Vijay shares an inside look into how electronics supply chains and manufacturing practices have evolved over his 35+ year career, the rise of EMS and ODM business models, and how the semiconductor industry is shifting in response to global pressures.

The discussion also explores best practices for building a culture of continuous improvement, balancing humans with automation, and adapting management approaches to local cultures.

Augmented Ops is a podcast for industrial leaders, citizen developers, shop floor operators, and anyone else that cares about what the future of frontline operations will look like across industries. This show is presented by Tulip, the Frontline Operations Platform. You can find more from us at Tulip.co/podcast or by following the show on LinkedIn.

Special Guest: Vijay Chinnasami.

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

If you like this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 100: Innovating Across the Manufacturing Supply Chain. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

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

Special Guest: Jane Arnold.

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

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

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

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

Transcript:

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

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

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

Jim, welcome to the show. How are you?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

JIM: Yeah, I totally agree with you.

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

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

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

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

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

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

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

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

Special Guest: Jim Heppelmann.

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

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

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

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

Transcript:

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

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

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

Christopher, how are you? And welcome.

CHRISTOPHER: Hi, Trond. How are you?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

TROND: Let's do it.

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

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

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

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

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

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

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

TROND: [laughs]

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

TROND: And how do you disprove this?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CHRISTOPHER: That's right.

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

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

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

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

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

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

[laughter]

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

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

CHRISTOPHER: Did you say five years or 50 years?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CHRISTOPHER: Exactly.

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

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

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

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

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

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

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

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

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

CHRISTOPHER: It's my pleasure.

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

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

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

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

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

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

Special Guest: Christopher Nguyen.

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

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

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

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

Transcript

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

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

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

MICHEL: Fine, thank you. How about yourself?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

And so there was not this kind of tension due to people losing their jobs and not having an alternative. And then, the transition was announced way ahead of time, so they had something like a 15-month period to plan for their transfer. And to my great surprise, the operators in the old plan were perfectly...were very helpful in figuring out the design for the new lines and contributed ideas. And there was no resentment of that situation.

TROND: In this particular example and in other examples, to what extent is production, you know, process redesign a technology challenge, and to what extent is it a human workforce challenge? Or do you not separate the two?

MICHEL: I try not to separate the two because you really have to consider them jointly. A technical solution that nobody wants to apply is not going to be helpful. And something everybody wants to apply but that doesn't work, is not going to be helpful either. So you have to consider both. And in this transition, by the way, between these two plants, most of the labor difficulties were in the new plant, not in the old one, because this plant became a section of the new plant. And none of the other lines in that new plant did anything similar, so it stood out as being very different from what all the other lines did.

What all the other lines did is you had a structure that is common in electronics assembly where you have rows of benches at which people sat and did one operation, and then the parts were moved in batches between these rows of benches. And instead of that, we put cells where the parts moved one at a time between different operations. And it was also organized so that it could be expanded from the current volume of work to higher volume of work. And so a lot more went into the design.

I was a consultant there, but I don't claim credit for the final design. It was the design of the people from the company. They actually got a prize within the company for having done something that was exceptionally good. And when I spoke with them a few years later, they had gone from having something like 20% of the space used for production in the new facility to having it completely full because they were able to expand this concept.

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TROND: Michel, I know that you have a consulting life and a consulting hat, but you also have a teaching hat and a teaching passion. Why did you write this recent textbook which is coming out on Routledge this fall, I believe, with Torbjø Netland from ETH? It's an Introduction to Manufacturing but with a very specific kind of industrial engineering perspective.

You told me when we talked earlier that there's a really specific reason why you wrote this textbook, and you have some very, I guess, strong views or worries about how manufacturing education, but perhaps the way it's taught really needs to change. And you feel like some schools are drifting away from the core. What's happening there?

MICHEL: Well, industrial engineering as a discipline is about 100 years old, take or leave a decade or two. It started out as...the way I describe it is the engineering of human work in the manufacturing environment. And it expanded to fields other than manufacturing, even at the time of pioneers like Frank and Lillian Gilbreth.

For example, we know the way operating rooms in hospitals work with the surgeon being assisted by nurses who hand all the tools to the surgeon; that particular form of organization is due to Frank and Lillian Gilbreth, industrial engineers who looked at the way operating rooms worked and figured that you really don't want to leave a patient with his belly open on the table while the surgeon goes to fetch the tool. You got to have some people giving the tools to the surgeon so that the surgeon can keep operating on the patient.

It sounds obvious now, but it wasn't obvious in 1910. And so they were immediately some applications outside of manufacturing, but the bulk of the work was on manufacturing. And the way it's evolved, especially in the past few decades, is that it's gotten away from that focus on human work. And when you look at the research interests of the academics in this field, you find that it's completely dominated by operations research and math.

TROND: So we're back to the math. [chuckles] So I find it fascinating that...well, you obviously have a deep insight into it, so you are sensitized to the challenges of overfocusing on one technical discipline as kind of the mantra and the fodder, I guess, the research data for all kinds of processes. I mean, why is math such a big problem, and what do you mean by human work in industrial manufacturing? Because to many people, the advanced work right now is about digitization, digitalization, and it has to do with machines and computers, and one would assume with big data or at least with data. Are you arguing against that trend?

MICHEL: No. I mean, if you ask the question of what is human work? The classical answer that I would give is what happens when the guy picks up the wrench. That's one answer. But what happens when the operator sees an alarm message on the control screen of a machine, that's a different answer, a more modern answer. So you had people with the torque wrench applying the right torque to a bolt manually, and then the torque wrench would tell him when the torque was achieved. That's one form of human work.

But monitoring and looking after multiple machines that are connected and have a central control system is also human work. You also have people doing it. And they have to feed these machines. They have to make sure that the machines have the right kinds of tools and dyes available to them. They have to maintain these machines. They have to program these machines, and they have to monitor them during production. And one particular problem with automatic systems is micro stoppages. Are you familiar with that term?

TROND: Well, explain it to all of us, micro stoppages. I mean stoppages, obviously, anything that stops the production line, whether it's a minor, major, I mean, that would be what I think you are saying.

MICHEL: Well, if it's a big problem, the operator doesn't solve it. The operator calls maintenance, and maintenance sends somebody to solve it. Micro stoppage is a problem that's small enough for the operator to deal with. And so, in daily life or in any office life, one very common micro stoppage problem is the copier, right? You tell the copier to print 20 collated copies of a document, and you walk away expecting to find these 20 copies ready when you come back. It doesn't happen because there are some paper jams and so you have to clear the paper jam and restart.

You have a lot of things like that in production where parts jam and shoots and stop coming down in automatic system. You have all sorts of issues like this which cause production lines to stop in a way that the operator can resolve in half a minute or a minute and restart. What these things cause is that you have to have an operator there.

And so if you really want to have an automatic system that are fire and forget...when you press a button, you move away to do something else while the machine goes through an automatic cycle. When that automatic cycle is finished, you come back. Micro stoppages prevent you from doing that. And they're very difficult to avoid, but they're a major problem, even today.

TROND: Michel, I wanted to keep talking about the educational part. But before that, I just wanted to benefit from your experience here and ask you a much more basic question which is so you're writing this textbook about the future or introducing prospective students to industrial engineering and manufacturing.

My question is, historically, factories were a very, very big part of manufacturing. Nowadays, meaning in the last few years after the pandemic and other things, a lot of us start to spend a lot more time on an issue, which I'm assuming you have spent a lifetime working on as well, which is supply chain which goes far beyond the factory because it's not located in any one factory, if anything, it's a system of many factories, and it's obviously the supplies of material flows into the factory.

And the reason I'm asking you about this is in thinking about the future, which I'll ask you about in a second, a lot of people are sort of factory of the future, this and that. And there are visions about how this is going to change. But it strikes me that manufacturing is and has always been so much more than the factory. What are the components that you really worry about? So, humans, you worry about humans. And you worry about materials. And then you obviously have to worry about the physical infrastructures that are regulating these things. What else goes into it on the macro level? What is this book about, I guess?

MICHEL: We're talking about supply chains as well because, as you mentioned, they're a very important part of manufacturing. And when you design a manufacturing system to make a product, you have to make decisions about your products, about components of your product, and what you make in-house, and what you buy from the outside.

And there's a major difference between supply chain issues relating to customers, on one hand, the suppliers on the other. It's not just suppliers; it's both sides, incoming supply chain and the outgoing as well. One major difference with what happens in the factory is that you don't control what other people decide, what other organizations decide. So when you manage a supply chain, you have to manage a network of organizations that are independent businesses.

How do you get this network of independent businesses to work with you, to cooperate with you, to make your manufacturing successful? That is a big challenge in supply chain management. Inside a factory, that's an environment you control. It's your organization. What management says is supposed to go; it doesn't always, but it's supposed to go. And you have a lot more control over what happens inside than over what happens in the supply chain.

And how much control you have over what happens in the supply chain depends greatly on your size. For example, if you're a small customer of a special kind of alloy that only has one manufacturer in the world, you're a very small customer to a very large manufacturer, a metals company. You're not in a position of strength to get that supplier to work with you.

If you're a car company making 10 million cars a year and you're dealing with a company that is making forgings for engine parts, you have a lot of control. You have a lot of influence. You represent a large part of their business. They can't afford to lose you. You can't afford to lose them. You can replace them if they don't perform. They can't afford to lose you. They might go out of business if they did. So it's a very different kind of position to be in.

And so when you deal with that sort of thing, you have to think through, what is my position with respect to suppliers and customers? Where is it? Where's the driving influence? And it's not always...power in a supply chain is not always resident with the company that does the final assembly of consumer products. In electronics, for example, semiconductor manufacturers are much more key than people who assemble computers.

TROND: I wanted to ask you a little bit about the trends and how these things are evolving in the next decade and beyond that. And one example you gave me earlier when we talked was pilots and jetliners because manufacturing in...well, the aviation industry is an example of an industry that, yes, it has an enormous amount of high tech. It's a very advanced science-based development that has produced air travel. But yet these pilots...and I experienced it this summer, a pilot strike stops everything.

So the role of people changes as we move into more advanced manufacturing. But people don't always disappear. What do you see as the biggest challenge of manufacturing and the role of manufacturing in the emerging society? What is going to happen here?
MICHEL: What I think is going to happen is that in many countries, the manufacturing sector will remain a large part of the economy, but as economies advance, it will have a shrinking share of the labor market. So it's a distant future, maybe like that of agriculture, where 2% of the population does the work necessary to feed everybody else.

And manufacturing is now about 10% of GDP in the U.S., 20% in Germany and Japan, about 10% in England, France, Italy. In China, we don't really know because they don't separate manufacturing from industry. And industry is a broader category that includes mining, and it includes road construction, et cetera. They don't separate out manufacturing, but really, it's a big sector of the economy.

And so it can remain a big sector, that's not a problem. But you have to think through a transition where the number of people that you employ doing this kind of work goes down, their level of qualifications go up, and the nature of the work they do evolves towards telling machines what to do and maintaining machines. So telling machines what to do can be programming machines when you develop processes, or it can be scheduling what work the machines do.

TROND: Is that incidentally why you have gone into teaching in a kind of an academic setting or at least influencing curriculum in an academic setting so much that you see a role here in the future? Beyond what's happening in factories today, you're quite concerned about what might happen in factories ten years from now, 20 years from now when these students become, I guess, managers, right? Because that's what happens if you get education in management at a good school, reading your hopefully great textbook. It takes a little time because you trickle down and become a manager and a leader in industry.

So I guess my question then is, what is it that you want these people to know ten years from now when they become leaders? What sort of manufacturing processes should they foster? It is something where humans still matter for sure, and machines will have a bigger part of it. But there's things we need to do differently, you think?

MICHEL: The airline pilot metaphor, you know, you have this $300 million piece of equipment. And how much money you make from operating it depends on these two people who are in the pilot's cabin. You have to pay attention to the work of people. And in most factories, the work of people today is an afterthought. So you put in machines. You put in production lines without thinking how will people get from the entrance of the building to where they actually work?

TROND: I was going to say it's a fascinating example you had with the airline industry in the sense that, I mean, honestly, even in the old industrial revolution, these machines were expensive, but I guess even more so. I don't know if you've done any research on this, but the amount of dollars invested per worker presumably has to go up in this future you are talking about here where we're increasingly monitoring machines, even these perhaps in the past viewed as low-skilled jobs or operator jobs.

I mean, you are operating, maybe not airplanes, but you're operating industrial 3D printers that cost hundreds of thousands of dollars with presuming error rates that could be catastrophic, either for you, for the production line, or for the product you're making.

MICHEL: Or photolithography machines that cost millions.

TROND: Right. But then that begs the question for me, Michel, how on earth is it possible? If you are right about this that education has been somewhat neglected and skills has been neglected, how's that even explainable? If you are a responsible factory manager or executive of a large manufacturing firm, how could it have gotten...and I'm obviously paraphrasing here. I don't know if you think it's that bad. But how could it get this bad that you actually had to come out and say it's a massive problem?

MICHEL: What happens is that you hear a lot about systems thinking, which, to me, it's pretty obvious there's more to a factory or more to a manufacturing system, to supply chain than the collection of its components; it's pretty obvious. And when you change the way a supplier delivers parts, it has an impact over what happens at the assembly workstations where these components are being used, for example.

You have to think of the whole as a system. And you have to think about whenever you make any changes to it; you have to think through how these changes affect the whole. What's happening is that there has been a great deal of specialization of skills; I'm not talking about factory workers here. I'm talking about engineers and managers that have been put into silos where they run production control. They become production control manager in the factory. Their next career move is to become production control manager in the factory of a different company.

TROND: So here's my open-ended question to you; you're sort of saying that industrial engineering, in one sense, needs to go back to its roots where it was. But the other side of the coin here is you're also talking about a world that's changing drastically. So my question is, the industrial engineer of the future, what kind of a person is this ideally, and what sort of skill sets and what sort of awareness does this person have?

MICHEL: The skill sets that this person should have are both technical and managerial. It's management and technology considered together. So they may not be able to write code, or they may not be able to design how to cut a piece of metal, or how to tweak the electrical properties of a circuit, but they know the importance of these things. They've been exposed to them through their education and career. And they have an appreciation for what they are.

So, for example, one particular task that has to be done in every manufacturing organization is technical data management. You have to manage the problem definition, the process definitions, which machines you use to do what, down to the process program that these machines run. All of this is data, technical data that has to be managed, put under revision control. And you'd expect someone with training in industrial engineering to understand the importance of revision control on this.

If you change something to the cutting program of a milling machine, you may affect what happens elsewhere. You may affect the mechanical properties of the product and make it difficult to do a subsequent operation later. And that's why before you implement this change in production, you have to have a vetting process that results in revision management. So I would expect an industrial engineer to understand that.

TROND: Well, you would expect an industrial engineer to understand that, but, I mean, some of the challenges that come from these observations that you're making here they impact all operators, not just engineers. And they certainly impact managers because they are about this whole system that you are explaining. So it sounds to me that you're mounting a pretty significant challenge to the future manufacturers, not just in skills development but in evolving the entire industrial system.

Because if we're going to make this wonderful spacecraft, and solve the environmental crisis, and build these new, wonderful machines that everybody expects that are going to come churning out every decade, we certainly need an upskilled workforce, but we need a whole system that works differently, don't we?

MICHEL: Yes. Can I give you a couple of examples?

TROND: Yeah.

MICHEL: One company outsourced the production of a particular component to a supplier then there were technical problems with actually producing this component with the supplier. So the customer company sent a couple of engineers to the supplier, and they found some problems with the drawing that had been provided to the supplier. And they made manual corrections to the drawings, the copies of the drawing in possession of the supplier. And it worked. It solved the immediate problem. But then, at the customer company, they didn't have the exact drawing. The only place with the exact drawings was at the suppliers. And a few years later, they wanted to terminate this supplier.

TROND: Aha.

MICHEL: You can see the situation. You want people to be able to understand that you just don't do that sort of thing.

TROND: Right. So there are so many kinds of multiple dependencies that start to develop in a manufacturing production line, yeah.

MICHEL: And then you find a company that's a subcontractor to the aircraft industry. And you find out they route parts through a process that has about 15 different operations. And the way they route these parts is they print a traveler that is 50 pages long, and it's on paper. And the measurements they make on the parts that they're required to make by their customer they actually record by hand on this paper. What's wrong with this picture?

TROND: So yeah, multiple challenges here.

MICHEL: Yes.

TROND: Are you sensing that these things are fixable? Are you optimistic in terms of this awareness of all aspects of the systems changing both among managers and next-generation industrial engineers, and perhaps even among the operators themselves to realize they're getting a more and more central role in the production system?

MICHEL: I won't try to prophesy what will happen to industry as a whole but what I'm confident about is that the companies that know how to address these problems will be dominant. Those are the sort of basic mistakes that really hurt you and hurt your competitive position. So there will be a selection over time that will eliminate people who do these kinds of mistakes.

TROND: Michel, I don't want to put you on the spot here. And you have spent your career researching and tracking Toyota as an excellent, excellent manufacturer that has graciously taught other manufacturers a lot. And also, people have copied and tried to teach them Toyota methods, even if Toyota wasn't trying to teach everyone.

Are there any other either individual companies or things that you would point to for the eager learner who is trying to stay on top of these things? I mean, so lean, obviously, and the Toyota Production System is still a reference point. But are there any other sources that in your career or as you're looking at the future where there is something to learn here?

MICHEL: Oh yes. Toyota is a great source of information, but it's by far...it's not the only one. One of the key parts of Toyota's management system is Hoshin Planning. Hoshin Planning didn't come from Toyota; it came from Bridgestone tires. And so that's one case where a different company came up with a particular method.

Honda is a remarkable company as well, so there are things to learn from Honda. HP was, under the leadership of its founders, a remarkable company. And they had their own way of doing things which they called The HP Way. Companies have recruited a lot of people...electronic companies have recruited a lot of people out of HP. And you feel when you meet the old timers who have experienced The HP Way, they feel nostalgia for it. And there were a lot of good things in The HP Way. They're worth learning about. So I also believe that it's worth learning about historical examples because history is still with us in a lot of ways.

The Ford Model T plant of 100 years ago was a model for a lot of things at the time. It also had some pretty serious flaws, namely, its flexibility. And you still see people putting up the modern-day equivalent of a Model T plant with new products and new technology but without thinking about the need. That particular plant may have to be converted in the not-too-distant future into making a different product. So it's always worth looking at examples from 100 years ago, even today, not for the sake of history but because, in a lot of ways, history is still with us.

TROND: Well, on that note, history is still with us; I thank you for this, Michel. And I shall remember to forget the right things, right? So history is still with us, but [laughs] you got to know what to remember and what to forget. Thank you so much.

MICHEL: Culture is what remains once you've forgotten everything.

TROND: [laughs] On that note, Michel, thank you so much for your time here and for sharing from your remarkable journey. Thank you.

MICHEL: You're welcome.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Lean Manufacturing. Our guest was Michel Baudin, author, and owner of The Takt Times Group. In this conversation, we talked about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work. And indeed, we are looking at a future where manufacturing is not going away.

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

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

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

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

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

Special Guest: Michel Baudin.

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.

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Trond's Takeaway:

Stanley Black & Decker is a huge organization where any improvements by tweaking their own operations or by adding insight from what happens along the whole supply chain can mean significant productivity gains. I find it interesting that they have their own version of the augmented lean approach tailored to where they are and, most importantly, building on the insight that the workforce is where the innovation comes from. By giving shop floor workers access to insights on big-picture manager deliberations, they are freed up to operate not only more efficiently but also more autonomously. When all of industry works that way, manufacturing will make tremendous advances more rapidly and sustainably than ever before.

Transcript:

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

In this episode of the podcast, the topic is Innovating Across the Manufacturing Supply Chain. Our guest is Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker.

In this conversation, we talk about lean leadership, productivity, the challenge of digital transformation across operations and supply chains, and how augmented lean means every organization has their own transformation approach.

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

Antonio, welcome to the podcast. How are you?

ANTONIO: I'm good. How are you doing?

TROND: I'm doing great. I'm looking forward to thinking and talking about manufacturing supply chains and the rollout of digital technology. So, Antonio, you are actually a business major by origin from North Texas, and then your master's is in HR. And then you're fashioning yourself as a lean leader and an operational expert working on productivity and now much on digital transformation. And you're heading the rollout of digital solutions for Stanley Black & Decker. I'm curious, what was it that brought a business major into the manufacturing field?

ANTONIO: For me personally, businesses is great. I'm a big advocate of free markets. And so for me, the whole time you think of how widgets are created and wanting to understand that aspect in manufacturing, creating widgets. Like you were saying, with a master's in human resource development, my thoughts there were learning that a lot of the cost from any organization is going to be labor and material. So having that understanding was great.

And then transitioning to making widgets and learning under some ultimate awesome leaders in the space along with great engineers that really, really, hand in hand taught me so many things. And then one of the leaders in lean as well having hands-on conversations, walking the site with this person that is known for lean just really, really strengthened my capabilities. But the thought of the digital side is always going to come into our space, in our world. And so to be able to do that for a large fortune 500 company is obviously amazing. I'm like a kid in the candy store.

TROND: [laughs]

ANTONIO: Those concepts really changed the way from an organizational side because business is business no matter how you look at it. We're trying to improve our margins and capture market share just like anyone else. But ultimately, it's just a different way of doing it.

TROND: I wanted to stop a little around lean first because in our pre-conversation you said lean touches everything. I'm just curious, what do you see as the key things in lean that you have learned that you are bringing into this work that we're going to be talking about a little bit?

ANTONIO: I think that it boils down to a way to create continuous improvement by impacting ultimately the lead time. I'm part of the global supply chain so obviously, I'm always looking at a holistic approach. That's why it's all aspects for me from a business standpoint. At the same rate, from a lean perspective, we can find waste in anything. So there are always opportunities to improve in that aspect in every single function.

Every function within the organization can be an aspect of lean. So that's the part for me that I get excited about, and I've touched every single function. So it's really an opportunity for any organization to continuously improve on and removing what they say muda from the origination of the concept in any organization.

TROND: I'm curious; some people would say that lean is or I guess was important early on but that contemporary organizations are somehow different, and digital, which we'll talk about, is one reason, but there are perhaps other things. What are some of the things that you, I mean, I don't know if you agree with this, but what are some of the things that you're incorporating into your thinking here that may be either different or where you have to adjust it to the organization you're actually in at any given moment? I'm just curious.

ANTONIO: You're thinking lean from a digital standpoint or just lean?

TROND: Well, lean was developed in its original form a very long time ago. So I guess the first question I'm asking is how can you be confident that the original insights are still valid? Is that because you're walking around and experiencing it every day, and it resonates with you? I guess, firstly, just curious about what lean generally means today in an organization like yours, and then obviously, we'll talk about the rollout of digital solutions, which you've been doing so much now.

ANTONIO: Right. And that's a great question, and I'm excited to be the person that has to answer that question.

TROND: [laughs] Well, you didn't think I was going to give you easy questions, Antonio. [laughs]

ANTONIO: Lean, the concept, I think, will never go away. And so for those that think that it will, really do not understand engineering from that standpoint because when you think about engineering, an engineer solves problems. And so we know number one, there's always going to be problems. I'm sure that there are a lot of people that say, "Hey, I got something for you to solve. I got a problem for you," so from that perspective, we know.

But then, on top of that, think about innovation from an engineering standpoint, as you see something improved, even if it's making it better, even if it's something like making it better for the customer, ultimately, that transition of change even the slightest or something large, every organization has to do it. They have to embrace it. And so a person that knows those techniques, that are really good and seasoned and experienced, which I would say I do fit in that; I feel mighty confident in that space, and I feel mighty confident in manufacturing, we could see it quickly. You see it immediately.

Like, you see a process, and it just stands out. And I think that you can't wish that away to be able to see the inefficiencies of any system. And if you do not have a system in your approach, then that to me is already folly, you know what I mean? Like, that's an error. If you can't create systems, especially in manufacturing, I think that that's no bueno.

[laughter]

TROND: Got it. I'm then curious, digital. How does digital factor into all of this? So I guess I'm understanding a little bit more of your conception of continuous improvement, lean, whatever you really want to call it, and engineers that are such a crucial part of the kind of organization you represent, Stanley Black & Decker.

So now, clearly, there's been a push in most organizations across fields to go digital and arguably, manufacturing organizations perhaps were resisting it a little bit because there was such an amount of automation in there already, and then now comes digital on top of that. And has it been easy? Has it been difficult? What goes into even the decision to say, "We're going to have a major digital transformation?" Tell me a little bit about the journey that you've gone through with Stanley in that respect.

ANTONIO: So, really great question. And so I'm going to take you down a little bit of a history lesson and introduce how it impacts. So when you think about things of the world, because you always have to relate to what's going on in the real world, you have the introduction of the smartphone. You have to credit that smartphone for that interaction of this interface because it's putting that into a lot of operators' hands to interface with something.

Now, when you think about digital, industry 4.0 touches a lot of things; it's very vast, very broad. But when you think about the insights and paper throughout your organization that's there but being able to in manufacturing...and I'll make this a little bit specific to manufacturers. There are so many points where you actually need data to improve throughout that process, and like I said, it's a system. And so if you can capture it in a digital way, now you can analyze it. Now it's an insight. Now you can take all of this, and you can do predictive analysis. You can add algorithms, AI, whatever you want once it's digital.

And it's transforming your operation to be able to enhance it in this digital way so you can advance and be a little bit more productive and get better, and so it still comes back to lean. [chuckles] Once you've created it digital, now it's like, what am I going to do with the data? Because you can do the wrong things with data. It can give you the wrong insight. And just making those decisions of where you are going to improve, I think that is really huge.

So for me, that transition starts with realizing the digital side, removing some of the paper. I mean, there are so many people that are old school I would say that do everything with paper. And if that paper was digital, then what could be? I'm smiling now because it gets me excited because there are so many processes that are old that people just pull out a paper and they use it even though we're in this digital age.

TROND: So I thought I would then move us a little bit into the aspect of having a digital platform. So digital means a lot of different things to different people. You say having access to digital gives us options basically because then you have data, but you have to do the right thing with it. First off, what kind of a decision and who was involved, I guess, in the decision at Stanley going digital in that sense? Because there are many different echelons of an organization that could potentially use data.

Who was the most excited, I guess, to use new data in your organization? How did that even come about? Was it a leadership decision? Was it mid-level managers that said, "Other organizations, our peers have more data?" Or was it analyzing, you know, Gemba Walks and walking around and saying, "Hey, the operators could be more productive with more data?" Where did the decision point come from?

ANTONIO: To answer your question, short answer would be leadership. We're pushing for the next edge in innovation and pushing forward to create change. And then it's what can be that thought, and I would say the collective. If you were to embrace true employee engagement and start from the shop floor, it's going to be things that they don't know that they're requesting, something digital, so to speak. They're just saying, "Hey, this would be cool. This is what I need in order to do my job effectively."

And then what about the supervisors to the middle managers that are trying to share insight of it's great to say that you hit your numbers or you produced your widget in a successful time or faster than you anticipated, but what about the opposite? What about when you did not meet your numbers? Being able to speak to that with data that's a huge win. Who wouldn't want that? And there are a lot of areas that are little dark areas in a manufacturing facility that you don't have that capability. And that's why you need some type of way to be able to shed light on those areas and capture that in a very effective way.

TROND: Tell us a little bit about the digital rollout process at Stanley. What went into it, and what is the situation? What sort of systems have you opted for, and how are you rolling them out?

ANTONIO: So within our organization, everything comes out with governance so thinking of and a way of controlling exactly what's completed, what's being done, what you are going to put within the facility, and then creating some type of uniformity around that. The interesting thing about our organization is we're a huge conglomerate. We produce many different parts and units. And it's just a lot of complexity and diversity as far as the people are diverse, but I'm just saying end product.

Manufacturing facilities...I'm global, so I'm facing all over the world different processes that we do and so being able to have a very tactic way to roll that out in a uniform way. That's really the strat there, really thinking it out. But then also allowing for those unique scenarios to come about, having what we call citizen developers. It's that employee engagement part, thinking about someone that's really close to the process. They may figure out a way that, hey, we need this type of solution, listening to them.

And then the fact, like I said, I'm global, I'm seeing way more than they are. And I can be like, and our team can look and say, "Hey, this actually could be used at several sites that look just like this one." And so we can get that MVP and create it in a very standard, uniform way so then we can roll it out on an enterprise level. And so all of this together is the way that we go about rolling out digital solutions.

TROND: So, Antonio, I'm curious about this because in classical automation, usually, it's a big sunk cost, and the system is stable, perhaps, but everyone has to learn it and do it one way. Is the current wave of digital transformation that you're talking about here does it allow for both strong governance, which you clearly need in a large organization, but also for those citizen developers to emerge with their more kind of not exactly bottom-up, but they are certainly factory-based, or they are site-based perhaps innovations?

Did you have to choose technologies that allowed for that, or how did that factor in? Because classic solutions of automation is like one size fits all, but you seem to be talking about, yes, the need for governance, but there's also the need for citizen developers. How did you enable those citizen developers?

ANTONIO: So the first thing is that you need to figure out something that's adaptable. And so for us, we use something zero code, so it's really, really easy for them to use. And so the thing is that you don't want to discourage innovation at all. You want to embrace employee engagement all that you can. At the same rate, there's another team that's going to make sure that cybersecurity and all of that that I'm playing within the confines and the rules, and if I do not, then definitely there'll be a discussion about it.

And so understanding that you're really balancing both, and you're controlling that citizen developer as much as you possibly can, being aware of what that individual may do. And at the same rate, watching and being able to take away their permissions if need be if we feel that it goes into...I don't want to say a danger, but it's not good from a governance standpoint of what they're doing due to some federal regulation or law or whatever have you. So it's just the balance of the two of having a platform that can give you that adaptability in order to control.

TROND: Antonio, can you expand a little bit on innovation? Again, in the context of a workplace that is becoming more and more automated, how do you inspire innovation? What does it mean for Stanley, innovation?

ANTONIO: When you think about what can be...let me give you an example of something that we created; I think that it will shed light. Every organization they go through physical inventory. So you have to count all your inventory and make sure that what your books say [laughs] that's what you have. It's just comparing those two from a financial standpoint. So you're going through that process.

And normally, this process is very manual where you're physically going; someone is sending out, making that count, writing on a sheet of paper of what they were able to capture, and then running that sheet of paper to some control room where everyone is conducting...basically calculating where you are now. And so everything's live. So you go, and you audit that area, and they come back.

So basically, someone is running around facilities. And if you look at some of our facilities, they're pretty ginormous, pretty big. So to go to one end to the other it's going to be a hike. And this is all on physical paper for the most part. This is all live, speed. So the thought came up when you say innovation, someone was like, "Is there a way to do this digitally? Why can't we do this digitally?" Just to speed things up, just to figure out, hey, where are we right now? Instead of getting all of these sheets of paper and then typing them again in some system.

And I go back to lean. That's rework. That's overprocessing. Even within this system, rework is someone already wrote it down on a sheet of paper. Now they're going to hand it to someone else to literally type it into another system. That redundancy can be removed. So you see that there is an opportunity there to save time because no one wins when we're doing a physical inventory. The site is shut down, and we're not making widgets. So you don't want that.

So anyway, there was a person that was like, "Hey, can we do this digital? There's an opportunity." So that's the innovation there. It starts with an idea and then sharing that idea saying, "Hey, is this possible? What can be? What is possible?" And then you have a very diverse team look at it along with accepting that idea. And you transform it into an application in order to conduct physical inventory. And we did just that, and it was huge.

And obviously, it's within, like I was saying, you get that MVP. And now we can just copy and paste that across the board to different sites and use it as much as we want from that standpoint with those same winnings, those same gains, and the same objective in order to help the site and use as much waste that is normally committed in a physical inventory.

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TROND: Antonio, you speak of apps. What are those apps that you speak about here, and how do you explain the concept of an app, I guess, to your operators? Because I'm assuming there is a bit of an educational journey there, too, when you're introducing certain new digital processes going, like you said, in a basic sense from paper to digital. And then you said it comes through these apps.

How do you explain the concept of apps, and how do they materialize, I guess, on the shop floor? I mean, they clearly are created. Are they created mostly by the vendors that you contract with, or are they created by your own engineers? Or are they created factory specifically, or how does this app development work? And what is an app?

ANTONIO: So they're created by our engineers. And this is actually pretty funny that you asked me what an app is. And so that thought is really important because this is something that we have to do out there on the floor. And so when approached with someone that you want to use this application, I don't think that I ever even say the word app to an operator as I have physically trained operators on an application. And it's just more so the process of what you would like them to do.

And one of the reasons of perfection, so to speak, is what you strive to do when it comes to the user interface and the user experience. You want to make the least amount of steps. You want to do the least amount to interfere with this individual that has a really, really important job to make widgets. And so the thought here is the explanation of what you're trying to accomplish and then the steps that they need to do to interact.

And like I said, what helps is obviously smartphones, you know, everyone's interacting with it. So, in our times today, I think that it's a little bit easier. If you were to take it maybe 15 years ahead, maybe it'd be a little bit more challenging, but I would say that not everyone is ready for that change. It's still new to them despite smartphones being there saying, "Hey, I have to interface with this iPad or a tablet, or touch screen," whatever have you; however, they're interacting.

So the ideal state is to create it where it's more automated. And so the application is just kind of like, it's a matter of fact. We're capturing all this data, and you're just doing your job. And we're just using triggers to be able to indicate what you're doing. So that's really how I would go about describing an app, never really saying app and just saying, "Hey, this is a process that we would like to use as you do your job really."

TROND: Antonio, would you speak specifically about Tulip as a digital solution? And what is that being used for, and how is that being rolled out? I mean, to the extent you can go into some detail, what is that platform doing for Stanley?

ANTONIO: For us, using Tulip is really, really advantageous because there are a few things that it's really, really great at. You can create pretty much what you want. I don't want to put it too much out there. And the easiest way where you don't...I mean, I have software engineers that work for me. But you don't have to be a software engineer; you could be just anyone. So that part makes it a great deal simple and then what it's capable of connecting to. So it can just easily integrate within your organization in order to achieve some of the things that you want to achieve, so from the standpoint of hey, we just need this very simplistic way of doing this.

And then what's more important? The UI. So it's like, what do you want this interface to look like and do? Because sometimes, I don't want to speak specifically to some organization or tool, but some tools that you can use make it very challenging with the user interface where it's just too much buttons or too difficult to get to what you want to. Versus, you have with Tulip a little bit more autonomy to make it and cater it to what needs to happen, where you've leaned out a lot of it and just say, hey, just come touch this button and do this, and that's it.

Because you want to make it simplistic, but maybe there's something else and another look, another view that you want to use. And so, using the same platform, you can make a view for someone else that will be looking at that data in a different way. And so that's the cool thing is it's all on one platform. So that makes it a little bit more powerful that from an operator standpoint, you've given them what they need, very simplistic, the limited amount of buttons. And then, for a different audience of a managerial role, you've given them the insights that will help to improve productivity within the shop floor.

TROND: What are some of the use cases that you then identified so far and are rolling out in these kinds of apps on that platform? And what are some of the things that one might think of? Or is that more of an iterative process that it's like, can you even map that out a year ahead where it's going to be used? Or is that like it's such an iterative process that it will evolve more organically? But either way, where's the starting point? What kinds of things have you now digitized this way?

ANTONIO: Within every manufacturing facility, they're going to say safety is first, and Stanley Black & Decker is no different. I can tell you what number one is, what 1A and 1B it’s...I can't say the other one is 2. So 1A is going to be safety, 1B will be quality. And so the difference here...and I want to differentiate something really quick because it's very important.

Being able to identify from the factory floor what's going on this is something totally different. From the operator's point of view and the data that they can create, that's different. Looking at other things is interesting, but what actually goes on on the manufacturing facility shop floor that type of data that's where it's important.

And so, to your question, you can, for instance, audit something. You can audit a process. That's something that's very, very easy. And you can do it in both realms. You can audit a process for safety. You can audit a process for quality. Those are two examples there. And obviously, you can advance that even more as you touch the product that you're making. And then once you touch the product that you're making, now you can relate that. That's where my business side comes in. Now I can take this beyond from a holistic approach.

So for me being global supply chain, this one place where it was touch, I can go backwards. So I can go further upstream to the vendor, to the site, to any other buffer in between that, let's say a distribution center, to the customer, back from the customer, and then a thread that goes all the way through. The insights are endless, and the capability and possibilities are endless when you can capture it all at the shop floor.

So that's really what we aim to do, really lighting up those dark spots and getting as much with the operator. And that's why operators, I mean, what's going on in our world and not just Stanley Black & Decker, as automation and digitizing the factory floor, this is going to definitely augment and amplify shop floor workers in a different way. And it's going to be really, really advantageous for you to be alongside that operator and enhance their skills to be able to be within a manufacturing facility to change because it's obviously changing. But you can make it where they're advantageous to the organization of what they do and give them a little bit more skill set.

It's almost like giving them more information, like going to university, so to speak, because they're able to see what they know. But now that cognitive data, we can take it from them digitally, and so now you can do more. You don't have to be thinking about that. It's like, oh yeah, we'll capture all that. Let's put something else on you. Because we'll take that cognitive data and store it for point solutions later on and now if need be. So it's a very interesting time within manufacturing of where we are now and what I foresee in the next 5, 10 years.

TROND: Do you think that manufacturing shop floors have trusted operators enough? Or was it just that the opportunity now of seeing more of the big picture is only now being realized with these digital apps so that this information is there and then you can trust them more? But it was interesting to me. I just want you to talk a little bit more about the new role of shop floor people, basically, that are now perhaps able to take on different things because of this new set of information that's being tracked.

ANTONIO: So when you really think about the frontlines, I would love to say and sit here and talk about how great I am and what I do for the organization. Oh, I think of all of these ideas. But for our organization and probably any organization, it's the people that make the widgets that are the most important people within the organization I would say. They're the workers, and the knowledge that they have of that process is so important.

At the same rate, we would say that the majority of those workers do not have fancy degrees or anything like that. And so we tend to think that possibly...well, I don't want to say that we tend to think that. It talks about the capability of what they're capable of, and so now with this, and if you can do it in a way for a digital transition, you can now look at what those capabilities are, the insight that they have. Okay, you do understand this process, then what's next? How do we improve it from a lean standpoint?

But you also intricately know, let's say, for instance, this machine you work on it every single day. But now we're going to create a way where you don't have to work so much on your, like I was saying, the things that you think about. We'll create something to do that for you. Now we would like for you to do something else. You see how this change comes up. We need you to just do this or that. And I don't want to be specific, but that's really how the change is occurring.

And to be honest with you, it's a huge win because there are many operators that actually enjoy...they want you to know and understand the data of what they do. It changes things because it can be a very technical job within manufacturing where you pull out a drawing. There's a certain specification that you have to hit, and that's going to make a difference if that part is manufacturable or not. And we're talking about sometimes you're pulling out calipers to get it within 2000s where it's got to be exact. It's almost like an exact science. That grace invariant is not that much.

And so, to be able to record that data digitally and view it that way, the operators are all for that because it helps to explain things that maybe they can't put into words, but the data will show it. And it's just like, "You see? You see what I'm saying? Right about this time at 4:00 o'clock, this machine always does this," I'm just giving an example. But you can see that from a data standpoint, and that will help the operator as far as transition into this new manufacturing operator, I believe.

TROND: So, Antonio, I think I'm now understanding a bit more about how this works on a given factory floor. Can you help me understand more about how this works all across the supply chain, which you were talking about earlier? Because now, I'm assuming the use case for you is not just one individual operator or sets of operators and teams doing one product in one location. You're talking about coordinating this across a larger supply chain. Now, how can these apps then come into play? Because now we're talking about different geographies, a lot of different contextual information that would need to be put into place.

How do these apps truly help smooth out the supply chain? It would seem to be a much perhaps more complicated challenge than just simply making an individual worker or team's life easier with safety and quality with precise work instructions. When you're talking supply chain, what do you really mean there? And what are the first, I guess, apps that are coming out that are going to truly impact the full supply chain?

ANTONIO: So know this, [laughs] it's like...I'm going to give an analogy because I want to make sure that you can understand because it can get really advanced when looking at things, so hear this out. So think about those pictures where you have the picture, and everything has a number. And so you go you're number one, and let's say number one is blue. So you fill in all the blue. And then number two is yellow or whatever. At the end, it's going to be a picture that you see, and you can recognize, oh my God, a parrot, when you're at the end.

So the way that the approach here is is that we know that it's a parrot. We understand that. And so the other functions within our organization know that it's a parrot, and maybe they're only focused on the blue, but they know that it's a parrot. And so, having certain datasets will fill in the blanks for them. Something that didn't have color now has more color, so they can make more of an informed decision on what they do because everything is connected. You cannot get away from the other.

So everything really starts where you make the widget, I think. It doesn't necessarily start there because you got to get the supplies to be able to make it. But what I'm saying is is that's the money time. But at the end of the day...and I'm going to go back to what I said earlier of how I summed up lean. Everything is lead time.

So I'll give you another analogy. I love kombuchas. When I go to the store, there's a certain kombucha that I want, and when it's not on the shelf, I'm going to go somewhere and get that kombucha. I'm not going to keep going to that store. And so, at the end of the day, this is the type of data that's needed throughout the whole global supply chain in order to ensure that our customer has that kombucha, so to speak. And all of that data insight is imperative to not only understand it but be able to do magic with it, so to speak, and make changes to continuously improve.

TROND: Interesting. As you're thinking about how these developments are affecting the future outlook in the manufacturing industry, or for your company, or maybe even wider for society, because some of these things, when they're compounded they, could have perhaps larger impact, what are some of the things that you think is going to come out of this in a 3 to 7 or 10-year timeframe? You've talked about shop floor operators becoming something even more special, perhaps. So I'm assuming that's one thing.

And then, if you want to think maybe about the larger workforce, what are some things that this will lead to? And then, finally, we just talked about the supply chain. Thinking ahead, what is likely to change when this has permeated throughout many organizations' supply chains with a lot more information available? What are the potentials here? What are the impacts?

ANTONIO: The main thing I think that will happen, and I think that it's already happening, is there will be a through thread through all the functions. I think that that's imperative. But I think that it will be a little bit easier with data. So the latter of those three that you was talking about from the future standpoint, I think that the through thread with that data as we advance and make even better applications for the shop floor to get even more data, you will be able to take that data to other functions to make changes, to improve, and reduce costs within your organization all across the board. So that's where the future will lead.

The former part of the question, as far as the change of the shop floor worker, I believe that from my perspective, I think that the world is changing. Education is changing. The cost of education is changing. And I think that from the older workforce, not to put an age on it, and what manufacturing was in the past is adapting. And the type of worker that is within a facility is different than it was because the people are different. We think different. We have Twitter, and Instagram, and Snapchat.

And so I'm throwing these things out here just saying, hey, we have a different workforce. They think different. And so I believe that manufacturers are adapting to this different workforce, and with that will come much change and much-needed change. And the capability of what a worker is expected to do, I think, will increase, but it will increase for the better. There are different roles for individuals to have within manufacturing facilities, and I think that we'll see that just come over time because we need data.

Data is going to be very, very important for any organization, and how we obtain that data, how we get that data, it's just better to have that person in the room having a big impact. And I'm saying that person, that operator in the room without having them in the room, so to speak, by getting their data to impact those decisions in their own way, but also using employee engagement with the data that they provide. So I think that's going to be really the change.

I think the number two question I kind of forgot. I apologize. I went from the last to the first.

TROND: No, it's fine. I mean, I was talking about the operators and then the advanced supply chains, which is, I guess, just another layer of complexity, and we have talked about it at length. But I'm just wondering, as these technologies, the digitization really advances and permeates throughout the supply chains, what are some of the cascading changes or not that might occur?

Because I'm assuming, just like you said, shop floor operators will have a different reality. They can do different things because some things are just taken care of or the beans are counted. They can do other things. What are those other things that organizations now can do because their supply chains will become more and more digitized?

ANTONIO: Yeah, those things are really...when you think about the footprint of what a facility needs to be, now that changes. Because one thing that's really, really important in any facility is space, so now this will impact it. Hey, we got this covered; could you go take care of these things? And then also I believe, so this is just going to be my opinion, I think that there's going to be more training. Now we can train up in another skill set to allow someone to have dual if not triple capability within their self to do more.

Let me tell you a little bit more about this machine because what we needed you for we good on that. Let's teach you about this other aspect of this machine in order to make it, you know, the upkeep of it, the PMs and TPMS, you know it. We've automated that and made it digital, but let's advance your knowledge a little bit more so you can understand. And I think that that's what we're about to witness here as we move forward.

To me, it's a really, really beautiful time. And it's going to be really, really interesting here in the next I would say ten would be the keymark, 5, especially with the climate today. And not to speak about the elephant in the room, but it truly is the perfect storm, all of these things happening. Like, going into a supply recession and then possibly having demand to drop, I mean, it's just a perfect storm of all of these things. But you'll see that those that are able to survive this will be better off because of it.

You never wish these things to happen. But you can say that you will improve, and you'll be stronger because it happened. And this also will impact what's needed in the future, especially on an operator level. So it's really interesting where we are today and how digitization will impact our lives and manufacturing from here on out. There won't be a point where it's not there. It will always exist for quite a bit of time unless there's some drastic change or an invention of some sort.

TROND: Antonio, the last question I'm going to just throw at you is, what are the training consequences? And how do you see training going forward in the medium-term future? Because you have pointed out that shop floor operators are going to be asked to do more things, more advanced things. They will get more of a bigger-picture view.

You're going to need a lot of true engineers, and then you might need a lot of engineers, meaning their engineering like they are trained with a mindset of an engineer in the sense that they are trained on improving, and suggesting, and tweaking, and adjusting the way that an engineer did. But surely, all of these people can't go to engineering school.

ANTONIO: [laughs]

TROND: How are you going to do this? Because the way I'm seeing you painting the picture of an emerging manufacturing workforce here, I mean, unless you're not talking about the same people, how are those same people going to adjust to this new reality?

ANTONIO: Right, yeah.

TROND: Is the UI going to be the key here, the UI just has to be simple the way you've explained that apps have to be kept simple so that training is limited? Or are you foreseeing that complexity still will increase so that people are going to have to become trained on still sophisticated piece of equipment? Because it could go two ways here, either you're doing advanced things, but you're keeping it simple still, or you're doing advanced things, and it's complicated. [laughs]

ANTONIO: So this is a great question, and I'm really excited to answer it. So the thought here is is, I'm going to take a CNC, a computerized numeric control machine. That is a very sophisticated piece of equipment, and an operator runs it already. No matter what they do, they're already running it, and so they're capable. And yes, they didn't go and get this advanced engineering, and those that receive those advanced engineering degrees they're worth every penny. It's teaching you on a vast scale.

But in a manufacturing facility, on what you're doing, you're removing some of the noise and saying, hey, I just need you to learn this. This is this process. So just this, just eat what's on your plate. Don't worry about any of this other stuff. And we'll guide you through. We will layer on, and layer on, and layer on the knowledge that we want you to have in order to enhance you on this process. And this process is core to manufacturing. See how that sounds a little bit different?

Because when you go and get your degree, I'm just going to pick engineering, you're learning all types of things, and they're all important. And there's a lot of physics and just a lot of things that you need to understand. At the end of the day, if you were to take an engineer off the streets that just got their degree and throw them in, how different would they be if you had a seasoned, experienced operator that knows this process and you compare the two? That would be an interesting comparison. I actually would like to see a study on that.

I think that, not to get deep, I just think that there would be a point where if you were to graph it where they would intersect, and that person with the advanced engineering would supersede this operator. But how long that would be would be interesting if you've created an environment and a very easy way through applications and digital solutions to improve this operator where they have knowledge and a different way of explaining it to them, all of these things where you've advanced and upped one. Like, you've upped this operator to this process. I think that would be interesting.

I think that that's going to be the future. You're going to have core competencies of manufacturing operators where they can feel proud. Despite that, they would be labeled blue-collar; I believe that their skill set and their knowledge would be probably more than what their label of blue-collar will be because they will be strategically very important to that manufacturing facility because of the knowledge that they know about that core competency of the process. And then just think about this, you learn one, you can learn something else. [chuckles] You know what I mean? And so I think that it just continues. So that's the way that I see it playing out.

TROND: Antonio, I think, to me at least, when I listen to this, it feels inspiring. And it certainly should feel inspiring to whether they are younger or older people who are interested in manufacturing because this spells a day and age where perhaps yet again, this kind of insight of knowing how to work machines and knowing how to coordinate with others on a shop floor or producing something tangible is going to be re-appreciated the way it was in other types of industrial upheavals and revolutions.

It's interesting to me that this is perhaps where we are, this inflection point where the kind of skill sets this will take and perhaps the kind of specialization that now seems perhaps within reach for a different cadre of people. Because clearly, MIT and, Carnegie Mellon, and UCL would have to scale up their training or offer everything they have for free online in order to train 10x, 100x, 1,000x more engineers.

Or these skills are just going to have to be taught in a combination of community colleges; I would assume, and on the shop floor directly by yourselves in these organizations themselves or perhaps a mix of the above. But either way, it would seem to me that it's not all that bleak of a future for manufacturing if what you're saying comes to --

ANTONIO: Fruition.

TROND: Fruition here.

ANTONIO: I agree. And this is really what I see, and that's why I'm excited. I'm happy to be a part of it. And it's one of those things...someone said this to me the other day "Industry 5.0." [laughs] I'm just like, okay. You can hear that concept, but from a societal standpoint and a person that is an advocate of free markets, I think that this is the moment in time in our world because we have to make widgets where we'll define what that is.

And before we talk about this industry 5.0 talk, the human part has to be addressed. And if you do it in the way that we're discussing, it makes for an interesting future. If you do it and bring other things into the discussion room already, I think that it changes basically what's being spoken about and not really discussing, okay, what is really going to move the needle and move us forward as a manufacturing group together? Because we compete against each other in some realms if we're in the same market, but it's all the same game no matter where you are.

And you're taking this from a guy that they would put in the plane and drop in a facility and now have to go through and just figure things out and could actually make change. But one of the things that I recognized everywhere I went in all the facilities that I've been to, all the facilities that I visited, were the people. The people were the important aspect. And you just definitely want to make sure that they're in the equation and in the dialogue of whatever change may happen. And I believe that platforms that allow that will be key for now and the future.

TROND: Antonio, you've been very generous with me, your time. It's been super interesting. Thank you so much.

ANTONIO: Thank you. I appreciate it.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim.

The topic was Innovating Across the Manufacturing Supply Chain. Our guest was Antonio Hill, Head of Manufacturing Digital Solutions, Global Supply Chain at Stanley Black & Decker. In this conversation, we talked about Lean leadership, productivity, and the challenge of digital transformation across operations and supply chains.

My takeaway is that Stanley Black & Decker is a huge organization where any improvements by tweaking their own operations or by adding insight from what happens along the whole supply chain can mean significant productivity gains. I find it interesting that they have their own version of the augmented lean approach tailored to where they are and, most importantly, building on the insight that the workforce is where the innovation comes from. By giving shop floor workers access to insights on big-picture manager deliberations, they are freed up to operate not only more efficiently but also more autonomously. When all of industry works that way, manufacturing will make tremendous advances more rapidly and sustainably than ever before. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and please rate us with five stars. If you liked this episode, you might also like Episode 94 on Digitized Supply Chain with insights from Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

Special Guest: Antonio Hill.

In this episode of the podcast, the topic is Decarbonizing Logistics. Our guest is Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg.

In this conversation, we talk about the huge tasks of mitigating and adapting to climate change throughout industrial supply chains.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 68: Industrial Supply Chain Optimization.

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Decarbonizing logistics without slowing economic growth is a formidable challenge which requires paradigm shifts across many industries, as well as adopting openness principles from the virtual internet onto the physical nature of the supply chain, as well as facilitating new business models, sharing, and standardization, and eventually dematerialization.

Transcript:

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

In this episode of the podcast, the topic is Decarbonizing Logistics. Our guest is Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg. In this conversation, we talk about the huge tasks of mitigating and adapting to climate change throughout industrial supply chains.

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

ALAN: I'm very well, thank you.

TROND: I'm super excited to have you, Alan, you know, an academic that has transformed and seen the transformation of a field that barely existed when you started. Some 40 years in academia and logistics and now being part of this exciting experiment with creating a whole new university focused on logistics. It's been quite a journey, hasn't it?

ALAN: It certainly has. I think this is my 43rd year as an academic. My colleagues often think maybe it is time to retire, but the subjects in which I specialize, which we'll be talking about in a few moments, like decarbonization, are sort of hot topics at the moment. So I'm very reluctant to phase myself out. So it's been an enjoyable 40-year career, I must confess.

TROND: How did you get to pick this area? It's obviously not; I mean, now, because of the pandemic and other things, logistics or at least supply chains is kind of on everybody's mind because we're not getting whatever product we want or maybe some sort of interest in green practices. And we're starting to realize that transportation is becoming more of an issue. People are worried about that. How did you get into this area?

ALAN: My interests initially were in transport and particularly freight transport. In fact, right at the beginning, it was actually a crime, believe it or not, which got me into this area.

TROND: [laughs]

ALAN: Because I'd done my masters at UBC in Vancouver. I returned to London to do my Ph.D. at the University of London. This was in 1976, a long time ago. And I had spent three or four months reading up on the subject of freight modal split, you know, why so much freight goes by road and so little by rail. And I'd compiled all my notes, and my briefcase was stolen.

[laughter]

So the day before that, I'd been to visit a professor at the London Business School who said to me, "The freight modal split topic has been very much researched." He said, "You're a young man. Why don't you go out and find something new to bring a new perspective to this subject?" And around then, the subject of...it wasn't called logistics back then; it was called physical distribution, right?

TROND: Hmm.

ALAN: Where you saw freight transport in a broader context linking it to inventory management, to production planning, to warehousing, and so forth. And so I began reading up on that subject. And that then became the main theme of my Ph.D., which I think was one of the first PhDs done in the UK on that subject. So you could say that it was the person that stole my briefcase way back in 1996 [laughs] that played a part in me discovering logistics as a field, and that's occupied me for 40 years in my academic career.

TROND: And on that journey, you have entered in and out of different fields. I noticed that you were a lecturer in economic geography in the beginning. So there's a very interesting, I find, physical component to logistics, obviously. How does geography enter into it for you?

ALAN: Well, I see transport and logistics as essentially a spatial subject. My Ph.D. focused on the geographical aspects of logistics, you know, where you locate the warehouses, how you route the vehicles, you know, so much logistics planning has a geographical component.

But the thing about logistics as an academic discipline is that it's drawn together academics from many different disciplines. Many have come from a mathematical background, from engineering, from economics, in my case, as I said, from geography. And that, I think, is one of the strengths of the subject area, that it has got this interesting interdisciplinary mix. And that allows us, in a sense, to deal with a whole range of policy issues, of industrial issues, I mean, from land use planning to environmental issues, which we'll be talking about in a moment. I've really enjoyed engaging with academics really from different disciplines over my career as an academic.

TROND: Well, and we'll talk about these things in a second. But, I mean, it's not just academics, right? Because the subject is so non-academic in a sense, right? [laughs] It's actually very alive, and it affects all of us. So people may not have been super aware of it. But, like you point out, it's very multidisciplinary.

Now, how did this startup University concept come about? You've moved to Hamburg or spent a lot of time in Hamburg with this KLU university for logistics, essentially, which sounds to me like a daunting prospect to create a new university based on a new discipline in Germany of all places.

ALAN: So I'd been 25 years in my previous university here in Edinburgh where I'd set up a master's program in the subject and a research center. And then, in my late 50s, I got the opportunity to go to Hamburg and to join what was a startup University. I mean, when I joined, I think we only had nine academic employees. We only had about 40 or 50 students in total. So it was a challenge.

And a bit of background on the university; it is a legacy project of a very wealthy man, Klaus-Michael Kühne, who is the majority owner of Kuehne+Nagel, which is the world's biggest freight forwarding company. And he also owns about a quarter of Hapag-Lloyd, one of the world's biggest shipping companies. And he, in a sense, wanted to give something back to the industry, and so he founded the university in 2010. So it's now 12 years old, and I think it's been a very successful enterprise.

We're still niche, obviously. We've got, I think, about 27 or 28 professors, about 500 students. But we have this focus on logistics and supply chain management. And there are also quite ambitious plans to globalize the university, to open up satellite KLUs around the world. So I was just very lucky really to get involved in this in the early stages and do my bit to help to shape this institution.

TROND: Well, you're lucky but obviously enormously accomplished. I wanted to talk a little bit about your 2018 book: Decarbonizing Logistics here. So this came out on Kogan Page. I also published on Kogan Page. It's a great UK-based publisher. Tell me a little bit about decarbonization overall and what you see as the main opportunities but also the challenges.

It seems to me there's a lot of talk of decarbonization, but the subject that you are attacking it from is one that points out a lot of the limitations of these visions of changing the world into a decarbonized world. They're very physical limits and very real practices out there in various industries. How can we kick off this discussion on decarbonization? What is the best way to understand the biggest challenge here?

ALAN: If we confine that to logistics, to put that into perspective, I think in my book, I reckoned...I pulled together as many numbers as I could, and I reckoned that logistics worldwide accounted for about between 10% and 11% of energy-related CO2 emissions. I've now revised that upwards, so I think it's probably now closer to 11% to 12%, most of that coming from freight transport but some of it from the buildings, from the warehouses, and the freight terminals. To my knowledge, nobody has yet carbon footprinted the IT and administrative aspects of logistics, but that could maybe be up half a percent or thereabouts.

And there's a general recognition that Logistics is going to be a very hard sector to decarbonize for three reasons: one, because of the forecast growth in the amount of freight movement worldwide over the next few decades. Second thing is because almost all the energy currently used in logistics is fossil fuel, right? So we're going to have to convert from fossil fuel to renewables.

And the third thing is the length of the asset life because ships would typically have an asset life of 25, 30, 35 years; planes, likewise, trucks are a bit shorter, maybe 10 to 15 years. But it's going to take us time to change that asset base away from fossil energy to renewables.

TROND: Well, I believe in the middle of your book, somewhere in chapter three, I read this quote that you had that the only way a restraining future increases in freight movement is basically to slow economic growth. That's not really very exciting of a prospect.

ALAN: Well, that's one of my five decarbonization levers to just reduce the amount of stuff that we have to move.

TROND: You must be a popular guy if you say that to industry leaders.

[laughter]

ALAN: Well, I think the challenge of dealing with a climate problem is so enormous that we really have to think out of the box and think of these radical suggestions. But in this case, a number of things can help us there; I mean, the development for circular economy, increasingly manufacturing and recycling will help to reduce the amount of stuff. A lot of the research suggests that people are prepared now to move to a sharing economy where they're less obsessive about owning things and more willing to share. In some sectors...look at electronics how we have managed to miniaturize products.

There's also 3D printing, which some people think will help us to reduce the amount of stuff that we need to move. It will help us to streamline our supply chains, reduce the amount of wastage in the production process. So it's not all about just people buying less. I mean, there are a number of trends I think we should --

TROND: I get that, but, Alan, I mean, 3D printing, I was just, again, reading from your book. You're not all that bullish on 3D printing, either. It's certainly not on the individual level this vision people might have in their heads that everyone's going to have a 3D printer, or the neighborhood will have a vast 3D printer network, and you can print everything locally. This whole decentralized idea of the world of material goods, essentially, where everything is printed on demand, you don't really see that as a very easy transition, do you?

ALAN: No, I don't. I think it's also a longer-term transition. I mean, there's a debate as to whether this will be truly a game changer. And maybe in the longer term, we will see a lot of consumer products printed in the home, and then we can greatly streamline supply chains. That is a long way off if it ever happens. Where I think it's more likely to reduce, freight demand is further back along the supply chain instead of business applications of 3D printing.

But there's an academic debate on this subject. Some people are quite upbeat about this, thinking 3D printing is going to be an effective decarbonizer. Others are a bit more skeptical. I mean, there are some forecasts being made about the net effect of 3D printing on the amount of air cargo in the future. But there's not necessarily a wide agreement on that. So I think the jury's out on this one, [laughs] on the net contribution 3D printing will make to decarbonization.

TROND: Alan, can you give me some tangible examples of what we're talking about here with logistics? Because, in essence, it's an unfair business to be in to decarbonize logistics in the sense that the subject as a whole is almost a victim of climate change. You're dealing with extractive or heavy industries that are moving about a lot of damaging [laughs] materials that they have extracted.

To turn this into a positive discussion is challenging, but there are a lot of attempts to do so. Maybe we can take trucking perhaps as an example. So transportation, obviously, of goods via air is challenging, and road and by ocean, I guess, is somewhat less climate impactful. But what is the prospect?

If we just take trucks, it's a modal transportation element. People understand truckers, and we see trucks on the road. It's a very visceral kind of element. What has happened there, and what would you see is the prospect there? People talk about electrification of trucks. What are the real prospects for change in trucking, transportation?

ALAN: I think one of the positive things here is that there are many things that can be done, and they're additive. Their net effects will be cumulative. They're going to be implemented over different timescales. So the sort of things that we can do today which yield a significant carbon saving would be to improve the aerodynamics of the vehicles, streamline them.

We can train the truck drivers to drive more fuel efficiently. I mean, I think that's recognized to be one of the most cost-effective ways of cutting carbon emissions and also, of course, reducing fuel costs as well. A lot of this would be self-financing for the trucking businesses.

Then looking to the longer-term, there are technologies that we'll be able to deploy. Here in Europe, there's been a lot of interest in platooning, where it's not just the fuel efficiency of the individual vehicle that you improve but convoys of vehicles that would then be closely coupled, if you like, on the motorway.

But many people see ultimately, the way we decarbonize road freight to get it down to zero emissions is through switching from diesel fuel to low carbon fuels, mainly batteries. I would have thought, certainly for smaller countries where the trucks travel shorter distances, maybe some use of hydrogen though I have to confess that I'm doubtful about the use of hydrogen in the road freight sector. I see we will need the hydrogen to decarbonize other sectors of the freight market, the ones you mentioned, aviation and shipping, because they don't have the same opportunity to electrify the operations that we will have in the road freight sector.

But I mentioned the importance of timescale here because if you look at Europe, I think there are 6.2 million trucks in Europe. We are replacing those trucks at about 200,000 or 300,000 a year. At that replacement rate, it's going to take us probably a couple of decades to entirely replace a diesel fleet with a fleet running on batteries or fuel cells, and therefore there are things we have to do in the interim.

So, in addition to the things I've mentioned, the shorter-term ones, we can fill the vehicles better. Typically in Europe, about 20% of truck kilometers are run empty. In some parts of the world, it's 30% or 40% of truck kilometers run empty. We need better load matching, you know, to get return loads because that would then help us to cut truck kilometers and thereby save energy and CO2.

TROND: You know, it strikes me that a lot of what you're talking about, I guess, resonates with the topic of this podcast because it's not just automating and making things enormously advanced in terms of technology per se. It is optimizing within this idea that you're using your assets differently, perhaps through digital means and organizing people and assets in a system in a better way. How would you say the progress is there?

Because there's, you know, we'll move to this in a second, there are these very high-profile projects, sequestration and such which we'll talk about that require technological leaps. But the kinds of things you're talking about here they are more tweaks, I guess, with better control of where your asset is, what's empty at given moments, and, like you said, platooning and other things, organizing people differently.

ALAN: I think the use of the word tweak may underestimate their contribution. It can be incremental, but it can still be quite significant, I think. So one thing is load matching; you know, if you're a trucking company or a truck driver and your truck is going to be returning empty, how can you find a return load? Or, if your vehicle is only partially loaded, how can you maybe pick up another load that will fill it to a greater extent?

Now, we have heard what we call freight exchanges, online freight exchanges now, for over 20 years where a trucker could go online, and it would be an online market, and they would be finding an available load. But that technology has been greatly upgraded recently with the application...well, moving to cloud computing, for example. But the application of artificial intelligence, machine learning, we can now take that level of transport solution to a new level.

TROND: You know, that's fascinating, Alan. My question, though, is, is the business model of the way that drivers are organized also needing to be optimized for that purpose? For example, if a driver works for a given company, what is the incentive for that company to have that driver take more load? I mean, is there a way that you can take someone else's cargo and then get evenly distributed? I don't know, the driver gets something for the inconvenience of going somewhere, and the company that owns the asset obviously gets part of it. There are business model changes needed too.

ALAN: Yes, again, a very good point. One important feature of the trucking industry, I think virtually everywhere in the world, is it's highly fragmented. Here in Europe, we've got over half a million small and medium-size carriers. I think about 80% of carriers only have one vehicle. So how do you engage that vast community of small operators in this process? Mobile computing has helped the mobile phone.

Now these owner-drivers, of course, have an obvious incentive to keep their vehicle as full as much of the time. For the bigger operators, many of them now operate control towers. So it's no longer the driver's decision to do this. I mean, the driver will be told where to go to pick up a load. But for these bigger companies as well, by deploying this technology, they can improve the efficiency of their operation. And as a cool benefit from all of that, you get the carbon reductions and the energy savings.

And we shouldn't just look at this in terms of Europe and in North America. If we look at this at a global level, these technologies that we've just mentioned are beginning to have a revolutionary effect in countries like India, in Indonesia, in African countries, where small operators with a mobile phone can now tap into these networks to find their next backload.

So it's not so much changing the business model; it's refining the business model and creating new commercial opportunities for these companies. So they're not doing this to decarbonize their operations. They're doing this to fill the vehicles, improve efficiency, and save money, but there will be carbon savings as a consequence.

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TROND: You know, your field is so fascinating for the myriad of different tactics that can be deployed here. Let's move for a second just to the bigger issues around energy, infrastructure, and ideas to change the way that that operates. Sequestration, for example, this idea of removing greenhouse gases, requires an enormous infrastructure. And I know you have written extensively on infrastructure overall. What is really at stake here with this type of process? We're talking about a futuristic, enormous industry that would be, I guess, on top of the existing logistics structure.

ALAN: Yes. It certainly will. I mean, I often flag this up to logistics businesses as the next huge business opportunity for so many of these companies. Because sequestration or carbon dioxide removal, I mean, drawing down the greenhouse gases already in the atmosphere is essentially a logistical process. We're going to be creating new supply chains, moving liquidized CO2 to places where it will either be buried in the ground or maybe used for some other purpose, like to make e-fuels.

But to put this into context, why is this happening? It's because we're almost certainly going to overshoot our carbon budgets. And so, if we want to commit to net zero, it is not simply a matter anymore of reducing emissions. We're also going to have to think about removing greenhouse gases already in the atmosphere. And to put that into perspective, I think last year; there were only about 18 or 19 plants in the world that were engaged in sequestration. And they only withdrew, I think, about 10,000 tons of CO2 from the atmosphere.

They're now projecting that by 2050 we'll, on an annual basis, be removing between 10 and 15 billion tons of CO2 from the atmosphere. And that is going to entail an enormous logistical exercise. But at the moment, thinking as at an early stage, we really haven't worked out where the best place will be to do the sequestration and where we will have to take the stuff to bury it in the ground.

TROND: In one of your presentations. You quoted an article from 2021 that says that the concept itself of net zero is basically a trap that it becomes kind of an excuse to do certain things as an extension of existing industries. These researchers have started to get second thoughts about something that they might even themselves have proposed. Is that the alternative view that you'd like to flag out there, or is this really a serious concern that we're putting too many eggs in one basket here?

ALAN: You're right. I mean, a lot of climate scientists are now seriously worried about the concept of net zero. I read the other day I think if you look at all the countries in the world that have committed to being net zero by 2050 or earlier and all the companies, I think 91% of the global economy is now covered by a net zero commitment. But I suspect a lot of people don't truly understand what net zero entails, I mean, realizing there's a big sequestration side to it, and it's not purely mitigation.

But I sympathize with the views of those who say that if we now get fixated with sequestration, if we realize we don't have to cut our emissions very quickly or dramatically because we can just leave it to future generations to pull down all the CO2 that we have put there. That is highly risky because the technologies we have for doing this are still fairly immature. And we're just not sure how we're going to be able to scale this up to the level I've just mentioned.

But there's an equity and ethical issue here that we should be leaving it to future generations to reverse the climate change processes that we have started. The last thing we want, of course, is for interest in sequestration to deflect attention from cutting emissions now. That's what we really need to do. Because the economic modeling on this suggests, it's an awful lot cheaper to stop emitting today than it will be in the future to remove those greenhouse gases from the atmosphere.

TROND: So let's talk a little bit about the future outlook then because there obviously are technologies on the table, on the books but also in development that do have certainly more renewable potential. There are improvements in renewables. There's the whole switching argument that eventually, once you switch, that is going to take effect.

But are you, I guess, pessimistic or optimistic that this switch or this future, as in 2050, which is kind of the climate future that most people are looking at, what is the prospect that we're anywhere close here? And where are the things where you think we should be putting our energies?

ALAN: One has to be optimistic in this area. I mean, if you're pessimistic, what do you gain? We have to look at the positives. And I think we will ultimately be able to decarbonize logistics. What concerns me is the speed at which we're doing it. Now, as I said, ultimately, we will do this by switching from fossil fuel to zero-carbon energy sources. In most cases, we're going to have to change the vehicles, the locomotives, the ships, the planes to do that, and that's going to be a long-term process.

Another thing which concerns me at the moment is there's a lot of disagreement as to what the dominant low-carbon fuel will be for the various future transport modes. So in the road freight sector, there's a debate as to whether we should be using batteries to do this or hydrogen. In the shipping sector, the main choice is between e-methanol or green ammonia. And some people think we should be using nuclear even. So a disagreement there. And then, on aviation, sustainable aviation fuel will be required in vast quantities to decarbonize aviation.

TROND: How are we going to do that? How are we going to do that, right? Isn't that the question? The vast amounts of forests or whatever agriculture is going to go to these biofuels.

ALAN: Well, I think biofuel will make a contribution. Personally, I think the main fuel we will use for aircrafts in the future is e-kerosene, which is a synthetic fuel which will use green electricity. Once we've decarbonized electricity, we can then use that to make green hydrogen, which we can then combine with other chemicals to make e-kerosene. Now at the moment, that's currently...we can do this currently, but it's two or three times more expensive than fossil kerosene.

But also, until we get the capability to do that, we will rely on biofuels. That's certainly true, not just for aviation but in the road freight sector and possibly to some extent in the shipping sector. But we got to make sure the biofuels are environmentally sustainable. Because, I mean, I was a real enthusiast for biofuels when I began to get involved in the climate change work. I thought it's biofuels that will allow us to decarbonize logistics until we did the lifecycle analysis.

And we discovered that if you make your biofuel with palm oil sourced from, I don't know, Indonesia or Malaysia, on a lifecycle basis, the emissions are three times those of the diesel that we are replacing. It just doesn't make sense at all. So we have to ensure that we're using feedstocks for the biofuels, which are genuinely sustainable. There's a limited quantity of those. So we have to see these as being of limited value short term, as transitional, until we move to the other fuels I've just mentioned.

TROND: But, Alan, it seems to me that as much as you're an enthusiast of various futuristic technologies, you're also saying that in the next ten years, there are a lot of operational things we can do. One idea that has been put forward that you've talked to me about is this idea, which needs to be explained, of the physical internet as a conceptual change in the logistics industry. Can you elucidate that concept? Because at face value, I don't quite understand it, but on the other hand, it's the principle here. It's not recreating the internet.

ALAN: No, yeah. I always have to say that the physical internet is not the Internet of Things because people, I think, often wrongly confuse the two things. The physical internet would be a physical manifestation, if you like, of the digital internet, applying the same principles, the same organizational principles that we have for moving emails to the movement of physical consignments.

So if you think what are the key features of the digital internet, open systems, standardized modules for moving information through the internet, we would be creating an open system. There'd be little proprietary asset-based logistics so that the warehouses, the freight terminals, the vehicles would be available for general access. And we would have to put in place, therefore, IT systems and market mechanisms to make that possible because that would then allow us to use that asset base an awful lot more efficiently.

The other thing which would, if I'd just add something else, is modularization. Because at the moment, we have got some degree of modularization obviously in pallets and containers and so forth, but we may have then to remodularize with a different type of handling equipment that would be nested and compatible to allow us to fill the vehicles better and to manage processes in the warehouses, for example.

TROND: It's surprising, I guess, a little bit to hear this, and maybe you can explain this to me. But at surface value, this whole international container standard and the way that that really changed shipping because there's, after all, one container. It looks the same pretty much everywhere. It was this big battle. And then there is this container, it doesn't quite work for air travel, but it works for freight, ocean-based shipping, and for land transport.

So one would have thought that that perspective is so ingrained in logistics because it was such a success story. But you're telling me that...did one rest too much on the laurels of that one success and then never extended this to other aspects of standardization? Or how do you explain that one element is so standardized and many, many, many other elements remain stuck in kind of that proprietary logic?

ALAN: It's a great point. So containerization was a game changer. I mean, it transformed international trade. And we've always been looking for a similar game changer, [laughs] you know, to be equally transformational. But there were still problems with containerization, you know, so that standardized the boxes and made it easier to transfer them between transport modes and so forth.

But if you look at the internal dimensions of a container, they're not all that compatible with the dimensions of the pallets inside, so you always waste some space. We call this the unit load hierarchy. So at the top end, we got the container, and then we come down to the next level, which would be the pallet load, and then the level below that would be the carton. And then you get down to the individual product. And it's at these lower levels in that hierarchy we don't have sufficient standardization. So there are many different sizes and shapes of pallets and stillages, and so forth. And it would be nice if we could converge on similar standardization at that level.

TROND: Fascinating. Let's move to the policy area in a second. I know that you did some work for Unilever a while back and developed a framework for decarbonization policy essentially or to understand the different factors that that will impact, and you called it the Timber Decarbonization Framework. And I'm just going to quickly recite these factors, and you'll explain why they all are here.

So technology, we've talked about technology, infrastructure, you know, obviously, the physical aspect of all these assets. And then market trends behavior which is interesting because behavior is not the first thing I would think of in logistics, [laughs] and then energy system and regulation. So there are many, many things here in this framework. But what does that mean for a policymaker? Because up until now, we've been talking about private sector optimizing their own portfolios, but there's also a wider concern here for policymakers or indeed for individuals.

ALAN: That's right. So a bit of background then on the project that we did for Unilever. The company had set itself this target to reduce the carbon intensity of its global logistics by 40% between 2010 and 2020, and it obviously had some ideas to how it could do that internally. But I thought over that time period, almost certainly, there'll be development outside Unilever's control, many of them at a national level, a macro level, which will help to decarbonize logistics, which would reinforce anything that the company was doing itself internally.

So they asked us to look at 13 of their main markets in the world and make an assessment as to what extent transport logistics were decarbonizing generally. And it was --

TROND: Only 13 markets. [laughs]

ALAN: Only 13 markets, that's right, I know. [laughter] I can tell you it was hard enough just doing it for 13 markets because that includes big markets like China and Brazil, and so forth. So we came up with the timber framework to say that these macro-level trends would fall basically into those six categories. And what we tried to do then was...this was a desk-based study. We tried to pull together as much data as we could for each of those six subject areas.

TROND: What was the most surprising of them for you, Alan? Technology is perhaps pretty obvious. And then infrastructure, I guess, for you in your field is very obvious. But some of the others, at least for me...and regulation, obviously, this was a regulatory concern as well. But what were some of the surprises, the biggest surprise when you were putting together this and realizing which factors were influential?

ALAN: I think it was the diversity which surprised us. Well, maybe I should qualify that because some of those countries were European countries where there's a lot of similarity. Many of them belong to the EU and therefore were governed by continental-wide regulatory policies.

But when you went into other countries, even countries you might think were similar in their level of development and in the maturity of their logistics industry, there were actually quite different approaches to the way in which they were decarbonizing. Just take one thing, for example, the freight modal split, you know, the division of freight traffic between transport modes can vary a lot between countries, and that can be quite a big determinant of the average carbon intensity of freight movement within that country.

But also, there's a feeling that it's the developed world that are doing the most innovative things in decarbonizing logistics. But we did find examples in less developed countries of quite clever initiatives. One often imagines that the lessons from decarbonizing logistics will transfer from the wealthier countries to the poorer ones. But there could be a scope, I think, for the movement of ideas and practices in the opposite direction as well.

TROND: Alan, let me ask you this. I mean, many times, when you know a lot about an area, you come to the conclusion that if I only ruled this system, things would be better.

ALAN: [laughs]

TROND: And thereby, in French, they say this dirigiste approach where you say government or me, the expert, or whoever it is, we are just going to set this straight. Is that the big wish for you or the experts in this domain that some master planner comes in and just kind of lays down the law? Or is the clue to these very necessary decarbonization strategies a more flexible framework?

ALAN: If I was that global dictator with special powers over logistics, I think the one thing I would prioritize would be pricing using the price mechanism. And things are progressing well in that direction. If you go to the World Bank website, there's a dashboard, and they show the extent to which carbon pricing schemes are developing around the world. And I think currently, almost a quarter of greenhouse gases emitted are in countries that have got some form of emissions trading or carbon taxation. So I think that needs to be extended.

What we're also seeing, of course, is the cost of carbon increasing. So the world's biggest emissions trading market is here in Europe. And I think over the past two years, or so, the price of carbon has rocketed; it's currently, I think, about €100 per ton of CO2. So extending these carbon pricing, carbon taxation schemes, and at the same time raising the cost of carbon will then incorporate carbon pricing into companies' balance sheets and their investment appraisal. And that, I think, will drive a lot of the changes we've been discussing. That includes the managerial, operational things right through to the technological things like switching to lower carbon fuels.

TROND: So at the end of the day then, Alan, you say there's a benefit to being optimistic, and I liked that message. But I do sense that there are some bumps in the road here. It's not going to necessarily be an easy technology fix or even an easy policy fix here. It seems the overall logistics framework it's not one industry; it seems to me. There are the logistics practices, and they are spread around every industry.

ALAN: Yes, you're right. I mean, I don't want to give the impression that any of this is going to be easy. It's going to be tough, but it will have to be done. And just to flag up some of the complexities, I've mentioned how in the trucking industry, we're going to have to shift from diesel trucks to probably battery ones predominantly. And again, almost all the discussion of that relates to Europe and in North America. But we got to do this at a global level.

At the moment, a lot of developing countries buy second-hand trucks from Europe or North America. And one thing that concerns me is that as Europe and North America accelerate the transition to low-carbon vehicles, they will want to dump a lot of their existing diesel vehicles. And the danger is they'll be dumped in less developed countries, where that will then slow their transition to the next generation of battery-powered vehicles.

So this is an area where we really have to take a truly global perspective on how we transform road freight because what's the point of us massively reducing our CO2 emissions in Europe if all we do is inflate emissions from other parts of the world? I mean, climate change is a global problem. We've got one atmosphere, and therefore we have to look at that bigger picture.

TROND: That's fascinating. It would seem to me that the solution would have to be something where you add incentive for everyone regardless of where you are in the pyramid of industrial transition to leapfrog essentially, right?

ALAN: Yes, yes, exactly. I think the key will be transferring technologies best practice from a lot of the more developed countries to the less developed world. I've just written a paper for the World Bank looking at how we tailor logistics, decarbonization to the needs of less developed countries, and that will be coming out in a few months' time. And I think that's going to be really one of our bigger challenges in this field.

TROND: Alan, it's fascinating to hear such an overview of a field and an expanding landscape that is so crucial to something that clearly is one of the bigger challenges of our time. Thank you so much for your time today.

ALAN: You're welcome. Thank you.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Decarbonizing Logistics. Our guest was Alan McKinnon, Professor of Logistics at the Kühne Logistics University of Hamburg. In this conversation, we talked about mitigating and adapting to climate change throughout industrial supply chains.

My takeaway is that decarbonizing logistics without slowing economic growth is a formidable challenge which requires paradigm shifts across many industries, as well as adopting openness principles from the virtual internet onto the physical nature of the supply chain, as well as facilitating new business models, sharing, and standardization, and eventually dematerialization. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 68: Industrial Supply Chain Optimization. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us because we would love to share your thoughts with other listeners.

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

Please share this show with colleagues who care about where industry and especially where industrial tech is heading.

To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube.

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

Special Guest: Alan McKinnon.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn and how to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip Interfaces (@tulipinterfaces), the frontline operations platform.

In Season 2 we honed in, covering a specific topics relevant to manufacturing, such as marketing, frontline operations, reshoring, digital lean, startups, supply chains, pricing strategies, the manufacturing software market workers, the low code/no- code issue, diagnostic manufacturing, operational data, life science manufacturing systems, the industrial tech transformation outlook, the future factory, the evolution of lean, and industrial interoperability. As you can see, these ranged from technical topics to HR to investing to management principles--all of which go into operating and innovating in manufacturing and industrial tech.

Guests featured in this episode:

Joe Sullivan (@sullivan_joe), host of The Manufacturing Executive podcast and founder of Gorilla 76 (@gorilla76)
Lydia M. Di Liello (@LydiaDiLiello), CEO and founder of Capital Pricing Consultants, and co-host of The WAM Podcast: Empowering Women in Manufacturing and Business. (@wam_podcast)
Yossi Sheffi (@YossiSheffi), Director, MIT Center for Transporation and Logistics (@MITSupplyChain)
Harry C. Moser (@reshorenow) founder and President of the Reshoring Initiative
Dr. Gunter Beitinger (@beitgugb) (@Siemens) SVP of Manufacturing at Siemens AG, Head of Factory Digitalization and Head of Product Carbon Footprint/SiGreen

Thanks for listening. If you like the show subscribe to augmentedpodcast. co or on your preferred podcast player. And rate us with 5 stars. If so, let us know by messaging us your thoughts. Hopefully, you'll find something awesome in this show or in other episodes. Please, if you do, let us know by messaging us. We would love to share your thoughts with other listeners.

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

Please share this show with colleagues who care about where industrial tech is heading.

To find us on social media is easy, we are Augmented Pod on LinkedIn and Twitter, and Augmented Podcast on Facebook and YouTube:

LinkedIn: https://www.linkedin.com/company/augmentedpod
Facebook: https://www.facebook.com/AugmentedPodcast/
Twitter: https://twitter.com/AugmentedPod
YouTube: https://www.youtube.com/channel/UC5Y1gz66LxYvjJAMnN_f6PQ

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

Special Guests: Dr. Gunter Beitinger, Harry C. Moser, Joe Sullivan, Lydia M. Di Liello, and Yossi Sheffi.