Bullotta's motto. The things he does, he does them well, and it is an internet of things, more than anything else. I, for one, am eagerly listening to what he predicts will happen next.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 4: A Renaissance of Manufacturing or episode 5: Plug-and-Play Industrial Tech. Augmented--the industry 4.0 podcast.

Transcript:

Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. In Episode 10 of the podcast, the topic is a Brief History of Manufacturing Software. Our guest is Rick Bullotta, Partner at TwinThread and Co-Founder of ThingWorx.

In this conversation, we talk about how Rick has shaped manufacturing software history and the lessons learned from being an early employee with Wonderware, the famous precursor to manufacturing automation, back in 1993, a company first sold to British engineering giant Siebe in 1998, which then merged with BTR to form Invensys, which in turn merged with French and multinational Schneider Electric and later the CTO.

Rick Bullotta was also the Co-Founder of Lighthammer Software which was later acquired by SAP. Then in 2009, founding ThingWorx, the first complete end-to-end technology platform designed for the industrial internet of things, which was acquired by PTC in 2003. We also touch on his current advice to founders in the industrial space, his board role at Tulip, and what he sees lie ahead for the industry.

Augmented is a podcast for leaders hosted by futurist, Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG Works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time every Wednesday. Augmented - the industry 4.0 podcast.

TROND: Rick, how are you today?

RICK: Good morning.

TROND: Well, it's a nice morning. I wanted to talk to you about some history.

RICK: Sure.

TROND: Well, you are a bit of a legend in this field, Rick. You've been basically part of almost every development in this field for several years. I wanted us to spend a little time today, not just going into your history of background as the founder of several startups that have had very significant impact on the industry but also just bring people in a little bit to the environment and how it has changed, and how based on your perspective, you see it evolving.

You had a degree from Cornell, and then you went on to fund several companies. Can you bring us back to those days when you were studying industrial engineering at Cornell? What was the environment then for manufacturing? And what was it that brought you into the thought that you would start engaging sort of entrepreneurial software development in manufacturing of all fields?

RICK: Just to be clear, I barely graduated. [laughter] So I had a fantastic time in college. But that was when I think we thought of engineers as mechanical engineers, or chemical engineers, the physical aspects of making things, building things, vending product as opposed to...I think software and technology was kind of a nascent concept there, at least certainly in manufacturing.

But I actually switched degrees from mechanical engineering to operations research mid-stride there, realizing that looking at pieces of broken metal under a microscope wasn't for me. So I graduated. My degree was in operations research, and actually, my first position was at a very progressive steel company called Lukens Steel, doing essentially industrial engineering work.

However, this was what? 1985, dawn of the PC, dawn of a new gen of computing. And some opportunities opened up there to kind of take on some additional responsibilities that involved applying computing to simulations and optimization models, all the stuff that I studied but never thought I'd actually practice. So I'd spend a lot of time in the local library checking out software, take the disc home, teach myself to code.

An opportunity then opened up to go into steel plant operation. So I used to run a heat-treating process. And that's one thing that a university degree won't prepare you for, having 15 steelworkers working for you. That's where you get a real education. You also quickly realize that the exception is the rule on the manufacturing floor. And we'll talk later about how it gave me a great appreciation of the importance of the role of people in this whole process and not just technology.

But yeah, I spent a few years in that role and then moved back over to an industrial computing group. And we were applying at the time very advanced technology, mini computers, very innovative user interfaces, high levels of automation to some of these processes back at the very site that I worked. And the very operations that I worked at was one of the first places for that. So that's kind of where I got into the technology side of things.

But I like to say I was blessed and lucky, right? This crusty, old steel company happened to be very, very committed to investing in technology. And it was a learning opportunity for me. And then, across the years, I moved into systems integration. I did some stuff in discrete manufacturing. I had the opportunity; again, luck sometimes happens here, to work for arguably the first well-known company in the industrial software space company called Wonderware, first IPO in the space. And I joined very early, which is kind of cool.

TROND: The Wonderware story is somewhat famous for people inside of manufacturing, but just in case, there are some listeners here who don't really appreciate how early Wonderware was. What was the situation when you created your first product? And why, in your account, has it become so emblematic of that early-early era? And what year are we talking about exactly when that entered the stage with Wonderware?

RICK: So late '80s, early '90s Wonderware came on the scene. I joined in; I believe it was '93. And my role there was actually in sales. So you'll find that a lot of my life experiences are all the elements that help build a successful business: sales, marketing, technology. So the founding team there...and there'll be a circle of life moment here in a little bit when we talk about how ThingWorx came to be.

The two key co-founders there, Dennis Morin and Phil Huber, recognized the value. And they harnessed the PC revolution and Microsoft Windows. So we're talking Wayback Machine when Windows looked like the Mac user interface. There wasn't a lot of PC application on the plant floor. There were some very interesting companies that I had worked with, competitors to Wonderware but a bit earlier companies like [inaudible 7:28]

But we were just kind of at that inflection point where people were comfortable with the role of the personal computer as this kind of human interface to all the automation systems that we had. What Dennis and Phil did was really twofold. And this, I think, ties into a lot of the innovation we're seeing today is they democratized the ability to build applications. They made it easy and fun.

So the whole experience wasn't coding; it was very visual. It leveraged kind of a drag and drop experience. You didn't need to understand software to apply it. You could build these incredible applications literally in minutes or hours, connect them to the physical world. I don't know if you've ever seen some of the classic applications they've built. But they're those process mimics, very dynamic graphics that represent the physical world.

And I learned a lot during that period about the importance of two things: one is ease of use and empowering others to build applications, particularly in the manufacturer domain. Second was, ironically, the importance of marketing. If there's one thing, that company did extraordinarily well in addition to having a great product was getting the message out there, maintaining a larger-than-life image. And the company grew rapidly to 5 million, 10 million, 15, 20, and on and on, and then IPOed.

But there wasn't anybody in history that didn't know the name. Go to a trade show...this is a company that kind of put some perspective. I think the first year I was there; we did about 20 million in revenues. We spent about a million-five on a party. That's kind of the priorities were well balanced there. But what an extraordinary group of people to learn from; I developed lifelong mentors and friends at that company that fast forward to my last company, some of those same people came and joined my team. So it was a complete honor to work with them again, so yeah.

TROND: So back in those days, what was it that Wonderware apart from the marketing side, and like you said, the menus and things...first of all, who was the target audience at this point? Was this still process engineers that were doing this, or was it still the IT department managing?

RICK: Typically process engineers, and that was the democratization, taking it out of that...let's go back to my time in the steel industry. We were writing Fortran code, PL/M code. We were writing code. We were creating database schema, all the kinds of classic development processes. And it was part of a corporate IT function. Now, this shifted to empowering two main groups, process engineers inside these manufacturing companies and, secondly, a new breed of systems integrators that were very, very focused on this automation domain.

So historically, they may have done the physical automation, the PLCs, the actuators, sensing distributed control systems. Now they were able to take on this role. Two other things happened. Just prior to the advent of things like [inaudible 10:42] and Wonderware, that user experience was physical gauges, and push buttons, and things like that, and sliders. Now, it became digital.

In a way, this was almost like magic at the time. It's virtual reality. It's like a lot of people the first time...I'll never forget my mother the first time she played solitaire on a PC and that virtual card dragging. It was just utter magic. Well, similar experience here, right? People were able to reproduce these and rapidly reconfigure. But to your point, I would say, yeah, it was those in-house process engineers and the systems integrators that helped implement these systems.

TROND: Were you all aware of how innovative you were? I mean, clearly, the marketing department thought you were something special. But did you realize at the time how timeless and etched into manufacturing history Wonderware would become later? Were you aware of how far ahead this was? Or were the customers telling you that clearly?

RICK: That's a great question. I think it was a combination of both. We had an almost cult-like customer following that was pretty unique, and it created a lot of energy. They knew we were doing something interesting. But we had very legitimate competitors who were also doing super cool stuff. I think another life lesson here was a lot of companies create great products. To bring great products to market at scale is a whole nother task. It's a whole nother challenge.

And I think what we had going for us was an absolutely extraordinary distribution channel, global distribution channels, and very energetic, bright people, independent businesses that could sell, support, implement this technology. That allowed us to achieve scale pretty quickly. But the customers were the primary feedback loop. We won all kinds of awards from the trade rags, all that kind of stuff. I definitely think it was the kinds of applications that the customers were building. That always gives you energy when you see that.

TROND: Rick, give me another sense of as we're sort of moving to your next company, just bring us back to that time with the early years of Wonderware. What were some of the things that were challenging to you on the application side then that today we would laugh off and it would just be like a line item? What were some of the things that were really complicated that you were so proud of having accomplished?

RICK: Well, let's just take the obvious, which is sort of the inverse of Moore's law. If we turn the clock back that many years, we have half as much compute power every year. And to have a very graphical dynamic user experience, it had to be reliable. I would not underestimate the incredible work that that development team did to take not only a new product in what we built with InTouch, which was the product at the time but also Windows itself. It wasn't evolved. It wasn't mature. It certainly wasn't targeted at these kinds of mission-critical applications.

So those were the kinds of things you had to work with. You had to make it robust, reliable, and take advantage of very, very limited compute and visualization capability at the time. It changed the modalities by way...people typically, you know, we were all used to keyboards at the time. Now it's touch; it's a mouse. It's a different means of interaction. And then how do you bring that? Some interesting challenges. Like, I'm a task worker down on the floor in protective equipment and gloves, and how do I interact with that? So all kinds of creative stuff to try and bring a whole new modality of human interaction to a pretty demanding segment.

TROND: So what then happened to you? What happened around you leaving Wonderware and moving on to the next challenges? Because you've also had a foray in larger companies, but then you immediately went back to the startup world. Give me a sense of what was your thinking then?

RICK: Sure. So there was a little detour as there are often in our careers. [laughs] I left and experimented. I actually came back to Wonderware a second time prior to my first startup in a product management role. I got to see M&A. So we got involved in a couple of key acquisitions that I was intimately involved in. So that was another learning experience for me. Then I saw this opportunity at a level above the Wonderwares of the world, of the OSIsofts of the world, of all these kinds of operational systems that we had. They were islands.

No one had that holistic view, a supervisor, an operator. No one was sharing information. And so the light bulb went off. This is actually about when the web technologies were starting to get a little traction, the browser, the Netscape effect, ubiquitous TCP/IP connectivity, Ethernet, and the plants. So that's when the light bulb went off. Let's see if we could do something not dissimilar from the way a Wonderware product will connect all your centers and controllers. Why not provide a unified way to see all the systems that you have? So basically, that's what became Lighthammer, and that was in 1998, we started that company.

But the intent was, again, to provide that unified view of first...it was called the Plant Information Portal. That was another cool word at the time, right? Portals. And so that was the objective, it's kind of unified visibility. I started the company with some colleagues that I knew from Wonderware. And we built, I think, something pretty groundbreaking there.

TROND: And the situation then was there was this need for almost like an information service to kind of...it was almost like an early portal for the industry in a sense.

RICK: I think what we found...the unique thing about the industrial space I like to say that everything's a legacy the moment it gets put in. Everything has proprietary APIs, interfaces, and protocols. My approach has always been solve hard problems because you're going to have fewer competitors, and the value is there. So we tried to solve a pretty hard problem, all these like debubblizing all these different crazy systems that were scattered around.

Yeah, so that's really what the objective was initially, unified visibility. But then we realized if people can see that information, why can't other systems? So it rapidly progressed from just being empowering people with information to empowering other lines of business systems. So your supply chain systems, warehouse systems, ERP systems can now be informed with real information in a timely manner. And that was what got us on SAP's radar.

TROND: Well, because the point was there that you started discovering the importance of standards. And there were standards at that time, but they were very basic web standards. And you started realizing that even in the side of the industrial field, you had to start depending on that. Is that also what got you involved in the intersection of interoperability and also open sourcing certain types of software?

RICK: Yeah. In fact, we were actively involved in a lot of open-source projects. I think that was also early in the open-source world. So if something was broken, no one was going to fix it for you; you fix it, right?

TROND: [chuckles]

RICK: So yeah, if you want to leverage and get value out of open source, you better be prepared to give back. So as a company, we definitely gave back to a lot of interesting projects that became part of the Lighthammer stack.

The other thing that I think is important to understand is, and this pattern repeats itself in my career, is building tools, not applications. My goal was always to empower people to build interesting stuff. They've got the ideas. They've got the innovations living inside them. But if it's hard, if there's friction at every point in the process, cost, time, whatever, they're not going to undertake it, so whether it was Wonderware stuff we were implementing, Lighthammer, ThingWorx. And nowadays, with solutions like Tulip, it really was all about that takedown friction, empower non-technical people to be innovators and do it fast.

TROND: So, Rick, then you got on SAP's radar. Tell me a little bit about not necessarily your experience there per se but just the difference for you in having straddled a startup that gets on the radar of a large company, and now you're working in a large company. What's the situation there? What is their understanding of the shop floor, and how does that all work? Because it gets more complicated when you're that kind of a software environment.

RICK: Well, I think SAP was a very good place to be for a number of reasons. SAP was dominant in the manufacturing vertical in terms of cost manufacturing. Customers, the vast majority of them ran SAP for their back-office systems. SAP had kind of light solutions for the manufacturing domain but a desire to go deeper. Secondly, they were launching a partner ecosystem at the time. We wanted to prove that, in fact, partners are an integral part to their offerings. So we were able to kind of get that visibility, but also, we started stealing some revenue.

So when you start taking customer spend instead of upgrading that module in my ERP system, I'm going to spend a couple hundred thousand dollars on my plant floor. That gets you on the radar too. Interesting sidenote, so after SAP, the salespeople told us something fascinating. If you think about in a typical manufacturing company, there's arguably four to seven times more blue-collar...I hate the term blue-collar, task worker, you know, frontline workers, so to speak. But that's got a new meaning nowadays as opposed to back office.

Secondly, we had something that not only had a user license for each manufacturing worker but also manufacturing site costs. So think about comparing selling something to the CFO’s office that will run in a data center. The scale and size of the deals were pretty substantial, and there was real value being created. So I think in the first year, our sales grew like 800%, 900% from a pretty good base, having that ready base of manufacturing customers to sell into a global company with global sales and support presence. It's pretty easy to get traction there.

TROND: But then you had a stint back at Wonderware before you went on to found a new company. What was that like? So you came back and now kind of almost running the show at Wonderware for a little bit.

RICK: No, not really because I think the company...this was an interesting dynamic. The company had grown substantially by that point, so from 60 people in my first experience to probably 800 at that point. I was a remote CTO. This was long before remote work was a thing. It was extremely challenging. And I just think those dynamics kind of made it probably not as effective as I could be. That said, some work that I had done in SAP research is what kind of led to the ideas behind ThingWorx.

And I actually think, to be blunt, I think Wonderware at the time could have realized those pretty well. Collectively, we could have brought that product to market probably faster of what became ThingWorx. But it just for a variety of reasons, it wasn't the right time, fit, location, all those kinds of things. So dove back into it again, got the band back together, so to speak.

TROND: How did that happen? Because at this point, you're not new to startups, and you have had a taste of the corporate world, in fact, in two leading positions, I guess. What is it that then motivates you to go back into that grind, and then you found a groundbreaking company? [laughs]

RICK: Part of it is you feel like you cheated on the test. You've got the scars. You've had the lessons learned. I think we had a pretty well-rounded idea on what the new product was going to be, how we were going to take it to market. So I think we actually went in with a pretty solid plan rather than just A; we're going to do some R&D.

Secondly, my business partners at Lighthammer were my business partners at ThingWorx, common investors. And some new folks that I worked with at Wonderware joined the team. It was sort of...I'm not going to say we couldn't fail. There were a lot of things we could have done wrong. But we had an incredible team of people with a lot of experience building companies like this, selling software like this. I had a pretty good feeling that we were on the right track there.

TROND: And what exactly was ThingWorx in the early days? Because you read things like machine to machine, and those are terms that only much later...today we call internet of things. But you guys were very, very early, honestly, in that domain to produce products in that space when most people were just starting. Machine to machine didn't mean anything to people back then.

RICK: And I think where we did well was going a little bit beyond that. And you'll see, once again, it's a pattern that repeats itself, the importance of people, the machines, and the other systems and processes that people have in their companies. Synthesizing all those together is actually where the value nexus is just massive. Any one of those taken in isolation or the connections between them, yeah, there's value to be done.

But so we went in kind of with a broad...rather than just machine to machine. And there were some companies doing cool stuff just for getting updates down to an MRI machine or whatever. But we tried to go beyond that. We also realized early on the classic issue; it's good to know what you don't know. And remote access over unreliable links and all that stuff was something...My team had primarily lived in what we would jokingly call the internets of things. Everything's on the local network. You have different considerations.

So we acquired a company, a super team, a small company that had a lot of expertise in the kind of internet of things and that remote connectivity, remote management, and that was this the second wave of rocket fuel to get things going.

TROND: That's interesting you say that because I think that temptation for many would be you're so far ahead, and you start building things, and you're building things in the future. But I mean, surely, the reality is the shop floor and other things, and you're dealing with poor internet connections. Forget skills. I mean, you're actually dealing with a network that doesn't scale to your idea.

RICK: Exactly right. And it was a very interesting balance between...I oversimplify kind of that industrial IoT is smart, connected operations and things like that, so factories, power plants, and then connected fleets of stuff, trucks, MRI machines, light towers, and cities, radically different requirements. One's 98% on-prem, one's 99.9% cloud, one's intermittent, unreliable, expensive connectivity, one's reliable, isolated.

So we built a platform to serve both of those tests. In retrospect, we probably made compromises along the way to accommodate that. But still, today, I think PTC’s revenue with ThingWorx is fairly well split between those two domains. But that was an interesting challenge on its own because the requirements were dramatically different.

TROND: But again, you got acquired. So is this a pattern in your companies? Or is it more a pattern in the field that, at a certain point...because, I mean, I'm making this up here. But is there something about the industry itself that lends itself very easily to just in order to get that scale, you have to be acquired, and it's very desirable? Or is it more a choice that you each time made to say we've built it to a certain scale?

RICK: I think in our segment, there are the rare few that an IPO track makes sense, and it's achievable. I think, for the most part, companies in our domain are...they're talking acquisitions to technology companies, cloud companies, enterprise app companies, industrial automation companies. So they have the luxury of we can be the innovation engine. It doesn't have to come off...

If you think about a BigCo that wants to build something organically, every dollar they submit...first of all, they're typically 10 to 20 times, and it's just reality, less efficient in developing software for a variety of reasons. And that money comes off the bottom line. So it's actually an interesting dynamic that it's almost more attractive for them as well.

But the ThingWorx story is super interesting in the sense that I told someone the other day...so Jim Heppelmann super visionary right there. He had this concept of the digital twin and IoT connected with products way back. And he actually took some of his best and brightest people, his CTO, a number of other people, moved them out of their office, put them in the Cambridge Innovation Center, and said, "Go create something."

Well, along the way, we got introduced to that team. And they came to the conclusion that, hey, it's going to be faster, cheaper. We can get to market capture mindshare quicker through acquisition. And if you think about it, that's a very...immature is not the right word. I don't even know what the word I'm looking for here, but it's you've just been given an opportunity to intrapreneur. You've got a clean sheet of paper, all the fun stuff after grinding out your day job for years.

And you make that decision to well; we're not going to do that. We're going to go buy a company. I have huge respect for that. And it turned out to be a very good decision for everyone involved. So that's actually how that happened. We were an intrapreneurial effort at a relatively large company, decided to go and become acquisitive instead. And that's worked out quite well.

TROND: So we haven't talked so much about the surrounding companies throughout these years. But were there other companies doing innovative things? I'm not so familiar with the history of all of the kind of less successful or less visible manufacturing IT companies throughout the early '90s. What was wrong with some of those, and why don't we talk about them? I mean, are they also still part of the picture? Were there smaller acquisitions that go into this history?

RICK: Yeah, there's actually a lot that we were doing right. It was a big enough pie that the gorilla, you know, in the segment might only have a 20-something percent market share. So it was still fairly fragmented. It's partially because of geography, partially because of different segments, and partially just because it was such a big opportunity. The companion market to a lot of what I was doing, for example, at Wonderware and Lighthammer, was the data side of it. So that's the historian companies.

Greatest example of that recently is the acquisition of OSIsoft by AVEVA for $5 billion, biggest little company you never heard of. I mean, just a fantastic success story. They stuck to what they did very well and built essentially a dominant market position. They had competitors with good products as well. But I think they're one of those success stories in that space that's only visible to most people now. We had competitors in almost every company I've ever worked at that had great solutions.

But this is, again, where I think the X factor stuff comes into play. Your go-to-market machine, the passion that your team and people have that's contagious. If people really believe and they interact with customers and partners, it's just magic. The second thing was, again, where you're really doing useful stuff for customers. Some companies were software companies. Some companies were really just integration companies masquerading as software companies. But, Trond, you know this. There's no shortage of bright people on this planet, and it's --

TROND: Well, sure, there's no shortage of bright people. But I guess this is the third segment that I wanted us to get into. You kind of have a third career now, which is this portfolio life, I guess. [laughs] You can characterize it yourself, but I don't know how to explain it otherwise where you're seeing, first of all, a number of companies and the maturity, I guess, in the space, that's a little different. But you are in a different stage in your career. And I want to eventually get to Tulip and discuss why you got involved with that. But first, maybe you can address some of these portfolio things that you're doing right now.

RICK: Sure.

TROND: Obviously, mentoring a lot more and getting involved on the board side. How do you see even just the last five years? What's happening right now? Where are we right now with manufacturing software?

RICK: So generically, I would say I'm doing manufacturing and adjacent stuff, kind of IoT industrial. I am so excited that it's cool again, right? Because it was for two decades. It was like --

TROND: Well, you were never concerned about that, surely. [laughs]

RICK: But, you know, what's the old...in the land of the blind, the one-eyed man is king. So if you were cool within your segment, you didn't have to be that great. And you could have done underselling what we achieved at the different companies. But I think it really has visibility now. There's investment money flowing into it. I think the increasing importance of...we kind of hit that little productivity inflection point where it started to flatten out. People are investing in technology.

The challenges around people there's just not a lot of know-how, or there's much less know-how about everything from manufacturing operations to the different tasks that get performed to the technologies. So, how do we offset that? So technology is starting to fill an increasingly important role of focused VCs, and focused investors, and focused incubators around this kind of stuff. I think that's probably the biggest change.

And then, like any technology segment, the building blocks, the Lego blocks that we build from, just get better and better and better. Someone that wants to add AI capabilities to their solution today, it's never been easier. I want to add Vision. Now, what you do with it can be very differentiating. But my point is that the building blocks we have today are just better than ever.

I think the challenge...what's changed maybe in a negative, I think the way you get to customers, get to market has changed and become more challenging. An example, if you think about a venture-funded or otherwise funded startup, turn the clock back 10 or 15 years. We primarily sold perpetual licenses plus maintenance. So you get a big chunk of revenue upfront.

Today in the SaaS and subscription world, in essence, we're all in the financing business. We're financing our cost of sales, our R&D., So the capital requirements for companies in our segment are bigger than they ever have been. And we see that with some of the raises, but that's just a reality. That dynamic perhaps even gets ignored sometimes, but it is a big change. Yeah, and then, you know, just to --

TROND: And what got you to Tulip?

RICK: So I think it was actually indirectly through Wonderware, if I recall. So Natan and team and Rony and team were looking around at comparables. What are some companies that have been successful growing a business in this space? And he kind of had the hit list of Wonderware folks that he wanted to talk to. And somewhere, somehow, I don't recall the exact moment, but we connected up, and I got it. When he explained what they were doing. The light bulb went off, and I said, "I'd love to be part of this." So I'm both an investor and advisor in the company. And also, I love smart people, like innovative people.

TROND: [laughs]

RICK: And there's no shortage of those in Natan’s team. So first visit there, seeing what they were doing, meeting the team, it was like, all right, there's something going on here.

TROND: So tell me what it is that you saw because I was also...I was at MIT at the time when Natan created the company. And I remember vividly going into the lab or whatever you want to describe his early workspace. Because that's what it was, right? It felt like a lab.

RICK: Sure.

TROND: But the stuff that was coming out was incredible. What do you think? Was it the product vision, or was it just a capability of the people that you saw early on? And now that you're looking at Tulip and its environment, what is being accomplished right now, would you say with this new app reality?

RICK: I think it was the aggregate of all the above. Because great example, if you recall the first demo scenario with the mixed reality projecting instructions onto the work –-

TROND: That was crazy. That demo was for me, the demo of all demos in the -- [laughs]

RICK: Absolutely.

TROND: It was crazy.

RICK: And I said, wow, you're taking a very fresh look at a problem here. And obviously, with their collective backgrounds, really interesting mix of skill sets, they're going to do cool stuff. And I think Natan and team would be the first to admit they were coming in with not a lot of domain knowledge. They had been involved in companies that made stuff, but there was a learning curve for sure. And that's what a lot of...not just myself, but they had a lot of advisors, customer feedback, brought in some folks into the team, and then just learned on the job training, engaging with customers, engaging in pilots.

So I think it took a year or two to kind of get grounded in what are some of the realities of the shop floor, not that they didn't have a good idea. But once that kind of confluence of smart people, customers starting to do cool stuff with it, and the end the product itself evolving, then that's kind of when the rocket took off.

TROND: Well, this is interesting what you're saying here because as I'm interviewing a lot of people who have innovated in this space, time and again, what comes back is this is not just your average software innovation garage. A lab is not a garage. Literally, you can be as smart as you are. You can have a big team of smart people. But unless you get coupled up with that manufacturing shop floor experience, you don't stand a chance, or you just can't build. You can't get past the demo.

Tell me more about that one because you have had it ingrained. We talked about this a few minutes ago. You started out that way. But there are so many more innovators these days that they can't; well, maybe they can start out, but they haven't started out on the shop floor, so many of them.

RICK: I wish they would...everybody who wants to get in this space needs to do...the equivalent of in law enforcement would be a ride-along. You go and spend a couple of nights working the streets. You realize how things really work. It's not like TV. It's not like you read in your textbooks. So there's no substitute for it, even if it's like super-concentrated real-world experience actually going out and spending some time with customers, real-world experience.

But I also think it's the third leg of the stool, which is important. It's the technology expertise and creating products. It's manufacturing domain knowledge and then figuring out how to get it in front of customers and sell it. We can never underestimate the importance of that. So that's another thing that I think Tulip took a lot of very iterative and A/B style testing approaches to go-to-market models and continue to innovate and experiment.

It's a challenging space to do low-touch, but they've found a niche with that, particularly as a means to plant seeds of customers that can take a first taste of the technology like, wow, that's pretty awesome. The holy grail, I think, for a lot of companies in our space to try to figure out how to do that. No one's really completely cracked the code yet. So it's a kind of combination model. But the domain expertise, a couple of key hires, for example, a great example is the hires they made in the pharmaceutical industry.

So life sciences now has become a really, really powerful vertical for Tulip as a result of bringing in civilian expertise plus the evolution of the product from a platform and tooling and some hardware to application, so the app marketplace that they launched. Now when I'm a buyer, you can approach not only that developer buyer, that integrator buyer, but now you can approach a business buyer and say, "I've got all these apps you can assemble together or just use as is." That was also a maturity thing. So it took the domain knowledge, interaction with customers, and then you can progressively build more into the software itself and less that the customer has to configure. That maturation has been pretty exciting to see.

TROND: Rick, we've been through a history here that's very, very exciting to me and, I think to listeners. What's next for the digital factory, for the manufacturing, execution systems, all these acronyms? I tried to shy away from them a little bit because we had so many, many other interesting things to talk about today.

But if you're looking to the next decade, the holy grail you mentioned, or this final integration project that would marry software, hardware, shop floor, and considering all the challenges that just the past year has brought us, and let's not even bring into it all of the other challenges of this decade and of this century, if you're going to go into the big words. Where are we headed?

RICK: I'll maybe focus on where I hope we head, which looks perhaps a little bit different. I started the discussion with one of the things that I learned in my first job working in the plant flow is the importance of people, the knowledge that they have, the experience that they have. People in a lot of our processes are still the sensor, the algorithm, and the actuator.

Like it or not, we haven't yet reproduced the human hand. We haven't yet reproduced the human brain. There are some really unique things about humans. And in that context, I hope that the next decade or so is about collaborative technology and how we use robotics, and AI, and information, and mixed reality to help people be better at what they do. And there's always a risk of dehumanization in something like that where people become interchangeable and they don their Iron Man assembly suit.

But I'll maybe take a more optimistic view that it's really...we're going to continue to increase productivity output. But there are so many roles like that that could benefit from the synthesis of all these cool technologies that we have. I maintain that there's no such thing as an AI market. There's no such thing as an IoT market; that they're all just building blocks, right? It's what we assemble to solve some actual problem that is interesting.

I'm hoping, and I'm confident, that the bar to implement these things becomes increasingly lower. AR is a great example today. It's hard. Building content is time-consuming and difficult. So maybe that's the next one that needs to bring the content creation to mixed reality, next-gen robotics, codebots, and some really interesting stuff happening there. The democratization of machine vision, and audio, and meta sensing that's happening.

TROND: But it's interesting you're saying they're still our building blocks, and they're still our collaboration challenges. And maybe those collaboration challenges are going to have to last longer than a decade, and maybe we need more building blocks. But what comes after that once a critical mass of building blocks get assembled? And you have watched this decade by decade that there's a certain coalescence of building blocks, and then a new platform is formed.

But still, in this industry, as you have said, so far, most of the time, these new platforms merge into the more traditional platform players, or they merge into more established. Is that a pattern that you see also in this decade? Or will we see the first mega conglomerates come out of completely new manufacturing combination platforms that are integrating all of these technologies and doing something truly new and can sustain their own new creation, whatever iteration of the manufacturing industry that would become?

RICK: And I don't know if it's going to be necessarily the suppliers that become the mega innovators. What may well happen is that the manufacturers themselves start to become because the tools have become so powerful that they become the mega. If you actually take a deep dive into a lot of really innovative manufacturing companies, it's the machines that they built to make the product. It's the processes they use to make the product. That's where some of the real breakthroughs happen. That doesn't come from outside. Now, sometimes suppliers can provide some of that equipment. So maybe this is just an amplifier for that.

And the second thing is I know is coming is this massive disintermediation of manufacturing. So we already have companies where the brand owner contracts the design of the product. It contracts people to make the products. It contracts people to service the product and sell the product. So they're literally just the brand name on top of it. Now you matrix that, right? Where you have companies with very, very flexible manufacturing capacity that's additive or traditional. Who knows, right?

But I think a manufacturing supply chain 10-20 years from now is going to look radically different. Fewer companies will be making stuff on their own. But the companies that are making stuff will be really applying some innovative technology to be flexible, versatile. That's never going to happen for grunt commodity stuff where the cost to produce matter; you do purpose-built. But increasingly, look at the proliferation rate on new product introductions and electronic products and so many different things in our lives, clothing, right? There are so many things that we could innovate faster if the manufacturing systems themselves could adapt faster. Maybe that's an outcome.

TROND: Well, I mean, whichever of these scenarios pan out, it seems to me that at least segments of this industry, if it remains, you know if you can talk about it as one industry anymore, is going to be super exciting. So that brings me, I guess, to just my closing question. If you were to advise a young person today who is maybe thinking about college, or they're thinking about should I follow my passion, which happens to be actually going and making and building things? Or should I get a theoretical education, or is that a false choice? Where should they go today?

There's this dichotomy between getting a four-year education versus just going and getting some skills like we have been talking about, so you have some inkling of where you actually need to be to understand in order to produce the innovations.

RICK: I think all the above, and let me elaborate on that a little bit. When I was in university, I created my own co-ops in the summer. So I worked...I sought them out. My son's at Drexel University now, and a co-op program is an integral part of his education there. For a lot of folks, getting kids particularly exposed to co-ops and those kinds of internships give you two things. It might tell you what you don't want to do just as much as you want to do, which is I think a lot of people in their career would wish they knew that earlier.

It helps you get that real-world experience and just interacting with people. So I think that aspect of in your university education doing a diverse and interesting set of co-ops would be very valuable. Having a liberal arts aspect to any technical education or focus skills education is still valid. You have to know how to read, write, speak, those kinds of things.

Design is ever increasingly important. The polymath is going to be a great skill to have. Secondly, learning has never been easier. You've got so many online resources as well. If you need a technical skill, I mean, I could probably learn neurosurgery on YouTube if I really needed to if there was no other option, you know, 60% chance that patient would live.

TROND: [laughs]

RICK: But we have so many different resources. I'm a believer in lifelong learning. So it's not a static thing. Certainly, a highly specialized skill if you're going to be geneticists doing CRISPR whatever, you need to spend 8-10 years of true rigorous study to master a lot of that kind of stuff. Maybe not; maybe that's even getting easier.

TROND: Ricky, you just brought me back to eighth grade and my one-week internship at the National Geological Lab, where I was sorting through minerals. And it's incredible how one week is etched into my mind. I don't think about it every time, and I haven't thought about it for years. But while you were just describing with seeking out these internships, you brought it all back to me. And I can almost remember how the Monday was different from the Tuesday rotation when I went through that institute. There is just no comparison to that kind of real-life experience.

RICK: And the other advice that I give any person is versatile set of skills. Do a sales role sometime in your life. You might hate it, you might despise it, but you're going to learn what the salespeople in your company go through. You might love it, and it becomes a career. Communications, what your marketing folks have. Having a diverse set of skills and getting exposure...maybe it happened accidentally for me. Those were the opportunities that presented themselves, but I think having that diverse skill set and toolbox is extremely valuable, particularly if you want to start a company.

TROND: Rick, I thank you so much. We have gone way over what I had promised and even my promise to our listeners to be very succinct. But this has been, for me, at least a fascinating roller coaster through your career and throughout manufacturing, both history and future. I thank you very, very much.

RICK: My pleasure.

TROND: You have just listened to Episode 10 of The Augmented Podcast with host Trond Arne Undheim. And the topic was A Brief History of Manufacturing Software. Our guest was Rick Bullotta, Partner at TwinThread and Co-Founder of ThingWorx. In this conversation, we talk about how Rick has shaped manufacturing software history and the lessons learned from being an early employee at Wonderware, the famous precursor to manufacturing automation, back in 1993, a company first sold to British engineering giant Siebe in 1998, which merged with BTR to form Invensys, which in turn merged with French multinational Schneider Electric and later the CTO.

Rick Bullotta was also the Co-Founder of Lighthammer Software, which was later acquired by SAP. Then in 2009, founding ThingWorx, the first complete end-to-end technology platform designed for the industrial internet of things, which was acquired by PTC in 2003. We also touch on his current advice to founders in the industrial space, his board role at Tulip, and what he sees lie ahead for the industry.

My take is that Wonderware, Lighthammer, and ThingWorx are prominent parts of manufacturing software history, and there's a chance that the 4th company he now is involved with, Tulip, also will be. I do things with things is Rick Bullotta's motto. The things he does, he does them well, and it is an internet of things, more than anything else. I, for one, am eagerly listening to what he predicts will happen next.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 4: A Renaissance of Manufacturing or Episode 5: Plug-and-Play Industrial Tech. Augmented- the industry 4.0 podcast.

Special Guest: Rick Bullotta.

Episode 86: Augmenting Industry: Reflections on Season 2

29 juni 2022 | 25 min

Welcome to episode #86 of the Augmented Podcast (@AugmentedPod). Today's episode will be a reflection on Season 2. Join host and futurist Trond Arne Undheim (@trondau) as he reflects on season 2 of the Augmented podcast, diving into a few highlights from the season.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip Interfaces (@tulipinterfaces), the frontline operations platform.

In Season 2 we honed in, covering a specific topics relevant to manufacturing, such as marketing, frontline operations, reshoring, digital lean, startups, supply chains, pricing strategies, the manufacturing software market workers, the low code/no- code issue, diagnostic manufacturing, operational data, life science manufacturing systems, the industrial tech transformation outlook, the future factory, the evolution of lean, and industrial interoperability. As you can see, these ranged from technical topics to HR to investing to management principles--all of which go into operating and innovating in manufacturing and industrial tech.

Guests featured in this episode:

Joe Sullivan (@sullivan_joe), host of The Manufacturing Executive podcast and founder of Gorilla 76 (@gorilla76)
Lydia M. Di Liello (@LydiaDiLiello), CEO and founder of Capital Pricing Consultants, and co-host of The WAM Podcast: Empowering Women in Manufacturing and Business. (@wam_podcast)
Yossi Sheffi (@YossiSheffi), Director, MIT Center for Transporation and Logistics (@MITSupplyChain)
Harry C. Moser (@reshorenow) founder and President of the Reshoring Initiative
Dr. Gunter Beitinger (@beitgugb) (@Siemens) SVP of Manufacturing at Siemens AG, Head of Factory Digitalization and Head of Product Carbon Footprint/SiGreen

Thanks for listening. If you like the show subscribe to augmentedpodcast. co or on your preferred podcast player. And rate us with 5 stars. If so, let us know by messaging us your thoughts. Hopefully, you'll find something awesome in this show or in other episodes. Please, if you do, let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guests: Dr. Gunter Beitinger, Harry C. Moser, Joe Sullivan, Lydia M. Di Liello, and Yossi Sheffi.

Episode 85: Industrial Cloud Interoperability

22 juni 2022 | 52 min

This week on the podcast, (@AugmentedPod) we have Leon Kuperman, CTO of CAST.AI (@cast_ai) Futurist Trond Undheim hosts (@trondau), this is episode #85 of Season 2 and the topic is: Industrial Cloud Interoperability. In this conversation, we talk about cloud interoperability, whether it exists, why it's needed and what it could accomplish. We also get into the technical underpinnings, such as Carita, containerization and the outlook for public private and hybrid clouds as well as the vendors that supply advanced infrastructures.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim, and presented by Tulip, the frontline operations platform.

Trond's takeaway: A.I. is a silent enabler of collaboration between systems, which by the same token affects collaboration between people and organizations, its technical complexity often limits the debate about the subject in non-specialist circles, which is a shame given the pivotal importance of cloud infrastructure in today's computing environment, the relative progress made on interoperability will determine the course of products, flexibility, security, and productivity.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. If you liked this episode, you might also like episode #17 Smart Manufacturing for All. Hopefully you'll find something awesome in these or in other episodes. And if so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Leon Kuperman.

Episode 83: Factory Journalism

15 juni 2022 | 41 min

This week on the podcast, (@AugmentedPod) we have Michelle Segrest, journalist, content creator, and owner of Navigate Content, Inc. (@michellesegrest) Futurist Trond Undheim hosts (@trondau), this is episode #83 of Season 2 and the topic is: Factory Journalism.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim, and presented by Tulip, the frontline operations platform.

Trond's takeaway: Factories are enormously diverse depending on industry country, region, and they also change over time. More importantly, they are thriving in a way that the public doesn't always appreciate. So getting inside a few factories from time to time might be the best service you could do yourself or for your kids to reorient yourself toward the importance of manufacturing or the important activity of making things, which has not gone away just because of the digital revolution.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. If you liked this episode, you might also like episode #79 The Future Factory. Hopefully you'll find something awesome in these or in other episodes. And if so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Michelle Segrest .

Episode 84: The Evolution of Lean

7 juni 2022 | 49 min

This week on the podcast, (@AugmentedPod) we have Torbjørn Netland, Chair of Production, Operations Management at the top Swiss university, ETH Zürich, and co-founder of Ethon AI (@tnetland) (@eth) (@ethon_AI). Futurist Trond Undheim hosts (@trondau), this is episode #84 of Season 2 and the topic is: The Evoltion of Lean.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim, and presented by Tulip, the frontline operations platform.

Trond's takeaway: Lean might be an ever-evolving concept centered around how to best improve industrial performance. The orthodoxy around what it means is not helpful yet clarity about clear objectives and tactics is important. The role of technology in Lean is controversial. But one thing is for certain, Lean is primary to technology.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. If you liked this episode, you might also like Episode #21: Lean Manufacturing in the USA with guest Karl Wadensten, who is the CEO of VIBCO. Hopefully, you'll find something awesome in these or other episodes. If you like what you hear, let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Torbjørn Netland.

Episode 79: The Future Factory

31 maj 2022 | 42 min

This week on the podcast, (@AugmentedPod) we have Dr. Gunter Beitinger, SVP of Manufacturing at Siemens AG joining us for a conversation about the digital transformation of the factory floor. Dr. Gunter is also the Head of Factory Digitalization and Head of Product Carbon Footprint/SiGreen (@beitgugb) (@Siemens). Futurist Trond Undheim hosts (@trondau), this is episode #79 of Season 2 and the topic is: The Future Factory.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim, and presented by Tulip, the frontline operations platform.

Trond's takeaway: It is pretty clear that the factory of the future will look different from that of the past but what's less clear is the path to get there and what role digitalization will play in getting us there. What does seem clear is that we still need creative heads. We need people to produce the needed innovations and machines to hum, along with to the human beat, as opposed to the other way around.

Thanks for listening. If you like the show, subscribe at augmentedpodcast.co or your preferred podcast, and rate us with five stars. If you liked this episode, you might also like Episode #21: The Future of Digital in Manufacturing.

Hopefully, you'll find something awesome in this show or in other episodes. Please, if you do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Dr. Gunter Beitinger.

Episode 82: Innovation Corridor in Connecticut

25 maj 2022 | 48 min

This week on the podcast, (@AugmentedPod) we have Marty Guay, Vice President of Business Development at Stanley Black & Decker (@StanleyBlkDeckr). In this conversation, futurist Trond Undheim (@trondau) and Marty talk about the Connecticut state initiative designed to enable technology adoption and workforce creation. This is Episode #82 of Season 2 and the topic is Innovation Corridor in Connecticut.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim, and presented by Tulip, the frontline operations platform.

Trond's takeaway: It has long been the thinking that government-sponsored workforce development is the only way to stimulate, learning, and training at scale. However, that may in fact not be the case and may at times have the adverse consequence that businesses don't invest themselves. But focused funding and initiatives do help and the CT innovation corridor seems well thought out and will presumably revitalize an important area around Hartford, which is much needed.

Thanks for listening. If you like the show, subscribe at Augmented podcast.com or your preferred podcast, and rate us with five stars. If you liked this episode, you might also like Episode #62: Manufacturing Excellence in Michigan. Hopefully, you'll find something awesome in this show or in other episodes. Please, if you do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Marty Guay.

Episode 77: Industrial Tech Transformation Outlook

18 maj 2022 | 54 min

This week on the podcast, (@AugmentedPod) we have Andrew Obin, the Managing Director with Bank of America Securities Equity Research. In this conversation, futurist Trond Undheim (@trondau) and his guest, Andrew discuss Wall Street's track record for digital transformation, where investments are made, and finally, Wall Street's tech trajectory. This is Episode #77 of Season 2 and the topic is: Industrial Tech Transformation Outlook.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim, and presented by Tulip, the frontline operations platform.

Trond's takeaway: Wall Street's track record for digital transformation isn't necessarily good because these processes are complex. But on the other hand, firms that exceed can command a premium and can forge ahead. The tech trajectory is interesting to watch as digital progress. Isn't a property of only tech companies anymore as that truth sinks in it will change by your actions as well.

Thanks for listening. If you like the show, subscribe at Augmented podcast.com or your preferred podcast, and rate us with five stars. If you liked this episode, you might also like Episode #21: The Future of Digital in Manufacturing.

Hopefully, you'll find something awesome in this show or in other episodes. Please, if you do let us know by messaging us. We would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Andrew Obin.

Episode 80: The Augmenting Power of Operational Data

11 maj 2022 | 28 min

This week on Season 2 of the Augmented Podcast (@AugmentedPod), Rony Kubat, CTO, and Cofounder of Tulip Interfaces (@tulipinterfaces) (@kubat), joins futurist Trond Undheim (@trondau) in a conversation about how operational data can be augmented using machine intelligence coupled with human interpretation.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform.

Trond's takeaway: The power of capturing both manual and machine processes in manufacturing comes when you start to be able to connect silos of shop data and shop floor data, to get a time series of the data attached to the product. It is not necessarily easy but when you achieve it, you have a great starting point for a true understanding of the manufacturing process.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. If you liked this episode, you might also like episode 81 Predictive to Diagnostic Manufacturing Augmentation which you can find at Augmented podcast.co/81.

Hopefully, you'll find something awesome in these or in other episodes. Please, if you do let us know by messaging us, we would love to share your thoughts with other listeners. The Augmented podcast is created in association with. The frontline operations platform connects the people, machines, devices, and the systems used in a production or logistics process in a physical location.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Rony Kubat.

Episode 78: Life Science Manufacturing Systems

4 maj 2022 | 40 min

Incoming! This week's episode of the Augmented Podcast (@AugmentedPod) features a conversation with futurist Trond Undheim (@trondau) and Dr. Gilad Langer, Manufacturing Practice Lead at Tulip (@tulipinterfaces). In this conversation, we talk about the evolution, the experiences, the challenges, and the future opportunities of life science manufacturing systems. This is episode 78 of Season Two and the topic is: "Life Science Manufacturing Systems."

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform.

Trond's takeaway: Life sciences are challenging: the traditional paradigm of industrial techniques. Bio-manufacturing in particular poses challenges to many existing systems. As such, the industry's work with ISP's pharma 4.0 initiative is bearing fruit. However, the dialogue with regulatory authorities still seems to be one the industry is trying to educate governments on. Nevertheless, once governments get it and are willing to adapt regulations, we might see even more rapid advances given the importance and scope of the new manufacturing techniques that are opening up and await regulatory approval.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast app and rate us with five stars. If you liked this episode, you might also like episode 31 Pharma 4.0 with Michelle Vuolo, who is a quality practice leader.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Dr. Gilad Langer.

Episode 81: From Predicative to Diagnostic Manufacturing Augmentation

27 april 2022 | 25 min

This week on the podcast (@AugmentedPod), Trond is in conversation with returning guest and colleague, Roy Shilkrot, the Lead Scientist at Tulip (@tulipinterfaces). In episode 81 of Season 2 of the Augmented podcast, the topic is "From Predictive to Diagnostic Manufacturing Augmentation." In this conversation, we're diving further into machine learning in manufacturing and emerging applications.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform.

My takeaway: Industrial tech is not what it used to be and will never again be a bulky inflexible instrument because we are entering an age with immediate feedback and rapid adjustments and a combination of human and machine. Diagnostic manufacturing is, in fact, quite different from a time when we had to wait until the product was onerous to see what could be wrong with it.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. If you liked this episode, you might also like episode 29 The Automated Micro-Factory, episode 22 Friedman's Factory: What is No-Code, or episode 19 Machine Learning in Manufacturing. You can listen to all of these episodes simply by pasting in the episode number after the URL.

The Augmented podcast is created in association with Tulip, the frontline operations platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Roy Shilkrot.

Episode 76: Low on Code, High on Process

20 april 2022 | 37 min

Joining us this week on the podcast (@AugmentedPod) is CEO and Founder of Pyze, Inc. (@PyzeInc) Prabhjot Singh (@psinghSF) Here with futurist Trond Undheim to talk all about business process intelligence, the workflows in manufacturing and logistics, and the future outlook for low-code in industrial applications, the episode is 76 and the topic is: "Low on Code, High on Process."

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform (@tulipinterfaces).

Trond's takeaway: Business process intelligence is the "why" of technology. Because smoother operations are where the value of technology is realized. The future outlook for low code in industrial operations is bright because it has the potential to streamline workflows in manufacturing and logistics. However, it is important to keep in mind that to leverage automation to do better decisions, and not just to squeeze out more with less--that starts with keeping in mind what the real problem is and steering with that in mind. If you don't know, figure out the problem and then invest in the process and if technology gets you there, invest.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. And if you liked this episode, you might also like episode 73 The Challenge of Front Line Operations. Hopefully, you'll find something awesome in these or in other episodes.

And if so, do let us know by messaging us because we would love to share your thoughts with other listeners. The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Prabhjot Singh.

Episode 75: Designing a Worker Friendly Industrial System

13 april 2022 | 42 min

This week on the podcast (@AugmentedPod), futurist Trond Undheim interviews Erik Mirandette, Head of Product and Ecosystem at Tulip Interfaces (@tulipinterfaces). This is episode 75 of Season 2: "Designing a Worker Friendly Industrial System."

In this conversation, we talk about what designing and redesigning a worker-friendly industrial system might entail, how to build capabilities and not point solutions that simply fix existing use cases to empower operators and workers along the way.

Trond's takeaway: It is unusual to hear the case for manufacturing efficiency, coupled with worker empowerment, and then expressed so clearly as a systems dynamics problem that needs to have an overall fix instead of just attacking.

Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. If you liked this episode, you might also like episode 73 The Challenge of Frontline Operations.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau) and presented by Tulip, the frontline operations platform.

The Augmented podcast is created in association with Tulip, the frontline operations platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Erik Mirandette.

Episode 67: Manufacturing 5.0

6 april 2022 | 38 min

This week on the podcast (@AugmentedPod), futurist Trond Undheim interviews Kathryn Kelley, Executive Director of the Ohio Manufacturing Institute at Ohio State University (@omiosumfg). This is episode 67 of Season 2, "Manufacturing 5.0"

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform (@tulipinterfaces).

Trond's takeaway: In this conversation, we talked about industrial trends in Ohio, across the US. Ohio, along with Michigan, California, and Texas, is where the bulk of US manufacturing habits are formed. That's why tracking their thinking is important. To think that it would take decades to roll out industry 4.0 in Ohio is mind-boggling. Can it be true?
This is why we need a new approach to industrial tech and one where training needs are drastically reduced and technology can be implemented in days and weeks instead of months and years. I believe that the opportunity exists now. Now let's roll it out, test it and see if it can happen. Training is key, and government and state-sponsored programs are an important component.

The Augmented podcast is created in association with Tulip, the frontline operations platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co
If you liked this episode, you might also like episode 49 Lean Manufacturing in the USA, episode 46 Manufacturing Training in Massachusetts, or episode 30 Rethinking Workforce Learning.

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Kathryn Kelley.

Episode 72: What is Tulip University

30 mars 2022 | 36 min

This week on the podcast (@AugmentedPod), futurist Trond Undheim interviews John Klaess, Head of Product Education at Tulip Interfaces (@tulipinterfaces). This is episode 72 of Season 2, "What is Tulip University."

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform.

Trond's takeaway: Training is crucial to contemporary manufacturing, though not in the way we usually think. Employees must train people all the time, which brings us to question, what if the technologies and the user interfaces were simplified.

The Augmented podcast is created in association with Tulip, the frontline operations platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co
If you liked this episode, you might also like episode 3 Reimagine Training, episode 46 Manufacturing Training in Massachusetts, or episode 2 How to Train Augmented Workers.

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Dr. John Klaess.

Episode 71: Trends in the Manufacturing Software Market

23 mars 2022 | 47 min

Episode 54: Industrial Pricing Strategies

16 mars 2022 | 38 min

Joining us this week on episode 54 of Augmented Season Two (@AugmentedPod), is Lydia M. Di Liello (@LydiaDiLiello), CEO and Founder of Capital Pricing Consultants, as well as the host of The WAM Podcast: Empowering Women in Manufacturing and Business. (@wam_podcast) The topic is: "Industrial Pricing Strategies."

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip Interfaces (@tulipinterfaces), the frontline operations platform.

Trond's takeaway: I can admit to often underestimating the strategic role of pricing in determining industrial developments, new entrance, exciting business models, and testing and or scaling the products.

I am not surprised that there are pricing consultants because the area is complex and you need both data and experience to play it right. The future of pricing is undoubtedly influenced by ever-evolving analytics about the purchasing habits of people in business. But is also shaped by the x-factors of the emerging future. At the end of the day, pricing is a high-priority leadership topic, but also, one that will be shaped by specialists.

Thanks for listening. If you like the show subscribe to augmentedpodcast. co or on your preferred podcast player. And rate us with 5 stars. If you liked this episode, you might also like episode 41 Scaling Software Movements, episode 50, The Last Mile of Productivity, or episode 49 Lean Manufacturing in the USA. Hopefully you'll find something awesome in these or other episodes. If so, let us know by messaging us your thoughts. We would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Lydia M. Di Liello.

Episode 68: Industrial Supply Chain Optimization

9 mars 2022 | 54 min

Episode 59: Early Startups Meet Industry 4.0

2 mars 2022 | 40 min

Episode 62: Manufacturing Excellence in Michigan

23 februari 2022 | 32 min

Episode 58: Manufacturing x Digital

16 februari 2022 | 46 min

Today’s guest is Chandra Brown, CEO, MxD for episode 58 of Augmented Podcast (@AugmentedPod). The topic is: Manufacturing x Digital.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip (@tulipinterfaces), the frontline operations platform.

In this conversation, we talked about manufacturing leadership, how to achieve digital manufacturing success, and fighting the skills and perception gap. We also touched on the activities of Chicago-based manufacturing institute MxD, and the future outlook of the industry.

Trond's takeaway: Manufacturing leadership is in good hands with Chicago-based manufacturing institute MxD. Digital manufacturing success takes constant R&D, experimentation, and learning from failure and success. If MxD’s approach took hold, the skills and perception gaps that persist would start to dissipate. As long as idea exchange continues to accelerate, the future outlook of the industry is bright.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 30, Rethinking Workforce Learning, 17, Smart Manufacturing for All, or 7, Work of the Future. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Chandra Brown.

Episode 60: Reshoring

9 februari 2022 | 38 min

Today's guest is Harry C. Moser, Founder and President, Reshoring Initiative (@reshorenow) on episode 60 of Augmented Podcast (@AugmentedPod). Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.

In this conversation, we talked about what reshoring is, why it is important now, what data there is on it, what the Reshoring Initiative is, and what the future holds for manufacturing.

Trond's takeaway: Reshoring remains a controversial topic. While the effects may be positive initially what does it mean for the cost and quality of existing supply chains? Is it even realistic to reshore big chunks of a globalized value chain? Which aspects are strategic and which are not? There are many questions, but the projections are alluring and domestically, in most traditional mass manufacturing states, it remains a popular topic and one would think the sustainability effects of more localized production would be a good thing. What's clear is that every nation, and particularly the US, depends on an educated workforce to be competitive. The real question might be: what does it take to create a more competitive world where opportunity exists on every shore?

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 50, The Last Mile of Productivity, episode 49, Lean manufacturing in the USA, or episode 18, Transforming Foundational Industries. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip (@tulipinterfaces), the frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ
See you next time. Augmented--industrial conversations that matter.

Special Guest: Harry C. Moser.

Episode 74: DMG MORI's Digital Lean Journey

2 februari 2022 | 53 min

Our guest is Marius Schmiedt, Head of Operational Excellence, DMG MORI (@dmgmorieu) for this episode of Augmented Podcast (@AugmentedPod). Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.

In this conversation, they talk about the operational aspects of industrial manufacturer DMG Mori's pathbreaking digital transformation.

[Please note that this episode was recorded in November of 2021.]

Trond's takeaway: Deep industrial transformation is not just a digital challenge but requires a fundamentally different operational mindset, beyond Lean Management, one that more fully embraces the potential of the human, industrial frontline worker. That shift is a journey, not a switch. On the other hand, the impact over time, measured in months not in years, is not only an immense return on investment but also a transition from dirty, dangerous and dull jobs to ever cleaner, safer and more exciting jobs.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 20, The Digitalization of Körber, episode 23, Digital Manufacturing in the Cloud, or episode 27, Industry 4.0 Tools. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip (@tulipinterfaces), connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guest: Marius Schmiedt.

Episode 73: The Challenge of Frontline Operations

19 januari 2022 | 44 min

Today our guest is Jason Dietrich, Head of Commercial Operations, Tulip (@tulipinterfaces), for episode 73 on Augmented Podcast (@AugmentedPod). Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform.

In this conversation, we talk about the following: What is frontline operations? What are the specific needs industrial companies have when they want to digitize their operations? What are some key Tulip use cases? What will industrial operations look like over the next decade?

Trond's takeaway: Frontline operations is an increasingly crucial business function whereby a function that previously was considered back office or lower priority, that of the industrial worker, receives increased attention. When the frontline gets digitally augmented and managerially supported, workers feel empowered just like white collar knowledge workers with desks. Connecting machines to serve people, simplifying technology implementation, operators can carry the key task of uniting industrial production with consumption needs. Over time, this might eradicate inefficiencies in the supply chain. This development will not only shape industrial operations over the next decade, in some companies, it is already in place.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 50, The Last Mile of Productivity, episode 49, Lean manufacturing in the USA, and episode 41, Scaling Software Movements. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guest: Jason Dietrich.

Episode 64: Marketing Mindset in Manufacturing

12 januari 2022 | 36 min

Our guest is Joe Sullivan (@sullivan_joe), host of The Manufacturing Executive podcast and Founder, Gorilla 76 (@gorilla76). This is episode 64 of the Augmented Podcast (@AugmentedPod), the topic is: Marketing Mindset in Manufacturing. In this conversation, we talked about marketing tips for manufacturers, best practices from foundations to demand generation, distinguishing yourself by consistency.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.

Trond's takeaway: The challenge of developing a credible yet effective marketing mindset is not unique to manufacturing, but it may be unusually underutilized in this sector. Industrial products and services are notoriously lagging behind the consumer market in terms of the sophistication of its communication, messaging, and use of social media. As industry seeks increasing relevance in people's life, or even seeks to recruit new talent from younger generations, that needs to change. Luckily, there are tools and practices to learn from and they are not rocket science, but take a willingness to commit.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 18, Transforming Foundational Industries, episode 46, Manufacturing Training in Massachusetts, or episode 53, Manufacturing Millennials . Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod

Facebook: https://www.facebook.com/AugmentedPodcast/

Twitter: https://twitter.com/AugmentedPod

YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guest: Joe Sullivan.

Episode 61: The Digital Journey of a Flower Wholesaler

22 december 2021 | 34 min

Today's guest on episode 61 is Andy Burton, Managing Director at Double H Nurseries Ltd (@DoubleHUK). In this conversation, we talk about the experience implementing the Tulip digital app in the midst of COVID-19 lockdown in the busiest flower season to try to save his flowers and turn on e-commerce without spending a fortune in time and resources and without deep digital skills in his workforce.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform.

Trond's takeaway:
Frontline operations is about so much more than technology, but getting technology right is often about picking the right technology for the skillset of your workforce. Implementing tech must be followed up by a complete tie-in with all your business processes, otherwise you enable speed in one part of the process and backlog in another. No-code apps make this easier, but not self evident.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guest: Andy Burton.

Episode 70: Disrupting Dialysis by Digital Operations

15 december 2021 | 39 min

In episode 70 of the Augmented podcast (@AugmentedPod), our guest is Marc Nash, Vice President of Manufacturing at Outset Medical, Inc. (@OutsetMedical). In this conversation, we talked about how Outset Medical is disrupting the dialysis industry by adopting industry 4.0 technologies.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform.

Trond's takeaway:

The Medical Device Industry is not the easiest industry to innovate within. Regulatory constraints, the complexity of managing software and hardware together, and staying on top of the logistical intricacies of the health care market, all of that complicates things. No Digital Lean journey is easy, but the fact that Outset Medical managed to build a greenfield factory in Tijuana, Mexico, with a bottom-up approach to process improvement through no-code apps that empower their workers, is an inspiring story.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 50, The Last Mile of Productivity, episode 33, Sustainable Manufacturing at Scale, or episode 63, Digitizing Medical Device Operations. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guest: Marc Nash.

Episode 63: Digitizing Medical Device Operations

8 december 2021 | 29 min

In episode 63 of the podcast, the topic is: Digitizing Medical Device Operations . Our guest is Dan Ron, Lead Engineer at Dentsply (@DentsplySirona)). Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim [(@trondau), presented by Tulip.

In this conversation, we talk about Implementing tulip and i4.0 concepts into a fast pace highly customized med device manufacturing. Digitizing work instructions. Simplification. Personalizing medical device product operations. The future of the industrial frontline worker.

Trond's takeaway: So much of what determines success with the rollout of technology in manufacturing has to do with simplification. You aim to simplify, you make the process simple, and you choose simple apps to start with. The end result is a simpler work process which makes you more efficient. If any of those steps are complex, you risk adding further complexity to an already messy reality of complicated supply chains, work processes, and workforce challenges. That's why customization of industry 4.0 approaches heavily depends on people who lead with clarity.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 36, Digital Lean, episode 29, The Automated Microfactory or episode 8, Productizing Quality. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process i n a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time.

Special Guest: Dan Ron.

Episode 66: Bridging the Physical-Digital Divide in Industrial Tech

1 december 2021 | 57 min

In episode 66 of the podcast, the topic is: Bridging the Physical-Digital Divide in Industrial Tech. Our guest is Rony Kubat (@kubat), CTO and co-founder, Tulip

In this conversation, we talk about the complexity of the shop floor and programming a physical-digital environment. What does Digital Lean mean to you? What is augmentation? What's next in industrial tech?

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.

Trond's takeaway:
The physical-digital environment is no joke. When you speak with a real technologist who not only has imagined what the future would look like, but who is involved in building it, integrating software and hardware on the factory floor, you realize how difficult it will be to transform industrial work. It is not just about industrial tech, it is about people. It is not just about neat software, or fancy hardware. It all has to work together. And, more importantly, it has to fit into the overall context of what people are already doing.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 44, No-code for IoT in the Cloud, episode 47, Industrial Machine Learning or episode 29, The Automated Microfactory.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guest: Rony Kubat.

Episode 65: The Ten Best Manufacturing Podcasts for 2021

24 november 2021 | 18 min

In episode 65 of the Augmented podcast, the topic is: The 10 Best Manufacturing Podcasts for 2021. Today, we we are reviewing manufacturing podcasts. We talk about why you should listen to manufacturing podcasts, what characterizes the best ones, what the top ten manufacturing podcasts are, in our opinion, and what makes a podcast great. The show is based on Tulip's blog post, The 10 Best Manufacturing Podcasts for 2021.

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau). The Augmented podcast is created in association with Tulip, a connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations. Tulip is also hiring. You can find Tulip at Tulip.co.

Trond's takeaway:
Manufacturing industry podcasts are emerging as a practical way to stay up to date, because you can listen on the fly: on your commute, while driving, when on a break, on your run, or even on the shop floor (if allowed). This is particularly useful in manufacturing, where a lot of the work consists of running around, standing up and being away from your desk (many workers don’t even have desks). At Tulip, where we are supporting the next generation of frontline operations, sharing knowledge and experience right when you need it, in a simple and accessible way, is a high priority. We currently have two podcasts of our own, Augmented, featuring industrial thought leaders, and Behind the Ops, talking to engineers and operators, which we have not included in this ranking (although we think they are great, too, but others can judge). If you have suggestions for podcasts that deserve a place among top manufacturing podcasts, please let us know.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 82, Making Industry Sexy with podcaster Scott MacKenzie host of the Industrial Talk podcast, or episode 53, Manufacturing Millennials with video podcaster Jake Hall, or the upcoming episode 64, Marketing Mindset in Manufacturing with podcaster Joe Sullivan, host of The Manufacturing Executive podcast. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod

Facebook: https://www.facebook.com/AugmentedPodcast/

Twitter: https://twitter.com/AugmentedPod

YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter.

Episode 69: How 5G Enables Manufacturing

22 november 2021 | 35 min

In episode 69 of the podcast, the topic is: How 5G Enables Manufacturing. Our guests are Rowan Högman, Head of 5G Industry Collaboration, Ericsson and Jeff Travers, Customer Success Lead - Dedicated Networks, Ericsson.

In this conversation, we talk about a new Ericsson report on how 5G Enables Manufacturing (Ericsson Industry Lab). We explore some surprising findings, discuss industry trends, as well as the current and future use cases as wireless networks take another step towards being trusted on the shop floor and they muse on the ultimate impact of 5G.

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.

Trond's takeaway:

5G isn't just another generation wireless networks, it is a game changer for trust, reliability, and industrial performance at the edge, that last mile which fixed broadband has struggled with. What remains to be seen is how widespread the rollout will be and, of course, how innovative industry will be in making use of the new network.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 44, No-code for IoT in the Cloud, episode 21, The Future of Digital in Manufacturing or episode 25, Industrial Tracking: Drones, Warehouses and Theme Parks. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter. See you next time.

Special Guests: Jeff Travers and Rowan Högman.

Episode 34: Making Factories in Space

17 november 2021 | 58 min

In episode 34 of the podcast, the topic is: Factories in Space. Our guest is William Bruey (@WillBruey), CEO and co-founder at Varda Space Industries.

William Bruey (@WillBruey): https://www.linkedin.com/in/will-bruey-8a2b5040/
Varda Space Industries (@Vardaspace): https://varda.com/

In this conversation, we talk about: Why build a factory in space? How R&D from the International Space Station facilitated this new development. What space manufacturing will entail in the short term. What the benefits are likely to be in terms of manipulating the terrestrial physical constraints of crystallization and sedimentation. We discuss what the near immediate use cases are, such as better semiconductors and fiber optics. We cover the futuristic use cases including 3D printing human organs without scaffolding.

Augmented (@Augmentedpod) is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum.

Trond's takeaway: Factories in space are closer than we think, and the reason is that NASA and others have spent 20 years doing R&D at the International Space Station. All that work can now come to fruition, but not as within the constraints of that remote vessel, but autonomously by deriving the results and building an independent rocket, lab and reentry capability. How exciting is that? Will it expand industrial performance on earth in this decade? What will it mean for further space exploration in the next? Change is afoot, but science, and space, are still endless frontiers. That might be a good thing, or we might get cocky.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 29, The Automated Microfactory, episode 33, Sustainable Manufacturing at Scale, or episode 13, Get Manufacturing Superpowers. Hopefully, you'll find something awesome in these or other episodes.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter.

Special Guest: William Bruey.

Episode 52: Unplanned Downtime

10 november 2021 | 38 min

In episode 52 of the podcast (@AugmentedPod), the topic is: Unplanned Downtime. Our guest is Simon Kampa, CEO & co-founder, Senseye (@senseyeIO).

In this conversation, we talk about Senseye's report, The True Cost of Downtime, investigating the impact of machine failure and unplanned downtime at the world’s largest manufacturers. We also discuss the coming consolidation of the industry due to technology maturity and customer preference for simplicity, quality, and scale. We briefly discuss the need for industrial interoperability in order to achieve the impact the industry desires.

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Augmented--the industry 4.0 podcast.

Trond's takeaway: The True Cost of Downtime in manufacturing is enormous. Interrupting factory production is the last thing you want, yet it's the most common response to anomalies in the production process. Does it have to be that way? Realtime analytics carries the promise of predictive maintenance which can be carried out in downcycles or certainly more opportunistically.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 8, Productizing Quality, episode 42, Business Beyond Buzzwords, and episode 19, Machine Learning in Manufacturing. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Simon Kampa.

Episode 44: No-code for IoT in the Cloud

3 november 2021 | 57 min

In episode 44 of the podcast, the topic is: No-code for IoT in the Cloud. Our guest is Rob Rastovich, CTO of ThingLogix (@ThingLogix): https://www.thinglogix.com/

In this conversation, we talk about what the Internet of Things (IoT) means for industrial business models. We discuss the impact of connected devices and the subscription based economy on industries as distant from the initial IT waves as agriculture.

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Augmented--the industry 4.0 podcast.

Trond's takeaway: The Internet of Things (IoT) is about to complete its hype cycle. We might finally see the smart cities we were promised in the nineties. With IoT, digital benefits comes to infrastructure, the last mile of sunk assets, long timelines, and nearly forgotten, but hard-earned public goods.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 29, The Automated Microfactory, episode 33, Sustainable Manufacturing at Scale, or episode 23, Digital Manufacturing in the Cloud. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Rob Rastovich.

Episode 53: Manufacturing Millennials

27 oktober 2021 | 55 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 53 of the podcast (@AugmentedPod), the topic is: Manufacturing Millennials. Our guest is Jake Hall, podcaster, The Manufacturing Millennial and Business Development Manager at Feyen Zylstra, a Grand Rapids, Michigan, US-based systems integrator.

In this conversation, we talk about The future generation in manufacturing, how we convince young people as well as how companies can make themselves attractive to this generation, and even more importantly, empowering the existing workforce.

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast--industrial conversations that matter.

After listening to this episode, check out:

The Manufacturing Millennial: https://www.themanufacturingmillennial.com/
Feyen-Zylstre (@feyenzylstra): https://www.feyenzylstra.com/
Jake Hall: https://www.linkedin.com/in/jacobrhall/

Trond's takeaway: Millennials are the key to the future of manufacturing, not just because they are a talent pool needed in the workforce but because they have the right mindset for change which is so beneficial to a manufacturing industry faced with the challenges and opportunities of industry 4.0 or smart manufacturing using digital solutions.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 11, Empowering Workers to Innovate, episode 3, Reimagine Training, or episode 12, Enterprise Wide Quality of Manufacturing.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Jake Hall.

Episode 50: The Last Mile of Productivity

20 oktober 2021 | 48 min

In episode 50 of the podcast (@AugmentedPod), the topic is: The Last Mile of Productivity. Our guest is Laurent Vernerey, former CEO Schneider Electric USA, and member of Tulip's Board of Directors.

In this conversation, we talk about the digital transformation journey for the manufacturing industry, as seen through the lens of Schneider Electric. From the early days where those that did not digitize got lost, to being able to track, capture and monitor, to today's reality which is all about change management and being able to achieve scale and into the future of agile, lean and digital which entails scaling and squeezing the value out of hardware implementation as well as drastically improving the customer experience.

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum.

Trond's takeaway: Benefiting from the industry luminaries I talk to every week, I'm eyeing a day when hardware, meaning physical objects of technological origin can scale at the speed and magnitude of software. This will create a whole new world, one where the sky's not the limit, in fact. We need it, because it might arrive just in time for us to avoid the complete destruction of our ecosystem--or at least to adapt to it--a set of issues we will tackle in forthcoming episodes about sustainability. For now, let's just work on the last mile, turning analytical improvements into intelligently preparing for unprecedented scale change in industrial manufacturing and innovation, all of which will be sorely needed soon enough.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 33, Sustainable Manufacturing at Scale, episode 42, Business Beyond Buzzwords, or episode 23, Digital Manufacturing in the Cloud. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Laurent Vernerey.

Episode 51: Designing Industrial Reality in 3D

13 oktober 2021 | 43 min

In episode 51 of the podcast (@AugmentedPod), the topic is: Designing Industrial Reality in 3D. Our guest is Marcelo Coelho (@marcelocoelho), Head of Design at Formlabs and Lecturer at MIT. In this conversation, we talk about the emerging practices of industrial design in additive manufacturing using 3D printing, machine learning and a high degree of customization.

After listening to this episode, check out:

Formlabs: https://formlabs.com/
Marcelo Coelho: https://cmarcelo.com/

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Augmented--the industry 4.0 podcast.

In this conversation, we talked about the emerging practices of industrial design in additive manufacturing.

Trond's takeaway: Making products accessible is hard work. Industrial design is not an afterthought. The best companies are getting good at it. Why? Because they are bringing customers closer to them, to learn and explore. In fact, people are potentially getting closer to the making process than ever before. We have the opportunity to literally shape our future, our surroundings, and the things we touch, use, work with and enjoy in our leisure. That's a whole other level of customization. Forget personalization, this is way more than being able to adapt to your personality, it is attuning to your very purpose and context as a situated human being with everchanging resources, needs and interests.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 36, Digital Lean, episode 29, The Automated Microfactory or episode 33, Sustainable Manufacturing at Scale. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

See you next time. Augmented--industrial conversations that matter.

Special Guest: Marcelo Coelho.

Episode 28: Making Industry Sexy

6 oktober 2021 | 50 min

In episode 28 of the Augmented podcast (@AugmentedPod), the topic is: Making Industry Sexy. Our guest is Scott MacKenzie, Host of the Industrial Talk podcast (@IndustrialTalk), https://industrialtalk.com/

In this conversation, we talk about Scott's personal journey from humble beginnings as a lathing contractor prepping houses for plastering and certified lineman climbing utility towers going to night school MBA to industrial marketer and educator and eventually becoming the leading independent industry podcast. We discuss his unique approach to industrial marketing and networking, focused on conversations to make introductions that turn into business. We chart the road ahead for industrial leaders, industrial tech, and marketing.

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Augmented--the industry 4.0 podcast.

Trond's takeaway: Building relationships is the foundation of business and should be the mainstay of marketing, even in the digital era. Podcasts have emerged as a powerful way to structure industrial conversations, and the Industrial Talk podcast is a masterful example of combining content marketing with authenticity, voice and a strong mission.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 39, Covering Industrial Innovation, episode 26, Manufacturing in Massachusetts, or episode 4, A Renaissance in Manufacturing. Hopefully, you'll find something awesome in these or other episodes. If so, do let us know by messaging us, we would love to share your thoughts with other listeners.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

Please share this show with colleagues who care about where industry and especially industrial tech is heading. To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter.

Special Guest: Scott MacKenzie.

Episode 49: Lean Manufacturing in the USA

29 september 2021 | 45 min

Episode 45: The Startup Studio for Manufacturing

22 september 2021 | 54 min

Episode 41: Scaling Software Movements

15 september 2021 | 52 min

In episode 41 of the Augmented podcast (@Augmentedpod), the topic is: Scaling Software Movements. Our guest is Hilarie Koplow-McAdams(@HilarieKM), Venture Partner at NEA (@nea).

Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform, where Hilarie as of this year serves as a Board of Director member, and is also associated with MFG.works, the industrial upskilling community launched at the World Economic Forum.

In this conversation, we talk about What Hilarie has learned from 35 years in the software industry, reflecting on her path, which includes taking Oracle from $100M to $11B revenue, via building a user movement of small business owners and their accountants at Intuit, integrating all those lessons with the simplicity that Salesforce brought to the customer relationship channeling the move towards trusted cloud computing, to Tulip, which enables the co-design of products and services in frontline operations, starting with the manufacturing shop floor.

**Trond's takeaway**: At hyper scale, growing software companies is not just about sales numbers and strategies, it is about channeling social movements. Perhaps it takes an outsider to see it and run with it? Hilarie has, through 35 years of trailblazing product and sales leadership, shown that truly understanding features, markets, users, and governance, is not just about following Silicon Valley personality cults. Rather, mega scale follows from tapping into big movements in the market, movements that are social in nature, delivering value to people who become believers and tell their friends, colleagues, and family about it. But although sales, at the core, may be social, it is also about being disciplined about market entry decisions and following through with a total solution.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 18, Transforming Foundational Industries, episode 42 Business Beyond Buzzwords, or episode 23, Digital Manufacturing in the Cloud. Hopefully, you'll find something awesome in these or other episodes.

The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

Augmented--industrial conversations that matter.

Special Guest: Hilarie Koplow-McAdams.

Episode 47: Industrial Machine Learning

8 september 2021 | 28 min

Episode 31: Pharma 4.0

1 september 2021 | 32 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 31 of the podcast (@AugmentedPod), the topic is: Pharma 4.0. Our guest is Michelle Vuolo, Quality Practice Lead, Tulip (@tulipinterfaces).

In this conversation, we talk about GxP, the collection of quality guidelines and regulations created to ensure that bio/pharmaceutical products are safe, meet their intended use, and adhere to quality processes during manufacturing, control, storage, and distribution, specifically in the context of emerging digital technologies that promise to improve efficiency, so-called industry 4.0 or, in this case, pharma 4.0 technologies.

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast--industrial conversations that matter.

After listening to this episode, check out:

Tulip: https://tulip.co/
Michelle Vuolo: https://www.linkedin.com/in/mtullie/

**Trond's takeaway**: Pharma 4.0 has to be executed with a diligence unseen in the software industry, which also explains why change has taken a while to come to pass. Now that the industry is taking on board the promise of such technologies, it also has to document its effects. The impact on digital technologies should be positive although it will not be without frustrations for those who want quick wins.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 33, Sustainable Manufacturing at Scale, episode 23, Digital Manufacturing in the Cloud, or episode 17, Smart Manufacturing for All.

Augmented--industrial conversations that matter.

Special Guest: Michelle Vuolo.

Episode 46: Manufacturing Training in Massachusetts

25 augusti 2021 | 59 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 46 of the podcast (@AugmentedPod), the topic is: Manufacturing Training in Massachusetts. Our guest is John Killam, President, MassMEP.

In this conversation, we talk about the important role of manufacturing in Massachusetts, fostering the next generation manufacturers, manufacturing workforce trends and actions, including how to recruit talent to the region and to our manufacturing firms.

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co (@tulipinterfaces), the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

After listening to this episode, check out MassMEP as well as John Killam's social profile.

MassMEP (@MassMEP): https://massmep.org/
John Killam (@JKillamMEP): https://www.linkedin.com/in/johnkillammassmep/

**Trond's takeaway**: Manufacturing is surprisingly important in Massachusetts, which most people don't necessarily see as a manufacturing state because it is a high cost state that competes mostly in high-end, technology infused manufacturing of specialty parts. However, with industry 4.0 that kind of manufacturing is on the rise, so the issue will only become more and more key--and the workforce will need to grow to keep up with the demand. In that, there is opportunity for young people. And new manufacturing jobs can be exciting jobs, too. In fact, most of them are.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 27, Industry 4.0 Tools, episode 17, Smart Manufacturing for All, or episode 11, Empowering Workers to Innovate.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: John Killam .

Episode 36: Digital Lean

18 augusti 2021 | 46 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 36 of the podcast (@AugmentedPod), the topic is: Digital Lean. Our guest is Edward Atkins, Head of Customer Success, Tulip (@tulipinterfaces).

In this conversation, we talk about achieving customer success with classic Lean Operations and beyond. We explore performance transparency, how operators can produce parts tracking procedures digitally, creating quality gates through inspection applications and being conscious about quality measurement. What am I making? At what quality? We also go into more complex things like work material flow, Kanban loops and taking inventory and how no-code is an exponential uplift since you no longer are burdened by logging and can collect as much information as you want.

After listening to this episode, check out Tulip's and Edward Atkins' profile on social media:

Tulip (@tulipinterfaces): Tulip.co.
Edward Atkins: https://www.linkedin.com/in/edward-atkins-77022213/

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast--industrial conversations that matter.

**Trond's takeaway**: Implementing digital lean on a client site especially enabling improvements to be done near 100 percent by the clients themselves is now possible with the leading frontline operations software and is game changing for industrial performance.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode **22, Freedman's Factory: What is nocode?, episode 24, Emerging Interfaces for Human Augmentation, and episode 14, Bottom up and Deep Digitization of Operations.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 36 of the podcast , the topic is: Digital Lean. Our guest is Edward Atkins, Head of Customer Success, Tulip

In this conversation, we talk about achieving customer success with classic Lean Operations and beyond. We explore performance transparency, how operators can produce parts tracking procedures digitally, creating quality gates through inspection applications and being conscious about quality measurement. What am I making? At what quality? We also go into more complex things like work material flow, Kanban loops and taking inventory and how no-code is an exponential uplift since you no longer are burdened by logging and can collect as much information as you want.

After listening to this episode, check out Tulip's and Edward Atkins' profile on social media:

Tulip (@tulipinterfaces): Tulip.co
Edward Atkins: https://www.linkedin.com/in/edward-atkins-77022213/

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast--industrial conversations that matter.

**Trond's takeaway**: Implementing digital lean on a client site especially enabling improvements to be done near 100 percent by the clients themselves is now possible with the leading frontline operations software and is game changing for industrial performance.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode **22, Freedman's Factory: What is nocode?, episode 24, Emerging Interfaces for Human Augmentation, and episode 14, Bottom up and Deep Digitization of Operations.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Eddy Atkins.

Episode 30: Rethinking Workforce Learning

11 augusti 2021 | 43 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 30 of the podcast (@AugmentedPod), the topic is: Rethinking Workforce Learning. Our guest is George Westerman, Senior Lecturer, MIT Sloan School of Management and Principal Research Scientist for workforce learning at the MIT Jameel World Education Lab.

In this conversation, we talk about how the industrial workforce learning system is broken. We touch on the history of "Pre-K to gray" workforce training. We discuss transforming the way workers get the skills they need to thrive in the context of the evolution of digital transformation. The trick is balancing work with learning, and changing the way learning happens. But what to learn? Westerman’s work has yielded the Human Skills Matrix. And how to learn? His research identified a new model of corporate learning and development called The Transformer CLO.

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday.

After listening to this episode, check out J-WEL as well as George Westerman's profile on social media:

J-WEL (@MIT_jwel): https://jwel.mit.edu/
George Westerman (@gwesterman): https://www.linkedin.com/in/georgewesterman/

**Trond's takeaway**: Rethinking workforce learning is necessary, important, and wide-ranging. It will be a massive effort with digital transformation at the heart but with the need for educational institutions, employers and the workforce all on board. Do we all agree what skills to teach or be taught? This is unlikely but developing a skills matrix is a start.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 17, Smart Manufacturing for All, episode 2, How to Train Augmented Workers, or episode 3, Reimagine Training.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Dr. George Westerman.

Episode 35: Analysts Shape Markets

4 augusti 2021 | 53 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 35 of the podcast (@AugmentedPod), the topic is: Industry Analysts Shape Markets. Our guest is Kim Knickle, Research Director, Verdantix.

In this conversation, we talk about The main role of industry analysts which is to identify and understand trends in their chosen sector. What is the role of analysts going forward? What will their function be? Who are the relevant suppliers at any given time?

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co (@tulipinterfaces), frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

After listening to this episode, check out Verdantix as well as Kim Knickle's social media profile.

Verdantix (@Verdantix): https://www.verdantix.com/
Kim Knickle (@KimKnickle): https://www.linkedin.com/in/kim-knickle/

Trond's takeaway: Industry analysts are surprisingly relevant in today's information rich markets, perhaps because of information overload or the need for trusted sources of information. But as industry morphs and categories change faster than before, can firms keep up with the markets, and can analysts create a still picture of an evolving situation? Analysts are traditionally helpful for the buying process. Navigating the manufacturing industry is becoming more and more difficult as traditional vendors are complemented by a myriad of startups. As analysts assess trends, create segment taxonomies, size up markets, and prepare industry forecasts:

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 42, Business Beyond Buzzwords, episode 32, Covering Industrial Innovation or episode 9, The Fourth Industrial Revolution post-COVID-19.

Augmented--industrial conversations that matter.

Special Guest: Kim Knickle .

Episode 29: The Automated Microfactory

28 juli 2021 | 56 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 29 of the podcast (@AugmentedPod), the topic is: The Automated Microfactory. Our guest is Brian Mathews, CTO, Bright Machines.

In this conversation, we talk about increasing the speed, scalability, and flexibility of manufacturing using an intelligent, software-driven approach. Can discrete manufacturing, that is, the production of distinct items such as electronics, automobiles, furniture, toys, smartphones, and airplanes, now achieve the same efficiencies that we have seen in the software world? What does the next decade look like?

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

After listening to this episode, check out Bright Machines as well as Brian Mathews' social media profile:

Bright Machines (@brightmachines): https://www.brightmachines.com/
Brian Mathews: https://www.linkedin.com/in/mathewsbrian/

Trond's takeaway: For fully software-enabled platforms to take hold, the automation of discrete manufacturing has to change exponentially in the years ahead. This has been long in the coming, and the impact is almost impossible to fathom. Factory-level automation is one thing. However, the onset of relatively mobile microfactories and the ability to remotely update, tweak, and even radically improve physical things that already left the initial production facility will not only change timelines, but might alter the very notion of what a product is. Given that the sci-fi that Brian and I both love is coming nearer reality--good luck to sci-fi writers trying to write about the next century's innovations. That is going to take some extra creativity.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 42, Business Beyond Buzzwords, episode 21, The Future of Digital in Manufacturing, or episode 27, Industry 4.0 Tools.

Augmented--industrial conversations that matter.

Special Guest: Brian Mathews .

Episode 33: Sustainable Manufacturing at Scale

21 juli 2021 | 40 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 33 of the podcast (@AugmentedPod), the topic is: Sustainable Manufacturing at Scale. Our guest is Scott N. Miller, Managing Director, Dragon Ventures.

In this conversation, we talk about his early experience building Roomba robot vacuum cleaner at iRobot, contract manufacturing challenges for startups, global hardware ecosystems, investing in hardware and industrial innovation, manufacturing strategy, the New Product Introduction (NPI) process, how to navigate the journey from prototype through high volume manufacturing including Shenzhen business models for 10K units factory first runs.

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

After listening to this episode, check out Dragon Ventures as well as Scott N. Miller's social media profile:

Dragon Ventures: https://www.dragonventures.org/
Scott N. Miller: https://www.linkedin.com/in/scottnmiller/

Trond's takeaway: Startups are ill equipped to handle global contract manufacturing challenges. They are essentially being asked to take on complex supply chain and product development procedures that even large companies themselves struggle with. Yes, there is a way to navigate this terrain, and those who do, can pick up tremendous bounties and might just change the world.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 18, Transforming Foundational Industries, episode 23, Digital Manufacturing in the Cloud or episode 9, The Fourth Industrial Revolution post-COVID-19.

Augmented--industrial conversations that matter.

Special Guest: Scott N. Miller.

Episode 8: Productizing Quality

14 juli 2021 | 40 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 8 of the podcast (@AugmentedPod), the topic is: Productizing Quality. Our guest is Surbhi Krishna Singh, CEO & co-founder, Firevisor.

In this conversation, we talk about How she once jumped over the boundary wall and escaped school, her engineering degree, working for Seagate and Micron and being an outlier-- a woman in engineering and manufacturing. We discuss productizing quality improvements in manufacturing and her startup, Firevisor.

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast--industrial conversations that matter.

After listening to this episode, check out Firevisor as well as Surbhi Krishna Singh's social media profiles:

Firevisor: https://firevisor.com/
Surbhi Krishna Singh (@SurbhiKrishna): https://www.linkedin.com/in/surbhi-krishna-singh-a7502718/

Trond's takeaway: At the Augmented podcast, in tracing and anticipating the contours of the emerging future of industry 4.0, we try to have a special focus on diversity, for example on women in manufacturing, or on young trailblazers, or individuals that represent both, as in this case. We need all perspectives onboard if industry is going to transform in a positive way. Industry is, in many ways, the last bastion of resistance against several areas of Diversity, Equity and Inclusion (DEI). I truly hope this is the last decade we need to describe industry this way. An issue so core to humankind such as manufacturing should be co-developed by all

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also other episodes featuring female trailblazers, and it's quite a list, such as episode 24, Emerging Interfaces for Human Augmentation, episode 32, Covering Industrial Innovation, episode 18, Transforming Foundational Industries, episode 16, A female fighter in a manufacturing SME, episode 7, Work of the Future, episode 3, Reimagine Training, or episode 2, How to Train Augmented Workers.

Augmented--industrial conversations that matter.

Special Guest: Surbhi Krishna Singh.

Episode 42: Business Beyond Buzzwords

7 juli 2021 | 50 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 42 of the podcast (@AugmentedPod), the topic is: Business Beyond Buzzwords. Our guest is Jeff Immelt, Venture Partner, NEA, former CEO of General Electric.

In this conversation, we talk about Jeff Immelt's new book Hot Seat, running a top tier manufacturing business, industrial tech, the impact of globalization, plant innovation, workforce training, global supply chain, virtual cloud connected value streams and what it is possible to do today and what was very difficult even a few years ago with the myriad of non-integrated enterprise IT and ERP systems and other challenges. Lastly, we discuss how industry will evolve in this decade.

After listening to this episode, check out NEA, GE, Jeff's book Hot Seat as well as Jeff Immelt's social media profile:

NEA (@nea): https://www.nea.com/
GE (@generalelectric): https://www.ge.com/
Hot Seat (book): https://www.simonandschuster.com/books/Hot-Seat/Jeff-Immelt/9781982114718
Jeff Immelt (@JeffImmelt): https://www.linkedin.com/in/jeffimmelt/

Trond's takeaway: Jeff Immelt's brave, honest, and wise book is unusually revealing and instructive. Jeff has shared not only how lonely it is at the top, but seemingly how few big choices and at times how many smaller choices you have at any given time. His struggles with industrial tech are near timeless. Nobody has all the answers in terms of getting organizational implementation of exponential tech right. Especially not if your organization is the size of GE. I was struck by the implication for leaders--be vulnerable or risk not only your own happiness but those of all your co-workers. Immelt's struggle was to digitize GE, a behemoth in transition. He chose to build an in-house capacity, at great cost, and with mixed results, but how many other options were there on the table? Hindsight is 2020. As Immelt points out, nowadays, low-code and no-code systems, such as Tulip, are about to transform frontline operations in ways we can only start to imagine. The promise is empowerment of workers and immense productivity gains from freeing up the human mind. Our challenges might, at times, seem or indeed be smaller in scale, but might feel equally overwhelming. Good to know then, that the folks at the top struggle as well.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 21, The Future of Digital in Manufacturing with Çağlayan Arkan, VP of Manufacturing Industry at Microsoft, episode 32, Covering Industrial Innovation with Amy Feldman, Senior Editor, Forbes, or episode 27, Industry 4.0 Tools with Carl B. March, Director, Industry 4.0 at Stanley Black & Decker.

Augmented--industrial conversations that matter.

Special Guest: Jeff Immelt.

Episode 22: Freedman's Factory: What is nocode?

5 juli 2021 | 43 min

In episode 22 of the podcast (@AugmentedPod), the topic is: Freedman's Factory: What is nocode? Our guest is Mark Freedman, Lean Practice Leader at Tulip for our new segment, Freedman's Factory, which you will always recognize within other Augmented episodes because of especially groovy music. Freedman's Factory will take us deep into the shop floor philosophy of Kaizen, and with that, into the heart of manufacturing excellence

In this conversation, which is the first episode of the new segment we have called Freedman's factory, which takes us deep into the shop floor philosophy of Kaizen. We introduced this new segment a month ago. In this episode of Freedman's Factory, we talked about Nocode in manufacturing. What is it? What existed before? What’s the difference it makes?

After listening to this episode, check out Tulip's and Mark Freedman's profile on social media:

Tulip (@tulipinterfaces): https://tulip.co/
Mark Freedman: https://www.linkedin.com/in/markjfreedman/

Trond's takeaway: Nocode for industrial applications is something truly special. Building on what we have come to know from contemporary software applications that don't have a learning curve, industrial nocode attempts the same thing, but with software written for the physical world, which is immeasurably harder to do because production cannot go down and you don't get second chances. I learned from Mark Freedman, that Tulip's deeply humanistic approach to nocode is rooted in the shopfloor experience, in trying to reflect, but also question factory floor behavior. I am on a learning journey. I still want to understand more the discrete tasks and functions that digital nocode apps make flow so naturally--work instructions, machine monitoring and other things. As always, the depth in Freedman's message lies, it seems to me, in his insistence on experience before tools, understanding before action, and understanding people, and the reasons behind their current process, way before introducing any kind of technology as a tool to simplify their life.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 10, A Brief History of Manufacturing Software, episode 6, Human-Robot Interaction challenges, or episode 1, Automation to Augmentation - the podcast's vision to build a movement. Also, if you missed the introduction to Freedman's Factory, listen to episode 15: Freedman's Factory: Introduction. Augmented-upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Mark Freedman.

Episode 23: Digital Manufacturing in the Cloud

30 juni 2021 | 71 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 23 of the podcast (@AugmentedPod), the topic is: Digital Manufacturing in the Cloud. Our guest is Jon Hirschtick, Head of SaaS, Onshape and Atlas Platform, PTC.

In this conversation, we talk about the story of SolidWorks, using agile methods, listening to the market, charting the evolution of CAD into SaaS, and its emerging and future iterations in the open source cloud and beyond.

Augmented is a podcast for industry leaders and operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast--industrial conversations that matter.

After listening to this episode, check out PTC, Solidworks, as well as Jon Hirschtick's social media profiles:

PTC (@ptc): https://www.ptc.com/en
Solidworks (@solidworks): https://www.solidworks.com/
Jon Hirschtick (@jhirschtick): https://www.linkedin.com/in/jonhirschtick/

Trond's takeaway: Digital manufacturing is moving to the cloud and that means a whole lot more than office software moving to the cloud. In fact, establishing a real-time digital thread, through next generation low-code and no-code systems, will reshape industry. The notion of factory production, distributed teams, product development, will all evolve significantly, and will enable personalization across industry and across any and eventually all of manufactured goods. The ramifications will be huge, but they won't automatically happen tomorrow, and the benefits will spread unevenly depending on who--be it corporations, nations, startups, or small- and medium enterprises--grabs the gauntlet first.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 43, Digitized Supply Chain, episode 24, Emerging Interfaces for Human Augmentation, or episode 21, The Future of Digital in Manufacturing.

Augmented--industrial conversations that matter to everyone.

Special Guest: Jon Hirschtick.

Episode 43: Digitized Supply Chain

23 juni 2021 | 45 min

Episode 40: Israel meets New England on Industry 4.0

21 juni 2021 | 52 min

In episode 40 of the podcast (@AugmentedPod), the topic is: Israel meets New England on Industry 4.0. This is a special episode where we tune into a webinar on Industry 4.0 put on jointly by AMHUB New England and the Israeli Economic Mission to North America. We have individual interviews mixed in with this webinar to provide a complete picture of this exciting meet.

Our guests are Michael Tamasi, Co-Chairman, MA Advanced Manufacturing Collaborative and CEO & owner, AccuRounds, Mark Goldfarb, CEO & co-founder, Sixdof Space, Carl B. March, Director, Industry 4.0, Stanley Black & Decker, Michael Zolotov, CTO & co-founder, Razor Labs, Lior Zadicareo, Chief Revenue Officer, Visual Factories, Natan Linder, CEO & co-founder, Tulip, Anat Katz, Minister Trade Affairs, Consulate General of New York, and Karin Chen, Head of Sectors, Automotive & Smart Mobility, Industry 4.0, Israel Export Institute

In this conversation, we talk about Industry 4.0 with top Israeli startups and advanced manufacturers from New England, notably the daunting challenges in manufacturing, the ever-changing context, the trailblazing tech solutions, Integrating it all - what do factory managers need to know, and last but not least, Partnering.

After listening to this episode, check out:

The New England Advanced Manufacturing Hub (AMHUB NE): https://mfg.works/amhub/amhub-new-england/
The Government of Israel’s Economic Mission to North America: https://embassies.gov.il/washington/AboutTheEmbassy/Pages/Economic-Mission.aspx#:
ISRAEL meets NEW ENGLAND: Advanced Manufacturing in Factories and Workplace: https://mfg.works/israel-meets-new-england/

If you liked this episode, you might also like to listen to individual episodes featuring each of the guests of this webinar, notably, episode 32, Visualizing Factories with guest Lior Zadicareo from Visual Factories, episode 27, Industry 4.0 Tools with Carl B. March from Stanley Black & Decker, episode 19, Machine Learning in Manufacturing with Michael Zolotov from Razor Labs, episode 25, Industrial Tracking: Drones, Warehouses and Theme Parks with Mark N. Goldfarb from Sixdof Space, or episode 26, Manufacturing in Massachusetts with Michael Tamasi, CEO of AccuRounds.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guests: Carl B. March, Lior Zadicareo, Mark N. Goldfarb, Michael Tamasi, Michael Zolotov, and Natan Linder.

Episode 24: Emerging Interfaces for Human Augmentation

16 juni 2021 | 51 min

Episode 39: Covering Industrial Innovation

14 juni 2021 | 50 min

Episode 21: The Future of Digital in Manufacturing

9 juni 2021 | 44 min

Episode 32: Visualizing Machines

8 juni 2021 | 35 min

Episode 19: Machine Learning in Manufacturing

4 juni 2021 | 42 min

In episode 19 of the podcast (@AugmentedPod), the topic is: Machine Learning in Manufacturing. Our guest is Michael Zolotov, CTO & co-founder at Razor Labs.

In this conversation, we talk about where we are with machine learning and AI for manufacturing. What are the main techniques? What is possible now? What will be possible soon?

After listening to this episode, check out Razor Labs: http://www.razor-labs.com/ as well as Michael Zolotov's profile on social media: https://www.linkedin.com/in/michael-zolotov-33a2b26b/

You may want to also be aware of the 'Israel meets New England' smart manufacturing event on June 9 and its organizers, the Israeli Trade Mission and Amhub New England:

The New England Advanced Manufacturing Hub (AMHUB NE): https://mfg.works/amhub/amhub-new-england/
The Government of Israel’s Economic Mission to North America: https://embassies.gov.il/washington/AboutTheEmbassy/Pages/Economic-Mission.aspx#:
ISRAEL meets NEW ENGLAND: Advanced Manufacturing in Factories and Workplace: https://mfg.works/israel-meets-new-england/

Trond's takeaway: Machine learning is definitely entering manufacturing over the next few years. Already, interesting experiments are underway to do simpler things such as prevent future downtime using sensor data already being captured by advanced machinery. Pure machine optimization can only get us so far, though. The real potential lies in complex business process optimization and simplification with augmented frontline operations. Technology plays a part, but clever workers, operators, and engineers will have to make intelligent use of the technologies available, they cannot just blindly implement. For that, we need reskilling--always learn.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 27, Industry 4.0 Tools, episode 13, Get Manufacturing Superpowers, and episode 14, Bottom up and Deep Digitization of Operations.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Michael Zolotov.

Episode 27: Industry 4.0 Tools

1 juni 2021 | 46 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 27 of the podcast (@AugmentedPod), the topic is: Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director, Industry 4.0 at Stanley Black & Decker.

In this conversation, we talk about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

After listening to this episode, check out Stanley Black & Decker (@StanleyBlkDeckr): https://www.stanleyblackanddecker.com/ as well as Carl B. March's profile on social media: https://www.linkedin.com/in/carlbmarch/

You may want to also be aware of the 'Israel meets New England' smart manufacturing event on June 9 and its organizers, the Israeli Trade Mission and Amhub New England:

The New England Advanced Manufacturing Hub (AMHUB NE): https://mfg.works/amhub/amhub-new-england/
The Government of Israel’s Economic Mission to North America: https://embassies.gov.il/washington/AboutTheEmbassy/Pages/Economic-Mission.aspx#:
ISRAEL meets NEW ENGLAND: Advanced Manufacturing in Factories and Workplace: https://mfg.works/israel-meets-new-england/

Trond's takeaway: Industry 4.0 requires a mindset shift, not just technology adoption. It's not just about you--whether you in this case is a big company or a top leader--rather, it is about bringing people, partners, SMEs, and the entire ecosystem along. To do so openness to learn, having a strategic roadmap so not chase all shiny objects, and investing in lighthouse factories that can illuminate the possibilities are each important ingredients.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 20, The Digitalization of Körber, episode 14, Bottom up and Deep Digitization of Operations, and episode 9, The Fourth Industrial Revolution post-COVID-19.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Carl B. March.

Episode 26: Manufacturing in Massachusetts

27 maj 2021 | 54 min

Episode 20: The Digitalization of Körber

26 maj 2021 | 48 min

Episode 25: Industrial Tracking: Drones, Warehouses and Theme Parks

25 maj 2021 | 31 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 25 of the podcast (@AugmentedPod), the topic is: Industrial Tracking: Drones, Warehouses and Theme Parks. Our guest is Mark N. Goldfarb, CEO & co-founder of the Israeli tech startup Sixdof Space, who is a panel speaker at an upcoming, fast-paced panel debate with Q&A on Industry 4.0 with top Israeli startups and advanced manufacturers from New England. The event is a collaboration between the New England Advanced Manufacturing Hub (AMHUB), a global network associated with the World Economic Forum, The Government of Israel’s Economic Mission to North America, Tulip, and Stanley Black & Decker.

In this conversation, we talk about positional tracking technology, touch on the many tracking technologies that exist out there- magnetic, UWB, Wifi, Lidar, LiFI - each with pros and cons. We then move into optical tracking, an approach that uses light, and as such requires a direct line of sight, but is less power hungry than other approaches. We discuss outfitting factory floors with this stuff to track movement by vehicles, people or goods in a manufacturing warehouse, and exciting futuristic use cases in remote surgery and landing space shuttles.

After listening to this episode, check out Sixdof Space, as well as Mark N. Goldfarb's profile on social media, as well as the Israel meets New England smart manufacturing event and its organizers, the Israeli Trade Mission and Amhub New England:

Sixdof Space (@SixdofS): https://www.sixdof.space/
Mark N. Goldfarb: https://www.linkedin.com/in/mark-n-goldfarb-822b/
The New England Advanced Manufacturing Hub (AMHUB): https://mfg.works/amhub/amhub-new-england/
The Government of Israel’s Economic Mission to North America: https://embassies.gov.il/washington/AboutTheEmbassy/Pages/Economic-Mission.aspx#:
ISRAEL meets NEW ENGLAND: Advanced Manufacturing in Factories and Workplace: https://mfg.works/israel-meets-new-england/

Trond's takeaway: Photonics is an exciting field with a tremendous amount of innovation. Optical tracking is only one use cases, there are many, many others for this platform technology.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, check out Augmented podcast on YouTube, and rate us with five stars. If you liked this episode, you might also like episode 14 Smart Manufacturing for All, episode 13 Get Manufacturing Superpowers, or episode 6 Human-Robot Interaction challenges.

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Mark N. Goldfarb.

Episode 18: Transforming Foundational Industries

19 maj 2021 | 44 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 18 of the podcast (@AugmentedPod), the topic is: Transforming Foundational Industries. Our guest is Dayna Grayson, General Partner and Co-founder of Construct Capital, a newly formed VC firm.

In this conversation, we talk about how Dayna became a trailblazing female VC, her early thesis around the SaaS model as transformative for industry to avoid ERP lock-in, her experience from being on the board of many famous startups, including Formlabs, Desktop Metal, Onshape, and frontline operations platform Tulip. She explains why she co-founded $140m fund Construct Capital in late 2020. We discuss how software is transforming industries that have arguably been somewhat stale since the industrial revolution. She shares her approach to invest in and scale the startups by non-linear and product design aware founders; we look at some recent investments of hers, engineer workflow tool Copia, EV charging software ChargeLab, fresh food assembly automation Chef Robotics. Finally, we touch on the future of manufacturing.

After listening to this episode, check out Construct Capital as well as Dayna Grayson's profile on social media:

Construct Capital (@constructcap): https://constructcap.com/
Dayna Grayson (@daynagrayson): https://www.linkedin.com/in/daynagrayson/

Trond's takeaway: Dayna Grayson is right about factories. The factory floor is a too limiting framework to use to understand emerging manufacturing firms. Production facilities might also become microfactories, industry products become tech platforms. What does industry look like 15 years from now? We won't even recognize it. The foundation is changing. Foundational industries, yes, but created in new types of foundries.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 13 Get Manufacturing Superpowers, episode 10 A Brief History of Manufacturing Software, or episode 11 Empowering Workers to Innovate. Augmented--the industry 4.0 podcast.

Special Guest: Dayna Grayson.

Episode 15: Freedman's Factory: Introduction

12 maj 2021 | 33 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 15 of the podcast (@AugmentedPod), the topic is: Freedman's Factory: Introduction. Our guest is Mark Freedman, Lean Practice Leader at Tulip for our new segment, Freedman's Factory, which you will recognize because of our new segment specific music, with a cool, shopfloor vibe.

In this conversation, we talk about Freedman's Factory. What is it? What do we hope to accomplish? We learn that Mark Freedman, whose job title is all about Lean--which has to do with eliminating waste and increasing productivity, is, in fact, all about Kaizen, which is somewhat more ambitiously Japanese and means change for the better, or in better English--continuous improvement, from Kai meaning change and Zen meaning peaceful, relaxed, and aware. We are aiming for all those things. In fact, we hope to probe deep into the manufacturing experience, learning from people who live it and breathe it. We also want to investigate what it means to explore Kaizen and Lean together with digitalization--and with the nocode approach. And, as Freedman says, with all change we have to respect the current state. In other words, before we start to change something, we have to take in what is and recognize why it is that way. If all of these things sound slightly philosophical, it is because they are. This is down-to-earth philosophy with a small p.

After listening to this episode, check out Tulip's and Mark Freedman's profile on social media:

Tulip (@tulipinterfaces): https://tulip.co/
Mark Freedman: https://www.linkedin.com/in/markjfreedman/

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 10 A Brief History of Manufacturing Software, episode 6 Human-Robot Interaction challenges, or episode 1 Automation to Augmentation - the podcast's vision to build a movement. Augmented--the industry 4.0 podcast.

Special Guest: Mark Freedman.

Episode 17: Smart Manufacturing for All

5 maj 2021 | 46 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 17 of the podcast (@AugmentedPod), the topic is: Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

After listening to this episode, check out CESMII as well as John Dyck's social profile:

CESMII: (@CESMII_SM) https://www.cesmii.org/
John Dyck: https://www.linkedin.com/in/johnsdyck/

In this conversation, we talked about democratizing smart manufacturing, the history and ambition of CESMII (2016-), bridging the skills gap in small and medium enterprises which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms and controls to radically impact manufacturing performance. We then have the hard discussion of why the US is (arguably) a laggard? John shares the 7 characteristics of future-proofing (interoperability, openness, sustainability, security, etc.). We hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade.

Trond's takeaway: US manufacturing is a bit of a conundrum. How can it both be the driver of the international economy and a laggard in terms of productivity and innovation, all at the same time? Can it all be explained by scale--both scale in multinationals and scale in SMEs? Whatever the case may be, future proofing manufacturing, which CESMII is up to, seems like a great idea. The influx of smart manufacturing technologies will, over time, transform industry as a whole, but it will not happen automatically.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 8 on Work of the Future, episode 5 Plug-and-play Industrial Tech, or episode 9 The Fourth Industrial Revolution post-COVID-19. Augmented--the industry 4.0 podcast.

Special Guest: John Dyck.

Episode 16: A female fighter in a manufacturing SME

28 april 2021 | 30 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 16 of the podcast (@AugmentedPod), the topic this week is: A female fighter in a manufacturing SME and our guest is Lena Jaentsch, Business Development Specialist at HERMA Group, a German manufacture, interviewed by futurist Trond Arne Undheim.

In this conversation, we talk about being a champion for new, efficient modes of production, bringing customers value, and also being a young woman in manufacturing. We discuss the challenges in today's manufacturing companies to explore new business models and stay on top of digitalization, yet keep a holistic view. Lena is an innovation manager, and calls herself "a strong but mostly single fighter in the SME space". We discuss the war for talent and why young people don't choose manufacturing. We also discuss the need for a global manufacturing community to exchange information, tools, solutions, and experiences--especially for women.

After listening to this episode, check out Herma as well as Lena Jaentsch's social profile.

Herma: https://www.herma.com/
Lena Jaentsch: https://www.linkedin.com/in/lena-jaentsch-8b0278191/

Trond's takeaway: With people like Lena on board, the future of any business would seem bright. She has the kind of limitless energy that characterizes what everyone should aspire to in the workforce. Moreover, she has the guts to go explore new things, learn, adapt, and implement. I predict Lena will move fast up the ladder. In small companies, you don't need many shopfloor managers like that, but you do need some, otherwise you will be left behind."

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 11 Empowering Workers to Innovate, episode 3 Reimagine Training, and episode 2 How to Train Augmented Workers.

Augmented--the industry 4.0 podcast.

Special Guest: Lena Jaentsch.

Episode 14: Bottom up and Deep Digitization of Operations

21 april 2021 | 33 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 15 of the podcast (@AugmentedPod), the topic is: Bottom up and Deep Digitization of Operations. Our guest is Dr. Damir Hrnjadovic, Managing Director, DMG MORI Digital GmbH

In this conversation, we talk about How DMG MORI was able to digitize their operations in a matter of weeks, using Tulip software. Lean, nocode startups are transforming experiences for factory owners because its shopfloor solutions are so easy to implement. We discuss whether nocode actually means process engineers can become independent from the IT department. Damir confirms that DMG MORI power users are able to do that even though not every shopfloor operator will create a Tulip application. We discuss upskilling in the German Mittelstand of small-and medium size businesses and how Damir feels like a time traveler when he encounters paper based processes on the shop floor.

After listening to this episode, check out DMG MORI as well as Dr. Damir Hrnjadovic's social profile.

DMG MORI (@dmgmorieu): https://us.dmgmori.com/
Dr. Damir Hrnjadovic (@DamirHrnjadovic): https://www.linkedin.com/in/dr-damir-hrnjadovic-3ba0485b/

Trond's takeaway: It is striking to hear about how Damir at times feels like a time traveler when encountering paper based operations at the shop floor, at other times, he can experience bottom up, deep digitalization in a matter of weeks. How can those to co-exist? Clearly shopfloor change is uneven at the moment, depending on whether you heard the right digital gospel and what the attitude of the IT department is to introducing changes that democratize IT.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 3 How to Train Augmented workers or episode 10 A Brief History of Manufacturing Software. Augmented--the industry 4.0 podcast.

Special Guest: Dr. Damir Hrnjadovic.

Episode 13: Get Manufacturing Superpowers

14 april 2021 | 35 min

Episode 12: Enterprise Wide Quality of Manufacturing

7 april 2021 | 37 min

Episode 11: Empowering Workers to Innovate

31 mars 2021 | 38 min

Episode 9: The Fourth Industrial Revolution post-COVID-19

24 mars 2021 | 35 min

Episode 10: A Brief History of Manufacturing Software

17 mars 2021 | 54 min

Episode 6: Human-Robot Interaction challenges

10 mars 2021 | 45 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 6 of the podcast, the topic is: Human-Robot Interaction challenges. Our guest is Kel Guerin, Chief Innovation Officer, Ready Robotics.

In this conversation, we talk about Trends in the robotic manufacturing community, Solutions, Robotic O/S, and The Future including a vision of a world where open robotic platforms dominate and no specialized skills required to operate robots.

After listening to this episode, check out Ready Robotics as well as Kel Guerin 's social profile.

Ready Robotics: https://www.ready-robotics.com/
- Ready.academy: https://ready.academy/
Forge OS/5: https://www.ready-robotics.com/forge-os5
Kel Guerin: https://www.linkedin.com/in/futureneer/

Trond's takeaway: The fact is that human-robot interaction has not developed at the pace of technology is a challenge. We now need to remedy this shortcoming. Change is underway. Is it happening fast enough? Are the interfaces simple enough to bring in scores of existing manufacturing workers or recruit new talent? If robots truly are to make manufacturing cool again, our tools to communicate with them--and our willingness to try--both need to improve. We have a ways to go, but the direction is good.

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by Artlist (@Art_list_io). The show can be found at http://www.augmentedpodcast.co/

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars on Apple Podcasts. If you liked this episode, you might also like Episode #2 How to Train Augmented Workers, Episode #3 Reimagine Training, or Episode #4 A Renaissance of Manufacturing.

Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday.

Augmented--the industry 4.0 podcast.

Special Guest: Kel Guerin.

Episode 7: Work of the Future

4 mars 2021 | 35 min

Episode 5: Plug-and-Play Industrial Tech

3 mars 2021 | 31 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode 5 of the podcast (@AugmentedPod), the topic is: Plug-and-play Industrial Tech. Our guest is Etienne Lacroix, CEO & Founder, Vention.

In this conversation, we talk about the Consumerization of Industry, high-mix manufacturers, shorter product life cycle, and end-to-end approaches. Etienne shares best practices for industrial workflows with 10x speeds to design, automate, order, deploy, comments on the appification of manufacturing software platforms, the legoification library of modular parts and factory equipment, and how it ties into empowering agile manufacturing. We discuss remaining complexities and briefly touch on the future.

After listening to this episode, check out the Vention well as Etienne Lacroix's social profile.

Vention https://vention.io/
Etienne Lacroix https://www.linkedin.com/in/etiennelacroix/

Trond's takeaway: Industrial tech is indeed becoming plug-and-play. We may be at the cusp of a lowcode and approaching a nocode environment, but that doesn't mean zero cognitive skills required, quite the contrary, at least if you want to shape the future development or lead the manufacturing process. Vention's Cloud-CAD makes use of engineering grade 3D in the web browser and is a big step forward. Many more exciting applications are to come, as we map out the use cases, get feedback from previously not sought-out parts of the shopfloor, and integrate those findings. Is a zero bottleneck future in the line of vision? Not now, but we are getting closer.

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co (@tulipinterfaces), the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum.Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by Artlist (@Art_list_io). The show can be found at http://www.augmentedpodcast.co/

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode #1 From Automation to Augmentation, Episode #4 A Renaissance of Manufacturing, or Episode #5 Plug-and-play Industrial Tech. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

Special Guest: Etienne Lacroix.

Episode 4: A Renaissance in Manufacturing

24 februari 2021 | 38 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode #4 of the podcast, the topic is: A Renaissance of Manufacturing. Our guest is Enno De Boer, Partner, Digital Manufacturing Lead, McKinsey.

Augmented is a podcast for leaders, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9 am US Eastern Time every Wednesday. Augmented--the industry 4.0 podcast.

In this conversation, we talk about What is digital manufacturing? How to transform operations strategy, best practices, specifically the World Economic Forum Global Lighthouse Factories. We also tackle future developments: How to stay up to date in this fast moving field? What’s next?

Trond's takeaway: is that manufacturing is indeed undergoing a renaissance. There should be a tremendous amount of excitement among policy makers, industry professionals, and frontline workers about the changes in play. Technologies are maturing. The digital factory is becoming a reality. For those who already took on board the lessons of lean manufacturing and are exploring the latest opportunities, automation has become augmentation. Yet, there's still a lot to learn. The World Economic Forum's Lighthouse factories is one place to seek inspiration.

After listening to this episode, check out the World Economic Forum Global Lighthouse Network, McKinsey's Operations practice, well as Enno De Boer's social profile.

World Economic Forum Global Lighthouse Network: https://www.weforum.org/projects/global_lighthouse_network
Enno De Boer (bio): https://www.mckinsey.com/our-people/enno-de-boer
McKinsey Manufacturing & Supply Chain practice area (@mckinsey_mfg): https://www.mckinsey.com/business-functions/operations/how-we-help-clients

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by @Art_list_io. The show can be found at http://www.augmentedpodcast.co/

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode #1 on From Automation to Augmentation or Episode #2 on How to Train Augmented Workers. Augmented--the industry 4.0 podcast.

Special Guest: Enno de Boer.

Episode 3: Reimagine Training

17 februari 2021 | 24 min

Episode 2: How to Train Augmented Workers

15 februari 2021 | 29 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode 2 of the podcast, the topic is how to train augmented workers. Our guest is Elisa Roth, doctoral student at the Institute for Manufacturing at the University of Cambridge in the UK, a Global Shaper by the World Economic Forum.

In this conversation, we talk about what industrial augmentation is and what industry 4.0 is. We discuss the training challenge in industrial operations, specifically the need to go beyond the traditional approach of formal training, apprenticeships, and on-the-job observation. I get her take on emerging training needs, what are the exact industry 4.0 skills we should be teaching? How to teach it and scale the teaching? Advice on how to learn and design learning journeys. Lastly, we discuss future developments: where is the manufacturing industry heading?

Trond's takeaway: Training augmented workers will have to happen in a much more streamlined way than previous training efforts. As Elisa Roth points out, first off, the organization needs to be on board with its workers integrating training into their workday. There is a lot of training available, but it might be of various quality, it might be hard to find exactly when you need it, and it may be hard to verify and get credit for informal learning. In short, it is going to require trust, as well.

After listening to this episode, check out the Institute for Manufacturing at the University of Cambridge in the UK, the Global Shaper Program by the World Economic Forum, as well as Elisa Roth's online profile:

The Institute for Manufacturing at the University of Cambridge in the UK: https://www.ifm.eng.cam.ac.uk/
The Global Shaper Community by the World Economic Forum: https://www.globalshapers.org/
Elisa Roth: https://www.linkedin.com/in/elisa-roth/

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by @Art_list_io. The show can be found at http://www.augmentedpodcast.co/

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars on Apple Podcasts. To nominate guests, to suggest exciting episode topics or give feedback, follow us on LinkedIn, looking out for live episodes, message us on Twitter @augmentedpod or our website's contact form. If you liked this episode, you might also like episode 3 on Reimagine Training, which will be live on Wednesday 17 Feb 2021. Augmented--the industry 4.0 podcast.

Special Guest: Elisa Roth.

Episode 1: Automation to Augmentation - the podcast's vision to build a movement

10 februari 2021 | 55 min

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In episode 1 of the podcast, the topic is: Automation to Augmentation. Our guest is futurist Trond Arne Undheim, host of the Augmented and Futurized podcasts, venture partner at Antler, ecosystem evangelist at Tulip, nonresident Fellow at the Atlantic Council, and co-founder of Yegii. Trond is a former Director of MIT Startup Exchange, Director of Standards Strategy & Policy at Oracle and National Expert for e-Government at the European Commission. Trond is a 4x author. His upcoming book, Future Tech: How to Capture Value from Disruptive Industry Trends, will be released on 3 March in the UK and Europe and on 30 March in the US. He holds a Ph.D. on the future of work and artificial intelligence and cognition.

In this conversation, we talk about the ambitious aim of the Augmented podcast to shift the discussion about industrial operations from Automation to Augmentation, bringing a bottom-up perspective that benefits the frontline worker.

After listening to this episode, check out Tulip as well as Trond Undheim and Natan Linder's social profile.

Tulip (@Tulip): https://tulip.co/
Trond Undheim (@trondau): https://trondundheim.com/
Natan Linder ((@linder78): https://www.forbes.com/sites/natanlinder/

Trond's takeaway: The path from Automation to Augmentation won't be linear. Clearly, there is still a place for automation. What we need to ensure is that industrial operations doesn't only become efficient but also maintains meaning for the frontline worker. The shift from automation to augmentation, starting with the appearance of 3D printing which is continuously improving, adding the revolutionary layer of lowcode and nocode apps on the manufacturing shop floor, has just begun, literally only a few years ago. This is why, discussing what is happening to make sure both industry insiders and those who depend on industry are comfortable, have a stake in the process, and can be part of the change, is so important. The Augmented podcast, aims to be part of the upskilling process that has to occur, equally and equitably, among government policy makers, industry leaders and frontline workers

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by Artlist (@Art_list_io).

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars on Apple Podcasts. To nominate guests, to suggest exciting episode topics or give feedback, follow us on LinkedIn, looking out for live episodes, message us on Twitter @augmentedpod or through our website's contact form. If you liked this episode, you might also like Episode #3 Reimagine Training, which will be live on Wednesday 17 Feb 2021. Augmented--the industry 4.0 podcast.

Special Guest: Natan Linder.

Episode 0: Introducing the Augmented podcast

2 februari 2021 | 3 min

Augmented Ops

Augmented Ops is a podcast for industrial leaders, shop floor operators, citizen developers, and anyone else that cares about what the future of frontline operations will look like across industries.

Om podden

Avsnitt

Rethinking Governance, eBR, and the CIO Role from the Ground Up with NextPharma’s Joel Fidalgo

Hannover Messe Unfiltered: The Real State of AI in Manufacturing

Preview: Hannover Messe Special

Operational Excellence at Enterprise Scale with Stanley Black & Decker’s Audrey Van de Castle

Digital Twins, Industrial AI, and the Future of Manufacturing with TwinThread's Erik Udstuen

Augmenting Craftmanship with Digital Technology at a Luxury Jeweler with Nicolette Naya

Scaling Open Source in Manufacturing with FlowFuse’s ZJ van de Weg

From Sony to Semiconductors: Digital Transformation in the Asia-Pacific with Vijay Chinnasami

Interoperability for Industry 4.0 with HighByte’s John Harrington

How AI Agents are Closing the Loop with Composabl’s Kence Anderson

Bringing Open Source to Industrial Automation with Arduino’s Fabio Violante

Frontline Operations in NASCAR with RFK Racing's Kevin Kidd

Inside a Next-Gen Medical Device Manufacturer

Uniting the Frontline Operations Community at Operations Calling

A New Approach to Pharma Manufacturing with Roche’s Daniele Iacovelli

The Past, Present, and Future of Industrial Software with Rick Bullotta

Episode 141: Reindustrializing America with Liz Reynolds

Episode 140: A Frontline Perspective on Industry 4.0 – Reflections on Season 1 of Augmented Ops

Episode 139: How to Architect Your Digital Strategy with Jeff Kramer

Episode 138: Democratizing Computer Vision with LandingAI’s Kai Yang

Episode 137: AI for the Frontline Engineer with Instrumental’s Anna Shedletsky

Episode 136: AI Takes Center Stage at Hannover Messe

Episode 135: Bringing Spatial Intelligence to Operations with Zerokey's Matt Lowe

Episode 134: Building Industrial Architectures with MQTT with HiveMQ’s Dominik Obermaier

Episode 133: Rethinking Our Approach to AI with Dr. Jay Lee

Episode 132: Open Source Software for Manufacturing with UMH's Alex Krüger

Episode 131: MQTT, Unified Namespace, and The New Industrial Data Stack with Litmus’s Vatsal Shah

Episode 130: Democratization, Gen AI, and the Future of Industrial Analytics with Seeq’s Lisa Graham

Episode 129: AI and the Human Element in Industry 4.0 with Jeff Winter

Episode 128: From Tailor to Technologist: A Digital Transformation Journey with Joachim Hensch

Episode 127: Venture Capital's Role in Digital Transformation with Lior Susan

Episode 126: Transforming Manufacturers’ Organizational Strategy with Dr. Jörg Gnamm

Episode 125: Rethinking Quality Control for Pharmaceuticals with Mark Buswell

Episode 124: Industrial Data Interoperability with Erich Barnstedt

Episode 123: Building a Manufacturing Software Marketplace with Diego Tamburini

Episode 122: Fixing the Failures of Industry 4.0 with Antonio Padovano

Episode 121: Looking Back and Looking Ahead

Episode 120: Digital Manufacturing in Turkey and Beyond with Efe Erdem

Episode 119: Industrial Design with Kimberly Andersson

Episode 118: Digital Manufacturing in the Cloud with Jon Hirschtick (Rebroadcast)

Episode 117: Manufacturing with Jessica Yen

Episode 116: The Market of Marketplaces with Jason Ray

Episode 115: Bridging the Physical-Digital Divide in Industrial Tech with Rony Kubat

Episode 114: The Industry 4.0 Journey with Scott Phillips

Episode 113: The Business Model of Lean with Jim Huntzinger

Episode 112: Humans, Robots, and the Future of Manufacturing with Anna Waldman-Brown

Episode 111: Operator 4.0 with David Romero

Episode 110: Executing on Manufacturing Technology with Jane Arnold

Episode 109: Augmenting Workers With Wearables with Andrew Chrostowski

Episode 108: Lean Operations with John Carrier

Episode 107: Explainability in AI with Julian Senoner

Episode 106: Post Lean with Frode Odegard

Episode 105: Product Lifecycle Management's Momentum in Manufacturing with Jim Heppelmann

Episode 104: A Scandinavian Perspective on Industrial Operator Independence with Johan Stahre

Episode 103: Human-First AI with Christopher Nguyen

Episode 102: Lean Manufacturing with Michel Baudin

Episode 101: How Academia Shapes Manufacturing with John Hart

Episode 100: Innovating Across the Manufacturing Supply Chain

Episode 99: Augmented Lean: The Book

Episode 98: Decarbonizing Logistics

Episode 97: Industrial AI

Episode 96: The People Side of Lean

Episode 95: Smart Manufacturing for All

Episode 94: Digitized Supply Chain

Episode 93: Industry 4.0 Tools

Episode 92: Emerging Interfaces for Human Augmentation

Episode 91: Reimagine Training

Episode 90: A Renaissance in Manufacturing

Episode 89: The Fourth Industrial Revolution post-COVID-19

Episode 88: The Future of Digital in Manufacturing

Episode 87: A Brief History of Manufacturing Software

Episode 86: Augmenting Industry: Reflections on Season 2

Episode 85: Industrial Cloud Interoperability

Episode 83: Factory Journalism

Episode 84: The Evolution of Lean

Episode 79: The Future Factory