Augmented Ops

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 "Executing on Manufacturing Technology" and our guest is Jane Arnold, board member at Aperio.ai and former VP of Manufacturing Technology at Stanley Black & Decker. In this conversation, we talk about advanced manufacturing technology, the importance of material flow, transparency, throughput, cost cutting, and captivating users with digital tools.

If you like this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 100: Innovating Across the Manufacturing Supply Chain. 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:

Execution is everything in manufacturing. You can have any technology you want, but it's only going to be as good as the execution, both among executives and among managers all along the supply chain and all across the factory.

Special Guest: Jane Arnold.

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.

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.

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.

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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.

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.

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.

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.

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.

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?

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.

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.

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

Special Guest: John Hart.