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Episode 103: Human-First AI with Christopher Nguyen Nov 23, 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 Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations in industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse. Augmented is a podcast for industrial leaders, process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Christopher, how are you? And welcome. CHRISTOPHER: Hi, Trond. How are you? TROND: I'm doing great. I thought we would jump into a pretty important subject here on human-first AI, which seems like a juxtaposition of two contradictory terms, but it might be one of the most important types of conversations that we are having these days. I wanted to introduce you quickly before we jump into this. So here's what I've understood, and you correct me if I'm wrong, but you are originally from Vietnam. This is back in the late '70s that you then arrived in the U.S. and have spent many years in Silicon Valley mostly. Berkeley, undergrad engineering, computer science, and then Stanford Ph.D. in electrical engineering. You're a sort of a combination, I guess, of a hacker, professor, builder. Fairly typical up until this point of a very successful, accomplished sort of Silicon Valley immigrant entrepreneur, I would say, and technologist. And then I guess Google Apps is something to point out. You were one of the first engineering directors and were part of Gmail, and Calendar, and a bunch of different apps there. But now you are the CEO and co-founder of Aitomatic. What we are here to talk about is, I guess, what you have learned even in just the last five years, which I'm thrilled to hear about. But let me ask you this first, what is the most formational and formative experience that you've had in these years? So obviously, immigrant background and then a lot of years in Silicon Valley, what does that give us? CHRISTOPHER: I guess I can draw from a lot of events. I've always had mentors. I can point out phases of my life and one particular name that was my mentor. But I guess in my formative years, I was kind of unlucky to be a refugee but then lucky to then end up in Silicon Valley at the very beginning of the PC revolution. And my first PC was a TI-99/4A that basically the whole household could afford. And I picked it up, and I have not stopped hacking ever since. So I've been at this for a very long time. TROND: So you've been at this, which is good because actually, good hacking turns out takes a while. But there's more than that, right? So the story of the last five years that's interesting to me because a lot of people learn or at least think they learn most things early. And you're saying you have learned some really fundamental things in the last five years. And this has to do with Silicon Valley and its potential blindness to certain things. Can you line that up for us? What is it that Silicon Valley does really well, and what is it that you have discovered that might be an opportunity to improve upon? CHRISTOPHER: Well, I learn new things every four or five years. I actually like to say that every four or five years, I look back, and I say, "I was so stupid five years ago." [laughs] So that's been the case. TROND: That's a very humbling but perhaps a very smart knowledge acquisition strategy, right? CHRISTOPHER: Yeah. And in the most recent five years...so before co-founding Aitomatic, which is my latest project and really with the same team...and I can talk a lot more about that. We've worked with each other for about ten years now. But in the intervening time, there's a four-and-a-half-year block when we were part of Panasonic. So we had a company called Arimo that was acquired by Panasonic for our machine learning AI skills and software. And I would say if you look at my entire history, even though I did start with my degree in semiconductor all the way down to device physics and Intel and so on, but in terms of a professional working career, that was the first time we actually faced the physical world as a Silicon Valley team. And anybody who's observed Silicon Valley in the last 15-20 years, certainly ten years, has seen a marked change in terms of the shift from hardware to software. And my friend Marc Andreessen likes to say, "Software is eating the world." If you look at education, you know, the degrees people are getting, it has shifted entirely from engineering all the way to computer science. And the punch line, I guess, the observation is that we Silicon Valley people do not get physical. We don't understand the manufacturing world. We don't know how to do HVAC and so on. And so when we build software, we tend to go for the digital stuff. TROND: Christopher, it's almost surprising given the initial thrust of Silicon Valley was, of course, hardware. So it's not surprising to me, I guess because I've been observing it as well. But it is striking more than surprising that a region goes through paradigms. CHRISTOPHER: Yeah. Yeah. And it's a global trend. It's the offshoring of low-end, shall we say, low-value manufacturing and so on. And we're discovering that we actually went a little too far. So we don't have the skill set, the expertise anymore. And it's become a geopolitical risk. TROND: Right. Well, a little bit too far, maybe, or not far enough. Or, I mean, tell us what it is that you're losing when you lose the hardware perspective, particularly in this day and age with the opportunities that we're about to talk about. CHRISTOPHER: Well, I can talk specifically about the things that touch my immediate spheres. Maybe you can think abstractly about the lack of tooling expertise and manufacturing know-how, and so on. But as part of Panasonic, the acquisition was all about taking a Silicon Valley team and injecting AI, machine learning across the enterprise. And so we were part of that global AI team reporting to the CTO office. And we found out very quickly that a lot of the software techniques, the machine learning, for example, when you think about people saying data is the fuel for machine learning and specifically labeled data, right? In the digital world, the Google place that I came from, it was very easy to launch a digital experiment and collect labels, decisions made by users. You can launch that in the morning, and by evening you're building examples. You can't do that in the physical world. Atoms move a lot more slowly. And so when you try to do something like predictive maintenance, you don't have enough failure examples to train machine learning models from. So all of the techniques, all of the algorithms that we say we developed from machine learning that seem to work so well, it turns out it worked so well because the problem space that we worked on has been entirely digital, and they all fail when it comes to manufacturing, the things that you can touch and feel, you know, cars that move and so on. TROND: I want to ask you this, Christopher, because the first company you helped co-found was, in fact, a contract manufacturer. Do you think that reflecting on this long career of yours and these various experiences, what was it that convinced you before others? I mean, you're not the only one now in the Valley that has started to focus on manufacturing and including hardware again, but it is rare still. What does it require to not just think about manufacturing but actually start to do compute for manufacturing? Is it just a matter of coming up with techniques? Or is it a whole kind of awareness that takes longer? So, in your case, you've been aware of manufacturing, acutely aware of it for decades. CHRISTOPHER: I would say there are two things, one is obvious, and the other was actually surprising to me. The obvious one is, of course, knowledge and experience. When we work on sonar technology that shoots a beam down an echogram that comes back to detect fish in the ocean, it's very necessary, not just convenient, but necessary for the engineers that work on that to understand the physics of sound waves travel underwater, and so on. So that education, I have long debates, and it's not just recently. When we were trying to structure a syllabus for a new university, I had long debates with my machine-learning friends, and they said, "We don't need physics." And I said, "We need physics." That's one thing. But you can concretely identify you need to know this. You need to know this. So if you're going to do this, learn the following thing. The thing that was more unexpected for me in the last five years as I sort of sound this bell of saying, hey, we need to modify our approach; we need to optimize our algorithms for this world, is a cultural barrier. It's kind of like the story of if you have a hammer, you want to go look for nails. So Silicon Valley today does not want to look for screwdrivers yet for this world. TROND: So you're saying Silicon Valley has kind of canceled the physical world? If you want to be really sort of parabolic about this, it's like software is eating the world, meaning software is what counts, and it's so efficient. Why go outside this paradigm, basically? If there's a problem that apparently can't be fixed by software, it's not a valuable problem. CHRISTOPHER: Or I can't solve that problem with my current approach. I just have to squint at it the right way. I have to tweak the problem this way and so on despite the fact that it's sort of an insurmountable challenge if you tried to do so. And concretely, it is like, just give me enough data, and I'll solve it. And if you don't have enough data, you know what? Go back and get more data. [chuckles] That's what I myself literally said. But people don't have the luxury of going back to get more data. They have to go to market in six months, and so on. TROND: Right. And so manufacturing...and I can think of many use cases where obviously failure, for example, is not something...you don't really want to go looking for more failure than you have or artificially create failure in order to stress test something unless that's a very safe way of doing so. So predictive maintenance then seems like a, I guess, a little bit of a safer space. But what is it about that particular problem that then lends itself to this other approach to automating labeling? Or what exactly is it that you are advocating one should do to bridge to digital and the physical AIs? CHRISTOPHER: I actually disagree that it is a safer space. TROND: Oh, it's not a safer space to you. CHRISTOPHER: That itself there's a story in that, so let's break that down. TROND: Let's do it. CHRISTOPHER: So, again, when I say Silicon Valley, it is a symbol for a larger ecosystem that is primarily software and digital. And when I say we, because I've worn many hats, I have multiple wes, including academia; I've been a professor as well. When we approach the predictive maintenance problem, if you approach it as machine learning, you got to say, "Do this with machine learning," the first thing you ask for...let's say I'm a data scientist; I'm an AI engineer. You have this physical problem. It doesn't matter what it is; just give me the dataset. And the data set must have rows and columns, and the rows are all the input variables. And then there should be some kind of column label. And in this case, it'll be a history of failures of compressors failing, you know, if the variables are such, then it must be a compressor. If the variables are such, it must be the air filter, and so on. And it turns out when you ask for that kind of data, you get ten rows. [laughs] That's not enough to do machine learning on. So then people, you know, machine learning folks who say they've done predictive maintenance, they actually have not done predictive maintenance. That's the twist. What they have done is anomaly detection, which machine learning can do because, with anomaly detection, I do not need that failure label. It just gives me all the sensor data. What anomaly detection really does is it learns the normal patterns. If you give it a year's worth of data, it'll say, okay, now I've seen a year's worth of data. If something comes along that is different from the past patterns; I will tell you that it's different. That's only halfway to predictive maintenance. That is detecting that something is different today. That is very different from, and it isn't predicting, hey, that compressor is likely to fail about a month from now. And that when we were part of Panasonic, it turns out the first way...and we solved it exactly the way I've described. We did it with the anomaly detection. And then we threw it over the wall to the engineer experts and said, "Well, now that you have this alert, go figure out what may be wrong." And half of the time, they came back and said, "Oh, come on, it was just a maintenance event. Why are you bothering me with this?" TROND: But, Christopher, leveraging human domain expertise sounds like a great idea. But it can't possibly be as scalable as just leveraging software. So how do you work with that? And what are the gains that you're making? CHRISTOPHER: I can show you the messenger exchange I had with another machine-learning friend of mine who said exactly the same thing yesterday, less than 24 hours ago. TROND: [laughs] CHRISTOPHER: He s…

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Episode 102: Lean Manufacturing with Michel Baudin Nov 16, 2022

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 Manufacturing. Our guest is Michel Baudin, author, and owner of Takt Times Group. In this conversation, we talk about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work and a future where manufacturing is not going away. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Lean manufacturing might mean many things, but industrial work has largely been a consistent practice over several hundred years, which is not necessarily a bad thing. Having said that, if we want to go about improving it, we might want to stay pretty close to the workforce and not sit in statistics labs far removed from it. Efficiency is tied to work practices, and they cannot be optimized beyond what the workforce can handle or want to deal with. As we attempt to be lean, whatever we mean by that, we need to remember that work is a thoroughly human endeavor. Transcript TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Lean Manufacturing. Our guest is Michel Baudin, author, and owner of Takt Times Group. In this conversation, we talk about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work and a future where manufacturing is not going away. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Michel, welcome. How are you? MICHEL: Fine, thank you. How about yourself? TROND: Things are good. Things are looking up. I'm excited to talk about lean manufacturing with you, having had such a rich, professional background. Michel, you're French. You originally, I think, were thinking of becoming a probability researcher, or you were actually, and then you went to Japan and studied Toyota. You have had this career in English, German, Japanese sort of consulting all the way back from 1987 onwards on exciting topics, lean manufacturing, and especially implementing it, right? The real deal. You've authored at least four technical books that I know about. And I think you listed probably a while back, having written 900 blog posts. You've been very busy. You are the owner of the Takt Times Group, which is a consulting firm on lean manufacturing. And you love math, but you have this very interesting attitude, which we'll talk about, which is math is great, but it's not always the best communication tool. Tell me a little about that to start off. You're a probability researcher that doesn't use math; I think that's fascinating. MICHEL: I use it, but I don't brag about it with people that it turns off. So I have to be in the closet for this because people who work in manufacturing usually focus on concrete things, things that they can see and touch, and abstraction is not something that they respond well to. So whenever you explain a principle, my approach is to state this principle and then dig into some very specific examples right away; otherwise, I'm losing the people I'm talking to. But anyway, that's what I've had to do. TROND: So, did I capture your background okay? I mean, you've had a very international life so far. I hope it's been enjoyable and not just professional because you've spent your time in Germany, and Japan, and in the U.S., So you're really enjoying the different kinds of manufacturing environments. Or is it that you just want to be close to where it's all happening? MICHEL: I've enjoyed living in many different countries. And so you mentioned I'm French. I was born and raised in France, but I'm an American citizen, and I spent most of my life in the U.S. I think of myself as being part French, part American, part German, part Japanese. Because when I'm in a country, I tend to immerse myself in the culture; I don't stay aloof from it. TROND: Well, I'm curious about that because in the abstract... so if we are in the world of math, then you could maybe say that efficiency techniques are global; that was the idea. Some people have that idea, let's say, that efficiency is a global thing, and there's one thing called efficiency, and everybody should just learn it because then it's all better. It seems to me that because you spent a lot of time in three different places, it shows up differently. MICHEL: I don't use the word efficiency so much because it's limited. There are techniques to improve manufacturing performance in every aspect of it, efficiency only being one of them, and these techniques are pretty universal. Now, when you're trying to help people in different countries, it's a postulate. You have to postulate what works in one place will work in another. So far, I haven't found any reason to believe otherwise. I have encountered many people who are saying things like, "This is country X, and these techniques don't work because our people are from country X." It's one of the most common techniques to refuse to implement anything new. The fact is the Toyota Production System wasn't supposed to be applicable to American workers until Toyota applied it with American workers in its joint venture with GM in the early 1980s at NUMMI specifically. It became a showcase. Later, Toyota opened its own factory in the U.S. in Georgetown, Kentucky, and applied the system there. And then, a few years later, it opened its own factory in France, and it worked with French workers. So it's really the idea that this only works in certain cultures or this only works in Japan. It's just the reality is different. It works pretty much everywhere. TROND: Well, that's fascinating, though, because, like you said, you have immersed yourself in these different factory and industrial cultures, if you may, and you are implementing lean in all of them or advising on lean methods. Why don't we start with that, then, perhaps? Tell me a little bit, what is lean to you? MICHEL: Lean to me...and I use the term less and less because I think over the past 30 years, it's lost a lot of its meaning. When it first came out, it was the latest in a number of labels that have been applied to the same thing. In the early 1980s, you talked about just-in-time then there was world-class manufacturing. A number of different terms were used and never really caught on. This one caught on. And the way I took it, I took it to mean generic versions of the Toyota Production System. There are very good reasons why you can't call what you're proposing to a company that makes frozen foods a Toyota Production System. There are also very strong reasons why you can't even go to a car company and do this. It's very awkward for a car company to openly admit to be using a competitor's system. So you have to have a label that refers to the content but doesn't refer to where it's coming from. TROND: So for you, at the basic level, if you strip away everything, it still is essentially the Toyota Production System, and lean is just to you, I'm just paraphrasing, it's a convenient wrapping for a way to explain it in a way that's non-threatening. But it is essentially the lessons from the Toyota Production System from a while back. MICHEL: That's the way I took it. That's why I adopted this label in the early 1990s, but a lot of time has elapsed since then. Because it became popular, very many people started using that label. And the content they were putting under it was pretty much...they were attaching this label to whatever they were doing. It has lost a great deal of its meaning which is why at this point, I rarely refer to it. TROND: So you're saying a lot of people are attaching lean to whatever they're doing, I mean, understandably so, Michel, right? Because it's become a very successful term. It sells books. It sells consulting. It does refer back to something that you think is real. So can you understand why people would do this if you are in consulting, or even in teaching, or you work in an industry, and you're managing something, why people would resort to this label? MICHEL: First of all, consultants have to have a brand name for what they're selling. It was useful. As a brand name, you have to call what you're offering by a given name, and clients look for this. It's a keyword they look for, and that's how they find you. So it's really necessary. I'm not criticizing consultants for using that. TROND: No, no, I understand it. And, I mean, you're also a little bit in a glass box in the sense that you are within the general tent of lean yourself. So I understand that. I fully understand it. MICHEL: What happens when it's successful is that more and more people jump on this bandwagon and say, okay, I'm going to offer a lean. When you look at what they're saying, it does not reflect the original content. By about 2000s, it had evolved into...what most consultants were offering was drawing value stream maps and organizing Kaizen events. Those two keywords are absent from the Toyota Production System. TROND: Can you explain...so this is interesting. Because I was going to ask you exactly this, what are the types of elements that you react to the most that you feel is really...because one thing is to say it diverged from the original content, but if it is kind of a valuable extension of something...but you're saying value streams and the Kaizens, the Kaizen practices they have very little to do with the Toyota Production System in your reading. MICHEL: That's right. The value stream mapping is a new name for a technique that they call; I mean the translation of the original name is, Materials and Information Flow Analysis (MIFA), Mono to Joho no Nagare in Japanese, flow of materials and information. So that's one idea. And there is a particular graphic convention that has actually evolved from Toyota that became the value stream mapping graphic convention, but it never was in the Toyota context. Mike Rother's own admission (He wrote Learning to See, which promoted this technique.) said it was not an important topic at Toyota. It has some uses, but if you go on factory tours in Japan, you don't see a lot of value stream maps. And so it's been taken...it was a specific tool for a specific purpose like figuring out how to work with a particular supplier. And then it was made into this supposedly all-powerful analytical tool that is the first thing that you have to do when you go into a factory is map its value streams, so that's taking a very small part of what Toyota does and make it into this big thing. As for Kaizen Events, it's actually an American invention. It's something that came out of...in the early 1990s; there were a number of executives who were frustrated with the slow pace of lean implementation with other methods. So they came up with this format they called the Kaizen Blitz, that became the Kaizen events. It's also traced back to some Japanese consulting firms, which found this particular format as a convenient way to make good use of a trip from Japan to the U.S. They would organize one-week events at their clients because it was a good way to justify essentially the cost and the trouble of flying over. TROND: I'm going to go with your story here. So let's say these two are kind of examples for you of things diverting from the original content. Why don't we speak about what the original content then is for a minute? What is the core of the Toyota production method or of lean in its original form for you? MICHEL: Well, the Toyota Production System is something I'm very interested in and still studying. And it's not a static thing. It's something that, for example, the first publication about it was from the early 1970s, an internal document from Toyota with its suppliers. And then there have been many, many other publications about it through the decades. And it's changed in nature, and the concepts of manufacturing have evolved. By definition, the Toyota Production System is what Toyota does. They're very good at making cars. And so it's always important to try to keep up with what it is they're doing, knowing that there is a 5 to 10-year gap between the time they come up with new concepts and the time that the rest of the world gets to know about them. And so, in the early 1990s, there were essentially concepts of how to organize production lines, how to lay out production lines, how to design operator workstations. And there were concepts on how to regulate and manage the flow of materials and the flow of information between stations and lines and between suppliers and customers. And there was also an approach to the management of people and the whole human resource management aspect of hiring people for careers, having career plans for everybody, including shop floor operators, managing to improve the operations based on this infrastructure. So it's a very rich concept, and it encompasses every aspect of manufacturing, logistics, and production control, all the way to accountability. So it's compared with other things like the Theory of Constraints or TPM that are much more limited in scope. There is an approach to quality that Toyota has. The quality improvement is not all of the Toyota Production System. It's a complete system for making a product covering all the bases. TROND: Let me just pick up on one thing, so you're saying it's a complete system. So one thing you pointed out was the HR aspect, and hiring people for careers is one thing, but you also said the career plans for shop floor operators. So I took two things from that, and I was going to ask about this because this has been used as one example of why you cannot implement the Toyota Production System in the same way in different countries, namely because that is one aspect of society that a company doesn't fully control because it is regulated. So, for example, in Europe and in France, which you know, really well, and Germany, you know, employment is regulated in a different way. If a company was going to have the same HR policy in three different factories in three different countries, they would have to have, first of all, obviously, follow the national regulation. But then they would have to add things on top of that that would, you know, specific employee protections that are perhaps not part, for example, of U.S. work culture. So that's one thing I wanted to kind of point to. But the other thing is interesting. So you said career plans for shop floor operators meaning Toyota has a plan for even the basic level worker meaning the operators, the people who are on the floor. And that seems to me a little bit distinct. Because in the modern workplace, it is at least co…

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Episode 101: How Academia Shapes Manufacturing with John Hart Nov 02, 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 "How Academia Shapes Manufacturing". Our guest is John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT. In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 92 on Emerging Interfaces for Human Augmentation. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: There has never been a more interesting time to be in manufacturing or to watch manufacturing. The tremendous breakthroughs that we are about to witness have been made possible by a confluence of emerging technologies and startup innovations, as well as a growing awareness of the importance of building human-centric technologies. We are indeed at a crossroads with profound challenges in the growing talent shortage, the need for workforce training, an aging industrial base, and the demands for manufacturing competency from the wider innovation ecosystem. We have to make progress fast, and innovations are just maturing to be able to do so at the scale and pace required. It will, again, be amazing to watch the manufacturing industry. Parts of it will perhaps, again, become the industry of industries. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind the new era of industrial operations where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory, and who is leading the change? And what are the skills to learn and how to stay up to date on manufacturing and industry 4.0. In this episode of the podcast, the topic is How Academia Shapes Manufacturing. Our guest is John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT. In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history. Augmented is a podcast for industrial leaders, for process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. John, how are you? Welcome. JOHN: I'm well, Trond. Great to see you. Thank you for having me. TROND: Well, I'm excited to have you talking about...well, hopefully, a lot of different things, but how academia gets to shape manufacturing, this fascinating venture that is manufacturing. But you yourself, John, you grew up in Michigan, is that right? You were close to this from an early age. JOHN: I was close to it. Yeah, I grew up in Royal Oak, Michigan, a suburb north of Detroit. If you know the Detroit Metro area, there are the mile roads, and the Detroit River is sort of Zero Mile. And I grew up between 14 and 15 Mile Roads, so in the hotbed of the good, old U.S. auto industry. TROND: Well, exactly. Because looking a little bit at your background here, you spent quite a few years as a summer intern at General Motors before you got yourself to...or actually perhaps in the beginning, in your undergrad years from UMichigan, is that right? JOHN: I did. After my first year at UofM, I worked as a summer intern at GM and went back a few years in a row in different roles in different areas. And honestly, when I decided to pursue a graduate degree and ended up at MIT, I thought I might just get my master's and go back and work in the auto industry, but things changed, and here we are today. TROND: Well, here we are today. You got yourself an undergrad from UMichigan. And you worked there for a little while, I believe, but then came to MIT with a master's, Ph.D. This is way back. But you won the prize for the best doctoral thesis in micro and nanotechnology. So that set you off on the path to rediscover nanomaterials, I guess. JOHN: Yeah, well, it's a really maybe exotic combination of topics. My master's thesis was on precision machine design, the design of these large mechanical couplings for industrial robots. And then, for my Ph.D., with the same advisor, I worked on carbon nanotube synthesis. But there you have the dipoles of manufacturing research, materials, processing, and mechanical design that have shaped how I've taken things forward since then. TROND: Well, but it is in these unique combinations that innovation starts to occur, right? JOHN: Yeah, exactly, combining different topics. And that's one reason I love manufacturing is that it is the union of materials processing, and automation, and software, and now also getting more interested in the organizational workforce aspects. It's a very rich, multidisciplinary layered topic. TROND: Yeah. And we'll explore this both from the organizational angle, and, indeed, I'm super interested in this material angle on things because it seems to me like you're exploring the very, very small nanostructures, but then you're then printing them on the very large canvas. So you're exploring materials from one extreme to the other. JOHN: Yeah. Well, it depends on your objective and what topic you're working on. There are cases in our research where we need to understand the formation of materials, not quite from the atom up but from the nanoscale or microscale up. And there are cases where we more or less abstract or coarse grain those link scales and focus on macroscale properties. TROND: Well, and then you also focus quite a bit on teaching. I noticed that you actually launched the first massive online course on manufacturing processes, and hopefully, we'll get to this a little bit as well. JOHN: Sure. TROND: But teaching and basically working on the next generation of manufacturers, whether they be the engineers or really anybody else, has certainly been one of the big challenges in manufacturing really forever. What is it that fascinates you so much about teaching this to a grander audience than the usual university audience? JOHN: Well, first, I'll say I believe that the top priority of universities, including in the area of manufacturing, is to educate future leaders and engineers. That said, the number of people we educate on our campus is a small fraction of those who could really benefit from what we teach and the way we teach. And that's not just geographically, but it's also in terms of their role in the workforce. So I believe manufacturing education should address all levels of the workforce. And to get at your question more directly, when I came to MIT, I was asked to take over our core undergraduate manufacturing class in the Department of Mechanical Engineering. And as I learned to teach the class for myself, I was intrigued by this emerging trend of digital learning, and this was 2015, 2016. And I was able to get some funding from MIT internally to create an online version of the course that would be offered free to the world, and probably 100,000 People have taken it so far. And it's been a great experience and evidence of how there is very broad interest in manufacturing really across the world. TROND: 100,000 people have taken this course. JOHN: Yeah. Well, I'll say 100,000 people have signed up for the course. This is the classic trade-off with online courses. It doesn't mean 100,000 people complete the course. It means that number signs up and hopefully took something away from it. It also speaks to the flexibility. You can sign up for a course and maybe just listen to one lecture, but if you take something valuable away from it, that's great. TROND: So I wanted to talk a little bit about how academia shapes manufacturing. And I know that there are, you know, you and I work at MIT, and you've had experiences obviously at University of Michigan. But there are other manufacturing centers and institutes all around the world. Could you lay out this landscape a little bit for us so that we get a sense of where the excellent centers of manufacturing are located? I mean, one structure, just to pick that, is manufacturing institutes, and I know that's sort of dear to your heart for a couple of different reasons that we'll get into. But what are some of the centers beyond MIT where there is activity that is organized in a way that really is something to focus on? JOHN: First, I think of in the U.S., Carnegie Mellon, Georgia Tech, Purdue, Michigan, Stanford, places that have defined manufacturing centers or have a body of work that relates to manufacturing that I would say there's a critical mass of faculty, and students, and affiliation with industry. Also, Penn State in the area of additive manufacturing and product design. It's hard to be comprehensive. I don't want to forget anyone big, but that's a sample of some of the notable ones. Internationally, a lot of activity in Europe; I admire the University of Cambridge, the Institute for Manufacturing there, where manufacturing is more or less a department, or it's within the Department of Engineering, which is analogous to what we would say is a school or college of engineering here in the U.S. And they have a broad set of activities that have been there for decades focused on manufacturing at the IFM. TROND: And if you think about the best schools to get educated in this topic, is it necessarily only the top brands? I mean, certainly, they have different roles. So when it comes to undergrads or even shorter, or I guess even community colleges have a really fundamental role in the formation of this sector, can you talk a little bit about that? JOHN: Oh, for sure. When you think of manufacturing education, we must think of the full stack of institutions that educate the workforce, from vocational institutions to community colleges where the student's goal may just be to complete a vocational program or complete a two-year degree and then exit the workforce, all the way to the four-year degrees, advanced degrees, and executive education. And given how manufacturing is paramount in the workforce and the economy, we need to educate folks at all those levels. But by far, the largest number of people are at those vocational community college levels and then to the bachelor's level. So I have a Ph.D. I love to mentor Ph.D. students. But that's a small fraction of the manufacturing workforce. TROND: What about in the U.S. setting? There's something called the Manufacturing USA, and there are these institutes that have sponsorship from various government agencies, most of them through the Department of Defense. But there's also a bunch at the Department of Energy and one, I guess, from the Department of Commerce. What is the role of basically government-sponsored sort of research and innovation activities in this field? It would strike me, I guess, that historically, it's quite important. JOHN: Certainly. You're alluding to the manufacturing innovation institutes, the MIIs that were started during President Obama's administration. Actually, MIT's work, the Production in the Innovation Economy study, and the Advanced Manufacturing Partnership, which emerged from that, was key in scoping the MIIs, and now there are 16 or so around the country. It's one example of public-private partnership. Public-private partnership is key to cultivating interest in manufacturing and also providing resources for technology translation and commercialization. I think the MIIs have had a great impact on awareness of manufacturing, on R&D, and really applied research in some critical technology areas. But it's only a small part of what we need to do to regrow and expand our industrial base in the U.S. TROND: So I want to move us shortly to MIT to discuss both your own research activity and how extensively you are now aiming to take a more organizing role to kind of get more out of all of the exciting work that's happening at MIT. But before that, I just spotted perhaps an older project of yours that I thought was extremely cool. You were once called a nanoartist, and you had this NanoArt Nanobliss gallery with visualizations. You previously mentioned Obama. I believe you made a NanoArt structure called Nanobama or something of that sort. How did this come about? And, again, I mean, I'm guessing this just sort of testifies to your interest in science communication as much as in the depths of science, which we'll get into in a moment. JOHN: You got it. The inspiration was how do we communicate what we're doing in the lab to broader audiences just to make them aware of what's happening in new technology, new materials? In that case, it was nanotechnology. If you don't mind, I'll tell you a bit more of the story. When I was an assistant professor at Michigan, we were doing a lot of work on carbon nanotube manufacturing, which was a follow-on from my graduate work at MIT. And I admired President Obama, or he was a presidential candidate at that time. And without implying a political inclination, I somehow put together the words nano and Obama in my mind. TROND: [laughs] JOHN: And I said, wow, it would be cool to have a Nanobama. So one thing led to another, and I actually worked with some students in my group to fabricate these little portraits out of carbon nanotubes representing Shepard Fairey's portrait of Obama that was used widely during that first presidential campaign. And I just posted it online, I think one day after the election, and it took off. It went viral, so to say, and was featured as Nature's Image of the Year. It was printed on the newspapers you used to get as you walk onto the subway in the morning around the world. There was a company that would syndicate this stuff, and they just sent it around. So it got a lot of attention. And it showed me the power of an image in communicating something. And, of course, President Obama, that was a historic election. The play on words was exciting, and also the fact that it was a little bit intriguing science and technology that was nano was interesting. And one more thing, a colleague of mine at Michigan then was working in the White House, and he said, "Hey, can you send us a Nanobama?" So I made this frame with a little piece of the real material, and a picture of it from the microscope sent it to Washington. I didn't hear anything about it until I got a call from the White House asking me to declare the value for the President's tax return because he decided to keep it; I kid you not. And then, after Obama left office, I was with my family at a bookstore in Wellesley, and I saw the book, the retrospective book of Pete Souza, the White House photographer. And I opened up the book, and I see a picture of Obama and John Boehner in the Oval Office in the middle of this book. And right on the doorframe is the Nanobama. So it actually made it to the White House, which was a pretty awesome feeling. TROND: It must be an awesome feeling, and, again, I think that, especially in this field of manufacturing which is so challenged at times, right? And people are talking about how these factories are greedy, or is this a great job, or whatnot. And there have been all of these historical moments. But then there is also th…

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Episode 100: Innovating Across the Manufacturing Supply Chain Oct 19, 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 Innovating Across the Manufacturing Supply Chain. Our guest is Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker. In this conversation, we talk about lean leadership, productivity, the challenge of digital transformation across operations and supply chains, and how augmented lean means every organization has their own transformation approach. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 94 on Digitized Supply Chain with insights from Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Stanley Black & Decker is a huge organization where any improvements by tweaking their own operations or by adding insight from what happens along the whole supply chain can mean significant productivity gains. I find it interesting that they have their own version of the augmented lean approach tailored to where they are and, most importantly, building on the insight that the workforce is where the innovation comes from. By giving shop floor workers access to insights on big-picture manager deliberations, they are freed up to operate not only more efficiently but also more autonomously. When all of industry works that way, manufacturing will make tremendous advances more rapidly and sustainably than ever before. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Innovating Across the Manufacturing Supply Chain. Our guest is Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker. In this conversation, we talk about lean leadership, productivity, the challenge of digital transformation across operations and supply chains, and how augmented lean means every organization has their own transformation approach. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Antonio, welcome to the podcast. How are you? ANTONIO: I'm good. How are you doing? TROND: I'm doing great. I'm looking forward to thinking and talking about manufacturing supply chains and the rollout of digital technology. So, Antonio, you are actually a business major by origin from North Texas, and then your master's is in HR. And then you're fashioning yourself as a lean leader and an operational expert working on productivity and now much on digital transformation. And you're heading the rollout of digital solutions for Stanley Black & Decker. I'm curious, what was it that brought a business major into the manufacturing field? ANTONIO: For me personally, businesses is great. I'm a big advocate of free markets. And so for me, the whole time you think of how widgets are created and wanting to understand that aspect in manufacturing, creating widgets. Like you were saying, with a master's in human resource development, my thoughts there were learning that a lot of the cost from any organization is going to be labor and material. So having that understanding was great. And then transitioning to making widgets and learning under some ultimate awesome leaders in the space along with great engineers that really, really, hand in hand taught me so many things. And then one of the leaders in lean as well having hands-on conversations, walking the site with this person that is known for lean just really, really strengthened my capabilities. But the thought of the digital side is always going to come into our space, in our world. And so to be able to do that for a large fortune 500 company is obviously amazing. I'm like a kid in the candy store. TROND: [laughs] ANTONIO: Those concepts really changed the way from an organizational side because business is business no matter how you look at it. We're trying to improve our margins and capture market share just like anyone else. But ultimately, it's just a different way of doing it. TROND: I wanted to stop a little around lean first because in our pre-conversation you said lean touches everything. I'm just curious, what do you see as the key things in lean that you have learned that you are bringing into this work that we're going to be talking about a little bit? ANTONIO: I think that it boils down to a way to create continuous improvement by impacting ultimately the lead time. I'm part of the global supply chain so obviously, I'm always looking at a holistic approach. That's why it's all aspects for me from a business standpoint. At the same rate, from a lean perspective, we can find waste in anything. So there are always opportunities to improve in that aspect in every single function. Every function within the organization can be an aspect of lean. So that's the part for me that I get excited about, and I've touched every single function. So it's really an opportunity for any organization to continuously improve on and removing what they say muda from the origination of the concept in any organization. TROND: I'm curious; some people would say that lean is or I guess was important early on but that contemporary organizations are somehow different, and digital, which we'll talk about, is one reason, but there are perhaps other things. What are some of the things that you, I mean, I don't know if you agree with this, but what are some of the things that you're incorporating into your thinking here that may be either different or where you have to adjust it to the organization you're actually in at any given moment? I'm just curious. ANTONIO: You're thinking lean from a digital standpoint or just lean? TROND: Well, lean was developed in its original form a very long time ago. So I guess the first question I'm asking is how can you be confident that the original insights are still valid? Is that because you're walking around and experiencing it every day, and it resonates with you? I guess, firstly, just curious about what lean generally means today in an organization like yours, and then obviously, we'll talk about the rollout of digital solutions, which you've been doing so much now. ANTONIO: Right. And that's a great question, and I'm excited to be the person that has to answer that question. TROND: [laughs] Well, you didn't think I was going to give you easy questions, Antonio. [laughs] ANTONIO: Lean, the concept, I think, will never go away. And so for those that think that it will, really do not understand engineering from that standpoint because when you think about engineering, an engineer solves problems. And so we know number one, there's always going to be problems. I'm sure that there are a lot of people that say, "Hey, I got something for you to solve. I got a problem for you," so from that perspective, we know. But then, on top of that, think about innovation from an engineering standpoint, as you see something improved, even if it's making it better, even if it's something like making it better for the customer, ultimately, that transition of change even the slightest or something large, every organization has to do it. They have to embrace it. And so a person that knows those techniques, that are really good and seasoned and experienced, which I would say I do fit in that; I feel mighty confident in that space, and I feel mighty confident in manufacturing, we could see it quickly. You see it immediately. Like, you see a process, and it just stands out. And I think that you can't wish that away to be able to see the inefficiencies of any system. And if you do not have a system in your approach, then that to me is already folly, you know what I mean? Like, that's an error. If you can't create systems, especially in manufacturing, I think that that's no bueno. [laughter] TROND: Got it. I'm then curious, digital. How does digital factor into all of this? So I guess I'm understanding a little bit more of your conception of continuous improvement, lean, whatever you really want to call it, and engineers that are such a crucial part of the kind of organization you represent, Stanley Black & Decker. So now, clearly, there's been a push in most organizations across fields to go digital and arguably, manufacturing organizations perhaps were resisting it a little bit because there was such an amount of automation in there already, and then now comes digital on top of that. And has it been easy? Has it been difficult? What goes into even the decision to say, "We're going to have a major digital transformation?" Tell me a little bit about the journey that you've gone through with Stanley in that respect. ANTONIO: So, really great question. And so I'm going to take you down a little bit of a history lesson and introduce how it impacts. So when you think about things of the world, because you always have to relate to what's going on in the real world, you have the introduction of the smartphone. You have to credit that smartphone for that interaction of this interface because it's putting that into a lot of operators' hands to interface with something. Now, when you think about digital, industry 4.0 touches a lot of things; it's very vast, very broad. But when you think about the insights and paper throughout your organization that's there but being able to in manufacturing...and I'll make this a little bit specific to manufacturers. There are so many points where you actually need data to improve throughout that process, and like I said, it's a system. And so if you can capture it in a digital way, now you can analyze it. Now it's an insight. Now you can take all of this, and you can do predictive analysis. You can add algorithms, AI, whatever you want once it's digital. And it's transforming your operation to be able to enhance it in this digital way so you can advance and be a little bit more productive and get better, and so it still comes back to lean. [chuckles] Once you've created it digital, now it's like, what am I going to do with the data? Because you can do the wrong things with data. It can give you the wrong insight. And just making those decisions of where you are going to improve, I think that is really huge. So for me, that transition starts with realizing the digital side, removing some of the paper. I mean, there are so many people that are old school I would say that do everything with paper. And if that paper was digital, then what could be? I'm smiling now because it gets me excited because there are so many processes that are old that people just pull out a paper and they use it even though we're in this digital age. TROND: So I thought I would then move us a little bit into the aspect of having a digital platform. So digital means a lot of different things to different people. You say having access to digital gives us options basically because then you have data, but you have to do the right thing with it. First off, what kind of a decision and who was involved, I guess, in the decision at Stanley going digital in that sense? Because there are many different echelons of an organization that could potentially use data. Who was the most excited, I guess, to use new data in your organization? How did that even come about? Was it a leadership decision? Was it mid-level managers that said, "Other organizations, our peers have more data?" Or was it analyzing, you know, Gemba Walks and walking around and saying, "Hey, the operators could be more productive with more data?" Where did the decision point come from? ANTONIO: To answer your question, short answer would be leadership. We're pushing for the next edge in innovation and pushing forward to create change. And then it's what can be that thought, and I would say the collective. If you were to embrace true employee engagement and start from the shop floor, it's going to be things that they don't know that they're requesting, something digital, so to speak. They're just saying, "Hey, this would be cool. This is what I need in order to do my job effectively." And then what about the supervisors to the middle managers that are trying to share insight of it's great to say that you hit your numbers or you produced your widget in a successful time or faster than you anticipated, but what about the opposite? What about when you did not meet your numbers? Being able to speak to that with data that's a huge win. Who wouldn't want that? And there are a lot of areas that are little dark areas in a manufacturing facility that you don't have that capability. And that's why you need some type of way to be able to shed light on those areas and capture that in a very effective way. TROND: Tell us a little bit about the digital rollout process at Stanley. What went into it, and what is the situation? What sort of systems have you opted for, and how are you rolling them out? ANTONIO: So within our organization, everything comes out with governance so thinking of and a way of controlling exactly what's completed, what's being done, what you are going to put within the facility, and then creating some type of uniformity around that. The interesting thing about our organization is we're a huge conglomerate. We produce many different parts and units. And it's just a lot of complexity and diversity as far as the people are diverse, but I'm just saying end product. Manufacturing facilities...I'm global, so I'm facing all over the world different processes that we do and so being able to have a very tactic way to roll that out in a uniform way. That's really the strat there, really thinking it out. But then also allowing for those unique scenarios to come about, having what we call citizen developers. It's that employee engagement part, thinking about someone that's really close to the process. They may figure out a way that, hey, we need this type of solution, listening to them. And then the fact, like I said, I'm global, I'm seeing way more than they are. And I can be like, and our team can look and say, "Hey, this actually could be used at several sites that look just like this one." And so we can get that MVP and create it in a very standard, uniform way so then we can roll it out on an enterprise level. And so all of this together is the way that we go about rolling out digital solutions. TROND: So, Antonio, I'm curious about this because in classical automation, usually, it's a big sunk cost, and the system is stable, perhaps, but everyone has to learn it and do it one way. Is the current wave of digital transformation that you're talking about here does it allow for both strong governance, which you clearly need in a large organization, but also for those citizen developers to emerge with their more kind of not exactly bottom-up, but they are certainly factory-based, or they are site-based perhaps innovations? Did you have to choose technologies that allowed for that, or how did that factor in? Becaus…

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Episode 99: Augmented Lean: The Book Oct 12, 2022

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 "Augmented Lean Prelaunch." Our guest is Natan Linder, in conversation with host, Trond Arne Undheim. In this conversation, we talk about the background of our co-authored book, Augmented Lean, a human-centric framework for managing frontline operations, why we wrote it, what the process has been like, the essence of the Augmented Lean framework, and the main lessons of this book for C-level executives across industry. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 96 on The People Side of Lean with Professor Jeff Liker. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Industrial revolutions are rarely chronicled as they are happening, but this industrial revolution will be. There is an ongoing shift in the way technology and workforce combine to produce industrial change, and it is happening now. We are lucky to be situated in the middle of it. And I personally feel fortunate that I was brought along for the ride. It has been a life-changing experience to realize the power and impact of living through a shifting logic of manufacturing and, perhaps more importantly, to realize that as excited as we can be about automation, an augmented workforce represents the best combination of the most important technology we have which is human workers themselves with the second best machines that humans create. The fact that making humans and machines work together is no trivial task has been pointed out before but documenting what happens when it does go well in the biggest industrial companies on the planet feels like a story that deserves to be told. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Augmented Lean Prelaunch. Our guest is Natan Linder, in conversation with myself, Trond Arne Undheim. In this conversation, we talk about the background of our co-authored book, Augmented Lean, a human-centric framework for managing frontline operations, why we wrote it, what the process has been like, the essence of the Augmented Lean framework, and the main lessons of this book for C-level executives across industry. Augmented is a podcast for industrial leaders, for process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Natan, good to have you in the studio. How are you today? NATAN: I'm great. How are you? It's been a minute. TROND: It's been a little minute for us. It's crazy with book launches. It takes a little out of you. And you are running a company in addition to that, so you had some other things on your plate too. NATAN: Yep, running a company and having a book coming is an, I don't know if an artifact, but definitely, company is a lot about changing the status quo. And the book tries to capture a movement. So I think they go along nicely. TROND: Yeah, Natan. And I wanted to bring us in a little bit and converse about why this book was written. Certainly, that's not my benefit. You brought it up to me. But what were we thinking about when writing this book? So I want to bring it back to way before I came into the picture with the book because it was your idea to write a book. What was on your mind? What were the main reasons that you thought I really want to write a book? NATAN: When I was coming up as an engineer...and my background, I'm not a pure manufacturing production type engineer, but I've been around it my entire career just because of the type of products that I've been involved with whether it's mobile phones, or robots of all sorts, 3D printers. So you get to spend a lot of time in these operational environments, shop floors, machine shops, and the like. And when we started working on Tulip, it was pretty clear pretty quickly that there's a moment that is emerging in operations that no one has captured the story. And this is back even; I don't know, maybe five or six years ago. We are maybe one or two years old, and I'm already starting to think about this post-lean, or classical lean movement that I'm sure is happening. That really is the genesis of the book in the early, early days. And fast forward to when we started talking, I think we got Tulip off the ground. But really, that was a platform to meet all those different people who helped operations transform digitally, whether it's all sorts of consultants, or academics who are researching operations, or business leaders, you know, tons of factory managers and the engineers that work with them, and the executive, so a whole bunch of people. And they're all basically talking about the same thing and the deficiencies in lean, the complexity of technology, and how they're trying to change, and it is so difficult. So I think that's a good description of the landscape before diving in to try and capture what the book attempts to capture. TROND: Yeah, Natan, I remember some of our early discussions. And we were dancing around various concepts because clearly, lean is a very broad perspective in industrial manufacturing focused on reducing waste and many other things. It's a broad concept that people put a lot of different things into. But I remember as you and I were thinking about how to describe this new phenomenon that we do describe in the book, we were thinking a little bit that a lot of these new influences come from the digital sphere. So there's also this term agile. There are some people who say, well, you know, let's just replace lean because it's an outdated paradigm. And I remember you were quite adamantly arguing that that's not the case. And this goes a little bit to the message in our book. We are in no way really saying that lean isn't relevant anymore. NATAN: On the contrary. TROND: Tell me a little bit about that. NATAN: A really simple way I think to frame it is that whether you're practicing lean formally or some variant of it, of lean, or Six Sigma, or some program that formalizes continuous improvement in your operation...and we're talking about frontline operations. We're talking about factories, and labs, and warehouses, and places like that. You are practicing lean because this is how the world..., even if you're not doing it formally; otherwise, you're not competitive. Even if you're in a bank or a hospital, you might be practicing lean. And that's where agile comes to the picture, and it was adopted widely by operations practice in general and pushed into areas that are not pure manufacturing. So, in a way, lean is a reality. Some organizations are more formal about it, some are less, but definitely, they're doing it. Here's the issue, and this is the main thesis of the book. When lean came about...and we know the catalyzing text. We know the teaching of Taiichi Ohno. We know about The Goal. We know about The Machine That Changed the World. And those are seminal texts that everybody reads. And we know about Juran and lots of great thinkers who thought about operations as a data-driven game, some from the school of thought of quality, some from pure operation research, some from how do you put emphasis on classic just-in-time, Kanban, Kaizen, all those continuous improvement things. But at the end of the day, all of that thinking, which still holds true, was not done when digital was top of mind, where data is everywhere, where people need to live in such data ecology. It was done, so to speak, in analog times. And it doesn't mean that the principles are wrong, but it doesn't mean they don't need to get augmented. And this is maybe the first time where this idea of augmentation, which, to me, augmentation is always about...I always think about augmentation from a people's perspective or an org perspective. It's just a collective of people. That's where it starts, and that's where we had something to say. So that's one aspect to think about. The second big one is actually very simple. It's kind of like; we heard ten years of industry 4.0 is going to change everything, and all we got is this lousy OEE graph. And that's kind of like a little tongue-in-cheek on we were promised flying cars, but we only got 140 characters. I mean, come on, stop talking about industry 4.0. It's like, who cares? If the tools and digital techniques and what have you is not adopted by the people actually doing the work, that then collectively, one engineer, another engineer, another operator, a team lead, the quality lead, and so on come together to transform their org, if that's not happening, then that's not sustainable transformation, and it's not very relevant. Again, augmentation. TROND: Right. And I think, Natan, that's where maybe some people are surprised when they get into this book. Because it would be almost tempting to dismiss us as traditionalists in the sense that we are not really going whole hog into describing digital as in and of itself, the core of this principle. So there is a little bit of a critique of agile as an idea that agile or using that as a kind of a description for all digital or digital, right? That digital doesn't change everything. And I guess I wanted to reflect a little bit on that aspect because I know that you, as a business leader now hiring a lot of people, we are spending a lot of energy bringing these two perspectives together, and it's not very obvious. You can't just take a digital person who is completely digital native and say, "Welcome to the factory; just do what you do. And because you do things better than everyone else, we are now going to adapt these factories." How do you think about that? In factories, you could conceive it as the IT versus OT, so operational technologists versus information technologists and the various infrastructures that are quite different when those two things come into play. NATAN: So my frame of reference is the most value...and it's a very engineery frame of reference because I'm an engineer at the end of the day. It's like, the most value gets unleashed when people truly change how they work and adopt a tool, and that's true for operations and manufacturing. But, by the way, it's also true for the greater business perspective. And a lot of people, when I talk to them about Augmented Lean, really take us to the realms of what is the future of work, and I think it's very timely. We're kind of in a post-COVID reality. Working remote has changed many things, working with data. Big ideas like citizen development, you hear them all over the place. And use of advanced platforms like the no-code/low-code that allow people to create software without being software engineers become a reality. So there's a much broader thing here. But if I focus for a second on what you're asking, the way I see it is when people truly change how they work, it means that they believe, and that belief translates into action, that the tool that they're using is the best way to do something. And they become dependent and empowered by it at the same time because they're not willing to go back to a state where they're not thinking and working with data, or back to the clipboard, or back to being dependent on an IT department or a service provider to give them some technical solution. People have become more self-sufficient. And it turns out that if you do that, and sometimes people would refer to that as you let people hack or go nuts in the factory floor or in whatever operational environment, that could be a concern to people, and that's a fair observation for sure. And that's where when you look at the book, when we were kind of constructing the framework we call Leader HG where HG stands for hack and govern... We are used to Silicon Valley startups being like, oh yeah, you all just need to hack. And that's a very glorious thing, and everybody understands that. And they want them to hack when they are a 50,000-person software company. They're still hacking, but they're doing it in a much more structured way, in a much more measured way. So even in hacking, there's governance. And in operational environment, governance is equally important, if not more, because you're making real things. That is something we've observed very empirically. Talking to a lot of people seeing what they do, it's like, yeah, we want the best ideas from people. How do we get it? What do we do? We tried this approach, that approach. And I think we were sometimes very lucky to be observers to this phenomena and just captured it. TROND: Yeah. And I wanted to speak to that a little bit. I want to thank you, actually, for bringing me into this project because you and I met at MIT but from different vantage points. I was working at Startup Exchange working with a bunch of very, very excellent MIT startups in all different domains, and you were an entrepreneur of several companies. But my background is more on the science and technology studies but also a management perspective on this. But I remember one of the things you said early on to me was, "I want to bring you in on this project, but don't just be one of those that stays at the surface of this and just has like a management perspective and writes future of work perspectives but from like a bird's eye view. Come in here and really learn and go into the trenches." And I want to thank you for that because you're right about many things. This one you were very right about. And this clearly, for me, became a true research project in that I have spent two years on this project, a lot of them in venues and factory floors, and discussing with people really at the ground level. And for me, it was really a foundational experience. I've read about many things, but my understanding of manufacturing, frankly, was lacking. And you could have told me as much, but I actually, frankly, didn't realize how little I knew about all of the factors that go into manufacturing. I had completely underestimated the field. What do you say to that? NATAN: It's interesting because I feel like the last two years, everything I think I know [laughs], then I found out that I don't know enough. It just kind of motivates you to do more work to figure out things because it's such a broad field, and it gets very, very specific. Just listening to your reflection on the past couple of years, the reality is that there is a gap in the popular understanding of what operations and manufacturing is all about. People think that stuff comes from some amorphous factory or machine that just makes the things. And they usually don't see, you know, we have those saying, like, you don't want to see how the sausage is made, which is obviously very graphic. But you also don't see how the car is made unless you're a nerd of those things and watch those shows like how things are made, but most people just don't. And they don't appreciate the complexity and what goes into it and how much technology and how much operation process it consumes. And as a society and as a set of collective economies and supply chains, it is so paramount to…

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Episode 98: Decarbonizing Logistics Oct 05, 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 Decarbonizing Logistics. Our guest is Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg. In this conversation, we talk about the huge tasks of mitigating and adapting to climate change throughout industrial supply chains. If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 68: Industrial Supply Chain Optimization. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Decarbonizing logistics without slowing economic growth is a formidable challenge which requires paradigm shifts across many industries, as well as adopting openness principles from the virtual internet onto the physical nature of the supply chain, as well as facilitating new business models, sharing, and standardization, and eventually dematerialization. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Decarbonizing Logistics. Our guest is Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg. In this conversation, we talk about the huge tasks of mitigating and adapting to climate change throughout industrial supply chains. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Alan, welcome. How are you? ALAN: I'm very well, thank you. TROND: I'm super excited to have you, Alan, you know, an academic that has transformed and seen the transformation of a field that barely existed when you started. Some 40 years in academia and logistics and now being part of this exciting experiment with creating a whole new university focused on logistics. It's been quite a journey, hasn't it? ALAN: It certainly has. I think this is my 43rd year as an academic. My colleagues often think maybe it is time to retire, but the subjects in which I specialize, which we'll be talking about in a few moments, like decarbonization, are sort of hot topics at the moment. So I'm very reluctant to phase myself out. So it's been an enjoyable 40-year career, I must confess. TROND: How did you get to pick this area? It's obviously not; I mean, now, because of the pandemic and other things, logistics or at least supply chains is kind of on everybody's mind because we're not getting whatever product we want or maybe some sort of interest in green practices. And we're starting to realize that transportation is becoming more of an issue. People are worried about that. How did you get into this area? ALAN: My interests initially were in transport and particularly freight transport. In fact, right at the beginning, it was actually a crime, believe it or not, which got me into this area. TROND: [laughs] ALAN: Because I'd done my masters at UBC in Vancouver. I returned to London to do my Ph.D. at the University of London. This was in 1976, a long time ago. And I had spent three or four months reading up on the subject of freight modal split, you know, why so much freight goes by road and so little by rail. And I'd compiled all my notes, and my briefcase was stolen. [laughter] So the day before that, I'd been to visit a professor at the London Business School who said to me, "The freight modal split topic has been very much researched." He said, "You're a young man. Why don't you go out and find something new to bring a new perspective to this subject?" And around then, the subject of...it wasn't called logistics back then; it was called physical distribution, right? TROND: Hmm. ALAN: Where you saw freight transport in a broader context linking it to inventory management, to production planning, to warehousing, and so forth. And so I began reading up on that subject. And that then became the main theme of my Ph.D., which I think was one of the first PhDs done in the UK on that subject. So you could say that it was the person that stole my briefcase way back in 1996 [laughs] that played a part in me discovering logistics as a field, and that's occupied me for 40 years in my academic career. TROND: And on that journey, you have entered in and out of different fields. I noticed that you were a lecturer in economic geography in the beginning. So there's a very interesting, I find, physical component to logistics, obviously. How does geography enter into it for you? ALAN: Well, I see transport and logistics as essentially a spatial subject. My Ph.D. focused on the geographical aspects of logistics, you know, where you locate the warehouses, how you route the vehicles, you know, so much logistics planning has a geographical component. But the thing about logistics as an academic discipline is that it's drawn together academics from many different disciplines. Many have come from a mathematical background, from engineering, from economics, in my case, as I said, from geography. And that, I think, is one of the strengths of the subject area, that it has got this interesting interdisciplinary mix. And that allows us, in a sense, to deal with a whole range of policy issues, of industrial issues, I mean, from land use planning to environmental issues, which we'll be talking about in a moment. I've really enjoyed engaging with academics really from different disciplines over my career as an academic. TROND: Well, and we'll talk about these things in a second. But, I mean, it's not just academics, right? Because the subject is so non-academic in a sense, right? [laughs] It's actually very alive, and it affects all of us. So people may not have been super aware of it. But, like you point out, it's very multidisciplinary. Now, how did this startup University concept come about? You've moved to Hamburg or spent a lot of time in Hamburg with this KLU university for logistics, essentially, which sounds to me like a daunting prospect to create a new university based on a new discipline in Germany of all places. ALAN: So I'd been 25 years in my previous university here in Edinburgh where I'd set up a master's program in the subject and a research center. And then, in my late 50s, I got the opportunity to go to Hamburg and to join what was a startup University. I mean, when I joined, I think we only had nine academic employees. We only had about 40 or 50 students in total. So it was a challenge. And a bit of background on the university; it is a legacy project of a very wealthy man, Klaus-Michael Kühne, who is the majority owner of Kuehne+Nagel, which is the world's biggest freight forwarding company. And he also owns about a quarter of Hapag-Lloyd, one of the world's biggest shipping companies. And he, in a sense, wanted to give something back to the industry, and so he founded the university in 2010. So it's now 12 years old, and I think it's been a very successful enterprise. We're still niche, obviously. We've got, I think, about 27 or 28 professors, about 500 students. But we have this focus on logistics and supply chain management. And there are also quite ambitious plans to globalize the university, to open up satellite KLUs around the world. So I was just very lucky really to get involved in this in the early stages and do my bit to help to shape this institution. TROND: Well, you're lucky but obviously enormously accomplished. I wanted to talk a little bit about your 2018 book: Decarbonizing Logistics here. So this came out on Kogan Page. I also published on Kogan Page. It's a great UK-based publisher. Tell me a little bit about decarbonization overall and what you see as the main opportunities but also the challenges. It seems to me there's a lot of talk of decarbonization, but the subject that you are attacking it from is one that points out a lot of the limitations of these visions of changing the world into a decarbonized world. They're very physical limits and very real practices out there in various industries. How can we kick off this discussion on decarbonization? What is the best way to understand the biggest challenge here? ALAN: If we confine that to logistics, to put that into perspective, I think in my book, I reckoned...I pulled together as many numbers as I could, and I reckoned that logistics worldwide accounted for about between 10% and 11% of energy-related CO2 emissions. I've now revised that upwards, so I think it's probably now closer to 11% to 12%, most of that coming from freight transport but some of it from the buildings, from the warehouses, and the freight terminals. To my knowledge, nobody has yet carbon footprinted the IT and administrative aspects of logistics, but that could maybe be up half a percent or thereabouts. And there's a general recognition that Logistics is going to be a very hard sector to decarbonize for three reasons: one, because of the forecast growth in the amount of freight movement worldwide over the next few decades. Second thing is because almost all the energy currently used in logistics is fossil fuel, right? So we're going to have to convert from fossil fuel to renewables. And the third thing is the length of the asset life because ships would typically have an asset life of 25, 30, 35 years; planes, likewise, trucks are a bit shorter, maybe 10 to 15 years. But it's going to take us time to change that asset base away from fossil energy to renewables. TROND: Well, I believe in the middle of your book, somewhere in chapter three, I read this quote that you had that the only way a restraining future increases in freight movement is basically to slow economic growth. That's not really very exciting of a prospect. ALAN: Well, that's one of my five decarbonization levers to just reduce the amount of stuff that we have to move. TROND: You must be a popular guy if you say that to industry leaders. [laughter] ALAN: Well, I think the challenge of dealing with a climate problem is so enormous that we really have to think out of the box and think of these radical suggestions. But in this case, a number of things can help us there; I mean, the development for circular economy, increasingly manufacturing and recycling will help to reduce the amount of stuff. A lot of the research suggests that people are prepared now to move to a sharing economy where they're less obsessive about owning things and more willing to share. In some sectors...look at electronics how we have managed to miniaturize products. There's also 3D printing, which some people think will help us to reduce the amount of stuff that we need to move. It will help us to streamline our supply chains, reduce the amount of wastage in the production process. So it's not all about just people buying less. I mean, there are a number of trends I think we should -- TROND: I get that, but, Alan, I mean, 3D printing, I was just, again, reading from your book. You're not all that bullish on 3D printing, either. It's certainly not on the individual level this vision people might have in their heads that everyone's going to have a 3D printer, or the neighborhood will have a vast 3D printer network, and you can print everything locally. This whole decentralized idea of the world of material goods, essentially, where everything is printed on demand, you don't really see that as a very easy transition, do you? ALAN: No, I don't. I think it's also a longer-term transition. I mean, there's a debate as to whether this will be truly a game changer. And maybe in the longer term, we will see a lot of consumer products printed in the home, and then we can greatly streamline supply chains. That is a long way off if it ever happens. Where I think it's more likely to reduce, freight demand is further back along the supply chain instead of business applications of 3D printing. But there's an academic debate on this subject. Some people are quite upbeat about this, thinking 3D printing is going to be an effective decarbonizer. Others are a bit more skeptical. I mean, there are some forecasts being made about the net effect of 3D printing on the amount of air cargo in the future. But there's not necessarily a wide agreement on that. So I think the jury's out on this one, [laughs] on the net contribution 3D printing will make to decarbonization. TROND: Alan, can you give me some tangible examples of what we're talking about here with logistics? Because, in essence, it's an unfair business to be in to decarbonize logistics in the sense that the subject as a whole is almost a victim of climate change. You're dealing with extractive or heavy industries that are moving about a lot of damaging [laughs] materials that they have extracted. To turn this into a positive discussion is challenging, but there are a lot of attempts to do so. Maybe we can take trucking perhaps as an example. So transportation, obviously, of goods via air is challenging, and road and by ocean, I guess, is somewhat less climate impactful. But what is the prospect? If we just take trucks, it's a modal transportation element. People understand truckers, and we see trucks on the road. It's a very visceral kind of element. What has happened there, and what would you see is the prospect there? People talk about electrification of trucks. What are the real prospects for change in trucking, transportation? ALAN: I think one of the positive things here is that there are many things that can be done, and they're additive. Their net effects will be cumulative. They're going to be implemented over different timescales. So the sort of things that we can do today which yield a significant carbon saving would be to improve the aerodynamics of the vehicles, streamline them. We can train the truck drivers to drive more fuel efficiently. I mean, I think that's recognized to be one of the most cost-effective ways of cutting carbon emissions and also, of course, reducing fuel costs as well. A lot of this would be self-financing for the trucking businesses. Then looking to the longer-term, there are technologies that we'll be able to deploy. Here in Europe, there's been a lot of interest in platooning, where it's not just the fuel efficiency of the individual vehicle that you improve but convoys of vehicles that would then be closely coupled, if you like, on the motorway. But many people see ultimately, the way we decarbonize road freight to get it down to zero emissions is through switching from diesel fuel to low carbon fuels, mainly batteries. I would have thought, certainly for smaller countries where the trucks travel shorter distances, maybe some use of hydrogen though I have to confess that I'm doubtful about the use of hydrogen in the road freight sector. I see we will need the hydrogen to decarbonize other sectors of the freight market, the ones you mentioned, aviation and shipping, because they don't have the same opportunity to electrify the operations that we will have in the road freight sector. But I mentioned the importance of timescale here because if you look at Europe, I think there are 6.2 million trucks in Europe. We are replacing those trucks at about 200,000 or 300,000 a year. At that replacement rate, it's going to take us pr…

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Episode 97: Industrial AI Sep 21, 2022

Show notes

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. The topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati. In this conversation, we talk about how AI does many things but to be applicable; the industry needs it to work every time, which puts additional constraints on what can be done by when. If you liked this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, and the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati. In this conversation, we talk about how AI does many things but to be applicable, industry needs it to work every time, which puts on additional constraints on what can be done by when. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Jay, it's a pleasure to have you here. How are you today? JAY: Good. Thank you for inviting me to have a good discussion about industrial AI. TROND: Yeah, I think it will be a good discussion. Look, Jay, you are such an accomplished person, both in terms of your academics and your industrial credentials. I wanted to quickly just go through where you got to where you are because I think, especially in your case, it's really relevant to the kinds of findings and the kinds of exploration that you're now doing. You started out as an engineer. You have a dual degree. You have a master's in industrial management also. And then you had a career in industry, worked at real factories, GM factories, Otis elevators, and even on Sikorsky helicopters. You had that background, and then you went on to do a bunch of different NSF grants. You got yourself; I don't know, probably before that time, a Ph.D. in mechanical engineering from Columbia. The rest of your career, and you correct me, but you've been doing this mix of really serious industrial work combined with academics. And you've gone a little bit back and forth. Tell me a little bit about what went into your mind as you were entering the manufacturing topics and you started working in factories. Why have you oscillated so much between industry and practice? And tell me really this journey; give me a little bit of specifics on what brought you on this journey and where you are today. JAY: Well, thank you for talking about this career because I cut my teeth from the factory early years. And so, I learned a lot of fundamental things in early years of automation. In the early 1980s, in the U.S, it was a tough time trying to compete with the Japanese automotive industry. So, of course, the Big Three in Detroit certainly took a big giant step, tried to implement a very good manufacturing automation system. So I was working for Robotics Vision System at that time in New York, in Hauppage, New York, Long Island. And shortly, later on, it was invested by General Motors. And in the meantime, I was studying part-time in Columbia for my mechanical engineering, Doctor of Engineering. And, of course, later on, I transferred to George Washington because I had to make a career move. So I finished my Ph.D. Doctor of Science in George Washington later. But the reason we stopped working on that is because of the shortage of knowledge in making automation work in the factory. So I was working full-time trying to implement the robots automation in a factory. In the meantime, I also found a lack of knowledge on how to make a robot work and not just how to make a robot move. Making it move means you can program; you can do very fancy motion. But that's not what factories want. What factories really want is a non-stop working system so they can help people to accomplish the job. So the safety, and the certainty, the accuracy, precision, maintenance, all those things combined together become a headache actually. You have to calibrate the robot all the time. You have to reprogram them. So eventually, I was teaching part-time in Stony Brook also later on how to do the robotic stuff. And I think that was the early part of my career. And most of the time I spent in factory and still in between the part-time study and part-time working. But later on, I got a chance to move to Washington, D.C. I was working for U.S. Postal Service headquarters as Program Director for automation. In 1988, post service started a big initiative trying to automate a 500 mil facility in the U.S. There are about 115 number one facilities which is like New York handled 8 million mail pieces per day at that time; you're talking about '88. But most are manual process, so packages. So we started developing the AI pattern recognition, hand-written zip code recognition, robotic postal handling, and things like that. So that was the opportunity that attracted me actually to move away from automotive to service industry. So it was interesting because you are working with top scientists from different universities, different companies to make that work. So that was the early stage of the work. Later on, of course, I had a chance to work with the National Science Foundation doing content administration in 1991. That gave me the opportunity to work with professors in universities, of course. So then, by working with them, I was working on a lot of centers like engineering research centers and also the Industry-University Cooperative Research Centers Program, and later on, the materials processing manufacturing programs. So 1990 was a big time for manufacturing in the United States. A lot of government money funded the manufacturer research, of course. And so we see great opportunity, like, for example, over the years, all the rapid prototyping started in 1990s. It took about 15-20 years before additive manufacturing came about. So NSF always looks 20 years ahead, which is a great culture, great intellectual driver. And also, they're open to the public in terms of the knowledge sharing and the talent and the education. So I think NSF has a good position to provide STEM education also to allow academics, professors to work with industry as well, not just purely academic work. So we support both sides. So that work actually allowed me to understand what is real status in research, in academics, also how far from real implementation. So in '95, I had the opportunity to work in Japan actually. I had an opportunity...NSF had a collaboration program with the MITI government in Japan. So I took the STA fellowship called science and technology fellow, STA, and to work in Japan for six months and to work with 55 organizations like Toyota, Komatsu, Nissan, FANUC, et cetera. So by working with them, then you also understand what the real technology level Japan was, Japanese companies were. So then you got calibration in terms of how much U.S. manufacturing? How much Japanese manufacturing? So that was in my head, actually. I had good weighting factors to see; hmm, what's going on here between these two countries? That was the time. So when I came back, I said, oh, there's something we have to do differently. So I started to get involved in a lot of other things. In 1998, I had the opportunity to work for United Technologies because UTC came to see me and said, "Jay, you should really apply what you know to real companies." So they brought me to work as a Director for Product Environment Manufacturing Department for UTRC, United Technology Research Center, in East Hartford. Obviously, UTC business included Pratt & Whitney jet engines, Sikorsky helicopters, Otis elevators, Carrier Air Conditioning systems, Hamilton Sundstrand, et cetera. So all the products they're worldwide, but the problem is you want to support global operations. You really need not just the knowledge, what you know, but also the physical usage, what you don't know. So you know, and you don't know. So how much you don't know about a product usage, that's how the data is supposed to be coming back. Unfortunately, back in 1999, I have to tell you; unfortunately, most of the product data never came back. By the time it got back, it is more like a repair overhaul recur every year to a year later. So that's not good. So in Japan, I was experimenting the first remote machine monitoring system using the internet actually in 1995. So I published a paper in '98 about how to remotely use physical machine and cyber machine together. In fact, I want to say that's the first digital twin but as a cyber-physical model together. That was in my paper in 1998 in Journal of Machine Tools and Manufacture. TROND: So, in fact, you were a precursor in so many of these fields. And it just strikes me that as you're going through your career here, there are certain pieces that you seem to have learned all along the way because when you are a career changer oscillating between public, private, semi-private, research, business, you obviously run the risk of being a dilettante in every field, but you seem to have picked up just enough to get on top of the next job with some insight that others didn't have. And then, when you feel like you're frustrated in that current role, you jump back or somewhere else to learn something new. It's fascinating to me because, obviously, your story is longer than this. You have startup companies with your students and others in this business and then, of course, now with the World Economic Forum Lighthouse factories and the work you've been doing for Foxconn as well. So I'm just curious. And then obviously, we'll get to industrial AI, which is so interesting in your perspective here because it's not just the technology of it; it is the industrial practice of this new domain that you have this very unique, practical experience of how a new technology needs to work. Well, you tell me, how did you get to industrial AI? Because you got there to, you know, over the last 15-20 years, you integrated all of this in a new academic perspective. JAY: Well, that's where we start. So like I said earlier, I realized industry we did not have data back in the late 1990s. And in 1999, dotcom collapsed, remember? TROND: Yes, yes. JAY: Yeah. So all the companies tried to say, "Well, we're e-business, e-business, e-commerce, e-commerce," then in 2000, it collapsed. But the reality is that people were talking about e-business, but in the real world, in industrial setting, there's no data almost. So I was thinking, I mean, it's time I need to think about how to look at data-centric perspectives, how to develop such a platform, and also analytics to support if one-day data comes with a worry-free kind of environment. So that's why I decided to transition to an academic career in the year 2000. So what I started thinking, in the beginning, was where has the most data? As we all know, the product lifecycle usage is out there. You have lots of data, but we're not collecting it. So eventually, I called a central Intelligent Maintenance System called IMS, not intelligent manufacturing system because maintenance has lots of usage data which most developers of a product don't know. But if we have a way to collect this data to analyze and predict, then we can guarantee the product uptime or the value creation, and then the customer will gain most of the value back. Now we can use the data feedback to close-loop design. That was the original thinking back in the year 2000, which at that time, no cell phone could connect to the internet. Of course, nobody believed you. So we used a term called near-zero downtime, near-zero downtime, ZDT. Nobody believed us. Intel was my first founding member. So I made a pitch to FANUC in 2001. Of course, they did not believe it either. Of course, FANUC in 2014 adopted ZDT, [laughs] ZDT as a product name. But as a joke, when I talked to the chairman, the CEO of the company in 2018 in Japan, Inaba-san that "Do you know first we present this ZDT to your company in Michigan? They didn't believe it. Now you guys adopted." "Oh, I didn't know you use it." So when he came to visit in 2019, they brought the gift. [laughs] So anyway, so what happened is during the year, so we worked with the study of 6 companies, 20 companies and eventually they became over 100 companies. And in 2005, I worked with Procter & Gamble and GE Aircraft Engine. They now became GE Aviation; then, they got a different environment. So machine learning became a typical thing you use every day, every program, but we don't really emphasize AI at that time. The reason is machine learning is just a tool. It's an algorithm like a support-vector machine, self-organizing map, and logistic regression. All those are just supervised learning or now supervised learning techniques. And people use it. We use it like standard work every day, but we don't talk about AI. But over the years, when you work with so many companies, then you realize the biggest turning point was Toyota 2005 and P&G in 2006. The reason I'm telling you 2005 is Toyota had big problems in the factory in Georgetown, Kentucky, where the Camry factory is located. So they had big compressor problems. So we implemented using machine learning, the support-vector machine, and also principal component analysis. And we enable that the surge of a compressor predicted and avoided and never happened. So until today -- TROND: So they have achieved zero downtime after that project, essentially. JAY: Yeah. So that really is the turning point. Of course, at P&G, the diaper line continues moving the high volume. They can predict things, reduce downtime to 1%. There's a lot of money. Diaper business that is like $10 billion per year. TROND: It's so interesting you focus on downtime, Jay, because obviously, in this hype, which we'll get to as well, people seem to focus so much on fully automated versus what you're saying, which is it doesn't really, you know, we will get to the automation part, but it is the downtime that's where a lot of the savings is obviously. Because whether it's a lights out or lights on, humans are not the real saving here. And the real accomplishment is in zero downtime because that is the industrialization factor. And that is what allows the system to keep operating. Of course, it has to do with automation, but it's not just that. Can you then walk us through what then became industrial AI for you? Because as I've now understood it, it is a highly specific term to you. It's not just some sort of fluffy idea of very, very advanced algorithms and robots running crazy around autonomously. You hav…

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Episode 96: The People Side of Lean Sep 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. The topic is "The People Side of Lean." Our guest is Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talk about how to develop internal organizational capability and problem-solving skills on the frontline. If you liked this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 84 on The Evolution of Lean. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Follow the podcast on Twitter or LinkedIn. Trond's Takeaway: Lean is about motivating people to succeed in an industrial organization more than it is about a bundle of techniques to avoid waste on a factory production line. The goal is to have workers always asking themselves if there is a better way. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is the People Side of Lean. Our guest is Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talk about how to develop internal organizational capability, problem-solving skills on the frontline. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Jeffrey, how are you? Welcome to the podcast. JEFFREY: Thank you. TROND: So I think some people in this audience will have read your book or have heard of your book and your books but especially the one that I mentioned, Toyota. So I think we'll talk about that a little bit. But you started out as an engineering undergrad at Northeastern, and you got yourself a Ph.D. in sociology. And then I've been reading up on you and listening to some of the stuff on the musical side of things. I think we both are guitarists. JEFFREY: Oh, is that right? TROND: Yeah, yeah, classical guitar in my case. So I was wondering about that. JEFFREY: So I play also a classical guitar now. I played folk and rock earlier when I was young. But for the last more than ten years, I've been only studying classical guitar. TROND: Well, so then we share a bunch of hours practicing the etude, so Fernando Sor, and eventually getting to the Villa-Lobos stuff. So the reason I bring that up, of course, beyond it's wonderful to talk about this kind of stuff with, you know, there aren't that many classical guitarists out there. But you said something that I thought maybe you could comment on later. But this idea of what happened to you during your studies of classical guitar actually plays into what you later brought into your professional life in terms of teaching you something about practicing in particular ways. So I hope you can get into that. But obviously, you've then become a professor. You are a speaker and an advisor, and an author of this bestseller, The Toyota Way. Now you run some consulting. And I guess I'm curious; this was a very, very brief attempt at summarizing where you got into this. What was it that brought you into manufacturing in the first place? I mean, surely, it wasn't just classical guitar because that's not a linear path. [laughs] JEFFREY: No. So for undergraduate, I had basically studied industrial engineering because I didn't really know what I wanted to do with my life. And my father was an engineer. And then I literally took a course catalog and just started reading the descriptions of different kinds of engineering. And industrial engineering was the only one that mentioned people. And in theory, industrial engineering is a systems perspective which integrates people, materials, methods, machines, the four Ms. And in the description from Northeastern University, they said it's as much about human organization as it is about tools and techniques. So that appealed to me. When I got to Northeastern...I was not a particularly good high school student. So I didn't have a lot of choices of what colleges I went to, so Northeastern was pretty easy to get into. But they had a cooperative education program where you go to school, and you work. You go back and forth between school and work and had a pretty elaborate system for setting you up with jobs. I got one of the better jobs, which was at a company called General Foods Corporation at the time, and they make things like Jell-O, and Gravy Train dog food, and Birds Eye vegetables, and a lot of other household names, Kool-Aid, all automated processes, even at that time in the 1970s. And they had been experimenting with something called socio-technical systems, which is supposed to be what I was interested in, which is bringing together the social and technical, which no one at Northeastern University had any interest in except me. But I was very interested in this dog food plant where they were written up as a case study pioneer. And the basic essence of it was to give groups of people who are responsible, for example, for some automated processes to make a certain line of Gravy Train dog food, give them responsibility for all their processes, and they called them autonomous workgroups. And what we try to do is as much as possible, give them all the responsibility so they can work autonomously without having to go and find the engineer or deal with other support functions, which takes time and is kind of a waste. So that fascinated me. I studied it. I wrote papers about it even in courses where it didn't fit. But the closest I could get to the social side was through sociology courses which I took as soon as I was able to take electives, which was about my third year. And I got to know a sociology professor closely and ultimately decided to get a Ph.D. in sociology and did that successfully, published papers in sociology journals at a pretty high level. And then discovered it was really hard to get a job. TROND: Right. [laughs] JEFFREY: And there happened to be an advertisement from an industrial engineering department at University of Michigan for someone with a Ph.D. in a social science and an undergraduate degree in industrial engineering. And I was probably the only person in the world that fit the job. And they were so excited to hear from me because they had almost given up. And I ended up getting that job quickly then getting to Michigan excited because it's a great university. I had a low teaching load. They paid more than sociology departments. So it was like a dream job. Except once I got there, I realized that I had no idea what I was supposed to be doing [chuckles] because it wasn't a sociology department. And I had gotten away from industry. In fact, I was studying family development and life’s course development, and more personal psychology and sociology stuff. So I was as far away as I could be. So I had to kind of figure out what to do next. And fortunately, being at Michigan and also being unique, a lot of people contacted me and wanted me to be part of their projects. And one of them was a U.S.-Japan auto study comparing the U.S.-Japan auto industry going at the same time as a study at MIT and Harvard that ultimately led to the book The Machine That Changed the World, which defined lean manufacturing. So this was sort of a competitive program. And they asked me to be part of it, and that's what led to my learning about Toyota. I mean, I studied Toyota, Nissan, Mazda mainly and compared them to GM, Ford, and Chrysler. But it was clear that Toyota was different and special. And ultimately, then I learned about the Toyota Production System. And from my perspective, not from people in Toyota, but from my perspective, what they had done is really solve the problem of socio-technical systems. Because what I was seeing at General Foods was workers who were responsible for technical process and then were given autonomy to run the process, but there was nothing really socio-technical about it. There was a technical system, and then there was social system autonomous work groups and not particularly connected in a certain way. But the Toyota Production System truly was a system that was designed to integrate people with the technical system, which included things like stamping, and welding, and painting, which were fairly automated as well as assembly, which is purely manual. And Toyota had developed this back in the 1940s when it was a lone company and then continued to evolve it. And the main pillars are just-in-time and built-in quality. They have a house, and then the foundation is stable and standardized processes. And in the center are people who are continuously improving. Now, the socio-technical part the connection is that just-in-time for Toyota means that we're trying to flow value to the customer without interruption. So if what they do is turn raw materials into cars that you drive, then anything that's turning material into a component or car physically is value-added, and everything else is waste. And so things like defects where you have to do rework are waste. And machines are shut down, so we have to wait for the machines to get fixed; that's waste. And inventory sitting in piles doing nothing is waste. So the opposite of waste is a perfect process. And Toyota also was smart enough, and all that they figured out was more like folk learning or craft learning. It was learning from doing and experience and common sense. And they didn't particularly care about linking it to academic theories or learning from academic theories, for that matter. So their common sense view is that the world is complicated. Humans are really bad at predicting the future. So the best we can do is to get in the ballpark with what we think is a good process and then run it and see how it fails. And then the failures are what lead to then the connection of people who have to solve the problems through creative thinking. So that was the integration that I did not see before that. TROND: Just one thing that strikes me...because nowadays, comparing the U.S. or Europe and Asia in terms of business practices, it's sort of like, oh, of course, you have to compare them because they are culturally different. But it strikes me that in the automotive industry, was it immediately really clear to you at the outset that there would be such striking differences between the Japanese and the U.S. auto industry? Or is that actually something that had to be studied? Or was it something that was known, but no one really knew exactly what the differences were? JEFFREY: So it wasn't like the American auto companies figured out that if they get good at using chopsticks, they'll be good at making cars. They weren't looking for something peculiar in Japanese culture. But they were addressing the more general problem, which was that Japanese companies were making small fuel-efficient cars at low cost with high quality. And none of the American companies could do that. The costs were higher. The quality was terrible compared to Japan. They took a long time to do everything, including developing cars. So somehow, the Japanese were purported, they weren't convinced this was true, but according to the evidence, the Japanese were purported to be better at just about everything. And the Americans wanted to know why particularly. And at that time, there had been an oil crisis, and there was a demand for small cars. The real question they were interested in is how could they make small cars that were competitive with the Japanese? So they had to understand what the Japanese were doing. Now, they realized that some of what the Japanese were doing were purely technical things that had nothing to do with culture. And then there was also a level of attention to detail and motivation that maybe was, for some reason, peculiar to Japan. But they needed to figure out how to replicate it in the United States. And then, in addition to that, they had Americans like Dr. Deming, who had gone to Japan and taught the Japanese supposedly quality control methods. And Japanese companies had taken quality control methods that were created in the United States more seriously than the American companies. So part of it was relearning what came from America to Japan and got done better. So it wasn't necessarily this kind of strange place, and how can we emulate this strange culture? TROND: Right. But that becomes then your challenge then, right? Because what you then discover is that your field is immensely important to this because what you then went on to do is...and I guess part of your consulting work has been developing internal organizational capability. These are skills that particular organizations, namely Toyota, had in Japan. So you're thinking that this then became...it's like a learning process, the Japanese learned some lessons, and then the whole rest of the automotive industry then they were trying to relearn those lessons. Is that sort of what has been happening then in the 30 years after that? JEFFREY: Yeah, the basic question was, why are they so good? Why are we so bad? And how can we get better in America? Then there were lots of answers to that question coming from different people in different places. My particular answer was that Toyota especially had developed a socio-technical system that was extremely effective, that was centered on people who were developed to have the skills of problem-solving and continuous improvement. And while the study was going on, they were doing a study out of MIT that led to The Machine That Changed the World. And around that same time, a joint venture between Toyota and General Motors had been formed called NUMMI. It was in California. And in their first year, it was launched in 1983, and in the first year, they had taken what was the worst General Motors plant in the world, with the worst attendance, the worst morale, workers who were fighting against supervisors every day, including physically fighting with them, terrible quality, and General Motors had closed the plant because it was so bad. And then, in the joint venture, they reopened the plant and took back 80% of the same workers who were like the worst of the worst of American workers. And within a year, Toyota had turned the plant around so that it was the best in North America with the best workers. TROND: That's crazy, right? Because wouldn't some of the research thesis in either your study or in the MIT study, The Machine That Changed the World, would have to have been around technology or at least some sort of ingenious plan that these people had, you know, some secret sauce that someone had? Would you say that these two research teams were surprised at finding that the people was the key to the difference here or motivating people in a different way? JEFFREY: Well, frankly, I think I probably had a better grasp that people were really the key than most other researchers because of my background and my interest in human-centered manufacturing. So I was kind of looking for that. And it was what the Toyota people would say...whenever they made a presentation or whenever you interviewed them, they would say, "People are kind of distracted by the tools and methods, but really at the center are people." And generally, most people listening to them didn't believe it, or it didn't register. Because Toyota did have cool stuff, like, for example, something called a kanban system, which is how do you move material around in the factory? Th…

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Episode 95: Smart Manufacturing for All Aug 31, 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 episode 17 of the podcast (@AugmentedPod), the topic is: Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.After listening to this episode, check out CESMII as well as John Dyck's social profile:CESMII: (@CESMII_SM) https://www.cesmii.org/ John Dyck: https://www.linkedin.com/in/johnsdyck/ In this conversation, we talked about democratizing smart manufacturing, the history and ambition of CESMII (2016-), bridging the skills gap in small and medium enterprises which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms and controls to radically impact manufacturing performance. We then have the hard discussion of why the US is (arguably) a laggard? John shares the 7 characteristics of future-proofing (interoperability, openness, sustainability, security, etc.). We hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade.Trond's takeaway: US manufacturing is a bit of a conundrum. How can it both be the driver of the international economy and a laggard in terms of productivity and innovation, all at the same time? Can it all be explained by scale--both scale in multinationals and scale in SMEs? Whatever the case may be, future proofing manufacturing, which CESMII is up to, seems like a great idea. The influx of smart manufacturing technologies will, over time, transform industry as a whole, but it will not happen automatically.Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 8 on Work of the Future, episode 5 Plug-and-play Industrial Tech, or episode 9 The Fourth Industrial Revolution post-COVID-19. Augmented--the industry 4.0 podcast. Transcript: TROND: Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. In Episode 17 of the podcast, the topic is Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute. In this conversation, we talked about democratizing smart manufacturing, the history, and ambition of CESMII, bridging the skills gap in small and medium enterprises, which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms, and controls radically impact manufacturing performance. We then have the hard discussion of why the U.S. is, arguably, a laggard. John shares the seven characteristics of future-proofing. And we hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade. Augmented is a podcast for leaders hosted by futurist, Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday. Augmented — the Industry 4.0 podcast. John, how are you today? JOHN: I'm well, Trond. How are you? TROND: I'm doing well. I'm looking forward to talking about smart manufacturing. What brought you to this topic, John? We'll get into your background. But I'm just curious. JOHN: This is my favorite topic, as you probably know. So I appreciate the chance to pontificate a little. I've been at this nexus between IT and OT for the last two decades of my career or more and found over these past two decades that this is one of the most complex pieces of manufacturing period, this sort of unique challenge between the world of operations and the world of IT. And the work I did at MESA (Manufacturing Enterprise Solutions Association) on the board and as the chairman of the board exposed me to a lot of the great vendors in this ecosystem. And through that work, I found that most of them struggle with the same things. We're all struggling in different ways. And so the opportunity to take one step back and look at this from a national and a global perspective and try to find ways to address these challenges became a very unique opportunity for me and one that I've enjoyed immensely. And so just the prospect of making a real difference in addressing these challenges as a nation and as an ecosystem has been just a privilege and one that I get really excited about. TROND: So, John, you mentioned your background. So you've worked in both startups...I think you were raising money for a startup called Activplant, but also, you have worked in large manufacturing for GE and Rockwell, so the big guys, I guess, in a U.S. context for sure. When this institution, C-E-S-M-I-I, CESMII, got started, what was its main objective, and what was the reason why this institution got launched? I guess back in 2016, which is not an enormous amount of time back. Give us a little sense of who took this initiative. And what is the core mission of this organization right now? JOHN: So Manufacturing USA is the umbrella organization under which these institutes, CESMII being one of them, were created. There are a total of 15 of these institutes, all funded with the exact same business model and funding model, and each of them having a different lens on the specific manufacturing problem that they're addressing. And ours, as the Smart Manufacturing Institute, is directly focused on creating a more competitive manufacturing environment by addressing innovation and research challenges that inhibit manufacturers from doing what they need to do in this fourth industrial revolution. So our mandate is to cut the cost of implementing smart manufacturing by 50%. Our mandate is to drive energy productivity, energy efficiency. Fundamentally, the agency that funds CESMII is the Department of Energy, which means that our overarching objective is to drive energy productivity as a basic metric. But we also believe that whether that's a direct challenge meaning addressing energy, performance energy efficiency directly, or an indirect outcome from a more efficient process, or a more effective supply chain, whatever that manufacturing initiative is, that we'll create a better product, a better process that will have direct and indirect impact on energy productivity, which is the connection back to our agency and the source of the funding that we have to accomplish these really important goals. TROND: And one of the really big identified gaps, also it seems, is this discrepancy between the big and the small industry players. So small and medium enterprises famously in every country is basically...the most of industry is consisting of these smaller players. They're not necessarily startups. They're not necessarily on this growth track to become unicorns. But they are smaller entities, and they have these resource constraints. Give me a sense of what you're doing to tackle that, to help them out, and to equip them for this new era. And maybe you could also just address...you called smart manufacturing industry 4.0, but I've noticed that that's not a term that one uses much. Smart manufacturing is kind of what you've opted for. So maybe just address that and then get to the small and medium-sized. JOHN: This is, I think, one of the really important observations that we try to make and the connections that we try to make to say that the status quo, the state of the industry today, Trond, is the result of three or four decades of what we did during the third industrial revolution. We began talking about the fourth industrial revolution many years ago. But we can't just turn that light switch on and assume that overnight everything we do now, despite the cultures we've created, the technologies we've created, the ways of doing things we've created, is now all of a sudden just new and exciting and different, and it's going to create that next wave of productivity. So when I talk about smart manufacturing and equating it with the fourth industrial revolution, it's truly the characteristics and the behaviors that we anticipate more so than what we're seeing. Because the critical mass of vendors and systems integrators, application and software products in this marketplace still resemble more of industry 3.0 than they do industry 4.0. And it's part of our vision to characterize those two only in the context of trying to accelerate the movement towards industry 4.0 or the fourth industrial revolution. Because it's that that holds out the promise of the value creation that we've been promised for ten decades but really aren't seeing. So that's the way we see the industry 4.0 versus the other concepts that we talk about. Digital transformation is another important term. All of that happens in the context of some initiative in a manufacturing operation to improve. We've been improving for three or four decades. What's different today? Well, it's not just relabeling [laughs] your portfolio to be industry 4.0 compliant. So anyway, that's a pet topic of ours just to help as a national conversation, as a set of thinking and thought leader organizations and individuals to put the spotlight on that and ensure that we're doing the things that we can to accelerate the adoption, and the behaviors, and the characterizations of what it really means to be industry 4.0. So to your point -- TROND: Yeah, I was just curious. The term revolution anyway is interesting in a U.S. context [laughter] and in any society. So it implies a lot of things, but it also certainly implies a speed that perhaps isn't necessarily happening. So there's all this talk now about how things are speeding up. But as you point out, even if they have some revolutionary characteristics, at the edge, there are some other things that need to happen that aren't necessarily going to happen at the speed of what you might imagine when you use the word revolution. It's not going to turn over like a switch. JOHN: That's exactly right. Well said, Trond. Manufacturing and bleeding edge never come together in the same sentence, and so it takes time for...and more so on the OT side than the IT side. Right out of the IT world, we have industrial IoT platforms. We have augmented reality. We have powerful AI machine learning tools. But what is the true adoption on the plant floor? Well, that's where the behaviors, and the cultures, and the characteristics of how we've always done things and the reluctance to adopt new things really comes in. And it's as much a part of the vendor and systems integration ecosystem as it is on the manufacturing side. And that's, again, this whole thing becomes...to drive (I really don't think it's a revolution to your point.) an evolution or accelerate the evolution towards Industry 4.0 requires the ecosystem to get engaged and to recognize these really important things have to change. Does that make sense? TROND: Yes. A lot of them have to change. And then to these small and medium enterprises, so I've seen a statistic that even in the U.S., it's around 98% of manufacturing. That is an enormous challenge, even for an association like yours. How do you reach that many? JOHN: Here's an interesting epiphany I had shortly after I came to CESMII and was working through exactly this challenge: how does an organization like ours access and understand the challenges they face and then look at the ecosystem that's there and available to serve them? The epiphany I had was that in my entire career with both big global corporations like Rockwell Automation and General Electric and specifically even the startup organization that I helped raise VC for and venture capital funding for and build and ultimately see acquired; I had never been in a small and medium manufacturing plant environment. The entire ecosystem is focused on large brands, recognized brands, and enterprises that have the potential for multisite rollouts, multisite implementation. And so the business models, the marketing models, the sales, the go-to-market, the cost of sales, everything in this ecosystem is designed towards the large enterprises called the Fortune 1000 that represent the types of characteristics that any startup, any Global Fortune 500 organization is going to go pursue. Which then says or leaves us with a really important conversation to say, how can the small and medium manufacturing organizations become part of this dialogue? How can we engage them? What does an ecosystem look like that's there to serve these organizations? And where an implementation organization like a good systems integrator can actually make money engaging in this way. And so that's where the needs of that ecosystem and our specific capabilities come together. The notion that democratization which is going to help the big manufacturers, and the big vendors, and the big integrators, and the big machine builders, the same things that we can do to cut the cost of deploying smart manufacturing for them, will enormously increase the accessibility of smart manufacturing capabilities for the small and medium manufacturers. And so that's where typically -- TROND: John, let's talk specifics. Let's talk specifics. So smart manufacturing, you said, and I'm assuming it's not just a community effort. You're intervening at the level also of providing a certain set of tools also. So if we talk about sensors, and data, and platforms, and control systems, these are all impacting manufacturing performance. To what extent can an association like yours actually get involved at that level? Is it purely on the standardization front, sort of recommending different approaches? Or is it even going deeper into layers of technology and providing more than just recommendations? JOHN: So the short answer is it depends on the domain, and the area of networking, and sensors and controls. Those are areas where longer-term research and investment to drive innovation to reduce the cost of connecting things becomes really important. And that's one of the threads or one of the investment paths that we pursue through what we call roadmap projects where there are longer, larger in terms of financial scope and further out impacts. We're hoping we'll have a dramatic impact on the cost of connecting machines and sensors and variable-frequency drives and motion systems or whatever sort of data source you have in an operation. So that's one track. The other piece which gets to the actual creation of technologies is more on the data contextualization, data collection, data ingestion side. And you mentioned the word standards. Well, standards are important, and where there are standards that we can embrace and advocate for, we're absolutely doing that. Part of the OPC Foundation and the standards that they're driving, MQTT and Sparkplug, becomes a really important area as well. And the work that MTConnect is doing to solve many of the same challenges that we believe we need to solve more broadly for a subset of machine classes more in a CNC machine tool side. But this effort, smart manufacturing, is happening today, and it's accelerating today. And we can't wait for standards to be agreed on, created, and achieve critical mass. So we are investing in a thin but vital layer of technologies that we can drill into if you'd like as a…

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Episode 94: Digitized Supply Chain Aug 24, 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 episode 43 of the podcast (@AugmentedPod), the topic is: Digitized Supply Chain. Our guest is Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT, Johnson & Johnson.In this conversation, we talk about why J&J puts operators at the center of its strategy, the empowerment effect of frontline operations apps, the evolution of personalized production, and how supply chain becomes an integral part of product development.After listening to this episode, check out J&J as well as Arun Kumar Bhaskara-Baba's social medial profile: J&J (@JNJNews): https://www.jnj.com/ Arun Kumar Bhaskara-Baba: https://www.linkedin.com/in/bhaskarababa/Trond's takeaway: "Operators are the key to the next phase of industrial evolution, that which involves the deep digitalization of manufacturing, its supply chain, production capacity, personalization, and with that the reinvention of factory production itself.Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 21, The Future of Digital in Manufacturing, episode 27, Industry 4.0 Tools or episode 10, A Brief History of Manufacturing SoftwareAugmented--conversations on industrial tech. Transcript: TROND: Augmented reveals the stories behind a new era of industrial operations, where technology will restore the agility of frontline workers. In Episode 43 of the podcast, the topic is Digitized Supply Chain. Our guest is Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson. In this conversation, we talk about why J&J puts operators at the center of its strategy, the empowerment effect of frontline operations apps, the evolution of personalized production, and how supply chain becomes an integral part of product development. Augmented is a podcast for leaders hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday. Augmented — the industry 4.0 podcast. TROND: Arun, how are you? ARUN: I'm doing great. How are you, Trond? TROND: Oh, it's wonderful to see you and hear you. I'm very excited. This is a big interview. You have really big responsibilities, Arun. We're going to get to that in a second. But global manufacturing that is a wide, wide topic. ARUN: Yes, indeed. But the bigger responsibility, but more importantly, what we are privileged is how we are impacting the lives of patients and customers around the world with our products. That comes with the privilege to work in the healthcare environment. TROND: Well, I'm glad you said that because as we're sort of tracing, I want to ask you a little bit about how you got to where you are. And I know from public records, at least, that you have part of your schooling in India. So you grew up in India, my assumption is, and you got your computer degree there. You worked in India for a little while for the Tata system. And then you made your way over to Michigan. You have your MBA from there. And then, from what I understand, you then had a bit of a career in automotive and then moved on to Dell. And this brings us to J&J. How did you end up in the U.S.? And how was that journey for you? You've come quite a bit of ways. ARUN: Yes. It's interesting that you asked how I ended up in U.S. For me, it was a choice of either going to Japan or to U.S., And I'm a vegetarian, so for me, U.S. was a better choice. Growing up when you're a kid, you have two years of experience, the decisions that you make, some priorities. TROND: That's funny, but you told me, Arun, that you came here with a briefcase and a $10 bill. ARUN: Yes. I was going to go -- TROND: That's, I guess, not an unusual immigrant story, but it is still quite striking. ARUN: Absolutely. I grew up in a very small middle-class family. So when I landed, I landed with a briefcase and a $20 bill, actually two $10 bills. And out of that, one $10 bill I still have as a reminder of where I started. TROND: Wow. And I cut your career a little short because you have had the opportunity to work in all of the BRIC countries, essentially. And you now manage teams across, I think, at least 28 countries. And that brings us, I guess, up to present day where I was alluding to this, but you have a very wide responsibility. We're going to talk about some of it. Can you tell me a little bit about your current role? ARUN: So, my team supports all the manufacturing operations for J&J across the globe. So we have 100-plus manufacturing plants in pharmaceutical, consumer, medical devices, and vision products. As I mentioned earlier, I am privileged to be in healthcare to serve our patients and customers. We are in 28 countries; my team is spread across. And it's a very humbling experience to really work in a global team and continue to support our operations across the world. TROND: Well, not only that 28 countries, but I understand you operate about 100 manufacturing sites, some obviously state of the art, very big and sprawling, others actually very small or at least mid-size and have all kinds of other issues. And J&J, you know, what is the breadth of products you make? I mean, you make vaccines. You make knees, artificial knees. What else do you guys make? ARUN: This is amazing. I used to work for Ford Motor Company and Dell. Definitely, they are also very strong in manufacturing. However, the manufacturing processes are very similar. It is either assembly process, marketing and manufacturing at Dell. I come to J&J, and any type of manufacturing, you say we got it. Whether you talk about process manufacturing or discrete manufacturing, we have that. So in the pharmaceutical area, we produce biological products where we actually grow live cells and make medicine out of it, as you mentioned, the vaccines and biological products. We also have big chemical products where we actually use big chemical reactions to produce the drugs. In medical devices, we have artificial knees and hips, which are more like a foundry operation. You take a mold, you put it in an artificial knee, and make it happen. And we have sutures that we produce. And in the consumer side, we have different types of liquids, gels, and tablets that we produce. And finally, in vision care is where we produce our lenses in a very high-velocity manufacturing. So if you look at the breadth of the manufacturing processes and products we support, we support almost every aspect of manufacturing. TROND: Well, this brings us to today's topic because we're going to talk a little bit about digitizing these operations, the supply chains, the whole thing, and think about what digital means to all of it, whether it's in pharma 4.0, or indeed, you know, manufacturing and industry 4.0. Can you maybe just kick us off a little bit and say what does digital mean to your business today? And what is your main take on how to approach it? ARUN: The first thing is really I see digital as a means to an end. So if you think about it, it's really why digital is the first and then why digital. We need to be very clearly understanding why we want to digitize. We are in the journey to transform our supply chain so that we can put our patients, our customers at the center of the supply chain and how we can get our products to our customers in a fast, nimble way and in an affordable way. If you think about healthcare, the key is affordability as well as the ability for us to deliver what they need where they need it. And if you think about even the vaccines that we are producing now, we are manufacturing only in some locations, but we have to distribute them everywhere, whether to sophisticated networks like U.S. or developing areas where we don't even have a lot of transportation like Africa. So how do we put the customer and the patient at the center? And how can we actually serve them in a much more faster way and in an affordable way? So that is the why behind our supply chain journey. And digitization is a very critical component of that transformation. How do we provide that end-to-end connectivity so that we can reach our customers and patients? How do we understand what is happening in the markets and react to those things quickly as well as respond quickly using digital? And then ensure that we are delighting our customers beyond just our products, that we have world-class products. But how do we make sure that we are delivering the same customer experience to our patients and customers? So for us, the work from the digital side is how do we build that end-to-end connectivity so that we can reach our customers and we can sense and respond very quickly? And finally, how do we make sure that we significantly improve your customer experience? TROND: I want to pick up on a couple of things, but let me first ask a basic question. I mean, when I think supply chain, I think back to business school where I was teaching for a while, and I think kind of a fairly dry subject that was a specialty subject. You either cared about it, and then you wanted to become an expert and obviously dominate the field. But now you're speaking of it as if it is a much more integrated part of product development, which I think that was certainly taught as two separate courses, even in the very immediate past. But do you think of the supply chain as completely integrated with what you do, what you produce? ARUN: Absolutely. If you think about where the healthcare is headed, if you think about personalized healthcare, if I'm taking a knee right now, we ship like six or seven knees to the surgeons so that they pick the right knee during the operation. And we are getting to a place where we take the picture of the knee, get it back, and make the product, and then 3D print it and give it to the surgeon. Or if you think about how we are personalizing where we are taking the blood from the patient and making the product that is very specific to the patient and shipping it to them. So this whole flow of here is my R&D, and then it goes to supply chain, and then we deliver it versus it is now becoming a connected world where this all comes together. So it's really a very integrated part of product development and supply chain. So we really look at that end to end. And then digital is the one that is actually accelerating that journey. Because I can now connect all of these things as a digital thread and then really push the envelope forward. TROND: But producing for a batch of one, I mean, it's enormously challenging at scale, no? ARUN: Yeah, absolutely. That is the trick, right? How do I produce that batch of one? And if you think about the future, where we can actually get to that and where we can produce batch of one for almost everything that we do is where we are headed. You're right; there are significant investments in terms of our manufacturing operations and the equipment that we need. And there is that balance between the scale that you need to have versus the personalization that is needed. And the balance is I don't think the pendulum can go either one way or the other. But really, we still have a lot more to move to the personalized level. How do we really become a full supply chain so that we can produce that batch of one wherever possible? And look at that from the customer and patient's angle, right? If you have somebody who has a traumatic surgery going on and they have a bone that we need to fix...and it is not the same from one trauma to another trauma. There you can't come back and say, okay, here is a batch of things that I'm producing, and I'm going to give it to you. So the customer expectations are also changing. As a patient and as a consumer, their expectations are also changing. And so we are moving to that batch of one. And how do you do it for different products? And how do you do it for different manufacturing processes is going to be tailored to that business model and then the product. TROND: So another thing that one might assume when we speak about this, because okay, batch of one, but it has to be an advanced system, and it's covering the globe. I mean, historically, if a factory has machinery or systems and digital technologies, it is a very monolithic, massive system. I understand that you have taken at least some care these days to focus on the operators. Why is that so crucial to you? And what does that mean for the kinds of technologies that you're putting into your factories nowadays? ARUN: So that's a very good question. If you think about where manufacturing is headed so that we can drive that flexibility, that approach so that we can quickly respond, we have to relook at our manufacturing operations. That means they need to be a lot more nimbler and a lot more flexible. And a lot of technologies are emerging, and that's all driving. But for us, at the end of the day, it all comes back to that operator. We are here to serve the operator. We call it #operatorrules. Because think about this, we can do all these flexible things. We can bring in automation. We can bring in robots and all of it. At the end of the day, there is an operator at the line who is making it happen. So how do we make sure that we put the operator at the center and then create the experience for the operator so that it makes it a lot easier? If you take any of our plants, the technology is growing very fast. We used to have an ERP system. The operator has to deal with an MES. The operator then has to look at the equipment interface that the equipment provider has given. Now I'm coming from technology and saying, okay, here is the smart glass. Wear the smart glass, and you can look at everything. Think about the operator, how complex we have made the operator's life. So we are trying to take a step back and say, how do we, first of all, make it simple? Number two is how do we empower them? So far, we all said that, oh, technology is either manufacturing engineering or the OT or IT people. We held the keys for the technology. But how do we really empower the operators so that they can make it flexible and then they can make it nimble? So that gives you the velocity that we need at our manufacturing operations. TROND: It's striking when you think about at least digital technologies now clearly. There have been machines in factories for centuries. I mean, that was sort of the various industrial revolution. So there have, of course, been machines that could be operated by operators to some degree. But the kind of control and the detail-level customization that's now becoming possible doesn't come naturally, does it? It takes a lot of attention to create those kinds of platforms. How do you see that evolving? For example, we said you have over 100 different sites, some of them large, others much smaller; what sort of approaches are you taking to experiment with these solutions? ARUN: So it's purpose-driven experimentation. Because to your point, when we have these large, fully automated factories, the key is how fast I can introduce new capabilities into that operation. Whereas when I go to a middle-tier factory with semi-automated or not as much automated, it is a very target problem-driven. I have an OEE problem. Let me figure out how do I experiment to bring the technology. But at both the spectrums, the key is to make sure that there is a good, r…

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