Augmented Ops

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Latest Episodes:

Episode 113: The Business Model of Lean with Jim Huntzinger May 03, 2023

Show notes

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

Jim Huntzinger, President of Lean Frontiers, joins us in this episode for a deep dive into the lean business model and all things lean accounting. We explore value stream versus product costing, the importance of lean coaching, the principles of Toyota Kata, and how these strategies can drive processes improvement and product development simultaneously. Throughout the conversation, we examine the value of transforming traditional business practices and the potential impact on organizational decision-making and growth.

If you like this show, subscribe at AugmentedPodcast.co. If you liked this episode, you might also like Episode 108: Lean Operations with John Carrier, or Episode 84: The Evolution of Lean with Torbjørn Netland. 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 lean business model is attractive to many manufacturing firms and still elusive to some of them, despite many examples of the principles in action popping up constantly. The business community should still spend more time on the interface between tech, logistics, and IT, and how all of that might interface with lean accounting. Strikingly, what we might think of as lean companies don't necessarily use lean practices across their business.

Special Guest: Jim Huntzinger.

Episode 112: Humans, Robots, and the Future of Manufacturing with Anna Waldman-Brown Apr 19, 2023

Show notes

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

In this episode, we’re speaking with Anna Waldman-Brown, PhD candidate in political economy and researcher at MIT. Our discussion dives deep into how manufacturers are automating welding processes, the role humans and robots will play in the future of the industry, and what these trends mean for small- and medium-sized enterprises in particular. We also explore the importance of collaboration between greener, tech-savvy automation engineers and the experienced shop floor operators whose skills and expertise are necessary to drive the production process.

If you like this show, subscribe at AugmentedPodcast.co. If you enjoyed this episode, you might also like Episode 92: Emerging Interfaces for Human Augmentation with Pattie Maes, or Episode 7: Work of the Future with Elisabeth Reynolds. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Special Guest: Anna Waldman-Brown.

Episode 111: Operator 4.0 with David Romero Apr 05, 2023

Show notes

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 Operator 4.0. Our guest is David Romero, Professor of Advanced Manufacturing at Tecnológico de Monterrey University in Mexico. In this conversation, we talk about the emergence of a smart and skilled operator who is helped by cognitive and physical augmentation, how this trend emerged, and how it will shape the future where we need more resilience.

If you like this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 104: A Scandinavian Perspective on Industrial Operator Independence with Johan Stahre. 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 operator is again at the center of the industrial process. This is a curious thing that seems to happen a few years after every major technological breakthrough or implementation once we realize how adaptable and capable a human workforce can be. That does not mean that technology is irrelevant but only that training humans to know every step of the work process is important in order to capture value by addressing and fixing errors and suggesting improvements.

Special Guest: David Romero.

Episode 110: Executing on Manufacturing Technology with Jane Arnold Mar 22, 2023

Show notes

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.

Episode 109: Augmenting Workers With Wearables with Andrew Chrostowski Mar 01, 2023

Show notes

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 cas…

Full show notes at the publisher

Episode 108: Lean Operations with John Carrier Feb 15, 2023

Show notes

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 wi…

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Episode 107: Explainability in AI with Julian Senoner Feb 01, 2023

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Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is "Explainability and AI." Our guest is Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talk about how to define explainable AI and its major applications, and its future. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 103: Human-First AI with Christopher Nguyen. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Explainability in AI, meaning knowing exactly what's going on with an algorithm, is very important for industry because its outputs must be understandable to the process engineers using it. The computer has not and will not use the product. Only a domain expert can recognize when the system is wrong, and that will be the case for a very long time in most production environments. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory, and who's leading the change? In this episode of the podcast, the topic is Explainability and AI. Our guest is Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talk about how to define explainable AI and its major applications, and its future. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Julian, welcome to the show. JULIAN: Hello, Trond. Thank you for having me. TROND: I'm excited to have you. You know, you're a fellow runner; that's always good. And you grew up in the ski slopes.; that makes me feel at home as a Norwegian. So you grew up in Austria; that must have been pretty exciting. And then you were something as exciting as a ski instructor in the Alps. That's every man and woman's dream. JULIAN: Yeah, I think it was very nice to grow up in the mountains. I enjoyed it a lot. But, you know, times have passed, and now I'm happy to be in Zurich. TROND: You went on to industrial engineering. You studied manufacturing and production at ETH. And you got interested in statistics and machine learning aspects of all of that. How did this happen? You went from ski instruction to statistics. JULIAN: Yeah, I was always impressed about watching stuff being made. I think it's a very relaxing thing to do. And I always wanted to become an engineer. When I was five years old, I wanted to become a ship engineer. So it was always clear that I wanted to do something with manufacturing and mechanical engineering. So I started actually doing my bachelor's in Vienna at Technische University. And for my master's, I moved to Zurich and studied Industrial Engineering. ETH has historically been very strong in machine learning research. Every student, no matter if you're interested or not, gets exposed to machine learning, statistics, and AI. It caught my attention. I thought there were very interesting things you can do when you combine both. So that's how I ended up doing research on interface and becoming an entrepreneur in this area. TROND: Yeah, we'll talk about your entrepreneurship in a moment. But I wanted to go to your dissertation, Artificial Intelligence in Manufacturing: Augmenting Humans at Work. That is very close to our interests here at the podcast. Tell me more about this. JULIAN: There is a lot of hype about AI. There's a lot of talk about self-aware factories and these kinds of things. These predictions are not new. We had studies in the 1970s that predicted there won't be any people in factories by the 1980s; everything will be run by a centralized computer. I never believed in these kinds of things. During my dissertation, I was interested in looking into how we can develop useful AI tools that can support people doing their jobs more effectively and efficiently. TROND: Right. But you already were onto this idea of humans at work. Where did you do your case studies? I understand ABB and Siemens were two of them. Give me a little sense of what you discovered there; pick any one of them. JULIAN: Sure. I'd love to start with the case that we ran with Siemens. I've worked quite a lot with Siemens in different use cases, but one of them was supporting frontline workers in complex assembly tasks on electronic products. So the aim was to help the worker check if the product has been assembled correctly. There are many connectors that could be missing or assembled in the wrong way. So the idea was to have a camera mounted to the workstation, and the worker would put the final product under the camera and get visual feedback if it has been assembled correctly or not. What we did here is really studying the psychological aspect of that. I would say most of my Ph.D. was really math-heavy and about modeling, but here we were interested in the psychological aspect. Because, in the beginning, we thought perhaps andon lights with green or red signals would be enough. But we got intrigued by the research question of does the worker actually follow these recommendations if it's just the green or red light? So we did an experiment, which I'm very excited about. So we got 50 workers that volunteered within Siemens to participate, which I'm very grateful for. We basically divided factory workers in two groups. We looked into the effect of explainability in the decisions that the AI makes. So we had one group that got basically just a recommendation to reject or to pass the product. And we had another group that got the exact same recommendations. But in addition to that, we provided visual feedback indicating the area where the AI believes that there could be an error. And the results of this experiment were perhaps not too surprising, but the effect size clearly was. We found that the people that did not get explanations for these recommendations were more than three times more likely to overrule the AI system, although the AI was correct. And I think this is a really nice finding. TROND: Well, it's super interesting in terms of trust in AI. And this topic of explainability is so much talked about these days, I guess, not always in manufacturing because people overlook that sometimes, people who are not in the industry. And they think about whether machines will take over and what decisions they're taking over and, certainly, if the machines are part of the decision making, what goes into that decision making? But as you were discovering more about explainability, what is explainability? And how is it different from even just being able to...it starts, I guess, with the decision of the AI being very clear because if that's not even clear, then you can't even interpret the decision. But then there's a lot of discussion in the industry, I mean, in the AI field, I guess, about interpretability. So can you actually understand the process? But you did this experiment, and it became very clear, it seems, that just the decision is not enough. Was it the visual example that was helping here? Or what is it that people want to know about a machine decision to make them trust the decision and trust that their processes, you know, remains a good process? JULIAN: I think I kind of see two answers to this question; one is the aspects of interpretability and explainability; perhaps I start with that. So these terms are often used interchangeably, and academics are still arguing about the differences. But there is now a popular opinion that I also share that these two things are not the same. So when we talk about interpretable AI, we think about models that have basically an interpretable architecture or functional relationship, so an example would be a linear regression. You have a regression line; it has a slope, it has an intercept. And you know how an X translates into a Y or an input to an output. Explainable AI is a fairly new research branch, which it's slightly different to that. It looks into more complex AI models like deep neural networks and ensembling techniques, which do not have this inherently interpretable model architecture. So a human, just by looking at it, cannot understand how decisions are formed. And what explainable AI methods do is basically reverse engineer what the model is doing by approximating the inner behavior of the model. So, in essence, we're creating a model of a model. Coming to your second question, so why might this be important in manufacturing? Basically, what I discovered during my research is that AI is still not trusted in the manufacturing domain, so people often do not understand what AI does in general, and I think explanations are a very powerful tool to simplify that. And a second use case of explainability is also that we can reduce complexity. So we can use more powerful methods to model more complex relationships. And we can use explainability on top of that to, for example, conduct problem-solving. TROND: Wow, you explain it very easily, but it's not easy to explain an actual AI model. Like, if you were to say, you know, here is the neural network model I used, and it had eight layers, good luck explaining that to a manufacturing worker or to me. JULIAN: So I think that explaining what a model is is also a different topic, and perhaps it's not even needed. You can still treat this as a mathematical function. I think it's really more about the decisions. We need to understand how decisions are formed, and there are different techniques to that. So when we talk about vision models, heat maps are a very interesting application. So we cannot really tell how the algorithm came up with a decision, but we can try to visualize the areas of an image where the neural network focused on to inform its decision. And we can, for example, see that certain areas are more highlighted than others, and perhaps that goes with the human intuition and creates more trust. TROND: You know, this topic is, for me, so fascinating because when we think about frontline workers or, indeed, engineers or quality managers on the shop floor, previously, they didn't have perhaps the tools available to open up the boxes, to open up the machines and look at the decisions that were being made. And, of course, that doesn't lead you to an enormous amount of confidence that what you're doing is good or bad or mediocre. You're not getting enough feedback. But it does seem to me that as machines and tools and algorithms on the shop floor become more and more complex, this is going to be a big effort. It doesn't sound very easy. And maybe you can characterize, you know, with the process today. These methods are just being applied on the shop floor. Do you have a sense that this general idea that things have to be explainable is a shared commodity on shop floors that are starting to use these techniques? Or would you say that it's enough of a challenge just to start experimenting with them, let alone trying to explain them to anybody around? I guess it's called a black box problem, right? JULIAN: Sure. I think in any use case where you have some kind of interaction between humans and AI, you've got to have explainability. It's going to be key. And there are also some less obvious use cases around explainable AI that I would also be happy to share. I think everywhere where you're going towards full automation, you perhaps don't need it, perhaps only if it's very high-stakes decisions being made. If you are rejecting products based on an AI that are very expensive, you might want to know why your line scrapped the products. In general, if you're going for automation, I would say explainability is nice to have. When we talk about augmentation, I think it's absolutely key. TROND: Yeah, it's absolutely clear. But we were talking before, and you were reminding me that in semiconductor production on an average production line, a large percentage of those components tend to be scrapped for quality reasons. And each of those components might be very, very expensive to manufacture. And it's a big problem. You have to recycle the part again, and they're made out of rare earth metals or whatnot. And it's a complicated thing, so it's not like you're just making wooden parts that you can just do over or plastic, and you can mold it again. These are like you said, they're expensive decisions that you're trusting machines to make. JULIAN: Yes. Talking about semiconductor industry, we have been also working on a different use case using explainable AI in semiconductors which I'm really excited about, and that is root cause analysis. In semiconductor manufacturing, as you mentioned, it's common that manufacturers throw away 15% to 20% of the chips they produce. We have car production lines that are standing still because of a chip shortage, so, obviously, this is a problem. What explainable AI can do here is we can try to model relationships in the manufacturing system to try to understand what causes these problems in the first place. So actually, when I was still a researcher back at ETH, I worked together with ABB semiconductors who exactly had this problem, so they had costly yield losses. And process engineers were struggling to find out where these losses were coming from. Because in semiconductor manufacturing, you often have hundreds of process steps, and each process, you can have even hundreds of process parameters such as temperatures and pressures. And you would like to know which of these process parameters do I need to adjust to avoid my yield losses. And if you have thousands, in this case, we had 3,600 different parameters that could have been suspects for yield losses. It's very hard to kind of track where yield losses are coming from. And the methods that are still used in industry are often based on linear methods. So we find this big effect, but we can't find the more hidden ones. And I think this is a very neat application of AI because you can use more complex models like neural networks or tree-based methods to model these relationships. So, in essence, we try to imitate the physical processes to learn the physical processes as they are. But since neural networks are very complex and we cannot really understand what they have been modeling, we need kind of explanations for that. And using these explanations, we can inform process engineers who are domain experts about what the model might have found. We're still acting upon correlations, not causation. But still, we can point towards certain areas that are interesting. And in the case of ABB, the AI guided the process engineers to suspicious processes, and the domain experts were able to come up with two improvement actions based on that input. Then they were able to reduce the scrap by more than 50% in one of their lines, which was, of course, substantial. And I think it's a very nice example of how humans and AI can collaborate and get more out of it. TROND: So, Julian, is that what augmentation of workers means to you? The augmented workforce is essentially a collaboration between man and machine in a deeper way than before? JULIAN: I think it's about expanding capabilities and getting teams of humans and machines that perform better and provide more value than either alone. I think that is what augmentation is about. MID-ROLL AD: In the n…

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Episode 106: Post Lean with Frode Odegard Jan 18, 2023

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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 nowada…

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Episode 105: Product Lifecycle Management's Momentum in Manufacturing with Jim Heppelmann Dec 07, 2022

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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 pr…

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Episode 104: A Scandinavian Perspective on Industrial Operator Independence with Johan Stahre Nov 30, 2022

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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…

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