Augmented Ops

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

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

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

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 94 on Digitized Supply Chain with insights from Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson.

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

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

Transcript:

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

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

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

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

Antonio, welcome to the podcast. How are you?

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

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

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

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

TROND: [laughs]

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

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

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

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

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

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

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

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

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

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

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

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

[laughter]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ANTONIO: [laughs]

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

ANTONIO: Right, yeah.

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

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

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

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

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

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

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

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

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

ANTONIO: Fruition.

TROND: Fruition here.

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

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

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

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

ANTONIO: Thank you. I appreciate it.

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

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

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

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

Special Guest: Antonio Hill.

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

In this episode of the podcast, the topic is "Augmented Lean Prelaunch." Our guest is Natan Linder, in conversation with host, Trond Arne Undheim.

In this conversation, we talk about the background of our co-authored book, Augmented Lean, a human-centric framework for managing frontline operations, why we wrote it, what the process has been like, the essence of the Augmented Lean framework, and the main lessons of this book for C-level executives across industry.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 96 on The People Side of Lean with Professor Jeff Liker.

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Industrial revolutions are rarely chronicled as they are happening, but this industrial revolution will be. There is an ongoing shift in the way technology and workforce combine to produce industrial change, and it is happening now. We are lucky to be situated in the middle of it. And I personally feel fortunate that I was brought along for the ride.

It has been a life-changing experience to realize the power and impact of living through a shifting logic of manufacturing and, perhaps more importantly, to realize that as excited as we can be about automation, an augmented workforce represents the best combination of the most important technology we have which is human workers themselves with the second best machines that humans create. The fact that making humans and machines work together is no trivial task has been pointed out before but documenting what happens when it does go well in the biggest industrial companies on the planet feels like a story that deserves to be told.

Transcript:

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

In this episode of the podcast, the topic is Augmented Lean Prelaunch. Our guest is Natan Linder, in conversation with myself, Trond Arne Undheim.

In this conversation, we talk about the background of our co-authored book, Augmented Lean, a human-centric framework for managing frontline operations, why we wrote it, what the process has been like, the essence of the Augmented Lean framework, and the main lessons of this book for C-level executives across industry.

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

Natan, good to have you in the studio. How are you today?

NATAN: I'm great. How are you? It's been a minute.

TROND: It's been a little minute for us. It's crazy with book launches. It takes a little out of you. And you are running a company in addition to that, so you had some other things on your plate too.

NATAN: Yep, running a company and having a book coming is an, I don't know if an artifact, but definitely, company is a lot about changing the status quo. And the book tries to capture a movement. So I think they go along nicely.

TROND: Yeah, Natan. And I wanted to bring us in a little bit and converse about why this book was written. Certainly, that's not my benefit. You brought it up to me. But what were we thinking about when writing this book? So I want to bring it back to way before I came into the picture with the book because it was your idea to write a book. What was on your mind? What were the main reasons that you thought I really want to write a book?

NATAN: When I was coming up as an engineer...and my background, I'm not a pure manufacturing production type engineer, but I've been around it my entire career just because of the type of products that I've been involved with whether it's mobile phones, or robots of all sorts, 3D printers. So you get to spend a lot of time in these operational environments, shop floors, machine shops, and the like.

And when we started working on Tulip, it was pretty clear pretty quickly that there's a moment that is emerging in operations that no one has captured the story. And this is back even; I don't know, maybe five or six years ago. We are maybe one or two years old, and I'm already starting to think about this post-lean, or classical lean movement that I'm sure is happening. That really is the genesis of the book in the early, early days.

And fast forward to when we started talking, I think we got Tulip off the ground. But really, that was a platform to meet all those different people who helped operations transform digitally, whether it's all sorts of consultants, or academics who are researching operations, or business leaders, you know, tons of factory managers and the engineers that work with them, and the executive, so a whole bunch of people. And they're all basically talking about the same thing and the deficiencies in lean, the complexity of technology, and how they're trying to change, and it is so difficult.

So I think that's a good description of the landscape before diving in to try and capture what the book attempts to capture.

TROND: Yeah, Natan, I remember some of our early discussions. And we were dancing around various concepts because clearly, lean is a very broad perspective in industrial manufacturing focused on reducing waste and many other things. It's a broad concept that people put a lot of different things into.

But I remember as you and I were thinking about how to describe this new phenomenon that we do describe in the book, we were thinking a little bit that a lot of these new influences come from the digital sphere. So there's also this term agile. There are some people who say, well, you know, let's just replace lean because it's an outdated paradigm. And I remember you were quite adamantly arguing that that's not the case. And this goes a little bit to the message in our book. We are in no way really saying that lean isn't relevant anymore.

NATAN: On the contrary.

TROND: Tell me a little bit about that.

NATAN: A really simple way I think to frame it is that whether you're practicing lean formally or some variant of it, of lean, or Six Sigma, or some program that formalizes continuous improvement in your operation...and we're talking about frontline operations. We're talking about factories, and labs, and warehouses, and places like that. You are practicing lean because this is how the world..., even if you're not doing it formally; otherwise, you're not competitive. Even if you're in a bank or a hospital, you might be practicing lean.

And that's where agile comes to the picture, and it was adopted widely by operations practice in general and pushed into areas that are not pure manufacturing. So, in a way, lean is a reality. Some organizations are more formal about it, some are less, but definitely, they're doing it.

Here's the issue, and this is the main thesis of the book. When lean came about...and we know the catalyzing text. We know the teaching of Taiichi Ohno. We know about The Goal. We know about The Machine That Changed the World. And those are seminal texts that everybody reads. And we know about Juran and lots of great thinkers who thought about operations as a data-driven game, some from the school of thought of quality, some from pure operation research, some from how do you put emphasis on classic just-in-time, Kanban, Kaizen, all those continuous improvement things.

But at the end of the day, all of that thinking, which still holds true, was not done when digital was top of mind, where data is everywhere, where people need to live in such data ecology. It was done, so to speak, in analog times. And it doesn't mean that the principles are wrong, but it doesn't mean they don't need to get augmented. And this is maybe the first time where this idea of augmentation, which, to me, augmentation is always about...I always think about augmentation from a people's perspective or an org perspective. It's just a collective of people. That's where it starts, and that's where we had something to say. So that's one aspect to think about.

The second big one is actually very simple. It's kind of like; we heard ten years of industry 4.0 is going to change everything, and all we got is this lousy OEE graph. And that's kind of like a little tongue-in-cheek on we were promised flying cars, but we only got 140 characters. I mean, come on, stop talking about industry 4.0. It's like, who cares?

If the tools and digital techniques and what have you is not adopted by the people actually doing the work, that then collectively, one engineer, another engineer, another operator, a team lead, the quality lead, and so on come together to transform their org, if that's not happening, then that's not sustainable transformation, and it's not very relevant. Again, augmentation.

TROND: Right. And I think, Natan, that's where maybe some people are surprised when they get into this book. Because it would be almost tempting to dismiss us as traditionalists in the sense that we are not really going whole hog into describing digital as in and of itself, the core of this principle. So there is a little bit of a critique of agile as an idea that agile or using that as a kind of a description for all digital or digital, right? That digital doesn't change everything.

And I guess I wanted to reflect a little bit on that aspect because I know that you, as a business leader now hiring a lot of people, we are spending a lot of energy bringing these two perspectives together, and it's not very obvious. You can't just take a digital person who is completely digital native and say, "Welcome to the factory; just do what you do. And because you do things better than everyone else, we are now going to adapt these factories." How do you think about that?

In factories, you could conceive it as the IT versus OT, so operational technologists versus information technologists and the various infrastructures that are quite different when those two things come into play.

NATAN: So my frame of reference is the most value...and it's a very engineery frame of reference because I'm an engineer at the end of the day. It's like, the most value gets unleashed when people truly change how they work and adopt a tool, and that's true for operations and manufacturing. But, by the way, it's also true for the greater business perspective.

And a lot of people, when I talk to them about Augmented Lean, really take us to the realms of what is the future of work, and I think it's very timely. We're kind of in a post-COVID reality. Working remote has changed many things, working with data. Big ideas like citizen development, you hear them all over the place. And use of advanced platforms like the no-code/low-code that allow people to create software without being software engineers become a reality. So there's a much broader thing here.

But if I focus for a second on what you're asking, the way I see it is when people truly change how they work, it means that they believe, and that belief translates into action, that the tool that they're using is the best way to do something. And they become dependent and empowered by it at the same time because they're not willing to go back to a state where they're not thinking and working with data, or back to the clipboard, or back to being dependent on an IT department or a service provider to give them some technical solution. People have become more self-sufficient.

And it turns out that if you do that, and sometimes people would refer to that as you let people hack or go nuts in the factory floor or in whatever operational environment, that could be a concern to people, and that's a fair observation for sure. And that's where when you look at the book, when we were kind of constructing the framework we call Leader HG where HG stands for hack and govern... We are used to Silicon Valley startups being like, oh yeah, you all just need to hack. And that's a very glorious thing, and everybody understands that.

And they want them to hack when they are a 50,000-person software company. They're still hacking, but they're doing it in a much more structured way, in a much more measured way. So even in hacking, there's governance. And in operational environment, governance is equally important, if not more, because you're making real things. That is something we've observed very empirically.

Talking to a lot of people seeing what they do, it's like, yeah, we want the best ideas from people. How do we get it? What do we do? We tried this approach, that approach. And I think we were sometimes very lucky to be observers to this phenomena and just captured it.

TROND: Yeah. And I wanted to speak to that a little bit. I want to thank you, actually, for bringing me into this project because you and I met at MIT but from different vantage points. I was working at Startup Exchange working with a bunch of very, very excellent MIT startups in all different domains, and you were an entrepreneur of several companies. But my background is more on the science and technology studies but also a management perspective on this.

But I remember one of the things you said early on to me was, "I want to bring you in on this project, but don't just be one of those that stays at the surface of this and just has like a management perspective and writes future of work perspectives but from like a bird's eye view. Come in here and really learn and go into the trenches."

And I want to thank you for that because you're right about many things. This one you were very right about. And this clearly, for me, became a true research project in that I have spent two years on this project, a lot of them in venues and factory floors, and discussing with people really at the ground level.

And for me, it was really a foundational experience. I've read about many things, but my understanding of manufacturing, frankly, was lacking. And you could have told me as much, but I actually, frankly, didn't realize how little I knew about all of the factors that go into manufacturing. I had completely underestimated the field. What do you say to that?

NATAN: It's interesting because I feel like the last two years, everything I think I know [laughs], then I found out that I don't know enough. It just kind of motivates you to do more work to figure out things because it's such a broad field, and it gets very, very specific.

Just listening to your reflection on the past couple of years, the reality is that there is a gap in the popular understanding of what operations and manufacturing is all about. People think that stuff comes from some amorphous factory or machine that just makes the things. And they usually don't see, you know, we have those saying, like, you don't want to see how the sausage is made, which is obviously very graphic.

But you also don't see how the car is made unless you're a nerd of those things and watch those shows like how things are made, but most people just don't. And they don't appreciate the complexity and what goes into it and how much technology and how much operation process it consumes. And as a society and as a set of collective economies and supply chains, it is so paramount to what's actually happening.

Just take things like sustainability or what happens with our planet. If we don't learn to manufacture things better and more efficiently with less people because we don't have enough people in operations, for example, our economies will start to crumble. And if we don't do it in a way that is not just sustainability from the perspective of saving the planet, also that, but if we don't become more efficient in our supply chains, then businesses will crumble because they can't supply their customers with the product that they need.

And this thing is never-ending because products have life cycles. Factories have life cycles. And the human species, that's what we do; we take technology, and then we turn it into products, and we mass produce it. That's part of how we survive. What we need is we increase awareness to this. And I think The Machine That Changed the World and Toyota Production System unveiled those concepts that you need to eliminate waste to build better organizations, to build a better product, to have happier customers; there's something really fundamental there that did not change.

The only thing that changed is that now we're doing it in a reality where the technology is out there; data is out there. And to wield it is difficult, and there is no escape from putting the people who do the work in the center. And to me, if we are capable of doing that, the impact of this is recharging or rebooting lean in the classic sense for the next three decades. And that's my personal hope for this book and the message we're hoping to bring in. We would love people to join that call and fly that flag.

TROND: Yeah. I wanted to take us now, Natan, to this discussion. A lot of people are saying, "Oh, you got to market manufacturing better, and then people will come to this area because there are interesting things to do there." But more broadly, if we think about our book and why people should read that, my first reflection is building on what I said earlier that I didn't realize not just the complexity of manufacturing but how interesting it was.

My take after two years of studying this is actually that there's no need to market it better because it is so interesting and fundamental for the economy that the marketing job, I think, essentially has already been done. And it's just there's a lag in the system for new employees, new talent. And society overall realizes how fundamentally it is shifting and reconfiguring our society.

But I guess I want to ask you more. What is the reason a C-level executive, whether they work in manufacturing, in some industrial company, or really, if they work in any company that is interested in what technology and manufacturing is doing to their business reality...how they can implement some of those ideas in their business. What would you say to them? I mean, is our book relevant to a business leader in any Fortune 500? Or would you say that our messages are kind of confined to an industrial setting?

NATAN: I think it applies to all of them. And the reason is that these types of roles that you're describing, folks will best be served if they learn from other people's experience. And what we tried to do in the book is to bring almost an unfiltered version of the stories of their peers across various industries, from medical devices, to pharmaceuticals, to classic discrete manufacturing, all sorts of industries. And they're all struggling with the same kind of stuff. And so those stories are meaningful and can contextualize the thinking of what those C-levels are actually trying to cope with.

What they're really trying to do, everybody, I'd say, is why do people think about and talk about those big terms of digital transformation? It's really because they want to make sure their companies don't stay behind or, in other words, stay competitive. This stuff is an imperative for organizations that have real operations that span digital and physical, and I don't know many that don't. Of course, there are some service industries that don't have anything but still have operations.

You can't avoid handling the subject and what it entails. It entails training your people differently. It entails defining technology stacks. It entails connecting using various technologies, protocols, what have you, across organizations and finding value in this data so you can make good decisions on how you run your billing cycles, or how you order your stock to build, or how you ship your end product and everything in between.

And I don't think that the book is groundbreaking in the sense that we're the first people who ever thought about it. But I think if we've done anything, is we've observed long and hard. And we've listened very carefully to what people are telling us that they did, and they struggled. And it's a timely book. And maybe in a decade, it's a classic, and, wow, these are good stories. And it's like reading about the first people booting up mainframes or PCs. And if that happens, I'm actually pretty happy.

But you know why I would be happy? Trond, let me tell you something, it's because technology, like, the human needs change much slower than how technology evolves and gets deployed, but still, good technological-driven transformation take a long time.

TROND: That's exactly what I was going to say is that the future is an interesting concept because what's tomorrow to some people is today for others. So you say we're not writing about something that's so new or unique but to industry overall and to some manufacturers, what we're writing about is the future because they haven't implemented it yet.

To some of Tulip customers, to some of the great companies that we have researched in the book, whether they be J&J, Stanley Black & Decker, DMG MORI, a lot of other companies in medical device side, and also smaller and medium-sized companies, even some startups that are implementing some the Augmented Lean principles, to them, this is of course not the future.

And maybe, you know, we're not saying that leaders who try to implement Augmented Lean need to change everything around; we're saying common sense things. It's just that; clearly, all of industry is not human-centric, right? There are parts of industry where you adjust 80% to your machines, and you make economic decisions purely based on the infrastructure efficiency improvements you're trying to make. I guess what we're saying is the innovation argument; people are the most innovative, and you have to restructure around your workforce, even if you are making machine and robot investments.

NATAN: Yeah, automation would always require strong reasons to automate that, you know, some of them are complexity, safety risk, things like that or throughput to like how much product do you need and that kind of stuff. But even if you have the best automation, you typically have people around it, and nothing is just only machine-driven or only human-driven.

The reality is that most stuff gets made through a combination of several manufacturing technologies working in unison with people at the beginning, middle-end doing things from the planning, to running automation setups and machinery, to taking the output, doing assembly, doing tests, audits and checks, and packaging, and logistics, and at the end of the day, human-intensive type of operation in most of the areas we roam, at least.

And as such, to think that in this day and age you don't focus on people is to me nuts when all those people carry a supercomputer called a smartphone in their hand and have uber-connected homes with a million CPUs streaming all this data, and we call that media, whatever. And they're so accustomed to interfacing to their world and their businesses through that.

And you and I are Gen Xers, and let's just think about the generation that comes after us and after us. These are digital natives par excellence. They expect as much, and organizations that don't do that, whether they choose the Augmented Lean approach or any other approach, they're just not going to have employees. That's a little bit of a problem.

TROND: Yeah. But it's important what you're saying in one respect which is there are many reasons to dismiss a book, a management book, a technology book. And one could be like; all these people are just that. And one, I guess, gut reaction when people look at the title or perhaps hear some of the things that you and I are saying is that, oh, these people are Luddites; they're against technology.

But I wanted to, certainly on my end, just to state very clearly there's nothing in our book that's against technology. We're simply saying to optimize for the simplest technology, that is, you know, to our great inspiration here, who was a big inspiration, I know, for you and now for me because you brought her into my sphere. Pattie Maes' perspective from MIT on Fluid Interfaces and the importance, you know, no matter what advanced technology you're going to bring into whatever context, if that context of the technology, the use interface is not a fluid interface, you are simply doing yourself a disservice.

You could have bought a $1 million CNC machine or maybe a $10 million whatever robot, but it has to work in your own organization, and this is just so important. So we're not against technologies. We're just saying these investments will be made. But you have to think about other things as you're making those investments. So I just wanted to make that point and hear your comment to that.

NATAN: Yeah, look, I have a slightly...I guess a complementary angle to this is like when you think about it; I think that technologically democratized organizations in the day and age we living in the future. And what makes, I think, Augmented Lean span beyond the frontline operation perspective is because it tells a story of democratizing operation where fundamentally before lean...and we're talking about the mass production era. Mass production came from a military structure, you know, divisions, and battalions, and commanders, and ranks, and all that kind of stuff.

Enters lean, and democratization starts. Forget technology. It starts because suddenly everybody on the Gemba Walk, you know, the walk where they have an equal voice to find problems on the shop floor, and list them up, and think about a solution, everybody has a voice. So these are fundamental things that shifted things like how you manage your warehouse, or how you do just-in-time, or how you are supposed to do continuous improvement. But you have to collect data to prove that this improvement is actually worthwhile doing.

And this is exactly what agile took, and this is exactly the transition you saw in, well, because the market moves so fast and the internet is here, and clouds are real, why don't we not spend two years in a bunker doing waterfall software development? And, boom, we're now talking sprints and all that kind of stuff. And no one is even questioning that. And that's a lean approach we call agile, lean approach to how you do software development.

And what I'm trying to say is, de facto, when I run a day in a company, like, I talk to my peers, and my leaders, and folks I work with on a daily basis. Everybody talks, yeah, we're on an operation sprint. We are on a marketing sprint. We are on a whatever sprint. What is that? That is a democratized organization with specific leaders owning functions and owning interfaces using tech stacks all over the place: the marketing stack, the sales stack, the HR stack, whatever.

And where we roam also, we're part of the operational or OT stack, and that's what they're doing. And all this book is doing is saying, like, hey, it's actually happening. Let's give this a name. Let's put the beacon on this. Let's try and find what's the commonalities. Let's get the best stories that share the successes and the failures. We have plenty of failures there in the book that teach you something at this moment in time and set up the next decade.

This next decade to me, is seminal. It's not very different to when technologies reached maturity, like clouds and what have you. 10, 15 years ago, you're talking about this thing, cloud, some people will go like, "What cloud? What are you talking about?" That's done. That's the disappearing edge of technology. Now we say AI and all that kind of stuff. And then the problem gets solved and disappearing, you know, it's like, so that's going to happen. I just think we gave it a good name and a good description at this point in time.

TROND: Natan, I love the...personally, I'm a runner. I love the metaphor of a sprint, and for a couple of reasons, not just because I know what a sprint is and what it takes. But I love the fact that a sprint in a management context refers to sprinting partly together because it's a team-based effort. So some people need to sprint a little faster in certain aspects of that team process in order to deliver things that the team needs.

But rounding up and thinking about how people can sprint with us, Natan, how should people think about learning more? So, obviously, reading the book. It's available on every bookstore, and Wiley published it, and it should be everywhere. There's even an e-book.

But beyond that, what are your thoughts about how people can get in touch, join the movement, join the sprint of thinking about Augmented Lean? Which by the way, there is no one Augmented Lean principle. It's a menu of choices. There are ways that you can engage. There are ways you can implement it. It's not like a one, three-step process that everybody has to do. But there are ways that people can connect. We have this Augmented Podcast. What are your thoughts if people are gelling with this message?

NATAN: I can talk about my heart's desire, okay, and my hallucination around this. And this is like, really, kind of living the dream and making sure democratization continues. If we are successful, at the moment, we are starting a movement. And there are millions of people who self-identify as lean Six Sigma quality professionals out there that know exactly what we're talking about viscerally. They spend their days trying to solve problems like that. They pore over data; they train people. They are the people creating the reports and trying to kind of help their organization take another step and another step in the never-ending journey of continuous improvement.

We need to work on a much larger manifesto for Augmented Lean, and this is not for you and me; this is for a greater community to come together. So my recommendation is if you dig this and this is something you want to do, you know where to find us; go to augmentedlean.com. There's a contact email, our contact information. And I guess we can share it for that purpose somewhere in Augmented Podcast or our various other channels. And tell us what you think. And just join us.

We're not sure exactly...we're starting from the excitement around launching the book with our close network of partners, and friends, and customers, and collaborators, and all our network. And it's a very exciting moment for us. But we're going to open it up, and it's going to be in the book tour, and it's going to be in various conferences.

And the first law of creating a movement is show up. So I'm calling everybody to show up if you're okay with lean and the way it's going so far for you and Six Sigma. But if you feel the need to change and observed or experienced some of the stuff we're talking about in Augmented Lean, come tell us about it, and let's shape it up and get people together. The internet is the best tool on the planet to do that, and we'll get it done. Stay safe.

TROND: Right. So, on that note, I want to round us off. I think that it should at least be clear from this conversation that both of us strongly feel that there are greater things ahead for industry and that manufacturing is not just a relevant piece of society, but there are things happening here that are coalescing that we are describing in the book, but that will happen independently of us and the very few examples we were able to put into the book.

And folks that are interested in exploring what that means for them as individuals, as knowledge workers in the factory floor, or as executives who just want to be inspired the way people were inspired by the Toyota lean movement or other movements, they should come and contact us. Natan, thanks for spending the time today.

NATAN: Yeah. Thanks, Trond. Always a pleasure. Will see you very soon.

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

The topic was Augmented Lean Prelaunch. Our guest was Natan Linder, in conversation with myself, Trond Arne Undheim. In this conversation, we talked about why we wrote a book and why C-level executives should read it.

My takeaway is that industrial revolutions are rarely chronicled as they are happening, but this industrial revolution will be. There is an ongoing shift in the way technology and workforce combine to produce industrial change, and it is happening now. We are lucky to be situated in the middle of it. And I personally feel fortunate that I was brought along for the ride.

It has been a life-changing experience to realize the power and impact of living through a shifting logic of manufacturing and, perhaps more importantly, to realize that as excited as we can be about automation, an augmented workforce represents the best combination of the most important technology we have which is human workers themselves with the second best machines that humans create. The fact that making humans and machines work together is no trivial task has been pointed out before but documenting what happens when it does go well in the biggest industrial companies on the planet feels like a story that deserves to be told. Thanks for listening.

If you liked the show, please subscribe at augmentedpodcast.co. And if you liked this episode, you might also like Episode 96 on The People Side of Lean with Professor Jeff Liker, who wrote the best-selling book, The Toyota Way. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us because we would love to share your thoughts with other listeners.

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

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

Special Guest: Natan Linder.

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

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

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

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

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

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

Transcript:

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

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

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

ALAN: I'm very well, thank you.

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

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

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

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

TROND: [laughs]

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

[laughter]

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

TROND: Hmm.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[laughter]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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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.

The topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati.

In this conversation, we talk about how AI does many things but to be applicable; the industry needs it to work every time, which puts additional constraints on what can be done by when.

If you liked this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation.

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation.

Transcript:

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

In this episode of the podcast, the topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, and the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati.

In this conversation, we talk about how AI does many things but to be applicable, industry needs it to work every time, which puts on additional constraints on what can be done by when.

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

Jay, it's a pleasure to have you here. How are you today?

JAY: Good. Thank you for inviting me to have a good discussion about industrial AI.

TROND: Yeah, I think it will be a good discussion. Look, Jay, you are such an accomplished person, both in terms of your academics and your industrial credentials. I wanted to quickly just go through where you got to where you are because I think, especially in your case, it's really relevant to the kinds of findings and the kinds of exploration that you're now doing.

You started out as an engineer. You have a dual degree. You have a master's in industrial management also. And then you had a career in industry, worked at real factories, GM factories, Otis elevators, and even on Sikorsky helicopters. You had that background, and then you went on to do a bunch of different NSF grants. You got yourself; I don't know, probably before that time, a Ph.D. in mechanical engineering from Columbia.

The rest of your career, and you correct me, but you've been doing this mix of really serious industrial work combined with academics. And you've gone a little bit back and forth. Tell me a little bit about what went into your mind as you were entering the manufacturing topics and you started working in factories. Why have you oscillated so much between industry and practice? And tell me really this journey; give me a little bit of specifics on what brought you on this journey and where you are today.

JAY: Well, thank you for talking about this career because I cut my teeth from the factory early years. And so, I learned a lot of fundamental things in early years of automation. In the early 1980s, in the U.S, it was a tough time trying to compete with the Japanese automotive industry. So, of course, the Big Three in Detroit certainly took a big giant step, tried to implement a very good manufacturing automation system.

So I was working for Robotics Vision System at that time in New York, in Hauppage, New York, Long Island. And shortly, later on, it was invested by General Motors. And in the meantime, I was studying part-time in Columbia for my mechanical engineering, Doctor of Engineering. And, of course, later on, I transferred to George Washington because I had to make a career move. So I finished my Ph.D. Doctor of Science in George Washington later.

But the reason we stopped working on that is because of the shortage of knowledge in making automation work in the factory. So I was working full-time trying to implement the robots automation in a factory. In the meantime, I also found a lack of knowledge on how to make a robot work and not just how to make a robot move. Making it move means you can program; you can do very fancy motion. But that's not what factories want.

What factories really want is a non-stop working system so they can help people to accomplish the job. So the safety, and the certainty, the accuracy, precision, maintenance, all those things combined together become a headache actually. You have to calibrate the robot all the time. You have to reprogram them.

So eventually, I was teaching part-time in Stony Brook also later on how to do the robotic stuff. And I think that was the early part of my career. And most of the time I spent in factory and still in between the part-time study and part-time working.

But later on, I got a chance to move to Washington, D.C. I was working for U.S. Postal Service headquarters as Program Director for automation. In 1988, post service started a big initiative trying to automate a 500 mil facility in the U.S. There are about 115 number one facilities which is like New York handled 8 million mail pieces per day at that time; you're talking about '88. But most are manual process, so packages.

So we started developing the AI pattern recognition, hand-written zip code recognition, robotic postal handling, and things like that. So that was the opportunity that attracted me actually to move away from automotive to service industry. So it was interesting because you are working with top scientists from different universities, different companies to make that work. So that was the early stage of the work.

Later on, of course, I had a chance to work with the National Science Foundation doing content administration in 1991. That gave me the opportunity to work with professors in universities, of course. So then, by working with them, I was working on a lot of centers like engineering research centers and also the Industry-University Cooperative Research Centers Program, and later on, the materials processing manufacturing programs.

So 1990 was a big time for manufacturing in the United States. A lot of government money funded the manufacturer research, of course. And so we see great opportunity, like, for example, over the years, all the rapid prototyping started in 1990s. It took about 15-20 years before additive manufacturing came about. So NSF always looks 20 years ahead, which is a great culture, great intellectual driver. And also, they're open to the public in terms of the knowledge sharing and the talent and the education.

So I think NSF has a good position to provide STEM education also to allow academics, professors to work with industry as well, not just purely academic work. So we support both sides. So that work actually allowed me to understand what is real status in research, in academics, also how far from real implementation.

So in '95, I had the opportunity to work in Japan actually. I had an opportunity...NSF had a collaboration program with the MITI government in Japan. So I took the STA fellowship called science and technology fellow, STA, and to work in Japan for six months and to work with 55 organizations like Toyota, Komatsu, Nissan, FANUC, et cetera.

So by working with them, then you also understand what the real technology level Japan was, Japanese companies were. So then you got calibration in terms of how much U.S. manufacturing? How much Japanese manufacturing? So that was in my head, actually. I had good weighting factors to see; hmm, what's going on here between these two countries? That was the time.

So when I came back, I said, oh, there's something we have to do differently. So I started to get involved in a lot of other things. In 1998, I had the opportunity to work for United Technologies because UTC came to see me and said, "Jay, you should really apply what you know to real companies." So they brought me to work as a Director for Product Environment Manufacturing Department for UTRC, United Technology Research Center, in East Hartford. Obviously, UTC business included Pratt & Whitney jet engines, Sikorsky helicopters, Otis elevators, Carrier Air Conditioning systems, Hamilton Sundstrand, et cetera.

So all the products they're worldwide, but the problem is you want to support global operations. You really need not just the knowledge, what you know, but also the physical usage, what you don't know. So you know, and you don't know. So how much you don't know about a product usage, that's how the data is supposed to be coming back. Unfortunately, back in 1999, I have to tell you; unfortunately, most of the product data never came back. By the time it got back, it is more like a repair overhaul recur every year to a year later. So that's not good.

So in Japan, I was experimenting the first remote machine monitoring system using the internet actually in 1995. So I published a paper in '98 about how to remotely use physical machine and cyber machine together. In fact, I want to say that's the first digital twin but as a cyber-physical model together. That was in my paper in 1998 in Journal of Machine Tools and Manufacture.

TROND: So, in fact, you were a precursor in so many of these fields. And it just strikes me that as you're going through your career here, there are certain pieces that you seem to have learned all along the way because when you are a career changer oscillating between public, private, semi-private, research, business, you obviously run the risk of being a dilettante in every field, but you seem to have picked up just enough to get on top of the next job with some insight that others didn't have. And then, when you feel like you're frustrated in that current role, you jump back or somewhere else to learn something new.

It's fascinating to me because, obviously, your story is longer than this. You have startup companies with your students and others in this business and then, of course, now with the World Economic Forum Lighthouse factories and the work you've been doing for Foxconn as well. So I'm just curious.

And then obviously, we'll get to industrial AI, which is so interesting in your perspective here because it's not just the technology of it; it is the industrial practice of this new domain that you have this very unique, practical experience of how a new technology needs to work. Well, you tell me, how did you get to industrial AI? Because you got there to, you know, over the last 15-20 years, you integrated all of this in a new academic perspective.

JAY: Well, that's where we start. So like I said earlier, I realized industry we did not have data back in the late 1990s. And in 1999, dotcom collapsed, remember?

TROND: Yes, yes.

JAY: Yeah. So all the companies tried to say, "Well, we're e-business, e-business, e-commerce, e-commerce," then in 2000, it collapsed. But the reality is that people were talking about e-business, but in the real world, in industrial setting, there's no data almost. So I was thinking, I mean, it's time I need to think about how to look at data-centric perspectives, how to develop such a platform, and also analytics to support if one-day data comes with a worry-free kind of environment. So that's why I decided to transition to an academic career in the year 2000.

So what I started thinking, in the beginning, was where has the most data? As we all know, the product lifecycle usage is out there. You have lots of data, but we're not collecting it. So eventually, I called a central Intelligent Maintenance System called IMS, not intelligent manufacturing system because maintenance has lots of usage data which most developers of a product don't know. But if we have a way to collect this data to analyze and predict, then we can guarantee the product uptime or the value creation, and then the customer will gain most of the value back.

Now we can use the data feedback to close-loop design. That was the original thinking back in the year 2000, which at that time, no cell phone could connect to the internet. Of course, nobody believed you. So we used a term called near-zero downtime, near-zero downtime, ZDT. Nobody believed us. Intel was my first founding member. So I made a pitch to FANUC in 2001. Of course, they did not believe it either. Of course, FANUC in 2014 adopted ZDT, [laughs] ZDT as a product name.

But as a joke, when I talked to the chairman, the CEO of the company in 2018 in Japan, Inaba-san that "Do you know first we present this ZDT to your company in Michigan? They didn't believe it. Now you guys adopted." "Oh, I didn't know you use it." So when he came to visit in 2019, they brought the gift. [laughs]

So anyway, so what happened is during the year, so we worked with the study of 6 companies, 20 companies and eventually they became over 100 companies. And in 2005, I worked with Procter & Gamble and GE Aircraft Engine. They now became GE Aviation; then, they got a different environment.

So machine learning became a typical thing you use every day, every program, but we don't really emphasize AI at that time. The reason is machine learning is just a tool. It's an algorithm like a support-vector machine, self-organizing map, and logistic regression. All those are just supervised learning or now supervised learning techniques. And people use it. We use it like standard work every day, but we don't talk about AI.

But over the years, when you work with so many companies, then you realize the biggest turning point was Toyota 2005 and P&G in 2006. The reason I'm telling you 2005 is Toyota had big problems in the factory in Georgetown, Kentucky, where the Camry factory is located. So they had big compressor problems. So we implemented using machine learning, the support-vector machine, and also principal component analysis. And we enable that the surge of a compressor predicted and avoided and never happened. So until today --

TROND: So they have achieved zero downtime after that project, essentially.

JAY: Yeah. So that really is the turning point. Of course, at P&G, the diaper line continues moving the high volume. They can predict things, reduce downtime to 1%. There's a lot of money. Diaper business that is like $10 billion per year.

TROND: It's so interesting you focus on downtime, Jay, because obviously, in this hype, which we'll get to as well, people seem to focus so much on fully automated versus what you're saying, which is it doesn't really, you know, we will get to the automation part, but it is the downtime that's where a lot of the savings is obviously. Because whether it's a lights out or lights on, humans are not the real saving here. And the real accomplishment is in zero downtime because that is the industrialization factor. And that is what allows the system to keep operating. Of course, it has to do with automation, but it's not just that.

Can you then walk us through what then became industrial AI for you? Because as I've now understood it, it is a highly specific term to you. It's not just some sort of fluffy idea of very, very advanced algorithms and robots running crazy around autonomously. You have very, very specific system elements. And they kind of have to work together in some architectural way before you're willing to call it an industrial AI because it may be a machine tool here, and a machine tool there, and some data here.

But for you, unless it's put in place in a working architecture, you're not willing to call it, I mean, it may be an AI, but it is not an industrial AI. So how did this thinking then evolve for you? And what are the elements that you think are crucial for something that you even can start to call an industrial AI? Which you now have a book on, so you're the authority on the subject.

JAY: Well, I think the real motivation was after you apply all the machine learning toolkits so long...and a company like National Instruments, NI, in Austin, Texas, they licensed our machine learning toolkits in 2015. And eventually, in 2017, they started using the embedding into LabVIEW version. So we started realizing, actually, the toolkit is very important, not just from the laboratory point of view but also from the production and practitioners' point of view from industry. Of course, researchers use it all the time for homework; I mean, that’s fine.

So eventually, I said...the question came to me about 2016 in one of our industry advisory board meeting. You have so many successes, but the successes that happen can you repeat? Can you repeat? Can you repeatably have the same success in many, many other sites? Repeatable, scalable, sustainable, that's the key three keywords. You cannot just have a one-time success and then just congratulate yourself and forget it, no. So eventually, we said, oh, to make that repeat sustainable, repeatable, you have a systematic discipline.

TROND: I'm so glad you say this because I have taken part in a bunch of best practice schemes and sometimes very optimistically by either an industry association or even a government entity. And they say, "Oh yeah, let's just all go on a bunch of factory visits." Or if it's just an IT system, "Let's just all write down what we did, and then share it with other people." But in fact, it doesn't seem to me like it is that easy.

It's not like if I just explain what I think I have learned; that's not something others can learn from. Can you explain to me what it really takes to make something replicable? Because you have done that or helped Foxconn do that, for example. And now you're obviously writing up case studies that are now shared in the World Economic Forum across companies.

But there's something really granular but also something very systemic and structured about the way things have to be explained in order to actually make it repeatable. What is the sustainability factor that actually is possible to not just blue copy but turn it into something in your own factory?

JAY: Well, I think that there are basically several things. The data is one thing. We call it the data technology, DT, and which means data quality evaluation. How do you understand what to use, what not to use? How do you know which data is useful? And how do you know where the data is usable?

It doesn't mean useful data is usable, just like you have a blood donation donor, but the blood may not be usable if the donor has HIV. I like to use an analogy like food. You got a fish in your hand; wow, great. But you have to ask where the fish comes from. [chuckles] If it comes from polluted water, it's not edible, right? So great fish but not edible.

TROND: So there's a data layer which has to be usable, and it has to be put somewhere and put to use. It actually then has to be used. It can't just be theoretically usable.

JAY: So we have a lot of useful data people collect. The problem is people never realized lots of them are not usable because of a lack of a label. They have no background, and they're not normalized. So eventually, that is a problem. And even if you have a lot of data, it doesn't mean it is usable.

TROND: So then I guess that's how you get to your second layer, which I guess most people just call machine learning, but for you, it's an algorithmic layer, which is where some of the structuring gets done and some of the machines that put an analysis on this, put in place automatic procedures.

JAY: And machine learning to me it's like cooking ware like a kitchen. You got a pan fry; you got a steamer; you got the grill. Those are tools to cook the food, the data. Food is like data. Cooking ware is like AI. But it depends on purpose. For example, you want fish. What do you want to eat first? I want soup. There's a difference. Do you want to grill? Do you want to just deep fry? So depending on how you want to eat it, the cooking ware will be selected differently.

TROND: Well, and that's super interesting because it's so easy to say, well, all these algorithms and stuff they're out there, and all you have to do is pick up some algorithms. But you're saying, especially in a factory, you can't just pick any tool. You have to really know what the effect would be if you start to...for example, on downtime, right?

Because I'm imagining there are very many advanced techniques that could be super advanced, but they are perhaps not the right tool for the job, for the workers that are there. So how does that come into play? Are these sequential steps, by the way? So once you figure out what the data is then, you start to fiddle with your tools.

JAY: Well, there are two perspectives; one perspective is predict and prevent. So you predict something is going to happen. You prevent it from happening, number one. Number two, understand the root causes and potential root causes. So that comes down to the visible and invisible perspective.

So from the visible world, we know what to measure. For example, if you have high blood pressure, you measure blood pressure every day, but that may not be the reason for high blood pressure. It may be because of your DNA, maybe because of the food you eat, because of lack of exercise, because of many other things, right?

TROND: Right.

JAY: So if you keep measuring your blood pressure doesn't mean you have no heart attack. Okay, so if you don't understand the reason, measuring blood pressure is not a problem. So I'm saying that you know what you don't know. So we need to find out what you don't know. So the correlation of invisible, I call, visible-invisible. So I will predict, but you also want to know the invisible reason relationship so you can prevent that relationship from happening. So that is really called deep mining those invisibles.

So we position ourselves very clearly between visible-invisible. A lot of people just say, "Oh, we know what the problem is." The problem is not a purpose. For example, the factory manufacturing there are several very strong purposes, number one quality, right? Worry-free quality.

Number two, your efficiency, how much you produce per dollar. If you say that you have great quality, but I spent $10,000 to make it, it is very expensive. But if you spend $2 to make it, wow, that's great. How did you do it? So quality per dollar is a very different way of judging how good you are. You got A; I spent five days studying. I got A; I spent two hours studying. Now you show the capability difference.

TROND: I agree. And then the third factor in your framework seems to be platform. And that's when I think a lot of companies go wrong as well because platform is...at least historically in manufacturing, you pick someone else's platform. You say I'm going to implement something. What's available on the market, and what can I afford, obviously? Or ideally, what's the state of the art? And I'll just do that because everyone seems to be doing that. What does platform mean to you, and what goes into this choice? If you're going to create this platform for industrial AI, what kind of a decision is that?

JAY: So DT is data, AT is algorithm, and PT is platform, PT platform. Platform means some common things are used in a shared community. For example, kitchen is a platform. You can cook. I can cook. I can cook Chinese food. I can cook Italian food. I can cook Indian food. Same kitchen but different recipe, different seasoning, but same cooking ware.

TROND: Correct. Well, because you have a good kitchen, right?

JAY: Yes.

TROND: So that's --

JAY: [laughs]

TROND: Right?

JAY: On the platform, you have the most frequently used tool, not everything. You don't need 100 cooking ware in your kitchen. You probably have ten or even five most daily used.

TROND: Regardless of how many different cuisines you try to cook.

JAY: Exactly. That's called the AI machine toolkit. So we often work with companies and say, "You don't need a lot of tools, come on. You don't need deep learning. You need a good logistic regression and support-vector machine, and you're done."

TROND: Got it.

JAY: Yeah, you don't need a big chainsaw to cut small bushes. You don't need it.

TROND: Right. And that's a very different perspective from the IT world, where many times you want the biggest tool possible because you want to churn a lot of data fast, and you don't really know what you're looking for sometimes. So I guess the industrial context here really constrains you. It's a constraint-based environment.

JAY: Yes. So industry, like I said, the industry we talked about three Ps like I said: problems, purposes, and processes. So normally, problem comes from...the main thing is logistic problems, machine, and factory problems, workforce problems, the quality problems, energy problem, ignition problem, safety problems. So the problem happens every day. That's why in factory world, we call it firefighting. Typically, you firefight every day.

TROND: And is that your metaphor for the last part of your framework, which is actually operation? So operation sounds really nice and structured, right?

JAY: [chuckles] Yes.

TROND: As if that was like, yeah, that's the real thing, process. We got this. But in reality, it feels sometimes, to many who are operating a factory; it's a firefight.

JAY: Sometimes the reason lean theme work, Six Sigma, you turn a problem into a process, five Ss process, okay? And fishbone diagram, Pareto chart, and Kaizen before and after. So all the process, SOP, so doesn't matter which year workforce comes in, they just repeat, repeat, repeat, repeat, repeat.

So in Toyota, the term used to be called manufacturing is just about the discipline. It's what they said. The Japanese industry manufacturing is about discipline, how you follow a discipline to everyday standard way, sustainable way, consistent way, and then you make good products. This is how the old Toyota was talking about, old one. But today, they don't talk that anymore. Training discipline is only one thing; you need to understand the value of customers.

TROND: Right. So there are some new things that have to be added to the lean practices, right?

JAY: Yes.

TROND: As time goes by. So talk to me then more about the digital element because industrial AI to you, clearly, there's a very clear digital element, but there's so many, many other things there. So I'm trying to summarize your framework. You have these four factors: data, algorithms, platforms, and operations. These four aspects of a system that is the challenge you are dealing with in any factory environment.

And some of them have to do with digital these days, and others, I guess, really have to do more with people. So when that all comes together, do you have some examples? I don't know, we talked about Toyota, but I know you've worked with Foxconn and Komatsu or Siemens. Can you give me an example of how this framework of yours now becomes applied in a context? Where do people pick up these different elements, and how do they use them?

JAY: There's a matrix thinking. So horizontal thinking is a common thing; you need to have good digital thread including DT, data technology, AT, algorithms or analytics, PT, platform, edge cloud, and the things, and OT operation like scheduling, optimizations, stuff like that.

Now, you got verticals, quality vertical, cost vertical, efficiency verticals, safety verticals, emission verticals. So you cannot just talk about general. You got to have focus on verticals. For example, let me give you one example: quality verticals. Quality is I'm the factory manager. I care about quality. Yes, the customer will even care more, so they care. But you have a customer come to your shop once a month to check. You ask them, "Why you come?" "Oh, I need to see how good your production." "How about you don't have to come? You can see my entire quality." "Wow, how do I do that?"

So eventually, we develop a stream of quality code, SOQ, Stream Of Quality. So it's not just about the product is good. I can go back to connect all the processes of the quality segment of each station. Connect them together. Just like you got a fish, oh, okay, the fish is great. But I wonder, when the fish came out of water, when the fish was in the truck, how long was it on the road? And how long was it before reaching my physical distribution center and to my home?

So if I have a sensor, I can tell you all the temperature history inside the box. So when you get your fish, you take a look; oh, from the moment the fish came out of the boat until it reached my home, the temperature remained almost constant. Wow. Now you are worry-free. It's just one thing. So you connect together. So that's why we call SOQ, Stream Of Quality, like a river connected.

So by the time a customer gets a quality product, they can trace back and say, "Wow, good. How about if I let you see it before you come? How about you don't come?" I say, "Oh, you know what? I like it." That's what this type of manufacturing is about. It just doesn't make you happy. You have to make the customer happy, worry-free.

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TROND: So, Jay, you took the words out of my mouth because I wanted to talk about the future. I'm imagining when you say worry-free, I mean, you're talking about a soon-to-be state of manufacturing. Or are you literally saying there are some factories, some of the excellence factories where you've won awards in the World Economic Forum or other places that are working towards this worry-free manufacturing, and to some extent, they have achieved it?

Well, elaborate for me a little bit about the future outlook of manufacturing and especially this people issue because you know that I'm engaged...The podcast is called Augmented Podcast. I'm engaged in this debate about automation. Well, is there a discrepancy between automation and augmentation? And to what extent is this about people running the system? Or is it the machines that we should optimize to run all the system?

For you, it's all about worry-free. First of all, just answer this question, is worry-free a future ideal, or is it actually here today if you just do the right things?

JAY: Well, first of all, worry-free is our mindset where the level of satisfaction should be, right?

TROND: Yep.

JAY: So to make manufacturing happen is not about how to make good quality, how to make people physically have less worry, how to make customers less worry is what is. But the reason we have a problem with workforce today, I mean, we have a hard time to hire not just highly skilled workers but even regular workforce.

Because for some reason, not just U.S., it seems everywhere right now has similar problems. People have more options these days to select other living means. They could be an Uber driver. [laughs] They could be...I don't know. So there are many options. You don't have to just go to the factory to make earnings. They can have a car and drive around Uber and Lyft or whatever. They can deliver the food and whatever. So they can do many other things.

And so today, you want to make workforce work environment more attractive. You have to make sure that they understand, oh, this is something they can learn; they can grow. They are fulfilled because the environment gives them a lot of empowerment. The vibe, the environment gives them a wow, especially young people; when you attract them from college, they'd like a wow kind of environment, not just ooh, okay. [laughs]

TROND: Yeah. Well, it's interesting you're saying this. I mean, we actually have a lack of workers. So it's not just we want to make factories full of machines; it's actually the machines are actually needed just because there are no workers to fill these jobs. But you're looking into a future where you do think that manufacturing is and will be an attractive place going forward. That seems to be that you have a positive vision of the future we're going into. You think this is attractive. It's interesting for workers.

JAY: Yeah. See, I often say that there are some common horizontal we have to use all the day. Vertical is the purpose, quality. I talked about vertical quality first, quality. But what are the horizontal common? I go A, B, C, D, E, F. What's A? AI. B is big data. C is cyber and cloud. D is digital or digital twin, whatever. E is environment ecosystem and emission reduction. What's F? Very important, fun. [laughs] If you miss that piece, who wants to work for a place there's no fun?

You tell me would you work for...you and I, we're talking now because it's fun. You talk to people and different perspectives. I talk to you, and I say, wow, you've built some humongous network here in the physical...the future of digital, not just professional space but also social space but also the physical space. So, again, the fun things inspire people, right?

TROND: They do. So talking about inspiring people then, Jay, if you were to paint a picture of this future, I guess, we have talked just now about workers and how if you do it right, it's going to be really attractive workplaces in manufacturing. How about for, I guess, one type of worker, these knowledge workers more generally? Or, in fact, is there a possibility that you see that not just is it going to be a fun place to be for great, many workers, but it's actually going to be an exciting knowledge workplace again?

Which arguably, industrialization has gone through many stages. And being in a factory wasn't always all that rosy, but it was certainly financially rewarding for many. And it has had an enormous career progression for others who are able to find ways to exploit this system to their benefit. How do you see that going forward?

Is there a scope, is there a world in which factory work can or perhaps in an even new way become truly knowledge work where all of these industrial AI factors, the A to the Fs, produce fun, but they produce lasting progression, and career satisfaction, empowerment, all these buzzwords that everybody in the workplace wants and perhaps deserves?

JAY: That's how we look at the future workforce is not just about the work but also the knowledge force. So basically, the difference is that people come in, and they become seasoned engineers, experienced engineers. And they retire, and the wisdom carries with them. Sometimes you have documentation, Excel sheet, PPT in the server, but nobody even looks at it. That's what today's worry is.

So now what you want is living knowledge, living intelligence. The ownership is very important. For example, I'm a worker. I develop AI, not just the computer software to help the machine but also help me. I can augment the intelligence. I will augment it. When I make the product happen, the inspection station they check and just tell me pass or no pass. They also tell me the quality, 98, 97, but you pass. And then you get your score. You got a 70, 80, 90, but you got an A. 99, you got an A, 91, you got an A, 92. So what exactly does A mean?

So, therefore, I give you a reason, oh, this is something. Then I learn. Okay, I can contribute. I can use voice. I can use my opinion to augment that no, labeled. So next time people work, oh, I got 97. And so the reason is the features need to be maintained, to be changed, and the system needs to be whatever. So eventually, you have a human contribute.

The whole process could be consisting of 5 experts, 7, 10, 20, eventually owned by 20 people. That legacy continues. And you, as a worker, you feel like you're part of the team, leave a legacy for the next generation. So eventually, it's augmented intelligence.

The third level will be actual implementation. So AI is not about artificial intelligence; it is about actual implementation. So people physically can implement things in a way they can make data to decisions. So their decision mean I want to make an adjustment. I want to find out how much I should adjust. Physically, I can see the gap. I can input the adjustment level.

The system will tell me physically how could I improve 5%. Wow, that's good. I made a 5% improvement. Your boss also knows. And your paycheck got the $150 increase this month. Why? Because my contribution to the process quality improved, so I got the bonus. That's real-world feedback.

TROND: Let me ask you one last question about how this is going to play out; I mean, in terms of how the skilling of workers is going to allow this kind of process. A lot of people are telling me about the ambitions that I'm describing...and some of the guests on the podcasts and also the Tulip software platform, the owner of this podcast, that it is sometimes optimistic to think that a lot of the training can just be embedded in the work process. That is obviously an ideal.

But in America, for example, there is this idea that, well, you are either a trained worker or an educated worker, or you are an uneducated worker. And then yes, you can learn some things on the job. But there are limits to how much you can learn directly on the job. You have to be pulled out, and you have to do training and get competencies.

As you're looking into the future, are there these two tracks? So you either get yourself a short or long college degree, and then you move in, and then you move faster. Or you are in the factory, and then if you then start to want to learn things, you have to pull yourself out and take courses, courses, courses and then go in? Or is it possible through these AI-enabled training systems to get so much real-time feedback that a reasonably intelligent person actually never has to be pulled out of work and actually they can learn on the job truly advanced things?

So because there are two really, really different futures here, one, you have to scale up an educational system. And, two, you have to scale up more of a real-time learning system. And it seems to me that they're actually discrepant paths.

JAY: Sure. To me, I have a framework in my book. I call it the four P structure, four P. First P is principle-based. For example, in Six Sigma, in lean manufacturing, there's some basic stuff you have to study, basic stuff like very simple fishbone diagram. You have to understand those things. You can learn by yourself what that is. You can take a very basic introduction course. So we can learn and give you a module. You can learn yourself or by a group, principle-based.

The second thing is practice-based. Basically, we will prepare data for you. We will teach you how to use a tool, and you will do it together as a team or as individual, and you present results by using data I give to you, the tool I give to you. And it's all, yeah, my team A presented. Oh, they look interesting. And group B presented, so we are learning from each other.

Then after the group learning is finished, you go back to your team in the real world. You create a project called project-based learning. You take a tool you learn. You take the knowledge you learn and to find a project like a Six Sigma project you do by yourself. You formulate. And then you come back to the class maybe a few weeks later, present with a real-world project based on the boss' approval.

So after that, you've got maybe a black belt but with the last piece professional. Then you start teaching other people to repeat the first 3ps. You become master black belt. So we're not reinventing a new term. It really is about a similar concept like lean but more digital space. Lean is about personal experience, and digital is about the data experience is what's the big difference.

TROND: But either way, it is a big difference whether you have to rely on technological experts, or you can do a lot of these things through training and can get to a level of aptitude that you can read the signals at least from the system and implement small changes, perhaps not the big changes but you can at least read the system.

And whether they're low-code or no-code, you can at least then through learning frameworks, you can advance, and you can improve in not just your own work day, but you can probably in groups, and feedbacks, and stuff you can bring the whole team and the factory forward perhaps without relying only on these external types of expertise that are actually so costly because they take you away. So per definition, you run into this; I mean, certainly isn't worry-free because there is an interruption in the process.

Well, look, this is fascinating. Any last thoughts? It seems to me that there are so many more ways we can dig deeper on your experience in any of these industrial contexts or even going deeper in each of the frameworks. Is there a short way to encapsulate industrial AI that you can leave us with just so people can really understand?

JAY: Sure.

TROND: It's such a fundamental thing, AI, and people have different ideas about that, and industry people have something in their head. And now you have combined them in a unique way. Just give us one sentence: what is industrial AI? What should people leave this podcast with?

JAY: AI is a cognitive science, but industrial AI is a systematic discipline is one sentence. So that means people have domain knowledge. Now we have to create data to represent our domain then have the discipline to solve the domain problems. Usually, with domain knowledge, we try with our experience, and you and I know; that's it. But we have no data coming out. But if I have domain become data and data become discipline, then other people can repeat our success even our mistake; they understand why. So eventually, domain, data, discipline, 3 Ds together, you can make a good decision, sustainable and long-lasting.

TROND: Jay, this has been so instructive. I thank you for spending this time with me. And it's a little bit of a never-ending process.

JAY: [laughs]

TROND: Industry is not something that you can learn it and then...because also the domain changes and what you're doing and what you're producing changes as well. So it's a lifelong --

JAY: It's rewarding.

TROND: Rewarding but lifelong quest.

JAY: Yeah. Well, thank you for the opportunity to share, to discuss. Thank you.

TROND: It's a great pleasure.

You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Industrial AI. And our guest was Professor Jay Lee from University of Cincinnati. In this conversation, we talked about how AI in industry needs to work every time and what that means.

My takeaway is that industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars.

If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

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

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

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

Special Guest: Jay Lee.

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

The topic is "The People Side of Lean." Our guest is Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talk about how to develop internal organizational capability and problem-solving skills on the frontline.

If you liked this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 84 on The Evolution of Lean.

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

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Lean is about motivating people to succeed in an industrial organization more than it is about a bundle of techniques to avoid waste on a factory production line. The goal is to have workers always asking themselves if there is a better way.

Transcript:

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

In this episode of the podcast, the topic is the People Side of Lean. Our guest is Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talk about how to develop internal organizational capability, problem-solving skills on the frontline.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Jeffrey, how are you? Welcome to the podcast.

JEFFREY: Thank you.

TROND: So I think some people in this audience will have read your book or have heard of your book and your books but especially the one that I mentioned, Toyota. So I think we'll talk about that a little bit. But you started out as an engineering undergrad at Northeastern, and you got yourself a Ph.D. in sociology. And then I've been reading up on you and listening to some of the stuff on the musical side of things. I think we both are guitarists.

JEFFREY: Oh, is that right?

TROND: Yeah, yeah, classical guitar in my case. So I was wondering about that.

JEFFREY: So I play also a classical guitar now. I played folk and rock earlier when I was young. But for the last more than ten years, I've been only studying classical guitar.

TROND: Well, so then we share a bunch of hours practicing the etude, so Fernando Sor, and eventually getting to the Villa-Lobos stuff. So the reason I bring that up, of course, beyond it's wonderful to talk about this kind of stuff with, you know, there aren't that many classical guitarists out there. But you said something that I thought maybe you could comment on later. But this idea of what happened to you during your studies of classical guitar actually plays into what you later brought into your professional life in terms of teaching you something about practicing in particular ways. So I hope you can get into that.

But obviously, you've then become a professor. You are a speaker and an advisor, and an author of this bestseller, The Toyota Way. Now you run some consulting. And I guess I'm curious; this was a very, very brief attempt at summarizing where you got into this. What was it that brought you into manufacturing in the first place? I mean, surely, it wasn't just classical guitar because that's not a linear path. [laughs]

JEFFREY: No. So for undergraduate, I had basically studied industrial engineering because I didn't really know what I wanted to do with my life. And my father was an engineer. And then I literally took a course catalog and just started reading the descriptions of different kinds of engineering. And industrial engineering was the only one that mentioned people. And in theory, industrial engineering is a systems perspective which integrates people, materials, methods, machines, the four Ms.

And in the description from Northeastern University, they said it's as much about human organization as it is about tools and techniques. So that appealed to me. When I got to Northeastern...I was not a particularly good high school student. So I didn't have a lot of choices of what colleges I went to, so Northeastern was pretty easy to get into. But they had a cooperative education program where you go to school, and you work. You go back and forth between school and work and had a pretty elaborate system for setting you up with jobs.

I got one of the better jobs, which was at a company called General Foods Corporation at the time, and they make things like Jell-O, and Gravy Train dog food, and Birds Eye vegetables, and a lot of other household names, Kool-Aid, all automated processes, even at that time in the 1970s. And they had been experimenting with something called socio-technical systems, which is supposed to be what I was interested in, which is bringing together the social and technical, which no one at Northeastern University had any interest in except me.

But I was very interested in this dog food plant where they were written up as a case study pioneer. And the basic essence of it was to give groups of people who are responsible, for example, for some automated processes to make a certain line of Gravy Train dog food, give them responsibility for all their processes, and they called them autonomous workgroups. And what we try to do is as much as possible, give them all the responsibility so they can work autonomously without having to go and find the engineer or deal with other support functions, which takes time and is kind of a waste.

So that fascinated me. I studied it. I wrote papers about it even in courses where it didn't fit. But the closest I could get to the social side was through sociology courses which I took as soon as I was able to take electives, which was about my third year. And I got to know a sociology professor closely and ultimately decided to get a Ph.D. in sociology and did that successfully, published papers in sociology journals at a pretty high level. And then discovered it was really hard to get a job.

TROND: Right. [laughs]

JEFFREY: And there happened to be an advertisement from an industrial engineering department at University of Michigan for someone with a Ph.D. in a social science and an undergraduate degree in industrial engineering. And I was probably the only person in the world that fit the job. And they were so excited to hear from me because they had almost given up. And I ended up getting that job quickly then getting to Michigan excited because it's a great university.

I had a low teaching load. They paid more than sociology departments. So it was like a dream job. Except once I got there, I realized that I had no idea what I was supposed to be doing [chuckles] because it wasn't a sociology department. And I had gotten away from industry. In fact, I was studying family development and life’s course development, and more personal psychology and sociology stuff. So I was as far away as I could be. So I had to kind of figure out what to do next.

And fortunately, being at Michigan and also being unique, a lot of people contacted me and wanted me to be part of their projects. And one of them was a U.S.-Japan auto study comparing the U.S.-Japan auto industry going at the same time as a study at MIT and Harvard that ultimately led to the book The Machine That Changed the World, which defined lean manufacturing. So this was sort of a competitive program. And they asked me to be part of it, and that's what led to my learning about Toyota. I mean, I studied Toyota, Nissan, Mazda mainly and compared them to GM, Ford, and Chrysler. But it was clear that Toyota was different and special.

And ultimately, then I learned about the Toyota Production System. And from my perspective, not from people in Toyota, but from my perspective, what they had done is really solve the problem of socio-technical systems. Because what I was seeing at General Foods was workers who were responsible for technical process and then were given autonomy to run the process, but there was nothing really socio-technical about it. There was a technical system, and then there was social system autonomous work groups and not particularly connected in a certain way.

But the Toyota Production System truly was a system that was designed to integrate people with the technical system, which included things like stamping, and welding, and painting, which were fairly automated as well as assembly, which is purely manual. And Toyota had developed this back in the 1940s when it was a lone company and then continued to evolve it.

And the main pillars are just-in-time and built-in quality. They have a house, and then the foundation is stable and standardized processes. And in the center are people who are continuously improving. Now, the socio-technical part the connection is that just-in-time for Toyota means that we're trying to flow value to the customer without interruption.

So if what they do is turn raw materials into cars that you drive, then anything that's turning material into a component or car physically is value-added, and everything else is waste. And so things like defects where you have to do rework are waste. And machines are shut down, so we have to wait for the machines to get fixed; that's waste. And inventory sitting in piles doing nothing is waste. So the opposite of waste is a perfect process.

And Toyota also was smart enough, and all that they figured out was more like folk learning or craft learning. It was learning from doing and experience and common sense. And they didn't particularly care about linking it to academic theories or learning from academic theories, for that matter.

So their common sense view is that the world is complicated. Humans are really bad at predicting the future. So the best we can do is to get in the ballpark with what we think is a good process and then run it and see how it fails. And then the failures are what lead to then the connection of people who have to solve the problems through creative thinking. So that was the integration that I did not see before that.

TROND: Just one thing that strikes me...because nowadays, comparing the U.S. or Europe and Asia in terms of business practices, it's sort of like, oh, of course, you have to compare them because they are culturally different. But it strikes me that in the automotive industry, was it immediately really clear to you at the outset that there would be such striking differences between the Japanese and the U.S. auto industry? Or is that actually something that had to be studied? Or was it something that was known, but no one really knew exactly what the differences were?

JEFFREY: So it wasn't like the American auto companies figured out that if they get good at using chopsticks, they'll be good at making cars. They weren't looking for something peculiar in Japanese culture. But they were addressing the more general problem, which was that Japanese companies were making small fuel-efficient cars at low cost with high quality. And none of the American companies could do that. The costs were higher. The quality was terrible compared to Japan. They took a long time to do everything, including developing cars.

So somehow, the Japanese were purported, they weren't convinced this was true, but according to the evidence, the Japanese were purported to be better at just about everything. And the Americans wanted to know why particularly. And at that time, there had been an oil crisis, and there was a demand for small cars. The real question they were interested in is how could they make small cars that were competitive with the Japanese? So they had to understand what the Japanese were doing.

Now, they realized that some of what the Japanese were doing were purely technical things that had nothing to do with culture. And then there was also a level of attention to detail and motivation that maybe was, for some reason, peculiar to Japan. But they needed to figure out how to replicate it in the United States.

And then, in addition to that, they had Americans like Dr. Deming, who had gone to Japan and taught the Japanese supposedly quality control methods. And Japanese companies had taken quality control methods that were created in the United States more seriously than the American companies. So part of it was relearning what came from America to Japan and got done better. So it wasn't necessarily this kind of strange place, and how can we emulate this strange culture?

TROND: Right. But that becomes then your challenge then, right? Because what you then discover is that your field is immensely important to this because what you then went on to do is...and I guess part of your consulting work has been developing internal organizational capability. These are skills that particular organizations, namely Toyota, had in Japan. So you're thinking that this then became...it's like a learning process, the Japanese learned some lessons, and then the whole rest of the automotive industry then they were trying to relearn those lessons. Is that sort of what has been happening then in the 30 years after that?

JEFFREY: Yeah, the basic question was, why are they so good? Why are we so bad? And how can we get better in America? Then there were lots of answers to that question coming from different people in different places. My particular answer was that Toyota especially had developed a socio-technical system that was extremely effective, that was centered on people who were developed to have the skills of problem-solving and continuous improvement. And while the study was going on, they were doing a study out of MIT that led to The Machine That Changed the World.

And around that same time, a joint venture between Toyota and General Motors had been formed called NUMMI. It was in California. And in their first year, it was launched in 1983, and in the first year, they had taken what was the worst General Motors plant in the world, with the worst attendance, the worst morale, workers who were fighting against supervisors every day, including physically fighting with them, terrible quality, and General Motors had closed the plant because it was so bad.

And then, in the joint venture, they reopened the plant and took back 80% of the same workers who were like the worst of the worst of American workers. And within a year, Toyota had turned the plant around so that it was the best in North America with the best workers.

TROND: That's crazy, right? Because wouldn't some of the research thesis in either your study or in the MIT study, The Machine That Changed the World, would have to have been around technology or at least some sort of ingenious plan that these people had, you know, some secret sauce that someone had? Would you say that these two research teams were surprised at finding that the people was the key to the difference here or motivating people in a different way?

JEFFREY: Well, frankly, I think I probably had a better grasp that people were really the key than most other researchers because of my background and my interest in human-centered manufacturing. So I was kind of looking for that. And it was what the Toyota people would say...whenever they made a presentation or whenever you interviewed them, they would say, "People are kind of distracted by the tools and methods, but really at the center are people."

And generally, most people listening to them didn't believe it, or it didn't register. Because Toyota did have cool stuff, like, for example, something called a kanban system, which is how do you move material around in the factory? They have thousands of parts that have to all be moved and orchestrated in complicated ways. And Toyota did it with physical cards.

And the concept was a pulse system that the worker; when they see that they're getting low on parts, they take a card and they post it. They put it in a box, and then the material handler picks it up. And they said, okay, they need another bin of these. On my next route, I'll bring a bin of whatever cards I get.

So they were replenishing the line based on a signal from the operator saying, "I need more." So it was a signal from the person who knows best what they need. And it also, from Toyota's point of view, put the employee in the driver's seat because now they're controlling their supply in addition to controlling their work process. And it didn't require that you predict the future all the time because who knows what is happening on the line and where they're backed up, and where they maybe have too many parts, and they don't need more? But the worker knows. He knows when he needs it and when he doesn't.

It was kind of an ingenious system, but the fact that you had these cards moving all over the factory and thousands of parts are moving just to the right place at the right time based on these cards, that was fascinating. So a lot of the consumers were more interested in that than they were in the people aspect, even though Toyota kept talking about the people aspect.

TROND: But so this is my question, then there was more than one element that they were doing right.

JEFFREY: There were multiple elements, yeah.

TROND: There were multiple elements. Some of them were structural or visual, famously.

JEFFREY: Right.

TROND: But you then started focusing, I guess, on not just the people aspect, but you started structuring that thinking because the obvious question must have been, how can we do some of this ourselves? And I guess that's my question is once you and the team started figuring out okay, there are some systematic differences here in the way they motivate people, handle the teams, but also structure, honestly, the organizational incentives minute by minute, how then did you think about transferring this? Or were you, at this point, just really concerned about describing it?

JEFFREY: Like I said, I was kind of unusual in my background, being somewhere between industrial engineering and sociology and being in industrial engineering departments. So maybe I wasn't as constrained by some of the constraints of my academic colleagues. But I never believed this whole model that the university gathers information structures that formulates it, then tells the world what to do. I never thought that made any sense. And certainly, in the case of lean, it didn't, and it wasn't true.

So the way that companies were learning about this stuff was from consultants, largely, and from people who had worked for Toyota. So anybody who had worked for Toyota, even if they were driving a forklift truck, in some cases, suddenly became a hot commodity. I consulted to Ford, and they were developing the Ford Production System.

They were using a consulting firm, and all their consulting firm's business was to poach people from Toyota and then sell them as consultants to other companies. And that company literally had people every day of the week who were in their cars outside the gates of Toyota. And as people came out, they would start talking to them to try to find people that they could hire away from Toyota.

TROND: It's funny to hear you talking about that, Jeff, right? Because in some way, you, of all people, you're a little bit to blame for the fame of Toyota in that sense. I mean, you've sold a million books with The New Toyota --

JEFFREY: Well, that was --

TROND: I'm just saying it's a phenomenon here that people obsess over a company, but you were part of creating this movement and this enormous interest in this. [laughs]

JEFFREY: I didn’t feel that that was...I personally had a policy because I had a consulting company too. So I personally had a policy that I would not hire somebody away from Toyota unless they were leaving anyway. That was my personal policy. But the important point was that there were a lot of really well-trained people coming out of Toyota who really understood the whole system and had lived it. And they could go to any other company and do magic, and suddenly things got better. [laughs]

And what they were doing was setting up the structures and the tools, and they also were engaging the people and coaching the people. They were doing both simultaneously, and that's how they were trained. Toyota had sent an army of Japanese people to America. So every person who was in a leadership position had a one-on-one coach for years, a person whose only reason for being in the United States was to train them. So they got excellent training, and then they were able to use that training.

And then other people once they had worked with a company and then that company got good at lean, then, within that company, you'd spawn more consultants change agents. Like, there was a company that I was studying called Donnelly Mirrors that made exterior mirrors for cars. And one of the persons that was trained by a Toyota person became a plant manager. And he ended up then getting offered a job as the vice president of manufacturing for Merillat Kitchen Cabinets. And now he's the CEO of the parent company that owns Merillat. And he's transformed the entire company.

So little by little, this capability developed where most big companies in the world have hired people with lean experience. Sometimes it's second generation, sometimes third generation. And there are some very well-trained people. So the capability still resides within the people. And if you have someone who doesn't understand the system but they just set up a kanban system or they set up quality systems, and they try to imitate what they read in a book or what they learned in a course; usually, it doesn't work very well.

TROND: Well, that was going to be my next question. Because how scalable is this beyond the initial learnings of Toyota and the fact that it has relied so heavily on consulting? Because there is sort of an alternate discourse in a lot of organizational thinking these days that says, well, not just that the people are the key to it but actually, that as a leader, however much you know or how aware you are of people processes, it is the organization itself that kind of has to find the answers.

So there's perhaps some skepticism that you can come in and change a culture. Aren't there organizations that have such strong organizational practices, whether they are cultural in some meaningful way or they're simply this is the way they've done things that even one person who comes in has a hard time applying a Toyota method? What do you think about that kind of challenge?

JEFFREY: Okay, so, anyway, I think what you said is...how I would interpret it is it’s a gross oversimplification of reality. So first of all, in the second edition of The Toyota Way, because I realized from the first edition, which was fairly early back in the early 2000s, I realized that some people were taking my message as copy Toyota, even though I didn't say that in the book. And I specifically said not to do that, but I said it in the last chapter.

So I put out the second edition a year ago, and I say it in the first page or first few pages. I say, "Don't copy Toyota," and explain why. And then, throughout the book, I say that, and then, in the end, I say, "Develop your own system." So it's probably repeated a dozen times or more with the hope that maybe somebody would then not ask me after reading it, "So, are we supposed to copy Toyota?"

So the reason for that is because, as you said, you have your own culture. And you're in a different situation. You're in a different industry. You're starting in a different place. You're drawing on different labor. You have maybe plants around the world that are in different situations. So the other thing I said in the book, which is kind of interesting and counterintuitive, is I said, "Don't copy Toyota; even Toyota doesn't copy Toyota."

TROND: So what does that mean? Did they really not?

JEFFREY: What it means is that...because Toyota had this dilemma that they had developed this wonderful system in Japan that worked great, but they realized that in auto, you need to be global to survive. So when they set up NUMMI, that was the first experiment they did to try to bring their system to a different culture.

And in reality, if you look at some of the cultural dimensions that make lean work in Japan, the U.S. is almost opposite on every one of them, like, we're the worst case. So if you were a scientist and you said, let’s find the hardest place in the world to make this work and see if we can make it work, it would be the United States, particularly with General Motors workers already disaffected and turned off.

So Toyota's perspective was, let's go in with a blank sheet of paper and pretend we know nothing. We know what the total production system is and what we're trying to achieve with it. But beyond that, we don't know anything about the human resource system and how to set it up. And so they hired Americans, and they coached them. But they relied a lot on Americans, including bringing back the union leader of the most militant union in America. They brought him back.

TROND: Wow.

JEFFREY: And said, "You're a leader for a reason. They chose you. We need your help. We're going to teach you about our system, but you need to help make it work." So that created this sort of new thing, a new organizational entity in California. And then what Toyota learned from that was not a new solution that they then brought to every other plant, whether it was Czechoslovakia, or England, or China. But rather, they realized we need to evolve a cultural system every time we set up a plant, starting with the local culture. And we need to get good at doing that, and they got good at doing it.

So they have, I don't know, how many plants but over 100 plants around the world and in every culture you can imagine. And every one of them becomes the benchmark for that country as one of their best plants. And people come and visit it and are amazed by what they see. The basic principles are what I try to explain in The Toyota Way. The principles don't change. At some level, the principle is we need continuous improvement because we never know how things are going to fail until they fail. So we need to be responding to these problems as a curse. We need people at every level well trained at problem-solving.

And to get people to take on that additional responsibility, we need to treat people with a high level of respect. So their model, The Toyota Way, was simply respect for people and continuous improvement. And that won't change no matter where they go. And their concept of how to teach problem-solving doesn't change. And then their vision of just-in-time one-piece flow that doesn't change, and their vision of building in quality so that you don't allow outflows of poor quality beyond your workstation that doesn't change.

So there are some fundamental principles that don't change, but how exactly they are brought into the plant and what the human resource system looks like, there'll be sort of an amalgam between the Japanese model and the local model. But they, as quickly as possible, try to give local autonomy to people from that culture to become the plant managers, to become the leaders. And they develop those people; often, those people will go to Japan for periods of time.

TROND: So, Jeff, I want to move to...well, you say a lot of things with Toyota don't change because they adapt locally. So my next question is going to be about future outlook. But before we get there, can we pick up on this classical guitar lesson? So you were playing classical guitar. And there was something there that, at least you said that in one interview that I picked up on, something to do with the way that guitar study is meticulous practice, which both you and I know it is. You literally will sit plucking a string sometimes to hear the sound of that string. I believe that was the example.

So can you explain that again? Because, I don't know, maybe it was just me, but it resonated with me. And then you brought it back to how you actually best teach this stuff. Because you were so elaborate, but also you rolled off your tongue all these best practices of Toyota. And unless you either took your course or you are already literate in Toyota, no one can remember all these things, even though it's like six different lessons from Toyota or 14 in your book. It is a lot.

But on the other hand, when you are a worker, and you're super busy with your manager or just in the line here and you're trying to pick up on all these things, you discovered with a colleague, I guess, who was building on some of your work some ways that had something in common with how you best practice classical guitar. What is that all about?

JEFFREY: Well, so, first of all, like I said, the core skill that Toyota believes every person working for Toyota should have is what they call problem-solving. And that's the ability to, when they see a problem, to study what's really happening. Why is this problem occurring? And then try out ideas to close the gap between what should be happening and what is happening. And you can view that as running experiments. So the scientific mindset is one of I don't know. I need to collect the data and get the evidence.

And also, I don't know if my idea works until I test it and look at what happens and study what happens. So that was very much central in Toyota. And they also would talk about on-the-job development, and they were very skeptical of any classroom teaching or any conceptual, theoretical explanations. So the way you would learn something is you'd go to the shop floor and do it with a supervisor.

So the first lesson was to stand in a circle and just observe without preconceptions, kind of like playing one-string guitar. And the instructor would not tell you anything about what you should be looking for. But they would just ask you questions to try to dig deeper into what's really going on with the problems or why the problems are occurring. And the lesson length with guitar, you might be sweating after 20 minutes of intense practice. This lesson length was eight hours.

So for eight hours, you're just on the shop floor taking breaks for lunch and to go to the bathroom and in the same place just watching. So that was just an introductory lesson to open your mind to be able to see what's really happening. And then they would give you a task to, say, double the productivity of an area. And you would keep on trying. They would keep on asking questions, and eventually, you would achieve it. So this on-the-job development was learning by doing.

Now, later, I came to understand that the culture of Japan never really went beyond the craftsman era of the master-apprentice relationship. That's very central throughout Japan, whether you're making dolls, or you're wrapping gifts, or you're in a factory making a car. So the master-apprentice relationship system is similar to you having a guitar teacher. And then, if you start to look at modern psychology leadership books, popular leadership books, there's a fascination these days with the idea of habits, how people form habits and the role of habits in our lives.

So one of my former students, Mike Rother, who had become a lean practitioner, we had worked together at Ford, for example, and was very good at introducing the tools of lean and transforming a plant. He started to observe time after time that they do great work. He would check in a few months later, and everything they had done had fallen apart and wasn't being followed anymore. And his ultimate conclusion was that what they were missing was the habit of scientific thinking that Toyota put so much effort into. But he realized that it would be a bad solution to, say, find a Toyota culture --

TROND: Right. And go study scientific thinking. Yeah, exactly.

JEFFREY: Right. So he developed his own way in companies he was working with who let him experiment. He developed his own way of coaching people and developing coaches inside the company. And his ultimate vision was that every manager becomes a coach. They're a learner first, and they learn scientific thinking, then they coach others, which is what Toyota does.

But he needed more structure than Toyota had because the Toyota leaders just kind of learned this over the last 25 years working in the company. And he started to create this structure of practice routines, like drills we would have in guitar. And he also had studied mastery. There's a lot of research about how do you master any complex skill, and it was 10,000 hours of practice and that idea.

But what he discovered was that the key was deliberate practice, where you always know what you should be doing and comparing it to what you are doing, and then trying to close the gap. And that's what a good instructor will do is ask you to play this piece, realize that you're weak in certain areas, and then give you an exercise. And then you practice for a week and come back, and he listens again to decide whether you've mastered or not or whether he needs to go back, or we can move to the next step.

So whatever complex skill you're learning, whether it's guitar, playing a sport, or learning how to cook, a good teacher will break down the skill into small pieces. And then, you will practice those pieces until you get them right. And the teacher will judge whether you got them right or not. And then when you're ready, then you move on. And then, as you collect these skills, you start to learn to make nice music that sounds good.

So it turns out that Mike was developing this stuff when he came across a book on the martial arts. And they use the term kata, which is used in Japanese martial arts for these small practice routines, what you do repeatedly exactly as the master shows you. And the master won't let you move on until you've mastered that one kata. Then they'll move to the second kata and then third. And if you ask somebody in karate, "How many katas do you have?" They might say, "46," and you say, "Wow, you're really good. You've mastered 46 kata, like playing up through the 35th Sor exercise.

So he developed what he called the improvement kata, which is here is how you practice scientific thinking, breaking it down into pieces, practicing each piece, and then a coaching kata for what the coach does to coach the student. And the purpose of the scientific thinking is not to publish a paper in a journal but to achieve a life goal, which could be something at work, or it could be that I want to lose weight. It could be a personal goal, or I want to get a new job that pays more and is a better job. And it becomes an exploration process of setting the goal.

And then breaking down the goal into little pieces and then taking a step every day continuously toward, say, a weekly target and then setting the next week's target, and next week's target and you work your way up the mountain toward the goal. So that became known as Toyota Kata. He wrote a book called Toyota Kata.

And then, I put into my model in the new Toyota Way; in the center of the model, I put scientific thinking. And I said this is really the heart and soul of The Toyota Way. And you can get this but only by going back to school, but not school where you listen to lectures but school where you have to do something, and then you're getting coached by someone who knows what they're doing, who knows how to be a coach.

TROND: So my question following this, I think, will be interesting to you, or hopefully, because we've sort of gone through our conversation a little bit this way without jumping to the next step too quickly. Because the last question that I really have for you is, what are the implications of all of this? You have studied, you know, Toyota over years and then teaching academically, and in industry, you've taught these lessons. But what are the implications for the future development of, I guess, management practice in organizations, in manufacturing?

Given all that you just said and what you've previously iterated about Toyota's ideas that not a lot of things change or necessarily have to change, how then should leaders go about thinking about the future? And I'm going to put in a couple of more things there into the future. I mean, even just the role of digital, the role of technology, the role of automation, all of these things, that it's not like they are the future, but they are, I guess, they are things that have started to change.

And there are expectations that might have been brought into the company that these are new, very, very efficient improvement tools. But given everything that you just said about katas and the importance of practicing, how do you think and how do you teach preparing for the future of manufacturing?

JEFFREY: And I have been working with a variety of companies that have developed what you might call industry 4.0 technologies, digital technologies, and I teach classes where a lot of the students are executives from companies where in some cases, they have a dual role of lean plus digitalization. So they're right at the center of these two things.

And what I learned going back to my undergraduate industrial engineering days and then to my journey with Toyota, I was always interested in the centrality of people, whatever the tools are. And what I was seeing as an undergraduate was that most of the professors who were industrial engineers really didn't have much of a concept of people. They were just looking at techniques for improving efficiency as if the techniques had the power themselves.

And what I discovered with people in IT, and software development, and the digital movement is often they don't seem to have a conception of people. And people from their point of view are basically bad robots [laughs] that don't do what they're supposed to do repeatedly. So the ultimate view of some of the technologists who are interested in industry 4.0 is to eliminate the people as much as possible and eliminate human judgment by, for example, putting it into artificial intelligence and having the decisions made by computers.

I'm totally convinced from lots of different experiences with lots of different companies that the AI is extremely powerful and it's a breakthrough, but it's very weak compared to the human brain. And what the AI can do is to make some routine decisions, which frees up the person to deal with the bigger problems that aren't routine and can also provide useful data and even some insight that can help the person in improving the process.

So I still see people as the ultimate customer for the insights that come out of this digital stuff, Internet of Things, and all that. But in some cases, they can control a machine tool and make an automatic adjustment without any human intervention, but then the machine breaks down. And then the human has to come in and solve the problem.

So if you're thinking about digitalization as tools to...and sometimes have a closed loop control system without the person involved. But in addition, maybe, more importantly, to provide useful data to the human, suddenly, you have to think about the human and what makes us tick and what we respond to. And for example, it's very clear that we're much better at taking in visual information than text information. And that's one of the things that is part of the Toyota Production System is visual management.

So how can you make the results of what the AI system come up with very clear and simple, and visual so people can respond quickly to the problem? And most of these systems are really not very good. The human user interface is not well designed because they're not starting with the person. And the other thing is that there are physical processes. Sometimes I kind of make a sarcastic remark, like, by the way, the Internet of Things actually includes things.

TROND: [laughs]

JEFFREY: And there's a different skill set for designing machines and making machines work and repairing machines than there is for designing software. There are a lot of physical things that have to go on in a factory, changing over equipment, be it for making different parts. And the vision of the technologists might be we’ll automate all that, which may be true. Maybe 30 years from now, most of what I say about people will be irrelevant in a factory. I doubt it. But maybe it's 100 years from now, but it's going to be a long time.

And there was an interesting study, for example, that looked at the use of robots. And they looked at across the world jobs that could be done by a human or could be done by a robot. And they found that of all the jobs that could be done by a human or a robot, 3% were done by robots, 97%...so this kind of vision of the robots driven by artificial intelligence doing the work of people is really science fiction. It's mostly fiction at this point. At some point, it might become real, but it's got a long way to go.

So we still need to understand how to motivate, develop people. But particularly, the more complex the information becomes and the more information available, the more important it is to train people first of all in problem-solving and scientific thinking to use the data effectively and also to simplify the data because we're actually not very good at using a lot of data. We actually can't handle a lot of bits of data at a time like a computer can. So we need simple inputs that then allow us to use our creativity to solve the problem.

And most of the companies are not doing that very well. They're offering what they call digital solutions, and I hate that term, on the assumption that somehow the digital technology is the solution. And really, what the digital technology is is just information that can be an input to humans coming up with solutions that fit their situation at that time, not generic solutions.

TROND: It's fascinating that you started out with people. You went through all these experiences, and you are directly involved with digital developments. But you're still sticking to the people. We'll see how long that lasts. I think people, from the people I have interviewed, maybe self-selected here on the podcast, people and processes seem enormously important still in manufacturing.

Thank you for your perspective. It's been a very rich discussion. And I hope I can bring you back. And like you said if in X number of years people are somehow less important...well, I'm sure their role will change, will adjust. But you're suspecting that no matter what kind of technology we get, there will be some role, or there should be some role for people because you think the judgment even that comes into play is going to be crucial. Is that what I'm --

JEFFREY: There's one more thing I want to add. If you look at industry 4.0, it'll list these are the elements of industry 4.0, and they're all digital technologies. But there's something that's becoming increasingly popular called industry 5.0, where they're asking what's beyond industry 4.0? Which has barely been implemented. But why not look beyond it? Because we've talked about it enough that it must be real.

Once we kind of talk about something enough, we kind of lose interest in it. We want to go on to the next thing. So none of these things necessarily have been implemented very well and very broadly. But anyway, so industry 5.0 is about putting people back in the center. So I call it a rework loop. Uh-oh, we missed that the first time. Let's add it back in.

TROND: So then what's going to happen if that concludes? Are we going to then go back to some new version of industry 4.0, or will it --

JEFFREY: Well, industry 4.0 is largely a bunch of companies selling stuff and then a bunch of conferences. If you go and actually visit factories, they're still making things in the same way they've always made them. And then there's a monitor that has information on a screen. And the IT person will show you that monitor, and the person on the floor may not even know what it is. But there's a disconnect between a lot of these technologies and what's actually happening on the shop floor to make stuff.

And when they do have a success, they'll show you that success. You know, there's like hundreds of processes in the factory. And they'll show you the three that have industry 4.0 solutions in there. And so it's a long way before we start to see these technologies broadly, not only adopted but used effectively in a powerful way. And I think as that happens, we will notice that the companies that do the best with them have highly developed people.

TROND: Fantastic. That's a good ending there. I thank you so much. I believe you've made a difference here, arguing for the continued and continuing role of people. And thank you so much for these reflections.

JEFFREY: Welcome. Thank you. My pleasure.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was the People Side of Lean. Our guest was Jeffrey Liker, academic, consultant, and best-selling author of The Toyota Way. In this conversation, we talked about how to develop internal organizational capability.

My takeaway is that Lean is about motivating people to succeed in an industrial organization more than it is about a bundle of techniques to avoid waste on a factory production line. The goal is to have workers always asking themselves if there is a better way.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 84 on The Evolution of Lean. Hopefully, you will find something awesome in these or in other episodes. And if you do, let us know by messaging us, and we would love to share your thoughts with other listeners.

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

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

Special Guest: Jeffrey Liker.

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

In episode 17 of the podcast (@AugmentedPod), the topic is: Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

After listening to this episode, check out CESMII as well as John Dyck's social profile:

• CESMII: (@CESMII_SM) https://www.cesmii.org/ 
• John Dyck: https://www.linkedin.com/in/johnsdyck/ 

In this conversation, we talked about  democratizing smart manufacturing, the history and ambition of CESMII (2016-), bridging the skills gap in small and medium enterprises which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms and controls to radically impact manufacturing performance. We then have the hard discussion of why the US is (arguably) a laggard? John shares the 7 characteristics of future-proofing (interoperability, openness, sustainability, security, etc.). We hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade.

Trond's takeaway: US manufacturing is a bit of a conundrum. How can it both be the driver of the international economy and a laggard in terms of productivity and innovation, all at the same time? Can it all be explained by scale--both scale in multinationals and scale in SMEs? Whatever the case may be, future proofing manufacturing, which CESMII is up to, seems like a great idea. The influx of smart manufacturing technologies will, over time, transform industry as a whole, but it will not happen automatically.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 8 on Work of the Future, episode 5 Plug-and-play Industrial Tech, or episode 9 The Fourth Industrial Revolution post-COVID-19. Augmented--the industry 4.0 podcast.

Transcript:

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

In Episode 17 of the podcast, the topic is Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

In this conversation, we talked about democratizing smart manufacturing, the history, and ambition of CESMII, bridging the skills gap in small and medium enterprises, which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms, and controls radically impact manufacturing performance. We then have the hard discussion of why the U.S. is, arguably, a laggard. John shares the seven characteristics of future-proofing. And we hear about two coming initiatives: Smart Manufacturing Executive Council & Smart Manufacturing Innovation Platform. We then turn to the future outlook over the next decade.

Augmented is a podcast for leaders hosted by futurist, Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday.

Augmented — the Industry 4.0 podcast.

John, how are you today?

JOHN: I'm well, Trond. How are you?

TROND: I'm doing well. I'm looking forward to talking about smart manufacturing. What brought you to this topic, John? We'll get into your background. But I'm just curious.

JOHN: This is my favorite topic, as you probably know. So I appreciate the chance to pontificate a little. I've been at this nexus between IT and OT for the last two decades of my career or more and found over these past two decades that this is one of the most complex pieces of manufacturing period, this sort of unique challenge between the world of operations and the world of IT.

And the work I did at MESA (Manufacturing Enterprise Solutions Association) on the board and as the chairman of the board exposed me to a lot of the great vendors in this ecosystem. And through that work, I found that most of them struggle with the same things. We're all struggling in different ways.

And so the opportunity to take one step back and look at this from a national and a global perspective and try to find ways to address these challenges became a very unique opportunity for me and one that I've enjoyed immensely. And so just the prospect of making a real difference in addressing these challenges as a nation and as an ecosystem has been just a privilege and one that I get really excited about.

TROND: So, John, you mentioned your background. So you've worked in both startups...I think you were raising money for a startup called Activplant, but also, you have worked in large manufacturing for GE and Rockwell, so the big guys, I guess, in a U.S. context for sure. When this institution, C-E-S-M-I-I, CESMII, got started, what was its main objective, and what was the reason why this institution got launched? I guess back in 2016, which is not an enormous amount of time back. Give us a little sense of who took this initiative. And what is the core mission of this organization right now?

JOHN: So Manufacturing USA is the umbrella organization under which these institutes, CESMII being one of them, were created. There are a total of 15 of these institutes, all funded with the exact same business model and funding model, and each of them having a different lens on the specific manufacturing problem that they're addressing. And ours, as the Smart Manufacturing Institute, is directly focused on creating a more competitive manufacturing environment by addressing innovation and research challenges that inhibit manufacturers from doing what they need to do in this fourth industrial revolution.

So our mandate is to cut the cost of implementing smart manufacturing by 50%. Our mandate is to drive energy productivity, energy efficiency. Fundamentally, the agency that funds CESMII is the Department of Energy, which means that our overarching objective is to drive energy productivity as a basic metric.

But we also believe that whether that's a direct challenge meaning addressing energy, performance energy efficiency directly, or an indirect outcome from a more efficient process, or a more effective supply chain, whatever that manufacturing initiative is, that we'll create a better product, a better process that will have direct and indirect impact on energy productivity, which is the connection back to our agency and the source of the funding that we have to accomplish these really important goals.

TROND: And one of the really big identified gaps, also it seems, is this discrepancy between the big and the small industry players. So small and medium enterprises famously in every country is basically...the most of industry is consisting of these smaller players. They're not necessarily startups. They're not necessarily on this growth track to become unicorns. But they are smaller entities, and they have these resource constraints.

Give me a sense of what you're doing to tackle that, to help them out, and to equip them for this new era. And maybe you could also just address...you called smart manufacturing industry 4.0, but I've noticed that that's not a term that one uses much. Smart manufacturing is kind of what you've opted for. So maybe just address that and then get to the small and medium-sized.

JOHN: This is, I think, one of the really important observations that we try to make and the connections that we try to make to say that the status quo, the state of the industry today, Trond, is the result of three or four decades of what we did during the third industrial revolution. We began talking about the fourth industrial revolution many years ago. But we can't just turn that light switch on and assume that overnight everything we do now, despite the cultures we've created, the technologies we've created, the ways of doing things we've created, is now all of a sudden just new and exciting and different, and it's going to create that next wave of productivity.

So when I talk about smart manufacturing and equating it with the fourth industrial revolution, it's truly the characteristics and the behaviors that we anticipate more so than what we're seeing. Because the critical mass of vendors and systems integrators, application and software products in this marketplace still resemble more of industry 3.0 than they do industry 4.0. And it's part of our vision to characterize those two only in the context of trying to accelerate the movement towards industry 4.0 or the fourth industrial revolution. Because it's that that holds out the promise of the value creation that we've been promised for ten decades but really aren't seeing. So that's the way we see the industry 4.0 versus the other concepts that we talk about.

Digital transformation is another important term. All of that happens in the context of some initiative in a manufacturing operation to improve. We've been improving for three or four decades. What's different today? Well, it's not just relabeling [laughs] your portfolio to be industry 4.0 compliant. So anyway, that's a pet topic of ours just to help as a national conversation, as a set of thinking and thought leader organizations and individuals to put the spotlight on that and ensure that we're doing the things that we can to accelerate the adoption, and the behaviors, and the characterizations of what it really means to be industry 4.0. So to your point --

TROND: Yeah, I was just curious. The term revolution anyway is interesting in a U.S. context [laughter] and in any society. So it implies a lot of things, but it also certainly implies a speed that perhaps isn't necessarily happening. So there's all this talk now about how things are speeding up. But as you point out, even if they have some revolutionary characteristics, at the edge, there are some other things that need to happen that aren't necessarily going to happen at the speed of what you might imagine when you use the word revolution. It's not going to turn over like a switch.

JOHN: That's exactly right. Well said, Trond. Manufacturing and bleeding edge never come together in the same sentence, and so it takes time for...and more so on the OT side than the IT side. Right out of the IT world, we have industrial IoT platforms. We have augmented reality. We have powerful AI machine learning tools. But what is the true adoption on the plant floor? Well, that's where the behaviors, and the cultures, and the characteristics of how we've always done things and the reluctance to adopt new things really comes in.

And it's as much a part of the vendor and systems integration ecosystem as it is on the manufacturing side. And that's, again, this whole thing becomes...to drive (I really don't think it's a revolution to your point.) an evolution or accelerate the evolution towards Industry 4.0 requires the ecosystem to get engaged and to recognize these really important things have to change. Does that make sense?

TROND: Yes. A lot of them have to change. And then to these small and medium enterprises, so I've seen a statistic that even in the U.S., it's around 98% of manufacturing. That is an enormous challenge, even for an association like yours. How do you reach that many?

JOHN: Here's an interesting epiphany I had shortly after I came to CESMII and was working through exactly this challenge: how does an organization like ours access and understand the challenges they face and then look at the ecosystem that's there and available to serve them? The epiphany I had was that in my entire career with both big global corporations like Rockwell Automation and General Electric and specifically even the startup organization that I helped raise VC for and venture capital funding for and build and ultimately see acquired; I had never been in a small and medium manufacturing plant environment.

The entire ecosystem is focused on large brands, recognized brands, and enterprises that have the potential for multisite rollouts, multisite implementation. And so the business models, the marketing models, the sales, the go-to-market, the cost of sales, everything in this ecosystem is designed towards the large enterprises called the Fortune 1000 that represent the types of characteristics that any startup, any Global Fortune 500 organization is going to go pursue.

Which then says or leaves us with a really important conversation to say, how can the small and medium manufacturing organizations become part of this dialogue? How can we engage them? What does an ecosystem look like that's there to serve these organizations? And where an implementation organization like a good systems integrator can actually make money engaging in this way.

And so that's where the needs of that ecosystem and our specific capabilities come together. The notion that democratization which is going to help the big manufacturers, and the big vendors, and the big integrators, and the big machine builders, the same things that we can do to cut the cost of deploying smart manufacturing for them, will enormously increase the accessibility of smart manufacturing capabilities for the small and medium manufacturers. And so that's where typically --

TROND: John, let's talk specifics. Let's talk specifics. So smart manufacturing, you said, and I'm assuming it's not just a community effort. You're intervening at the level also of providing a certain set of tools also. So if we talk about sensors, and data, and platforms, and control systems, these are all impacting manufacturing performance.

To what extent can an association like yours actually get involved at that level? Is it purely on the standardization front, sort of recommending different approaches? Or is it even going deeper into layers of technology and providing more than just recommendations?

JOHN: So the short answer is it depends on the domain, and the area of networking, and sensors and controls. Those are areas where longer-term research and investment to drive innovation to reduce the cost of connecting things becomes really important. And that's one of the threads or one of the investment paths that we pursue through what we call roadmap projects where there are longer, larger in terms of financial scope and further out impacts. We're hoping we'll have a dramatic impact on the cost of connecting machines and sensors and variable-frequency drives and motion systems or whatever sort of data source you have in an operation. So that's one track.

The other piece which gets to the actual creation of technologies is more on the data contextualization, data collection, data ingestion side. And you mentioned the word standards. Well, standards are important, and where there are standards that we can embrace and advocate for, we're absolutely doing that.

Part of the OPC Foundation and the standards that they're driving, MQTT and Sparkplug, becomes a really important area as well. And the work that MTConnect is doing to solve many of the same challenges that we believe we need to solve more broadly for a subset of machine classes more in a CNC machine tool side. But this effort, smart manufacturing, is happening today, and it's accelerating today. And we can't wait for standards to be agreed on, created, and achieve critical mass.

So we are investing in a thin but vital layer of technologies that we can drill into if you'd like as a not-for-profit, not to compete in the marketplace but to create a de facto standard for how some of these really important challenges can be addressed, and how as a standard develops and we fund the deployment of these innovations in the marketplace and kind of an innovation environment versus a production environment. Not that they don't turn into production environments, but they start as an innovation project to start and prove out and either fail quickly or scale up into a production environment.

So this idea of a de facto standard is a really important idea for us. That's our objective. And that's what we believe we can build and are building is critical mass adoption for really important ideas. And we're getting support from a lot of the great thought leaders in the space but also from a lot of the great organizations and bodies like, as I mentioned, the OPC Foundation, The Industrial Internet Consortium, the German platform industry 4.0 group responsible in Germany for industry 4.0.

We're working towards and aligning around the same principles and ideas, again, to help create a harmonized view of these foundational technologies that will allow us to accomplish the dramatic reduction of the cost of connecting and extracting information from and contextualizing that information. And then making it available in ways that are far more consistent and compelling for the application vendor.

The bar or the threshold at which an application developer can actually step into the space and do something is in a pretty high space. If you kind of look back, and I know this analogy is probably a little overused, but what it took to build applications for devices and phones, smart devices, and smartphones before Apple and Android became commonplace meant that you had to build the entire stack every single time. And that's where the industry is today. When you sit down in front of a product, you're starting from scratch every time, regardless of the fact that you've created an information model for that paper-converting machine 100 times in 20 different technology stacks.

When I start this project, it's a blank slate. It's a blank sheet of paper every single time. Is that value-add? Is that going to help? No. And yet it requires a tremendous amount of domain expertise to build that. So the notion of standardizing these things, abstracting them from any individual to technology stack, standardizing on them, making them available in the marketplace for others to use that's where democratization begins to happen.

TROND: So what you are about to create is an innovation platform for smart manufacturing. Will that be available then to everybody in the U.S. marketplace? Or is it actually completely open for all of the industry, wherever they reside? And what are the practical steps that you would have to take as a manufacturer if you even just wanted to look into some of the things you were building and maybe plug in with it?

JOHN: So we're not about to build, just a minor detail there. We've been working on this for a couple of years. And we have a growing set of these implementations in the marketplace through the funded projects that we were proud to be able to bring to the marketplace. So the funding, and right now within the scope of what we're doing here as an institute, the funds that we deploy as projects, these grants, essentially mean that we spend these grants, these funds in the U.S. only.

So in the context of what we do here, the smart manufacturing innovation platform, the creation of these profiles, the creation of the apps on top of the platform by our vendor ecosystem and domain experts in this ecosystem those are largely here and exclusively here in the U.S, I should say. So from that perspective, deployments that we have control over in terms of funding are uniquely here in the U.S. What happens beyond that in terms of where they're deployed and how they're deployed, we know we live in a global manufacturing environment. And as our members who want to deploy these capabilities outside of the U.S., those are all absolutely acceptable deployments of these technologies.

TROND: But, John, so all of these deployments are they funded projects so that they're always within involvement of grant money, or is some part of this platform actually literally plug and play?

JOHN: So there are several threads. The projects that we fund are obviously one thread. There's another thread that says any member of ours can use any implementation of our platform or can use our platform and any of the vendors that are here as a proof of concept or pilot, typically lasting 3,4,5,6 months for free of charge. What happens then that leads to the third component is after your pilot, there's one of two things that's going to happen. The system will be decommissioned, and you ideally, well, I shouldn't say ideally...you fail fast, the system is decommissioned, and folks move on.

Ideally, the pilot was a success. And that generates a financial transaction for the parties involved in that. And that organization moves towards a production rollout of these capabilities. So CESMII's role then diminishes and steps away.

But this notion of a pilot actually came from a conversation with one of our great members here at Procter & Gamble. They talk about innovation triage and the complexity of just innovating within a large corporate environment like Procter & Gamble. The fact that just to stand up the infrastructure to invite a vendor, several vendors in to stand up their systems costs hundreds of thousands of dollars and takes months and months and months just to get started.

This notion that we can provision this platform in minutes, bring our vendor partner technologies to bear in minutes allows them to execute what they call innovation triage. And it really accelerates the rate at which they can innovate within their corporation, but it's that same idea that we translate back down to small and medium manufacturing, right? The notion that you don't have to have a server. You don't have to sustain a server. You don't have to buy a server to try smart manufacturing in a small and medium manufacturing environment.

If you've got five sensors from amazon.com and lightly industrialized Raspberry Pi, you have the means to begin the smart manufacturing journey. What do you do with that data? Well, there are great partner organizations like Tulip, like Microsoft Excel, even Microsoft Power BI that represent compelling democratized contemporary low-cost solutions that they can actually sustain.

Because this isn't just about the cost of acquiring and implementing these systems, as you know. This is also about sustaining them. Do I have the staff, the domain expertise as a small and medium manufacturer to sustain the stuff that somebody else may have given me or implemented here for me? And so that's just as an important requirement for these organizations as the original acquisition and implementation challenges.

TROND: It's so important what you're talking about here, John, because there's an additional concept which is not so pleasant called pilot purgatory. And this has been identified in factories worldwide. It's identified in any software development. But with OT, as you pointed out, with more operational technologies, with additional complications, it is so easy to just get started with something and then get stuck and then decide or maybe not decide just sort of it just happens that it never scales up to production value and production operations.

And it seems like some of the approaches you're putting on the table here really help that situation. Because, as you mentioned, hundreds of thousands of dollars, that's not a great investment for a smaller company if it leads to a never-ending kind of stop and start experimenting but never really can be implemented on the true production line.

JOHN: Yeah. Spot on, Trond. The numbers that we're seeing now...I think McKenzie released a report a couple of months ago talking about, I think, somewhere between 70% and 80% of all projects in this domain not succeeding, which means they either failed or only moderately succeeded. And I think that's where the term pilot purgatory comes in.

I talk almost every chance I get about the notion that the first couple of decades of the third industrial revolution resulted in islands of automation. And we began building islands of information as software became a little more commonplace in the late '80s and '90s. And then the OTs here in the last decade, we've been building islands of innovation, this pilot purgatory.

The assumption was...and I get back to the journey between where we thought industry 3.0 or the third industrial revolution became the fourth industrial revolution. The idea was that, man, we're just going to implement some of these great new capabilities and prove them out and scale them up. Well, it gets back to the fact that even these pilots, these great innovative tools, were implemented with these old ideas in these closed data siloed ways and characterizations.

And so yeah, everybody's excited. The CEO has visibility to this new digital transformation pilot that he just authorized or she just authorized. And a lot of smart people are involved, and a lot of domain experts involved. The vendors throw cash at this thing, and the systems integrators, implementers, throw cash at this thing. And even if they're successful, and broadly, as an individual proof of concept, there are points of light that say, we accomplished some really important things.

The success is not there, or the success isn't seeing that scaled out, and those are the really nuanced pieces that we're trying to address through this notion of the innovation platform and profiles. The notion that interoperability and openness is what's going to drive scale, the notion that you don't have the same stovepipe legacy application getting at the same set of data from the same data sources on the shop floor for every unique application, and that there are much more contemporary ways of building standardized data structures that every application can build on and drive interoperability through.

TROND: Yeah, you talk about this as the characteristics of future-proofing. So you mentioned interoperability, and I guess openness which is a far wider concept. Like openness can mean several things. And then sustainability and security were some other of your future-proofing characteristics. Can you line up some of those for us just to give some context to what can be done? If you are a factory owner, if you're a small and medium-sized enterprise, and you want to take this advice right now and implement.

JOHN: Yeah, we've tried as an association, as a consortia, Trond, it's not just CESMII staff like myself who are paid full-time to be here that are focused on identifying and developing strategies for the challenges that we believe will help manufacturing in the U.S. It's organizations that are members here and thought leaders from across the industry that help us identify these really fundamental challenges and opportunities.

And so, as an institute, we've landed on what we call the smart manufacturing first principles. There are seven first principles that we believe characterize the modern contemporary industry 4.0 compliant, if you will, strategy. And just to list them off quickly, because we have definitions and we have content that flushes out these ideas, sort of in order of solve and order of importance for us, interoperability and openness is the first one. Sustainable and energy efficient is the second one, security, scalability, resilient and orchestrated, flat and real-time, and proactive and semi-autonomous.

And so these we believe are the characteristics of solutions, technologies, capabilities that will move us from this world of pilot purgatory and where we've come from as an ecosystem in this third industrial revolution and prepare us for a future-proof strategy whether I'm a small and medium manufacturer that just cares about this one instance of this problem I need to solve, or whether I'm a Fortune 10 manufacturing organization that understands that the mess that we've created over the last 25 years has got to make way for a better future.

That I'm not going to reinvest in a future...not that I can rip and replace anything I've got, but I've got to invest in capabilities moving forward that represent a better, more sustainable, more interoperable future for my organization. That's the only way we're going to create this next wave of productivity that is held out for us as a promise of this new era.

TROND: John, you have alluded to this, and you call it the mess that we've created over the last 25 years. We have talked about the problems of lack of interoperability and other issues. This is not an easy discussion and certainly not in your official capacity. But why is the U.S. a laggard? Because, to be honest, these are not problems that every country has, to a degree, they are but specifically, the U.S. and its manufacturing sector has been lagging. And there is data there, and I think you agree with this. Why is this happening? And are any of these initiatives going to be able to address that short term?

JOHN: So this is probably the most important question that we as a nation need to address, and it's a multifaceted, complex question. And I think the answer is a multifaceted, complex response as well. And we probably don't have time to drill into this in detail, but I'll respond at least at a 30,000 foot-level. Even this morning, I saw a friend of mine sent me a link about China being called out today officially as being a leader in this digital transformation initiative globally, as you've just alluded to.

So, from our perspective, there are a couple of important...and like I said, really understanding why this is the case is the only way we're going to be able to move forward and accelerate the adoption of this initiative. But there are a number of reasons. The reason I think China is ahead is in part cultural, but it's also in part the fact that they don't have much of the legacy that we've built. Most of their manufacturing operations as they've scaled up over the last decade, two decades, really since the World Trade Organization accepted China's entry in this domain, their growth into manufacturing systems has been much, much more recent than ours.

And so they don't have this complex legacy that we do. There are other cultural implications for how the Chinese manufacturing environment adopts technologies. And there's much more of a top-down culture there. Certain leaders drive these activities and invest in these ways. Much of the ecosystem follows. So that's, I'll say, one perspective on how China becomes the leader in this domain very quickly.

Europe is also ahead of the U.S. And I think there are some important reasons why that's the case as well. And a part of it is that they have a very strong cultural connection to the way government funds and is integrated with both the learning and academic ecosystem there in most of Europe as well as with the manufacturing companies themselves. It seems to have become part of their DNA to accept that the federal government can bring these initiatives to the marketplace and then funds the education of every part of their ecosystem to drive these capabilities into their manufacturing marketplace.

We, on the other hand, are a much more American society. We are individualistic. The notion that the government should tell manufacturers what to do is not a well-accepted, [laughs] well-adopted idea here in the U.S. And that's been a strength for many manufacturers, and for many, many years.

The best analogy that I can come up with right now in terms of where we are and where we need to go and CESMII's role in all of this, and the federal government's role in all of this, which I think brings a healthy blend of who we are as a nation and how we work and how we do things here together with a future that's a little more also compatible with these notions of adopting and driving technology forward at scale, is the reality that in 1956, President Eisenhower convinced Congress to fund the U.S. Interstate Highways and Defense Act to build a network of interstate highways, a highway network across this country to facilitate much more efficient flow of people and goods across this country.

Apparently, as a soldier, many decades before, he had to travel from San Diego to Virginia in a military convoy that took him 31 days to cross the country [laughs], which is a slight aside. It was apparently the catalyst that drove the passion he had to solve this problem. And that's the role that I think we can play today, creating a digital highway, if you will, a digital catalyst to bring our supply chains together in a much more contemporary and real-time way and to bring our information systems into a modern industry 4.0 compliant environment.

And that's setting those, creating those definitions, defining those characteristics, and then providing the means whereby we can accelerate this ecosystem to move forward. I think that's the right balance between our sense of individualism and how we do things here in the U.S. versus adopting these capabilities at scale.

TROND: That's such a thoughtful answer to my question, which I was a little afraid of asking because it is a painful question. And it goes to the heart, I guess, of what it means to be an American, to be industrial, and to make changes. And there is something here that is very admirable. But I also do feel that the psychology of this nation also really doesn't deeply recognize that many of the greatest accomplishments that have been happening on U.S. soil have had an infrastructure component and a heavy investment from the government when you think about the creation of the internet, the creation of the highway system. You can go even further back, the railways.

All of those things they had components, at least a regulation, where they had massive infrastructure elements to them whether they were privately financed or publicly financed, which is sort of that's sort of not the point. But the point is there were massive investments that couldn't really be justified in an annual budget.

JOHN: That's right.

TROND: You would have to think much, much wider. So instead of enclosing on that end then, John, if you look to the future, and we have said manufacturing is, of course, a global industry also, what are you seeing over this next decade is going to happen to smart manufacturing?

So on U.S. soil, presumably, some amount of infrastructure investment will be made, and part of it will be digital, part of it will be actually equipment or a hybrid thereof that is somewhat smartly connected together. But where's that going to lead us? Is manufacturing now going to pull us into the future? Or will it remain an industry that historically pulls us into the future but will take a backseat to other industries as we move into the next decade?

JOHN: Yeah, that's another big question. We've been talking about smart manufacturing 2030, the idea that smart manufacturing is manufacturing by 2030. And a decade seems like a long time, and for most functions, for most areas of innovation, it is, but manufacturing does kind of run at its own pace. And there is a timeline around which both standardization and technologies and cultures move on the plant floor. And so that's a certain reality. And we were on a trajectory to get there. But ironically, it took a pandemic to truly underscore the value of digital transformation, digital operations, and digital workers, I can certainly say in the U.S. but even more broadly.

So a couple of important data points to back that up. Gartner just recently announced the outcome of an important survey of, I think, close to 500 manufacturing executives here in the U.S. in terms of their strategic perception of digital transformation, smart manufacturing. And I think they specifically called it smart manufacturing.

And it was as close to unanimous as anything they've ever seen; 86% or 87% of manufacturing executives said that now digital transformation, smart manufacturing is the most strategic thing they can invest in. What was it a year ago? It was probably less than half of that. So that speaks to the experience these organizations have gone through. And the reality that as we talk about resilience, some people talk about reshoring, and some of that will happen.

As we talk about a future environment, that's...I shouldn't say disruption-proof but much more capable of dealing with disruption not just within the four walls of the plant or an enterprise but in the supply chain. These capabilities are the things that will separate those that can withstand these types of disruptions from those that can't. And that has been recognized.

And so, as much as these executives are the same ones that are frustrated by pilot purgatory, it's these executives that are saying, "That's the future. We've got to go there." And we're seeing through this pandemic...we hear CESMII are saying the manufacturing thought leaders understand this and are rallying around these ideas more now than ever before to ensure that what we do in the future is consistent with a more thoughtful, more contemporary, future-proof way of investing in digital transformation or smart manufacturing.

TROND: John, these are fascinating times, and you have a very important role. I thank you so much for taking time to appear on my show here today.

JOHN: Trond, I appreciate that. I appreciate the privilege of sharing these thoughts with you. These are profound questions, and answering the easy ones is fun. Answering the hard questions is important. And I appreciate the chance to have this conversation with you today.

TROND: Thanks. Have a great day.

JOHN: You too.

TROND: You have just listened to Episode 17 of the Augmented Podcast with host Trond Arne Undheim. The topic was Smart Manufacturing for All. Our guest is John Dyck, CEO at CESMII, the Smart Manufacturing Institute.

In this conversation, we talked about democratizing smart manufacturing and the history and ambition of CESMII, bridging the skills gap in small and medium enterprises, which constitute 98% of manufacturing. We discuss how the integration of advanced sensors, data, platforms, and controls radically impact manufacturing performance. We then have the hard discussion of why the U.S. arguably is a laggard. We heard about two coming initiatives: the Smart Manufacturing Executive Council & the Smart Manufacturing Innovation Platform. We then turned to the future outlook over the next decade.

My takeaway is that U.S. manufacturing is a bit of a conundrum. How can it both be the driver of the international economy and a laggard in terms of productivity and innovation, all at the same time? Can it all be explained by scale, both scale in multinationals and scale in SMEs? Whatever the case may be, future-proofing manufacturing, which CESMII is up to, seems like a great idea. The influx of smart manufacturing technologies will, over time, transform industry as a whole, but it will not happen automatically.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 8 on Work of the Future, Episode 5 on Plug-and-play Industrial Tech, or Episode 9 on The Fourth Industrial Revolution post-COVID-19.

Augmented — the Industry 4.0 podcast.

Special Guest: John Dyck.

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

In episode 43 of the podcast (@AugmentedPod), the topic is: Digitized Supply Chain. Our guest is Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT, Johnson & Johnson.

In this conversation, we talk about why J&J puts operators at the center of its strategy, the empowerment effect of frontline operations apps, the evolution of personalized production, and how supply chain becomes an integral part of product development.

After listening to this episode, check out J&J as well as Arun Kumar Bhaskara-Baba's social medial profile:

•  J&J (@JNJNews): https://www.jnj.com/ 
• Arun Kumar Bhaskara-Baba: https://www.linkedin.com/in/bhaskarababa/

Trond's takeaway: "Operators are the key to the next phase of industrial evolution, that which involves the deep digitalization of manufacturing, its supply chain, production capacity, personalization, and with that the reinvention of factory production itself.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 21, The Future of Digital in Manufacturing, episode 27, Industry 4.0 Tools or episode 10, A Brief History of Manufacturing Software

Augmented--conversations on industrial tech.

Transcript:

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

In Episode 43 of the podcast, the topic is Digitized Supply Chain. Our guest is Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson.

In this conversation, we talk about why J&J puts operators at the center of its strategy, the empowerment effect of frontline operations apps, the evolution of personalized production, and how supply chain becomes an integral part of product development.

Augmented is a podcast for leaders hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday.

Augmented — the industry 4.0 podcast.

TROND: Arun, how are you?

ARUN: I'm doing great. How are you, Trond?

TROND: Oh, it's wonderful to see you and hear you. I'm very excited. This is a big interview. You have really big responsibilities, Arun. We're going to get to that in a second. But global manufacturing that is a wide, wide topic.

ARUN: Yes, indeed. But the bigger responsibility, but more importantly, what we are privileged is how we are impacting the lives of patients and customers around the world with our products. That comes with the privilege to work in the healthcare environment.

TROND: Well, I'm glad you said that because as we're sort of tracing, I want to ask you a little bit about how you got to where you are. And I know from public records, at least, that you have part of your schooling in India. So you grew up in India, my assumption is, and you got your computer degree there. You worked in India for a little while for the Tata system. And then you made your way over to Michigan. You have your MBA from there.

And then, from what I understand, you then had a bit of a career in automotive and then moved on to Dell. And this brings us to J&J. How did you end up in the U.S.? And how was that journey for you? You've come quite a bit of ways.

ARUN: Yes. It's interesting that you asked how I ended up in U.S. For me, it was a choice of either going to Japan or to U.S., And I'm a vegetarian, so for me, U.S. was a better choice. Growing up when you're a kid, you have two years of experience, the decisions that you make, some priorities.

TROND: That's funny, but you told me, Arun, that you came here with a briefcase and a $10 bill.

ARUN: Yes. I was going to go --

TROND: That's, I guess, not an unusual immigrant story, but it is still quite striking.

ARUN: Absolutely. I grew up in a very small middle-class family. So when I landed, I landed with a briefcase and a $20 bill, actually two $10 bills. And out of that, one $10 bill I still have as a reminder of where I started.

TROND: Wow. And I cut your career a little short because you have had the opportunity to work in all of the BRIC countries, essentially. And you now manage teams across, I think, at least 28 countries. And that brings us, I guess, up to present day where I was alluding to this, but you have a very wide responsibility. We're going to talk about some of it. Can you tell me a little bit about your current role?

ARUN: So, my team supports all the manufacturing operations for J&J across the globe. So we have 100-plus manufacturing plants in pharmaceutical, consumer, medical devices, and vision products. As I mentioned earlier, I am privileged to be in healthcare to serve our patients and customers. We are in 28 countries; my team is spread across. And it's a very humbling experience to really work in a global team and continue to support our operations across the world.

TROND: Well, not only that 28 countries, but I understand you operate about 100 manufacturing sites, some obviously state of the art, very big and sprawling, others actually very small or at least mid-size and have all kinds of other issues. And J&J, you know, what is the breadth of products you make? I mean, you make vaccines. You make knees, artificial knees. What else do you guys make?

ARUN: This is amazing. I used to work for Ford Motor Company and Dell. Definitely, they are also very strong in manufacturing. However, the manufacturing processes are very similar. It is either assembly process, marketing and manufacturing at Dell. I come to J&J, and any type of manufacturing, you say we got it. Whether you talk about process manufacturing or discrete manufacturing, we have that.

So in the pharmaceutical area, we produce biological products where we actually grow live cells and make medicine out of it, as you mentioned, the vaccines and biological products. We also have big chemical products where we actually use big chemical reactions to produce the drugs. In medical devices, we have artificial knees and hips, which are more like a foundry operation. You take a mold, you put it in an artificial knee, and make it happen. And we have sutures that we produce.

And in the consumer side, we have different types of liquids, gels, and tablets that we produce. And finally, in vision care is where we produce our lenses in a very high-velocity manufacturing. So if you look at the breadth of the manufacturing processes and products we support, we support almost every aspect of manufacturing.

TROND: Well, this brings us to today's topic because we're going to talk a little bit about digitizing these operations, the supply chains, the whole thing, and think about what digital means to all of it, whether it's in pharma 4.0, or indeed, you know, manufacturing and industry 4.0. Can you maybe just kick us off a little bit and say what does digital mean to your business today? And what is your main take on how to approach it?

ARUN: The first thing is really I see digital as a means to an end. So if you think about it, it's really why digital is the first and then why digital. We need to be very clearly understanding why we want to digitize. We are in the journey to transform our supply chain so that we can put our patients, our customers at the center of the supply chain and how we can get our products to our customers in a fast, nimble way and in an affordable way.

If you think about healthcare, the key is affordability as well as the ability for us to deliver what they need where they need it. And if you think about even the vaccines that we are producing now, we are manufacturing only in some locations, but we have to distribute them everywhere, whether to sophisticated networks like U.S. or developing areas where we don't even have a lot of transportation like Africa.

So how do we put the customer and the patient at the center? And how can we actually serve them in a much more faster way and in an affordable way? So that is the why behind our supply chain journey. And digitization is a very critical component of that transformation. How do we provide that end-to-end connectivity so that we can reach our customers and patients? How do we understand what is happening in the markets and react to those things quickly as well as respond quickly using digital?

And then ensure that we are delighting our customers beyond just our products, that we have world-class products. But how do we make sure that we are delivering the same customer experience to our patients and customers? So for us, the work from the digital side is how do we build that end-to-end connectivity so that we can reach our customers and we can sense and respond very quickly? And finally, how do we make sure that we significantly improve your customer experience?

TROND: I want to pick up on a couple of things, but let me first ask a basic question. I mean, when I think supply chain, I think back to business school where I was teaching for a while, and I think kind of a fairly dry subject that was a specialty subject. You either cared about it, and then you wanted to become an expert and obviously dominate the field.

But now you're speaking of it as if it is a much more integrated part of product development, which I think that was certainly taught as two separate courses, even in the very immediate past. But do you think of the supply chain as completely integrated with what you do, what you produce?

ARUN: Absolutely. If you think about where the healthcare is headed, if you think about personalized healthcare, if I'm taking a knee right now, we ship like six or seven knees to the surgeons so that they pick the right knee during the operation. And we are getting to a place where we take the picture of the knee, get it back, and make the product, and then 3D print it and give it to the surgeon.

Or if you think about how we are personalizing where we are taking the blood from the patient and making the product that is very specific to the patient and shipping it to them. So this whole flow of here is my R&D, and then it goes to supply chain, and then we deliver it versus it is now becoming a connected world where this all comes together.

So it's really a very integrated part of product development and supply chain. So we really look at that end to end. And then digital is the one that is actually accelerating that journey. Because I can now connect all of these things as a digital thread and then really push the envelope forward.

TROND: But producing for a batch of one, I mean, it's enormously challenging at scale, no?

ARUN: Yeah, absolutely. That is the trick, right? How do I produce that batch of one? And if you think about the future, where we can actually get to that and where we can produce batch of one for almost everything that we do is where we are headed. You're right; there are significant investments in terms of our manufacturing operations and the equipment that we need. And there is that balance between the scale that you need to have versus the personalization that is needed.

And the balance is I don't think the pendulum can go either one way or the other. But really, we still have a lot more to move to the personalized level. How do we really become a full supply chain so that we can produce that batch of one wherever possible? And look at that from the customer and patient's angle, right? If you have somebody who has a traumatic surgery going on and they have a bone that we need to fix...and it is not the same from one trauma to another trauma. There you can't come back and say, okay, here is a batch of things that I'm producing, and I'm going to give it to you.

So the customer expectations are also changing. As a patient and as a consumer, their expectations are also changing. And so we are moving to that batch of one. And how do you do it for different products? And how do you do it for different manufacturing processes is going to be tailored to that business model and then the product.

TROND: So another thing that one might assume when we speak about this, because okay, batch of one, but it has to be an advanced system, and it's covering the globe. I mean, historically, if a factory has machinery or systems and digital technologies, it is a very monolithic, massive system. I understand that you have taken at least some care these days to focus on the operators. Why is that so crucial to you? And what does that mean for the kinds of technologies that you're putting into your factories nowadays?

ARUN: So that's a very good question. If you think about where manufacturing is headed so that we can drive that flexibility, that approach so that we can quickly respond, we have to relook at our manufacturing operations. That means they need to be a lot more nimbler and a lot more flexible. And a lot of technologies are emerging, and that's all driving. But for us, at the end of the day, it all comes back to that operator. We are here to serve the operator. We call it #operatorrules.

Because think about this, we can do all these flexible things. We can bring in automation. We can bring in robots and all of it. At the end of the day, there is an operator at the line who is making it happen. So how do we make sure that we put the operator at the center and then create the experience for the operator so that it makes it a lot easier?

If you take any of our plants, the technology is growing very fast. We used to have an ERP system. The operator has to deal with an MES. The operator then has to look at the equipment interface that the equipment provider has given. Now I'm coming from technology and saying, okay, here is the smart glass. Wear the smart glass, and you can look at everything. Think about the operator, how complex we have made the operator's life. So we are trying to take a step back and say, how do we, first of all, make it simple?

Number two is how do we empower them? So far, we all said that, oh, technology is either manufacturing engineering or the OT or IT people. We held the keys for the technology. But how do we really empower the operators so that they can make it flexible and then they can make it nimble? So that gives you the velocity that we need at our manufacturing operations.

TROND: It's striking when you think about at least digital technologies now clearly. There have been machines in factories for centuries. I mean, that was sort of the various industrial revolution. So there have, of course, been machines that could be operated by operators to some degree.

But the kind of control and the detail-level customization that's now becoming possible doesn't come naturally, does it? It takes a lot of attention to create those kinds of platforms. How do you see that evolving? For example, we said you have over 100 different sites, some of them large, others much smaller; what sort of approaches are you taking to experiment with these solutions?

ARUN: So it's purpose-driven experimentation. Because to your point, when we have these large, fully automated factories, the key is how fast I can introduce new capabilities into that operation. Whereas when I go to a middle-tier factory with semi-automated or not as much automated, it is a very target problem-driven. I have an OEE problem. Let me figure out how do I experiment to bring the technology.

But at both the spectrums, the key is to make sure that there is a good, robust architecture principles. There is good, robust security, and then there is a good data architecture. But from a solutions point of view, how do we make sure that these are modular? Think about the mainframe days where you need to know all those to run the application to now you have apps on your device.

So how do we break these monolithic technologies that are running the operations into smaller apps by bite-sized chunks that we can actually deploy very quickly or pull it out? And that gives me the flexibility to say for a large site; I'm going to deploy all these 100 apps so that they can run it as a suite. Whereas when I go to a smaller site, I might only deploy two of those applications for a specific problem. So it's kind of like really breaking down by, number one, by purpose. Number two, having a good consistent architecture. And number three, really breaking these monolithic things into smaller apps and nimble apps that we can drive.

TROND: I know that you've tried some of Tulip's solutions. Tulip is an app system. But clearly, the bar to completely replace any number of advanced technologies that have developed over literally decades is not done overnight. How do you see the journey that app developers on the manufacturing shop floor...what sort of journey are they going to have with you to prove themselves over time to gradually solve many of these very ambitious problems?

I mean, you describe them pretty eloquently, but they're different in each factory, like you pointed out. And we're dealing with operators, some of whom are very advanced and have taken all kinds of industry 4.0 courses and others who have not. So this is a bit of a journey.

ARUN: Yeah, it is a journey, but there are similarities in this journey. If you think about maintenance of the equipment, it used to be a stronghold of those engineers that are sitting somewhere, and they get to the equipment when there is help needed. Look at where we are now. With operator asset care, we are empowering the operators to own that equipment and drive it. So that is the same journey that we have to go through from the digital side.

And the key is, first of all, making sure that we have platforms like Tulip and others that help us to be able to quickly develop those apps, of course, in a very consistent framework. Especially for us when we are in a regulated industry, some of those framework and validation things become extremely critical. How do you set those boundaries?

The second thing is educate the operators so that they feel empowered that they own the work that they are doing, and they can shape it in the way they need to do it and to continue to train them. And then the third level is to really train the rest of the organization. The management and then the operations leaders all need to be digitally savvy to drive that and then see the value. So it is a journey, but you need to be very clear about why we are doing it and putting the operators at the center and helping them.

The thing that is going to help us is this whole COVID pandemic situation. If you think about the digital savvy of almost the entire world, it has significantly improved. Every operator, whether we like it or not, yeah, they might not have a degree, but they know how to order their Uber Eats. They know how to use an app. So we are seeing digital literacy coming up very fast. So this is a great opportunity for us to drive that transformation. But you're right; it is a journey.

TROND: But you also mentioned regulated industry. I mean, to what extent can some of these apps kind of slide in between the cracks and do stuff that was never covered by regulation? And to what extent do you actually need to take very, very good care that you are, I guess, also updating the regulations and knocking on the doors of governments and telling them that "Look, there's an app for this too."? [chuckles] And we need to upgrade the regulatory framework to take that into account. So it seems to be a bit of both.

ARUN: Yes, you absolutely hit the nail on the head. You need to do both. One is, first of all, have a good, robust architecture. That's why the platforms like Tulip will need to ensure that the architecture is robust so that it has enough control so that we can drive this validation and qualification, those things, and giving the parameters of the freedom for the operators within those constraints. And let's not forget cybersecurity, which is a huge thing, especially when we come to the OT cybersecurity as well. And on the other side...sorry.

TROND: No, no, go ahead. On the other side...

ARUN: On the other side, we need to continue with the regulators and work with the regulators to make sure that they understand what we are doing. We are now working with the regulators to educate them on real-time release. How can we actually use the data rather than having to produce these samples and batches as opposed to relying on continuous data that is coming that shows that your process is in compliance?

So working on both sides with the framework so that it is robust as well as regulators to make sure that they understand how the technology is transforming. At the same time, the compliance is improving. Think about it, when you're doing samples, one, you're taking one sample from a batch. But when you're doing continuous sampling, you have the whole sample, whole product batch data you have in your hands. So we'll continue to work with them to make sure that the regulators are also coming with us on that journey.

TROND: How is pharma 4.0 going? I mean, the acronym is the same as industry 4.0. Is 4.0 actually happening, or are we still in 3.0?

ARUN: In pharma-world, I would say we still have 2.0 to 3.12 to 3.33. And there are some great examples where we have the 4.0 when I talk about what we are doing with the personalized solutions when we talk about how we are bringing IoT to the forefront, how we are doing real-time release with digital twins of our whole process. Now we have digital twins, even for bioreactors, which are very difficult to characterize. So yes, the journey is there.

The key is to keep in mind why we are doing it to really make sure that we have the patients that are waiting for our products in mind and then really transform around to support them. So the journey is continuing. Yes, there are very good examples for pharma 4.0. But are we there yet? No. But is everybody working together to get there? Yes.

TROND: Let's talk a little bit about this operator and the training of an operator because training the workforce is something I ask a lot of the people who come on this podcast about just because technology is one thing but training people on the technology to implement it in a fruitful way is a whole other challenge. What approach are you taking at the whole J&J complex when it comes to training your existing future and even training your ecosystem around you?

ARUN: A couple of things there; one is, first of all, making sure that you start with the user experience in mind and design everything from there. So you need to start with the design aspect. The second thing is how do we make it simple? The more simple you make it, the less training. How many people are getting trained on how to use an iPhone? So really, how do we make it simpler?

But actually, in the future, I'm thinking...and this I actually got from one of your podcasts, Trond, is, are we going to get to a point where there is no interface? So can we get our apps to a state where there is no interface, then your training becomes a lot more part of the evolution rather than you have to go; oh, now I need to learn this, and I need...no, it should be so intuitive. It's like gesturing with my hands.

So how do I get to that state? Hopefully, that state comes in soon, as you've been discussing with some of them. But for me, it is really how do we keep on making it so simple that it becomes intuitive? And it starts with the design, where you put the operator at the center and design around the operator.

TROND: Can we talk a little bit more specifically about the digitized supply chain? Because it is such a core to what you're up to. And I know that there are some characteristics that you care about the most one of them I think you mentioned to me was being very responsive. But what are the priorities when you are redesigning a supply chain? What are the kinds of things that are top of mind for you? And where do you start?

ARUN: You start with the customer experience. How do we make sure that that is clear on how it is impacting the customer experience? Now to help with the customer experience, how do we drive that responsiveness in your supply chain so that you can respond very quickly to what is happening at the demand side, the customer side, and then link it back?

Then the next one is really the resiliency. How do we build that resiliency in supply chain so that we can react very quickly? If there is one thing that COVID taught us is that resiliency in our supply chains actually helped the world in one way to survive this pandemic and continue to survive. So how do we drive that resiliency in the supply chain?

TROND: What do you think about these very traditional concepts that have been part of...and, you know, you had the start of your career in automotive. Lean management is something that everybody wanted to copy, and the Toyota processes and a lot from the country you chose not to study in [laughs] essentially because you weren't convinced they were vegetarian enough.

But anyway, what do you think about the heritage from lean and mixed in with some of the agile tradition from software? Is that altogether creating a new paradigm? And what does that look like, and who's describing it? If you would maybe describe where some of your influences come from when you are designing such a large organization around these principles.

ARUN: At the heart, the lean principles and agile principles are still really valid. Like, if you think about lean, what it is saying is think about the floor, eliminate the waste, and continue to improve and zero defects as possible. So that mindset has to be there for us to even look at digital. What digital is doing is actually helping us to implement lean even faster. How do you get there?

Now, from responsiveness, and we talked a lot about the responsiveness, and reacting, and resiliency that requires this agile mindset, this traditional boundaries of I'm going to go from plan, source, make, deliver. This is becoming a network. The only way you can survive in that network is having that agile mindset where we bring people together very quickly, get the problem solved, deliver that MVP, and don't look back and then move on to the next one.

So the agile principles around bringing the teams together very quickly to focus on the key priorities and delivering on the MVP aligned with the lean thinking to make sure that there is no waste and we are really getting the floor done actually is a great combination of these two. And these are the two things that need to come together even for us to roll out the digital solutions very quickly in our operations.

And COVID has been a great example if you think about how we came together to deliver a product for the instruments in a very quick way across the world in a virtual way. It has been a great example that shows that it can be done. So that's where the lean foundations and then the agile mindset are extremely critical, even for us to drive this digital transformation.

TROND: If you think about how this was built, what are some of the best influences that help you along the way? We talked a little bit about startups that bring the app mindset and maybe some of the agile thinking. It doesn't necessarily come from startups, but certainly, it does exist with startups. Where are these industry practices that you are increasingly embodying at J&J? Where do you think they come from?

ARUN: Actually, they come from many places. And for startups, really one of the places where we can actually see how their mindset is there in terms of test and learns, and learning from failure, and more. And even I'm looking at some of the journeys like how companies like Tulip are evolving as well. Especially those companies from a startup to accelerating phase, that's where we are seeing a lot of the learnings that we can learn.

And one of the big things that we at J&J look at is how can we look at our CEO and saying, "Hey, we need to act like a 135-year-old startup."? So how do we actually look at it? And to your point, where we are looking for, we are looking for everywhere; one is really those startups. But more importantly, those startups that got that first phase and are now accelerating, that's where all the processes need to come together.

And then, at the end of the day, we still have to be reliable. And we are in a regulated industry. So how do we make sure that the patient safety, product quality are the top priority and our processes are reliable? That's where the established companies also help us on how we continue to drive that.

TROND: Yeah, because that's what I guess I wanted to drive to because there is an established idea in the established industry to look for industry best practices. And in the manufacturing space, there are these lighthouse projects. Companies on their own might have lighthouse projects that are especially good. And the World Economic Forum has lighthouse factories. In fact, they have designated places around the world where they have tracked and figured out that they are of sufficient quality to put up as inspirational lighthouses for others.

What is your view on how well that works as a practice? For example, you have 100 sites. Is it possible to tell one site to become more like Site A? Because look at site A how well they're doing. Isn't that also a bit of a challenging message to communicate?

ARUN: Yeah.

TROND: No one likes to be like, all right, I understand. [laughs] My golf swing is not up to par, I get it. I need to look at my neighbor over here. It's not always a fantastic message.

ARUN: [laughs] But speaking of that, actually, we have five sites that are lighthouse sites. And we have one that is going to come up with one of the projects that we're working on as well is in one of the sites with Tulip for the lighthouse site. But the thing is, knowledge grows by sharing. The more you share, the more you're going to grow the knowledge and the faster the adoption is going to be. You're absolutely right.

It does not mean that just because this is a lighthouse site, they are at a pedestal, and then everybody else is in another place. I actually look at it the other way around. What did those lighthouse sites do that we can actually copy and paste, so I don't have to reinvent? And then I can focus on something else as well. So the lighthouse sites are helping us to really share that knowledge so that we can learn from one another. We can build on it. And then we eliminate the need for us to redo the things that they have gone through.

But you're absolutely right; that doesn't mean that those are the only sites that are doing everything and everybody else is not. But sometimes, the copycats that are coming behind the lighthouse might be the best of things because they can get lighthouse practices and implement and then really show that they can actually transform their manufacturing operations much more faster.

TROND: Well, and that's true in the history of manufacturing that you can actually leapfrog. It is still a field where if you do many things right, you definitely make a difference. I wanted to shift tact a little bit, Arun, and move to coming years. What are some of the industry developments that you are the most excited about?

So we've talked generally about digital. We've talked about personalization. What are some of the things that are going to be most crucial to get right and even just like in the year ahead? It's been a very...it's been a wild ride in the last 12 to 15 months. What's going to hit us in the next year, and what are you focused on?

ARUN: So let me break it into a few different areas. One is purely from the technology side of it. If we look at how 3D printing is going to evolve and how it is going to help us to change significantly, how the digital twin and digital threads that are coming up fast that we can actually connect. And then, more importantly, how the machine learning and AI models that are coming up that help us to be responding very quickly. So I'm very excited about those areas, how 3D printing is transforming our operations, how we are able to bring digital twins, digital thread, and machine learning to really drive that end-to-end thread all the way to the customer.

The second area is, from a mindset point of view, is how resiliency and responsiveness has become kind of like a norm. If you think about the COVID pandemic, what it has done is how that resiliency and responsiveness has become a norm. So how do we actually drive that and don't lose that as we come out of the pandemic and then go forward?

And the final one is I'm going to go back and harp on the culture side of it. How do we drive that culture where we let operators be empowered and learn from it and let them be the kings? And we also have the operator hashtag #operatorrules. And we support that culture change, the digital change, and which is really going to be accelerated because they are becoming more and more digital savvy. So there is the technology aspect. And there is actually the responsiveness. And finally, how do we drive the digital savvy across the organization?

TROND: So my last question, and I don't know how fair that question is in the context that you're in, because I could imagine that given the amount of factors that are moving at any given moment, very long-term thinking seems perhaps a little farther away from your everyday life. Because there are so many things that could go wrong literally every minute.

But if you permit yourself and me to think a little bit longer term, towards the next decade, are these things on the digital side, you know, digital twins, and AI, and machine learning, and 3D printing, as this decade moves to a close, are there other things on your horizon as well that will even more drastically transform the landscape? I mean, are digital factories going to be really coming into the scene and really transforming the way?

Are we going to recognize a factory even in the next decade? Or am I kind of overblowing this, and things are just fairly complicated, and it's going to take quite a long time to shake out and integrate all these technologies with all of the workforce challenges and cultural challenges that you just pointed out?

ARUN: Imagining the future, first of all, I really love the idea of almost no interface, intuitive use of technology. Can we get to that? That's one. The second thing is, yes, there will still be big manufacturing areas. Some of them are tied to the physics and biology, so we cannot change, but everything else can actually significantly change. And if you think about can we actually do a factory in a box very quickly for vaccine production in a developing world that cannot afford and we deploy it very quickly?

So will we get to a point where it becomes more of Lego blocks that we can assemble very quickly and get it up and running and everything has an equal and digital model that we really don't have to worry about it? It is not about the digital twin of my operations. But if I take the digital twin of my patient's body and the digital twin of operations, think about how easy it is for me to actually respond to that personalized request or personalized medicine.

Since you let me imagine and let my thoughts flow a little bit more broadly, it's really bringing the digital equivalence. So can I actually take my digital equal and to respond to the digital twin to get the personalized product for me either in a batch of 1 or even maybe a batch of 10 if batch of 1 is not possible? So the factories of the future, yes, some of them might not significantly change, but most of them will be that flexible way to bring them together for specific product or specific customer and being able to re-assemble very quickly to do something else.

And then the intelligence, can it move to the equipment so that the equipment itself can rearrange itself based on the customer base? But then, what is the implication to the workforce? And what is the implication to the operators? So this way of getting those operators to be a lot more digital savvy and really helping to manage this complexity will be a great foundation. But at the same time, that is something that we all need to watch. Yes, all of this can happen. But we need to watch for how do we bring our people together?

TROND: Yeah, and I could just imagine putting myself back in my old government days, scratching my head about self-regulating systems in the medical field, right? [laughs]

ARUN: Yes.

TROND: That would seem to be a little bit of a challenge as well. So there are so many interesting challenges. But it seems to me that even if you are occupied every minute with operational challenges and even just digitizing a supply chain without fundamentally changing its logic, it's going to take all men and women on deck. It's a cultural challenge. It is not just a technology challenge.

ARUN: Absolutely. It is. It is a cultural challenge.

TROND: Well, look, it's been fascinating to hear, and I hope I can check back in with you. It seems to me that if we had had this interview just even just 15 months ago, some of these challenges might have looked a little bit less rosy, and we wouldn't have been discussing about the next decade. I'm assuming that a lot of things for you in your business have really, I guess, opened up throughout this pandemic. Is that right?

ARUN: Yeah.

TROND: Some of these opportunities just weren't there before.

ARUN: Absolutely. A lot of the acceleration...first of all, we are privileged to serve our patients. And we have a big part in helping the world get through the pandemic, our vaccine. And even how we have brought in digital twin into our vaccines in a very faster way was enabled by the pandemic situation.

The whole digital acceleration of some of our solutions that were sitting on the shelf for almost six to nine months, the demand for them grew up within the first few months of the pandemic. So the digital acceleration of our operations has happened. The third thing, as I said earlier, is the digital savvy of our day-to-day citizen is helping us to bring these much more faster to our patients and customers around the world.

TROND: That's a very interesting statement. Because when you cannot innovate faster than your end client, then you're really dealing with the total ecosystem here. You actually depend on your end client to be caught up with all of these technologies. It's a fascinating challenge and probably very important too because there isn't a little bit of an insurance policy there, no Arun. Because if you cannot be more advanced than your end user is, at least you have the time to, or you have to take the time to educate the end user and get their real feedback on what needs to happen.

So that leaves me on an optimistic note, and if you have any last statement...I certainly thank you for your time. And if you have a last challenge, you know, there are so many challenges where you could launch, but if you think to your fellow industry executives, what is the one thing maybe you want to leave them with what you think is a shared challenge that people should focus more on in industry these days?

ARUN: Keep the operator at the center #operatorrules. Let's make sure that we empower them. We help them to be as digitally savvy as possible. That will actually help us to move these needles much more faster.

TROND: Arun, I thank you so much. It's been a pleasure. And I hope I can invite you back someday.

ARUN: Definitely. It has been great, Trond.

TROND: You have just listened to Episode 43 of the Augmented Podcast with host Trond Arne Undheim. The topic was Digitized Supply Chain. Our guest was Arun Kumar Bhaskara-Baba, Head of Global Manufacturing IT at Johnson & Johnson. In this conversation, we talked about why J&J puts operators at the center of its strategy.

My takeaway is that operators are the key to the next phase of industrial evolution that which involves the deep digitalization of manufacturing, its supply chain, the production capacity, personalization, and with that, the reinvention of factory production itself.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 21: The Future of Digital in Manufacturing, Episode 27: Industry 4.0 Tools, or Episode 10: A Brief History of Manufacturing Software.

Augmented — conversations on industrial tech.

Special Guest: Arun Kumar Bhaskara-Baba.

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

In episode 27 of the podcast (@AugmentedPod), the topic is: Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director, Industry 4.0 at Stanley Black & Decker.

In this conversation, we talk about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

After listening to this episode, check out Stanley Black & Decker (@StanleyBlkDeckr): https://www.stanleyblackanddecker.com/ as well as Carl B. March's profile on social media: https://www.linkedin.com/in/carlbmarch/ 

You may want to also be aware of the 'Israel meets New England' smart manufacturing event on June 9 and its organizers, the Israeli Trade Mission and Amhub New England:

• The New England Advanced Manufacturing Hub (AMHUB NE): https://mfg.works/amhub/amhub-new-england/
• The Government of Israel’s Economic Mission to North America: https://embassies.gov.il/washington/AboutTheEmbassy/Pages/Economic-Mission.aspx#:
• ISRAEL meets NEW ENGLAND: Advanced Manufacturing in Factories and Workplace: https://mfg.works/israel-meets-new-england/

Trond's takeaway: Industry 4.0 requires a mindset shift, not just technology adoption. It's not just about you--whether you in this case is a big company or a top leader--rather, it is about bringing people, partners, SMEs, and the entire ecosystem along. To do so openness to learn, having a strategic roadmap so not chase all shiny objects, and investing in lighthouse factories that can illuminate the possibilities are each important ingredients.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 20, The Digitalization of Körber, episode 14, Bottom up and Deep Digitization of Operations, and episode 9, The Fourth Industrial Revolution post-COVID-19. 

Augmented--upskilling the workforce for industry 4.0 frontline operations.

Transcript:

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

In Episode 27 of the podcast, the topic is Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director Industry 4.0 at Stanley Black & Decker.

In this conversation, we talk about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

Augmented is a podcast for leaders hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the manufacturing upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday.

Augmented — the industry 4.0 podcast.

Carl, how are you today?

CARL: I'm doing great, Trond. Good to see you.

TROND: Yeah, this is fantastic. We've spent a lot of time together, Carl. We've gotten to know each other. This industry 4.0 is bringing us together.

CARL: Quite a bit. And there's so much going on in this space, especially here in New England. So it's an exciting time.

TROND: Yeah, for sure. Carl, I wanted to talk a little bit about you and your background. You're an engineer. And now you're deeply steeped in industry 4.0. Maybe I'll just ask that question, why did you become an engineer? And how did you end up where you are right now? Was it an obvious path for you? Or did you always want to go into manufacturing?

CARL: I guess from the beginning, I was always a tinkerer, so just growing up and hanging around mechanical equipment, my desire was always to break and fix. [laughs] So eventually, I got wind of a teacher who, in fact, was my music teacher. And he asked me what did I want to do? I said I wanted to break and fix equipment and all of these things. And he said, "Well, you want to be a mechanical engineer." [laughs] So I kept that with me from maybe nine years old, and that's the path I went. Eventually, I did my first degree in mechanical engineering. And then eventually, I did an automotive systems engineering graduate degree.

TROND: Wow. And so then, in the beginning, you were headed for the automotive industry.

CARL: Yeah, yeah. It was always a desire around cars. So my father had all the cars that needed to be fixed. And where I'm from, we're in the Caribbean. I'm from Jamaica originally. It was one of those luxuries that you had where you just dispose of your vehicles once they start giving some problems. So we fixed the cars. [laughs] That's what we had to do.

TROND: [laughs] So you ended up with a bunch of cars then, not just fixing them, but you ended up with a bunch that are not used.

CARL: [laughs] Exactly. And taking parts from one and putting on the other. [laughs]

TROND: That's funny. That's funny. Well, so you did that for a while. And you were in automotive, which is an exciting field in and of itself. And then you went into consulting for a bit as well. So you've done a little bit of that.

CARL: And so the interesting thing is once I did my first degree, which was mechanical engineering, I had the opportunity to start working in the manufacturing environment. And I actually started off in mining and refining. So I was in alumina refining for a while, and then I went back and did the automotive degree. And then, coming out of that, it was the wonderful time in Detroit where everything was a bit uncertain. So though I started off in automotive there, after that degree, I went back to my roots of reliability engineering, which is more along the lines of operational excellence in the manufacturing environment.

TROND: You know, it's kind of fascinating today because automotive has gone full circle.

CARL: Yes, it really has.

TROND: It's like, nobody...who would have guessed [laughs] that automotive was going to go from glory days to, like, it's all over to a renaissance of mobility?

CARL: I've gotten the opportunity to observe that, especially as a consultant, as I eventually went into consulting. More than half of my 20-plus years in manufacturing has been in the consulting space. So, while consulting, that's where I really started to see many sectors, from the very advanced sectors in aerospace and automotive down to what we call base materials, which is going back to the dirt, the mining and refining pieces. And just seeing the range of technology adoption across all fields as it relates to operational excellence was an eye opener for me.

And when I think about this topic of industry 4.0 which really it's not an old topic. It really came about in 2011 or so, which was the mid of my consulting career. And that's when I made a pivot in my consulting, where I started to focus a lot more on the technology enablement within these respective spaces.

TROND: Well, let's dig deeper into it. Because you're indeed, you know, you're with Stanley Black & Decker. You run a lot of their industry 4.0 activities, especially on the analytics and the value stream side. But let's get into the topic more because, as you said, 2011 is not a long time ago. And I hear industry 4.0, by the way, seems to be more of a European term than an American term. Here it’s like smart manufacturing because manufacturing is the main thing. But at Stanley, you guys somehow chose the international term industry 4.0. Why don't you, for the benefit of all of us, just tell us how you define it? What is --

CARL: So industry 4.0 is this terminology referring to the fourth industrial revolution. So it stems back to the first industrial revolution having to do with mass production and steam being used as a driver. Then eventually, it went into the second, where we started to get some computers in the space and started to be able to take advantage of some of those things. The third having to do more with automation. So we started to put a lot more robots and robotics within the manufacturing space.

And interestingly, then we started to do a little bit more sensorization. But in the 2011 or 2010 period of time, that's when we started to make a lot of advances in big data, cyber-physical systems. So that's where those applications started to come into the manufacturing environment, AI, artificial intelligence, anything related to analytics in the manufacturing environment. That's where we're starting to consider the industry 4.0.

And one other thing, there are probably three main elements that differentiate the fourth industrial revolution from its predecessors; one is vertical integration. Vertical integration is what we call from the top floor to the shop floor. You're able to pass data back and forth and get information on what's happening at any given time, at whatever level it is in your production process.

The second is horizontal integration. And that's where you start to look across your value chain. So you're looking at data coming from your supplier, and data coming from your customer, and data within your own manufacturing environment.

And then the third one is integrated product lifecycle. So this is one of the most interesting pieces of industry 4.0 in that you're actually getting feedback, even though the customer doesn't even know you're getting that feedback. And you're getting feedback into your product lifecycle and your product design. And you're designing it to manufacture well, and you're designing it to basically fulfill the purpose of the end consumer, so all of that feedback loop that's taking place there. And what enables it is a part of what we refer to as industry 4.0.

TROND: That's super interesting. And can you comment a little bit on how that translates then into Stanley Black & Decker's digital transformation journey? Because, arguably, and I meant to have it here, I have, you know, I have a bunch of tools in my arsenal. [laughs] I might actually run and go get that. But they weren't always digital; mine happens to be battery operated. And hopefully, I can run and get it in a second; I really wanted it in this tape.

But it has been a journey for you as well, and I guess it's a continuing journey because sensors and all that stuff take quite a bit to transform an entire kind of suite of products into a set of connected arguably industry 4.0 tools. So I'm curious, where would you say you guys are in that transformation process?

And how ready is the world for a fully sensorized reality where everything is connected? I guess the maximal vision of industry 4.0, which is this idea of industrial Internet of Things where everything is starting to connect and yield analytics. Because you took the...these are also difficult things to do, right? The vertical integration, all of these things are difficult. But this full vision, we are a step away from that so far, this full sensorization.

CARL: Yeah, it has not all become a reality as yet. And as you can imagine, the maturity is going to be different depending on the sector, the industry that you're dealing with. But if I was to look back for a second on the journey that we've had at Stanley Black & Decker, I joined the company maybe about three years ago when we made a very interesting pivot in the way that we were approaching industry 4.0. I'll speak on that in a second.

But prior to that point in time, Stanley Black & Decker has always been an innovator in this space. We do make tools, and we're the number one tools company in the world. But we also serve a lot of our other businesses, automotive and aerospace, in particular, in providing fasteners, et cetera. And as a result of this diversity, it made sense for a company like ours with 100-plus sites to be able to start working in smart manufacturing.

And the process was that there were a couple of chosen sites that were given a bit more license to integrate industry 4.0 elements within their four walls, and they were referred to as lighthouse factories. So it was very decentralized, not very organized from the standpoint of having certain standards that would scale well. And this is where we started to see a lot of productivity gains, efficiencies within those sites.

Then in 2017, we did a study internally and determined that let's go after this in the right way, which is to organize ourselves to have a program. And as a result of organizing this program, that's where I came in as one of the first few hires within the program to centralize what we're doing. And then, I ended up leading our analytics value stream. We also had value streams related to connected factory, automation, et cetera. And that's where we started to go after it in the right way.

And I think as a result of that, the gains that we've had and the learnings that we've had over the past three years have been tremendous. And if you compare this to the typical approach, especially that I've seen in my consulting years, is that there's a term that was coined by either McKinsey or the World Economic Forum, I can't remember now, called the pilot's purgatory.

A lot of companies I observed they'll start something. They'll start one use case here, another use case there, nothing linked. And they'll do some form of pilot, but it never scales. It would fizzle out in some way. Somebody would move on from one role to the next. The interest isn't there. So, as a result of that, they will continuously stay in the same place, and there will be no roadmap for movement.

TROND: And how do you avoid that destiny of the pilot purgatory? There are many theories on how to do that. And I would say probably every manager of some seniority would say, "Yeah, yeah, I know about that issue, and we don't have that issue here." [laughter]

CARL: But if we're honest with ourselves, it's very easy to fall into pilot purgatory because, first of all, it is very easy to move after the first shiny object or the next shiny object that catches our eye. That's just the way human nature is. One of the things that we've learned is the value of having a strategic roadmap and especially related to industry 4.0. So one of the things that I'm currently working on with our small to medium size enterprises, small to medium-sized manufacturers is we're trying to enable them with two things, one is to assess yourselves.

And we are currently using a framework from Singapore called SIRI, which is Smart Industry Readiness Index. We're making that available to our small to medium-sized enterprises for us to work with them on assessing where are you with respect to these 16 dimensions of industry 4.0? And you don't need to be at the very top band for any one of these, really. You need to look at where you are with respect to peers, with respect to the best practices, and with respect to where you need to be to meet your business objectives. So once we do the assessment, we are able to filter that out in terms of what should be prioritized on the strategic roadmap.

The second thing that we're offering is given what we've done so far; we have a wealth of experience in this space as well as what we've gathered in terms of partners who have been giving us use cases that can apply to these 16 dimensions. We're then able to work with the manufacturer to specify this is what your roadmap should look over the next three to five years if that's your planning horizon.

You focus on these elements first, these dimensions first, but more specifically, these specific use cases. And these use cases are foundational. These use cases will provide you with some return that will help to fund the rest of your program, et cetera. So I think those two things between the assessment and having a strategic roadmap are critical enablers to avoiding this pilot purgatory.

TROND: That's fantastic. We'll talk a little more about SIRI hopefully later because it relates to the work you and I are doing with the World Economic Forum and our AMHUB network. And we are hoping to bring it in really to play in New England, you know, across the sector. But before we get to that, I wanted to ask you a couple of questions about this physical manufacturing 4.0 facility where I believe you actually work out of sometimes in Hartford, this, I guess, 23,000 square foot center.

So it's a physical kind of advanced manufacturing center like its own little kind of demo factory and training center also, I guess, for your smart factory initiatives. How did that get started? Well, it's the middle of a pandemic. But what do you intend to use it for? And what were you using it for before the pandemic? Because I'm assuming you've had a quiet period like all of us.

CARL: Yes, we have. We've had quite a quiet period over the past year and some. But in 2019, we opened the space, and what we actually did...I'm referring back to when we started to go about this in a different way in 2017. We had one of our...well, our key leader Sudhi Bangalore was, brought in from the outside to lead this program. And he was named the VP of our industry 4.0. Since then, he's been also named as CTO for global operations.

But this was one of Sudhi's visions in that we would not only have the team to do this industry 4.0 enablement in a standardized and centralized way, but we would also have an innovation space that you can physically touch, feel, experience the elements of industry 4.0 all the way from automation. So you'll see the robotics. You'll see the automated mobile robots. You will see the automated conveyors, the machine centers all of these things, as well as data flowing back and forth and analytics being displayed.

All these things were intended to be experienced because within our own factory and network; the expectation was that some of what we'll be trying to get to our sights would be new. And we wanted to make sure that individuals, especially plant leaders, would be able to come in and really feel and experience what good looks like.

At the same time, it was also a vision of our CEO as well as our CFO to use the space within Hartford, and Hartford was chosen as a location for a specific reason because we wanted to work with the city. We wanted to work with the state around making Hartford some central innovation hub for New England and hopefully the nation. So that's where this space came into being. And we had a grand opening in April of 2019. So it was always intended for us internally, but it was always intended for the public in a measured way to be able to come in and experience it.

And then finally, I'd probably say that in terms of what we're thinking going forward, we hope to get back into the space sometime soon. We hope to obviously reopen to manufacturers in the region. But then we also want to be able to utilize more of our partners as well, our technology partners, so that they too can show some of their solutions in the space as well.

TROND: It's so important, I think, to emphasize that technology...well, because of the danger in the shiny objects that you just addressed before that, it is precisely for that reason because when you have this experiential sense of what the technology can accomplish, and on the shop floor, there is so much of that right? Robots. It's very visual and tactile. You can clearly much more easily see how you could adopt it. So it sounds quite important to have a demo factory like that.

CARL: Absolutely

TROND: What do you think is the path forward? So you said you guys are engaging with a bunch of different actors that are not your obvious partners. You're engaging with SMEs in a deeper way than before. You have startup engagements but at a very early stage with the STANLEY+Techstars Accelerator. So you're engaging with organizations that are very different than the mothership. Why do you have such a distributed strategy?

CARL: So, I think a lot of this comes from the innovative culture that we live in. We recognize that innovation comes from many places, disparate sources. And we recognize that we won't know everything. We don't know everything. And especially when we're trying to break new ground, we need to be able to tap into all the resources that we can in order to do so and in a relatively efficient but also agile and quick way.

So a couple of years, probably also coinciding with the 2017 time period, we started working with a group called Techstars. And as some might know, Techstars is an international organization that basically incubates relatively new startups and helps them along the way. And there's some partial investment, generally, with the program. But our first round of investments in Techstars was companies that were focused on additive manufacturing.

The current round, which was just completed maybe a few weeks ago, a couple of weeks ago, had cohorts that were related to artificial intelligence, analytics mostly. And we had a couple of robotics ones in there as well, local robots, which all of this is really to ensure that we're able to keep our pulse on everything that's going on.

So to your earlier question about the shiny object, noticing the shiny object is not a bad thing because you have to keep your pulse on what's going on. And as people innovate and as more and more people enter the space and as more things are democratized and commoditized, you want to make sure that you're able to pull in what's needed at any given time.

So that's what we've been trying to do in different ways within our industry 4.0 program, specifically within our Techstars program. And then, we also have another group called Stanley Ventures, which also directly invests in some startups as well. So we're doing it on multiple fronts.

TROND: That's interesting. I wanted to get into the learning aspect. And maybe the humbling part here is both for you and I, and I'll speak for myself, but we're expected to both be experts on industry developments and then simultaneously be evangelists for the same, which is sort of to intermix roles in industry always. But it's complicated. How do you feel like you are able to stay on top of all these things?

Because it's one thing as a company, as Stanley, to have all these investments to have all these things available, theoretically, that you could pull from. But then, now as an individual, I just wanted to address how you, just to take that as an example, how do you engage? Because you and I are both engaged, and we're supposed to be those leaders. And we are building networks that we'll get into in a second that are helping us do that.

But how do you reflect around your own ability to cut this balance between looking at all the shiny objects, making sure you don't miss any of them, and then advising not only your company and implementing stuff but then also being an advisor to the general ecosystem about what is worth looking at and where are things in the maturity scale to keep everything kind of calibrated?

CARL: Yeah, and it can be difficult. And that's where we have to strike a balance. When we started off our program, we recognized that we couldn't build everything internally. So we had to rely on a robust partner ecosystem, probably having somewhere close to 30-plus different partners doing any one given thing at any one time. And then the learning that we got from that was that as a result of that, we were able to get further quicker. We were able to understand a little bit more about the space and what's truly revolutionary and what isn't.

And then we've recognized over time that we still have to have some portion of our time still spent evaluating what's new and coming out. We're able to do that because we are organized in a way to do that, and we have processes around that. And we have individuals who are more focused on innovation versus deployment. And we're probably able to do that because we're a larger company. And this is just how we're set up. Now, the concern that we have for manufacturing, in general, is that the majority of the space is made up of small to medium size enterprises, which don't have this luxury. They have very few individuals.

TROND: I mean, it's just not possible.

CARL: It's not possible for them to do it, which is why we've made the pivot and said to ourselves if we're trying to uplift the entire system, and as they say, a rising tide lifts all boats, right? If we're to uplift the entire manufacturing sector and manufacturing ecosystem, we need to focus on those who make up the majority of it, which is 95%-plus small to medium-sized enterprise.

And we can filter through some of the noise for them. And how we do that is provide a consolidated technology map against a framework so that they don't have to go through the filtering and figuring out what's good, what's not, how much is this going to be worth to me, et cetera. Because we've actually done some of that on our own. And then we just provide to them that based on where you are and your dimensions that you need to focus on, these are the four or five use cases for that specific dimension.

Now, let's talk through and filter. Let's cut to the chase here; how much will this be worth to you? What will be the return on your investment based on what this costs and based on what it will give back to you in terms of impact value? And I think being able to assist in that way I think is critical to getting everyone else a bit more involved in industry 4.0.

TROND: Yeah, and to that point, you and I are both engaged in...so one of those 30 partners, I'm assuming you would count the World Economic Forum as part of those. And you and I are both engaged in the advanced manufacturing platform there and a bunch of initiatives.

CARL: Absolutely.

TROND: We're not going to cover all of those, but there's one in particular that you and I are responsible for here in New England, which is the Advanced Manufacturing Hub, which is a global network of organizations which were the forum itself, which also started out with a centralized organization of the largest firms. So the likes of Stanley Black & Decker in all fields have realized a version of the same thing that you were saying that if the entire world of industry is going to really take up industry 4.0, they also need to work in a distributed way.

And these networks that we have joined in with...well, maybe you could just give your version. What do you think AMHUB New England is and should be doing? And what are some of the things you are excited about that we are starting to launch here? Because it's very new. It got picked up last year, launched under the worst [laughs] possible conditions during a pandemic. I mean, launch a social network during a pandemic, and you will realize what a tricky task is. But anyway, we're in year two. We're into it. There's still a pandemic, and we're doing some virtual events. What are you excited about? AMHUB New England, what is it to you?

CARL: I think the wonderful thing about the network is that we're not the first ones going at this. This is an ever-expanding network within the World Economic Forum. And everyone knows the World Economic Forum like you said, is a collection of all the leaders of the top companies. And then we're focused on the manufacturing space. So we're talking about the top manufacturers in the world coming together and trying to figure this out.

And the Advanced Manufacturing Hubs, I think we're probably close to 13 or so now in the network. It changes numbers every now and again, but we're not the first, and we've definitely had the opportunity to learn from some of our predecessors. We've had others in the U.S. that have been at this for a couple of years before we have that we're learning how they've integrated with public organizations, so integrated with the county and the state and non-profit institutions in the region to be able to go after their objectives. So that's one of the things that we're obviously trying to do: bring public organizations and get them involved along with the private.

We've also recognized, and I think we've had a passion within our own group here around upskilling. We recognize that this is a critical factor for enabling manufacturing in our region. We need to not only deploy and get new technologies, but we also need to upskill our workforce to meet the demands of these new technologies in our environment. So from my perspective, Trond, we have a lot of work to do.

We, fortunately, have a lot of manufacturers, most of them small, within the region who are interested who are enthusiastic about what the path ahead of us looks like. And I think within the next couple of months, or next few months, as we continue to engage that community, we will be able to provide them with more opportunities to upskill and get to where they need to be with respect to their workforce.

TROND: Yeah, and it's fascinating. I mean, you said the World Economic Forum has a bunch of related activities. But it's also true, and I just interviewed someone (That's a podcast episode that's actually coming out this morning.) who's on the panel that you are on, Michael Tamasi, as well so about manufacturing in New England. Because clearly, there's an established network and ecosystem here already we're building on. And this happens, I think, in all of the New England states and Connecticut, for sure. You and I have been engaging with some of the actors there. There are trade associations. There are state and federally-funded organizations like the MEP system and various other kinds of manufacturing networks.

So from my point of view, it's not substituting for all of this. It's just partnering with all of them and just trying to join the efforts that they're already doing but from the perspective of a global picture. So it's getting, hopefully, if we succeed, the best of breed essentially making sure that all of the activities that we are putting on make local sense here in New England, showcase New England, so there's a showcasing aspect of this, and we have a lot I think to be proud of.

I mean, there's Stanley Black & Decker, clearly a behemoth really in industrial tech and in the manufacturing sector worldwide, but there are a lot of other companies also startups contributing and making headway, and then we have a lot to learn.

I wanted to maybe just discuss for a second this event that we're putting on in June here on Israel meets New England. What do you think is the attraction of having two regions meet? So, in this case, it's Israeli startups. But in other events, we might bring in, like you said, the SIRI folks from Singapore who you're working with to measure progress and benchmark in the field, or we could collaborate with even with Michigan, which is another major, major U.S. manufacturing hub. Or it could be Italy or Spain and many of the other networks that exist worldwide. What do you think the attraction is to gain that kind of regional cohesion?

CARL: I think over time, we've recognized that gone are the days when we think innovation is restricted to a particular country or a region or anything like that. I think we're very much aligned on the fact that technology and innovation in the industry 4.0 space is not restricted. So it makes sense that when we think about sharing of best practices that, we go all over the world, and that's part of the reason why if you think about the World Economic Forum, it has a global network of advanced manufacturing hubs.

Each hub may focus a little bit differently on slightly different topics. Some will overlap, but they are also tapping into the expertise and the ideas from their local regions with the intent that we will go across regions and share with each other.

So this upcoming event, I think, is a wonderful one sponsored by the Advanced Manufacturing Hub here in that it's allowing us to see a couple of...or have a conversation with a couple of innovators from another region, and in this situation, it's Israel. But in the future, we will use other regions as well to bring them in, hear a little bit more about what they've been working on, what has been important in their region, which might be slightly different from us, and then have a bit of discourse between us around what the future holds for technology and innovation in general.

TROND: Well, let me profit from that segue into the future. What is next for you in the digital factory? And what does the next decade look like for you in terms of, I guess, your own business-connected industrial tools, perhaps? You're very, very engaged with the networks and the maker movement. And broadly, your thoughts in industrial tech and where that's heading, and maybe even some comment on this upskilling challenge that you mentioned, I mean, what will happen to all of these things?

It's a mixed bag of challenges that they're all somewhat related. You can't have progress in technology without the skilled labor force and all that stuff, and somewhat dependent on technology development. But what do you see happening here? Are we entering at least at the very least a decade where manufacturing will leap forward somewhat faster than it has done before? Will it start to change this impression that manufacturing is hard and difficult and we're dealing with a slow-moving kind of system? Or do you see that that's going to still be the case?

CARL: I'm quite optimistic. I think based on what I've seen at least in the past three years, I think, the way that manufacturing has moved, it gives me optimism that there will be a significant leap in what we're doing going forward. It took a little bit of time, as I said, from 2011 till about maybe 2016-2017, for people to start to really gain a certain amount of interest and get past a bit of skepticism.

At this point, there are enough proven use cases across the board that individual companies and individuals recognize that this is not just a shiny new object or fly-by-night use case. These are things that are here to stay and will be critical to business going forward. So I think as a result of that, first of all, there will be quite a bit of acceleration of efforts.

The second thing is we decry the pandemic and its effects and everything else. But I have to say that there are certain mindsets that have been shifted as a result of the experience. There's more of a need and interest around being able to monitor your remote operations. So now people are more interested in connectivity than there were before. They're more interested in insights and analytics than they were before. Because now they can't necessarily be by the machine, by the production process, by the production line 24/7 or 24 hours a day. But instead, they can benefit from all of these technologies that will allow them to get the most out of their equipment.

They also recognize how important the workforce is. We always decry automation has taken away jobs, but I'll say no; in fact, the studies that have been done show that those who lead in innovation actually also have an uptick in workforce of some 50% instead of the opposite, which is what the myth would typically tell you. So all of these things coming together, I think, will help us move forward quicker going forward.

And then the third piece that I will mention finally is around upskilling going forward. It's absolutely critical that we upskill our workforce. In the U.S. for many years, and we've seen the charts and the data around the amount of retiring workers in the manufacturing sector, so we have a lot of skills and knowledge that will be leaving manufacturing and have already left.

So to replace those individuals, we need individuals of the younger demographic who will, one, come in with knowledge of processes. But the ones that are coming in they're not interested in our grandfather's factory. They're more interested in what can I do differently in this space with the use of technology and innovation to do twice as much work in half as much time? Which is a good thing. We want them to come in with that mindset. And I think with the advancements in technologies; we will be able to do that.

But what would be critical is to be able to upskill them, give them the right skill sets around these technologies, around the production processes as well as there's going to be a tremendous amount of marketing and PR to get folks interested in manufacturing. Because manufacturing is a very exciting sector. It's buzzing, and it actually has quite a lot of open jobs, frankly, that need to be filled, but we need to upskill individuals to fill those jobs.

TROND: You have just listened to Episode 27 of the Augmented Podcast with host Trond Arne Undheim. The topic was Industry 4.0 Tools and Analytics. Our guest is Carl B. March, Director of Industry 4.0 at Stanley Black & Decker. In this conversation, we talked about what industry 4.0 means, the importance of upskilling the entire manufacturing industry, and the lessons from Stanley Black & Decker's digital transformation journey.

My takeaway is that industry 4.0 requires a mindset shift, not just technology adoption. It's not just about you, whether you, in this case, is a big company or a top leader; rather, it is about bringing people, partners, SMEs, and the entire ecosystem along. To do so, openness to learn, having a strategic roadmap so not chase all shiny objects and investing in lighthouse factories that can illuminate the possibilities are each important ingredients.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 20: The Digitalization of Körber, Episode 14: Bottom-up and Deep Digitization of Operations, and Episode 9: The Fourth Industrial Revolution post-COVID-19.

Augmented — upskilling the workforce for industry 4.0 frontline operations.

Special Guest: Carl B. March.

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

In episode 24 of the podcast (@AugmentedPod), the topic is: Emerging Interfaces for Human Augmentation. Our guest is Pattie Maes, Professor at the MIT Media Lab.

In this conversation, we talk about augmenting people instead of using or making smart machines, AI summers and AI winters, parallels between AI and expert systems and why we didn't learn our lessons, enabling people to perform better through fluid, interactive, immersive and wearable systems that are easy to use, how lab thinks about developing new form factors, and much more.

After listening to this episode, check out MIT Media Lab as well as Pattie Maes's social profile:

• MIT Media Lab: @medialab (twitter) https://www.media.mit.edu/ (web)
• Pattie Maes: https://www.media.mit.edu/overview 

Trond's takeaway: Augmenting people is far more complex than developing a technology or even experimenting with form factors. Instead, there's a whole process to exploring what humans are all about, discovering opportunities for augmentation and tweaking it in dialogue with users. The Media Lab's approach is work intensive, but when new products make it out of there, they tend to extend a human function as opposed to becoming just a new gadget.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 19, Machine Learning in Manufacturing, episode 7, Work of the Future, or episode 13, Get Manufacturing Superpowers. 

Augmented--industrial conversations.

Transcript:

TROND: Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. In Episode 24 of the podcast, the topic is Emerging Interfaces for Human Augmentation. Our guest is Pattie Maes, Professor at the MIT Media Lab.

In this conversation, we talk about augmenting people instead of using or making smart machines. We discuss AI summers and AI winters, the parallels between AI and expert systems and why we didn't learn our lessons, enabling people to perform better through fluid, interactive, immersive, and wearable systems that are easy to use, and how the lab thinks about developing new form factors, and much more.

Augmented is a podcast for industry leaders and operators hosted by futurist Trond Arne Undheim, presented by Tulip.co, the frontline operations platform, and associated with MFG.works, the industrial upskilling community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern Time, every Wednesday. Augmented — the industry 4.0 podcast.

Pattie, how are you today?

PATTIE: Hi. I'm doing great. Thank you. Thanks for having me.

TROND: Oh, sure. I'm very excited to have you. And in fact, I just feel like the audience should get to know you. I know a lot of them do because you have become an innovator that has a stage on TED. And obviously, a lot of people at MIT know you. But I wanted to just recognize that you were one of the early PhDs in AI, right? 1987 is not a time when --

PATTIE: Yeah. [laughs]

TROND: Is that what we call the second wave of AI? It's certainly not the -- [laughs]

PATTIE: The grandmother of AI, yeah. [laughs]

TROND: You're not a recent convert to this topic. That's for sure.

PATTIE: So yes, I actually studied artificial intelligence long before it was such a big deal or the big deal that it is right now. But actually, soon after doing my Ph.D. in AI, I became more and more interested in a related problem, the problem of not artificial intelligence but intelligence augmentation, or how can we make people more intelligent, more productive, support them in making better decisions? So soon after my Ph.D., I veered more in that direction.

TROND: Well, and that's what we will talk about because you have indeed been on the MIT faculty for 30 years exploring these topics in various kinds of bifurcations. And you have been the advisor to scores of startup founders also. And, of course, people might think that goes through the territory at MIT, but the numbers are really still staggering, and also the performance of some of those startups, including Tulip, which we'll talk about, but also many other startups and many other innovation projects that didn't quite make it to startups. But they still created a lot of attention around the world for the promising demos or the things they suggested about what the future of technology might look like.

So I would like first to just recognize that you've achieved, I guess, the amazing feat of not just innovating a lot yourself, but you must be an amazing innovation mentor. And you certainly have inspired a lot of people that I personally know in AI, and in human augmentation, and beyond. And I wanted, first of all, just to see if I could have you reflect a little bit on your journey, which I imagine...well, first of all, it's a nice wordplay from Belgium to Boston.

PATTIE: Yeah, so I came here after my Ph.D. actually, and of course, wanted to be in the place in the world where the most exciting research was going on in my area. [laughs] And so initially, I ended up at the AI Lab, but I soon after actually accepted a job at the Media Lab. And what really attracted me there was that the lab is very application-driven. We're very interested in really working towards things that can be deployed in the real world, that can make a difference in the real world, that can be through for-profit startups.

But sometimes that is actually in other ways by just freely giving away tools and technologies or maybe starting a not-for-profit to really disseminate something and make something accessible to larger groups of people. So I've always been very attracted to the practical aspect and trying to make a difference really with the work that we do. And as a result, several companies have been created out of my research group.

TROND: Was this something you set out to do? When you were in Belgium, getting your degree at the Vrije Universiteit in Brussels, were you thinking I am going to go to America and become an innovator? Was that in your mind?

PATTIE: No, I think a lot of that sort of happened accidentally, actually. And one reason I think why I'm interested in practical applications and real-world deployment is that I was never really interested in the technology for the sake of the technology. I'm not one of these people who gets really excited about purely just the technology, the algorithms, and so on. I want to make my life easier and other people's lives easier. And that has always been what motivates me and my work.

TROND: And that gets us to intelligence augmentation. Because I guess in some sense, the Media Lab is all about that topic to some extent. And I wanted to also address the fact that not only are you doing the work in your lab, but I think at least for the last few years, you've had the academic responsibility across the lab, and you have shepherded the lab, arguably, through one of its more difficult times.

So surely, you have also experienced innovation and the tricky things that show up with innovation across a plethora of fields. But generally, people at the Media Lab are hired, I guess because they think about application. What is it that is so different when you...so let's just start with that. When you start with a human in mind from the get-go, what is the difference that makes?

PATTIE: So I think; indeed, our philosophy is always to be, like I said, application-driven. And what that means is that we take a closer look at the ultimate target users and their place or where they live or work, and how the technology could make a difference there and could change things there. So rather than starting from the technology and trying to maybe optimize some algorithm that does X, we actually work closely with target users. We really study their lives today to understand what the pain points are, what the opportunities are for technologies to make a difference and support them in being more effective, more productive.

TROND: But you have experienced both sort of AI summers and winters. Is one of the reasons that AI [laughs] tends to get into trouble that it always is very myopic about the technology focus, or is it a more complicated reason why there are these summers and winters? [laughs]

PATTIE: Well, I think that that is indeed a primary problem. So yes, there have been several AI summers and winters. Probably a lot of your listeners are young enough that they don't realize that there was another hype cycle for AI that happened sort of in the '80s and '90s with the emergence of expert systems, so-called expert systems. These were not based on machine learning and neural network techniques but instead were typically based on rule-based systems.

But they were very sophisticated. They had typically a lot of knowledge built in about a particular problem like, say, making a certain diagnosis, or doing some planning, or what have you. So the systems in laboratory settings were very impressive and were often outperforming experts at doing some scheduling problem, or planning problem, or diagnosis, or recognition problem.

But what happened when they were put into the workplace or when people tried to integrate them into the real world was that they basically encountered all sorts of obstacles. One of the obstacles was that people wouldn't necessarily trust the machine, the expert system. They didn't quite know how to work with it or where to fit it into their workflow. They weren't always able to get explanations for why the machine was making a certain decision.

It was very hard to correct the knowledge of the system and give it new information or to update its information if it wasn't correct. So there wasn't really a lot of transparency, a lot of controllability, interpretability. And that ultimately was the downfall of expert systems. And so yeah, at that time, just like now, there were many startups, millions of dollars pumped into all of this. The conferences and exhibits were extremely popular, and all of that died down. And we entered an AI winter where suddenly there was very little interest from the real-world businesses in AI.

Now, of course, we are in another summer, in another hype cycle. And I am actually very worried that we are making exactly the same mistakes because most of the AI systems that are being developed are being developed very much not in the context of where they ultimately will be used or not with the collaboration of the people who ultimately will use these tools. And so we will encounter exactly the same problems of trust and transparency, and controllability, and interpretability.

So, in my work, I've always been emphasizing a different approach. And I like to not call it artificial intelligence but rather maybe augmented human, or augmented intelligence, or maybe human-centric AI because our approach is one where we start out by studying what people are already doing in a certain work environment, whether that is a manufacturing floor or a doctor in the hospital, and so on.

And we actually work together with them or think about how we can support the people that are there to do their work better, to be more effective at their work. And so it's a totally different way of looking at a problem. We try to optimize for the person and the technology together to perform better. We don't try to optimize for the algorithm or the system to become better without thinking about how that system will be integrated into our real lives and real-world scenario.

TROND: Well, this is super interesting. I want to go into a couple of examples of things that you have done with your students and otherwise in a second. But first, why have we not learned collectively this lesson? I mean, what is it? I mean, is this something you think is happening across the board with technology? Or is it even just specific to this machine learning AI environment that we...are we so tempted by the potential impact of the use cases that we’re just getting carried away into the algorithms'depth and then forget the user? Or why haven't people said this is not good enough?

PATTIE: I think that it is actually a broader problem with development of digital technologies. All of the technologies that we use today whether it is maybe AI systems or whether it is social networking services and so on, they mostly have been designed and built by engineers, by teams that just consist of engineers and not people that come from very different backgrounds, for example, more social humanities backgrounds, et cetera.

One of the reasons that I was very excited to join the Media Lab as opposed to a computer science department is that it is very interdisciplinary. And we really recognize and try to emphasize that interdisciplinarity is extremely important in innovation, in creating things that ultimately will be successful and will be able to make a positive difference basically and a positive impact.

So that means involving not just engineers but also designers, people who can really think about making things fluid, seamless about how it integrates into workflow, and so on. But also people from humanities backgrounds, and social scientists, and so on. So I think it's important to have that broader perspective to make or to create technologies that ultimately are desirable and ultimately really improve our lives.

TROND: But, Pattie, take me inside of a week in the Media Lab. Because when you describe it this way, it sounds almost so intuitive and simple that I'm wondering why people need to travel to the Media Lab to learn this. Because if it was just simple to just hire a team with different skills, and it will happen, there surely is some other type of magic ingredient.

What does a week look like in your lab? How do you draw out the kind of creative energy...maybe it's helpful if you take Arnav Kapur's AlterEgo, which most people know as just that video that went viral. And they're like, imagining the future of computing with just this device where he's not even speaking, but he's kind of just basically controlling, it would seem, the computer with his jaw. Now, fantastic video; how does something like this come out of your lab?

PATTIE: So we are a very open laboratory. So, in addition to attracting creative, entrepreneurial people and really cultivating a very interdisciplinary team, we engage a lot in conversations, in discussions with others, with the outside world, which is actually pretty rare still for people in universities. [laughs] So, for example, we have member companies.

We have a consortium of companies that fund the Media Lab, and they, pre-COVID at least, come and visit on a daily basis. Every day we have at least ten different companies visiting to see the work, to engage in discussions, to give us feedback. They don't direct the work, but they can be critical. They can see opportunities for where to take it, and so on. And we engage in a very iterative type of style of work, where we quickly prototype something. Like in the case of AlterEgo, it looked pretty ridiculous the way it was glued together with some cardboard and other things that we could find in the lab. [laughs]

But we create these very early prototypes that are very clunky, don't work very well. But those make a certain future more visible. They envision what is possible or make it more concrete. And then we invite a lot of feedback from all of these visitors, from all of these people with different backgrounds. And they see opportunities for oh, maybe I would use it this way. Or maybe it's really exciting in that application domain, or I see this or that problem with the technology.

So that's really the technique that we pursue, attract a very diverse team of highly creative entrepreneurial people but from very different backgrounds, and engage in a lot of team innovation, and do very iterative types of design, making prototyping, and then getting feedback from really everyone, not just these companies that come and visit but our own families, and of course, the target users of the technologies that we build. So that's the secret sauce, so to speak, [laughs] or the secret to how Media Lab innovation works.

TROND: Take us back maybe to 2012 or something. And in the lab, you have two bright people; one is Rony Kubat, who also had a background from the Computer Science and AI Lab at MIT, but then had already come over to study with you. And then you had Natan Linder, who had industry background and had been already head of a Samsung lab in Israel. Now the two of them show up during their masters, I guess, and then ultimately PhDs but masters, I guess, in this context, and they start developing something.

Can you tell me a little bit about those early days, early conversations you had with them about what each of them were doing, and your reflections on to what extent some of the early work they did with you how that transpired into what now, 2014 I believe, turned into Tulip Interfaces? And now, in 2021 went on the Gartner calendar, essentially, as a manufacturing execution system.

And more broadly, aspirationally, it's a frontline operations platform that can transform the way that workers are working at the frontlines, augmenting them and really changing manufacturing as we know it today with a kind of a no-code system. So this was like fast forward 2012 to 2021. Where were they back then? What was it that you taught them specifically? What were they working on? And how did you work together?

PATTIE: What motivated this work initially was this whole realization, in 2012, that we were living in these two parallel worlds, and it's still very much the case. [laughs] We live in the physical world, and then there's this whole digital world with information about all the things around us in the physical world that we are engaged in and so on, the people we're meeting with, and so on.

And we realized that or we were frustrated really that these two types of experiences were not connected. For example, if I pick up a book, I can look at the pages, the beautiful pictures in the book, read the back cover to see what people have to say about it. But ideally, at that moment, I will also have access to the rating on Amazon and what others have said about that book or not because that's extremely relevant at that moment when I'm considering whether that book may be an interesting book for me to read.

So we were very interested in creating experiences that are more integrated, where our physical lives are more integrated with the digital information that exists about everything around us and all of our actions and experiences. So we experimented with different types of augmented reality systems to bridge that gap and to make the digital information and services available in the physical world.

So that's really where the work that Natan and Rony did and what led to Tulip where that started. They were experimenting with building systems that have an integrated camera and projector so that the machine can see what is happening and can project relevant information onto whatever it is looking at. So that people can get, for example, relevant reviews when they're looking at a product that they want to buy.

So we actually developed all sorts of prototypes to illustrate this vision of this integrated augmented reality. For example, at that time, together with Intel, we built up an example of a store that has the two integrated, that has physical products; I believe it was cameras. And then there was a projector system that would recognize what camera you were looking at or picking up, and it would give you additional information about it. So it would point out the features by actually pointing at the different buttons on the camera and what was so special about them, et cetera.

We also built an augmented desk for a learning context, for an educational context. And in all of these cases, we worked with partners, for example, for the education context to think about how this augmented reality could be used in the context of schools. We worked with Pearson, who's the leading developer of course books and school books, and so on.

We then also worked with Steelcase on how this augmented reality technology could be used on the manufacturing floor. How could it help people in real-time by giving them feedback about what they were doing, maybe giving them real-time instructions projected onto their workspace, or maybe alerting them that something wasn't done right or a step was forgotten, and so on?

And that work with Steelcase ultimately and with some other sponsors as well like GSK, for example, which does drug development, all of that led to the spin-off to Tulip being created as a company that can really realize that whole vision of an augmented manufacturing place where you can have real-time information provided. But you can also track the whole manufacturing floor in real-time and have very detailed data, and analytics, and intelligence about which steps may cause more errors or which steps in the process, say, take a lot of time, and so on. So you have this real-time insight also into the manufacturing floor that we've never had before.

TROND: It's fascinating that you picked this...that they picked this example and that you are kind of explaining it now. Because I want to give people the right sense of what it takes to produce an innovation that turns into a commercial, true product because I saw a version of the product you were explaining now in 2014, in the fall when I was at the Startup Exchange. And I was one of the first in their then Tulip lab with seven employees.

But that demo of something that had a camera and a sensor only this spring turned into what Tulip called their vision product. And it's only now coming to market. So here is arguably some of the brightest people working with you, a very experienced mentor, working from 2012 to a demo in 2014. But then they had to take all kinds of other things to market first, and only now, in 2021, is this coming out. I find that an incredible timeline and path.

PATTIE: Yeah, it's surprising to me as well, although I have seen it happen multiple times. We think that technology moves really fast. But then, in practice, for an invention like this to ultimately make a difference in the real world typically takes ten years or more. I have had that experience with other technologies that we've invented in the past. Actually, an earlier technology that we invented in our lab was recommendation systems that recommend a book to you because you also liked these other books or because people who also liked the books that you buy also bought this book that is being recommended to you.

We invented that technology in '94 [laughs] when browsers were just available. And we were talking a lot to Media Lab member companies about how exciting this would be and how it would personalize the whole online experience if you could get these recommendations from other people like you. And there was excitement among the member companies, but they were at that time saying, "Well, we're not sure that people are ultimately going to feel comfortable giving their credit cards over the internet to buy something. So it seems very exciting, and it's a great vision, but we don't see this happening."

[laughter]

That was companies like Blockbuster [laughs] and other companies that now are bankrupt, maybe because they didn't take this seriously enough. [laughs] But so because these larger companies were a little bit skeptical about this whole vision that we were portraying of online commerce and recommendations and so on, we started a company ourselves called Firefly in '94 and ultimately sold it to Microsoft actually in '98.

But we were just way too far ahead. We were too early. And most people weren't ready to buy things online. Most companies weren't ready to partner with us. And we actually sold a company in '98 at a time when briefly, everybody thought that internet commerce was dead, was not going to take off. A year later, [laughs] our company would have been ten times as much or worth ten times as much as what we sold it for.

So, unfortunately, we sold it at the wrong time when there was a lot of pessimism about...and it's hard to believe that now, [laughs], especially now during COVID, that everybody pretty much buys everything online. But yeah, back then in '98, that was not at all clear. And we were too early, basically. So in my experience, it always takes at least 10 to 15 years, even for a technology that seems ready to be deployed to ultimately make a difference in the real world.

TROND: Well, the digitalization of physical infrastructure like you started with is a different thing, though, and even more complicated than the trust to buy something online, which I guess is vaguely related to you have to trust that something abstract is actually going to have a consequence.

But Rony and Natan told me that they even basically slept over in factories and studied these workers for days and weeks on end, and I guess Tulip is still studying workers. It's not immediately obvious what is the contribution on the factory floor, is it? I mean, it's not as easy as to say, "We have this fancy digital thing that we're going to give you." But why is it so much more complicated?

PATTIE: Yeah, I think it's always complicated. [chuckles] And it is important to really understand the context, the actual context of where some technology is going to have to fit in. I remember very well when Rony and Natan were visiting the factories, and they would come back with amazing stories, to our minds, very primitive ways in which everything [laughs] was being done at that time, still a lot of use of paper records, for example, for collecting information.

So it was a big gap that had to be bridged [chuckles] really between the vision that we had of this totally connected manufacturing place with all of this real-time data, real-time instructions and advice, being able to also modify things and edit this whole digital layer or digital support system in real-time by the people on the floor, and the managers, and so on. There was really a big gap from that reality of paper-based systems in a very low-tech context to that vision that we had of this smart manufacturing floor.

TROND: And how far are we getting with this, and how quickly will it go now? Would you say that this has been a decade of exploration and a lot of these things have been sorted out? Or would you say some quick wins happened, and then some of the slower things they are just slow? Any kind of technology will take the time it takes to fully understand how you can contribute.

I guess I'm asking this in the context of another technology that a lot of people are putting a lot of hope in these days, especially perhaps during COVID, you know, robotics on the manufacturing floor and maybe the merging of AI or machine learning and robotics. How do you see these things?

How disruptive will any kind of digital device, or software system, or augmented system that should benefit workers how disruptive can these devices and systems become? And have we hit some sort of momentum, or is this still going to be kind of case-by-case basis, and the hype is just not going to be true in this domain?

PATTIE: I think we have to accept that progress necessarily is slow. [laughs] I mean, I think the potential is there. But in my experience, really reaching that potential involves learning a lot of hard lessons along the way, but progress is being made. It's just not as quick as we would like it to be. And I think the same will be true for this vision of smart manufacturing, including the use of robotics, which is even more challenging because you have moving parts, [laughs] which means that things break down quicker and that there are also more safety constraints and so on as well.

But yeah, progress will continue to be made. And I think it's very important for companies to engage with all of these new technologies, and to do experiments, and to start integrating some of these new technologies in their workplace, or you end up like the Blockbuster [laughs]example that I gave earlier where they said, "We'll deal with this later or when it becomes more important," and then they were bankrupt.

TROND: Well, it strikes me that you're not going to give me timelines because it depends on so many things. But if you look at the future of, I guess, cognitive enhancement more generally or certainly these immersive and sometimes wearable systems that you have been building for 30 years, you have an interesting role because you are, of course, inspiring a lot of hype just because the products you build are so fascinating, and they seem so simple.

But you are also combining this with being very careful about the predictions that are surrounding it. So tell me a little bit about what the future holds for these things. I mean, are we to expect more of these fascinating devices coming on market, or are you exploring a lot more of those in your lab right now?

PATTIE: Oh yeah.

TROND: Where is it at the moment on the experimental stage?

PATTIE: There's never a shortage of interesting new ideas for us to work on. I always have way too many or more than I have students to work on them. [laughs] But one area that we are exploring in the lab right now is we want to go beyond systems that help people with providing information. The focus on digital technologies, whether it is laptops, or watches, or smartphones, has been primarily on communication and also the system giving you information.

And with the work that we talked about so far today, the focus was on giving them that information integrated into whatever they are doing so that they don't have to try to juggle between the physical and then the digital information that may be relevant to whatever physical stuff somebody is doing. But we're trying now to go beyond systems that give you information and are interested in looking at how digital devices can help people with issues such as attention, motivation, memory, learning, grit even, creativity.

We think that given that all of us are now sort of forever after cyborgs, we always have technology with us. We have our smartphones never far [laughs] away from our body. Many of us wear a smartwatch as well. And so we have this opportunity now to use these systems to help people with a lot more than just giving them access to information.

The systems increasingly have sensors integrated that can sense what the person is doing, where they are, maybe even what their heart rate is, and whether they are maybe a little bit anxious at the moment or not, or maybe the opposite. Maybe they're too sleepy; they're not engaged.

So increasingly, systems will have a better sense like that of the state of a person, the cognitive state of a person, and will help the person with being in the state that they want to be in. For example, we've been building glasses that have built-in sensors for sensing brainwave activity as well as for sensing eye movements. And that pair of glasses it's called the AttentivU project.

It can actually give you feedback about your own attention level. Are you being highly attentive right now? Or are you being distracted? Are you fatigued? And so on. And we use that information to help a person to be aware of the fact maybe that a driver of a truck should be taking a break because they're too fatigued, or it can help a person who's listening to a lecture be more attentive because the system can tell them when their attention is waning.

So we think that this is an exciting new direction to really go beyond just giving a person information about whatever job they're doing, or whatever they're working on, or are thinking about, or doing, but going beyond that and helping them with those skills that are really important for being successful in life that all of us struggle with, and that all of us keep having to work on.

TROND: Fascinating. That's fascinating. I want to ask you what is your goal with all of these activities? Because you are an innovator, but innovators are always motivated. Good innovators are always motivated by something. What is it ultimately that you have been trying to achieve over these years?

PATTIE: I really want to help people. [laughs] I did study computer science and artificial intelligence. But my goal is not to create smarter, more capable machines or algorithms. I ultimately want to help people with machines, with AI. I want to enable them to live their best lives and to grow and learn and ultimately become the person that they would like to be.

TROND: So you have a very optimistic view on a future that a lot of people are scared about right now. Some people might be scared about AI. They might be scared about what they're seeing around them. How do you maintain this very optimistic vision? Is it because you feel like you have agency? You get clever students come in and work on your ideas.

I guess I'm just trying to say that usually, I would ask people what is the best way to stay up to date and kind of model what you're doing? And the obvious thing would be they should try and come and apply and come to your lab. Now, some people will achieve that, not very many, right? It's a small space, so there are limits.

PATTIE: [laughs] [crosstalk 43:43]

TROND: The other advice would be to pay to get to the Media Lab and become a corporate sponsor; that seems to be another avenue. But do you have any other less obvious ways that people can emanate some of this spirit that I think you...because you're sharing an entire approach to how to understand technology, how to develop technology, but also a vision of what technology should be doing for us. You kind of have a philosophy. You told me a philosophy with a small p about technology. How should people try to learn more about it, engage with that kind of philosophy?

PATTIE: Yeah, I do think it is the role of the Media Lab to be optimistic really and to see the potential of emerging technologies in improving people's lives. That is really sort of our unique focus among all university research laboratories. We look at emerging technologies, and we try to be positive thinkers or optimistic thinkers in terms of how those technologies can ultimately empower people to improve their own lives, their communities, and their environment, the natural world around them as well.

We try not to be naive, [laughs] in that quest at the same time. And we are very much aware that all of the powerful technologies that we work on can be abused, can be used in very negative ways as well. But I think that that is ultimately not a reason not to engage in these endeavors. Basically, we try to invent the future that we want to live in, [laughs] or that's really what we are working on.

And we try to be inclusive in that process by, again, not just involving the students and researchers in the lab but really the target communities like people on a manufacturing floor and how do they want to work with AI, and robotics, and augmented reality, et cetera? So we basically involve the target users, companies that are involved in a particular sector, and so on as well. And so yeah, I think that there are many opportunities really for people to be involved.

I would also like to say that, especially now with COVID, all laboratories have become much more open and, for example, lecture series, showcases, virtual open houses, and so on. There are no limits to how many people can attend because it's all [laughs] online anyway these days. So it's actually nice that that has opened up the laboratory more and makes it possible for more people to get involved, to be part of conversations, to listen to talks, see demonstrations, and so on.

TROND: That's fascinating. And I think just in closing, you mentioned this acronym that's typically used in psychological studies, the WEIRD acronym, Western, Educated, Industrialized, Rich, and Democratic. And it seems to me that that is a very, very specific user group, but it is far from the only one. So maybe in closing, my last question would be, how does one, you know, because others might be developing technology on other continents or other places.

How do you avoid this bias of jumping into a lane that other people have created that is this lane? It's maybe demos from Western labs. It's use cases in highly industrialized factories or whatever it is or created for the New York Fifth Avenue consumer market. Those are not the only technologies we should be building. So how do we do it otherwise?

PATTIE: Yes, I fully agree. And meanwhile, today, I talked about my work. And my work is indeed mostly focused on the Western developed world and technologies that might be available here. There's a lot of work happening at the Media Lab with other communities, both within the United States, less fortunate communities, maybe than the ones that many of my technologies are designed for.

There's a lot of work, for example, with people in Africa on use of different technologies. So we try to...maybe we cannot develop technologies for everyone, [laughs] but we try to be explicit about who some technologies are designed for and not assume that they would generally be usable. And we try to work with the target communities that they are designed for. And definitely, we're not exclusively working with or designing technologies for the Western, richer world.

TROND: Well, thank you so much, Pattie. This has been very enlightening. It turns out that advanced technology is complicated and slower, but perhaps more sustainable when it's developed that way. And that's an interesting lesson. Thank you so much.

PATTIE: Thank you. It was a pleasure.

TROND: You have just listened to Episode 24 of the Augmented Podcast with host Trond Arne Undheim. The topic was Emerging Interfaces for Human Augmentation. And our guest was Pattie Maes, Professor at the MIT Media Lab.

In this conversation, we talked about augmenting people instead of using or making smarter machines and enabling people to perform better through fluid, interactive, immersive, and wearable systems that are easy to use, developing new form factors, and much more.

My takeaway is that augmenting people is far more complex than developing a technology or even experimenting with form factors. Instead, there's a whole process to exploring what humans are all about, discovering opportunities for augmentation, and tweaking it in dialogue with users. The Media Lab's approach is work intensive, but when new products make it out of there, they tend to extend a human function as opposed to becoming just a new gadget. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 19: Machine Learning in Manufacturing, Episode 7: Work of the Future, or Episode 13: Get Manufacturing Superpowers.

Augmented — industrial conversations.

Special Guest: Pattie Maes.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode 3 of the podcast, the topic is: Re-imagining workforce training. Our guest is Sarah Boisvert, Founder and CEO Fab Lab Hub, LLC and the non-profit New Collar Network.

In this conversation, we talk about re-imagining workforce training, industry 4.0., what do you mean by “New Collar” jobs? We discuss the mushrooming of Fab Labs. What skills are needed? How can they be taught? How can the credentials be recognized? .What has the impact been? Where do we go from here.

After listening to this episode, check out Sarah Boisvert's online profile as well as the New Collar Network:

•  Sarah Boisvert https://www.linkedin.com/in/sarah-boisvert-3a965031/ 
• The New Collar Network (@NewCollarNetwrk): http://newcollarnetwork.com/
• Fab Lab Hub (@FabLabHub): http://fablabhub.org/

Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by @Art_list_io.

Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars on Apple Podcasts. To nominate guests, to suggest exciting episode topics or give feedback, follow us on LinkedIn, looking out for live episodes, message us on Twitter @augmentedpod or our website's contact form. If you liked this episode, you might also like episode 3: How to Train Augmented Workers. Augmented--the industry 4.0 podcast.

Transcript:

TROND: Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory? Who's leading the change, and what are the key skills to learn? How to stay up to date on manufacturing and industry 4.0.

Augmented is a podcast for leaders in the manufacturing industry, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, that is M-F-G.works, the open learning community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern, every Wednesday. Augmented — the industry 4.0 podcast.

In episode 3 of the podcast, the topic is Reimagining Workforce Training. Our guest is Sarah Boisvert, Founder and CEO of Fab Lab Hub and the non-profit New Collar Network. In this conversation, we talk about reimagining workforce training, industry 4.0, and what do you mean by new collar jobs? Fab Labs, what skills are needed? How can they be taught? How can the credentials be recognized? What has the impact been, and where do we go from here?

Sarah, how are you doing today?

SARAH: I'm doing well. How are you?

TROND: I'm doing fine. I'm excited to talk about reimagining workforce training, which seems to be an issue on your mind, Sarah. You are a founder yourself. You have been actively involved in advanced manufacturing. I understand part of your story is that your company manufactured and sold the Lasik eye surgery back in 1999. So you've been involved in manufacturing for a while. We're here to talk about something very exciting. You say new-collar jobs is the big focus. I know you didn't invent the term. Can you give me a sense of what new-collar jobs refers to, first of all?

SARAH: Sure. It is a term that was coined by Ginni Rometty, who was then the CEO of IBM. She's now the executive chair. And it refers to blue-collar jobs that have now become digital. And so many of our jobs...if you just think about your UPS man who now everything's not on paper, it's all in a handheld tool that he takes around on his deliveries. And all jobs are becoming digital. And so I thought that Ginny's term encapsulated exactly what's happening, and the technologies that we used to use just in manufacturing are now ubiquitous across industries.

TROND: You have also been instrumental in the MIT spinout project called Fab Labs. Just give us a quick sense, Sarah; what are Fab Labs? Not everybody is aware of this.

SARAH: Fab Labs are workshops and studios that incorporate many different kinds of digital fabrication. So we are taking the ones and zeros, the bits of CAD designs, and turning them into things that you can hold in your hand. And it covers topics like 3D printing, and laser cutting, and CNC machining. But Neil Gershenfeld, who founded the international Fab Lab Network, likes to say the power of digital fabrication is social, not technical.

TROND: You know, this brings me to my next question, what skills are needed? So when we talk about new-collar jobs and the skills and the workforce training, what exact skills is it that we need to now be more aware of? So you talked about some of them. I guess digital fabrication, broadly, is another. Can you go a little bit more into what kind of skills you have been involved in training people for?

SARAH: Well, when I first started this project, I had always been interested in workforce training, obviously, because I had a manufacturing company, and I needed to hire people. And we had worked with the community college near our factory to develop a two-year curriculum for digital manufacturing. But I had in mind exactly what I needed for my own company and the kinds of skills that I was looking for.

And so a lot of Fab Labs, because we have about 2,000 Fab Labs around the world, heard about this program and started asking me, "Could you make a curriculum for us?" And there were so many of them that I thought I needed to come up with something that is going to fit most of the Fab Labs.

And so I interviewed 200 manufacturers in all kinds of industries and from startups to Fortune 10 and so companies like GE, and Boeing, and Apple, and Ford, as well as companies in the medical device space. What they all told me they wanted was...the number one skill they were looking for was problem-solving. And that's even more important today because we're getting all these new technologies, and you haven't got some guy in the back of the machine shop who has done this before. And we're getting machines that are being built that have never been built before. And it's a whole new space.

And the second thing they were looking for was hands-on skills. And I was particularly looking at operators and technicians. They were also looking for technical skills like CAD design, AI. Predictive analytics was probably the number one skill that the international manufacturers' CEOs were looking for. And I got done, and I thought, well, this is all the stuff we do in Fab Labs. This is exactly what we do. We teach people how to solve problems.

And so many of our labs, particularly in places like Asia or Africa where there was tremendous need and not enough resources, necessity is the mother of invention. And so many of our Fab Labs invent amazing things to help their communities. And I thought, well, we don't need a two-year curriculum because the need for the employers was so extreme. I thought we need something more like what we do in Fab Labs.

TROND: And how can these skills be taught? What are the methodologies that you're using to teach these skills that aren't necessarily, you know, you don't need to go to university, as you pointed out, for them? But they have to be taught somehow. What are the methods you're using?

SARAH: Well, I did a lot of research trying to nail that down when I got done figuring out what it was people needed in the factories. And it seemed like digital badges were the fastest, easiest, most affordable way to certify the ability of a badge earner to work with a particular skill set. And they were developed by IBM and Mozilla probably decades ago now and are used by many organizations to verify skills.

And it's a credential that is portable and that you can put on your digital resume and verify. There is an underlying standard that you have to adhere to; an international standards body monitors it. And there's a certain level of certainty that the person who says they have the skill actually has it.

TROND: That's a good point because, in this modern day and age, a lot of people can say that they have gone through some sort of training, and it's hard to verify. So these things are also called micro certifications. How recent is this idea to certify a skill in that digital way?

SARAH: I think that these particular badges have been around for decades, and people like Cisco, and IBM, and Autodesk have been using them for quite a long time, as well as many colleges, including Michigan State, is one that comes to mind that has a big program. And they can be stacked into a credential or into a higher-level course. So we stack our badges, for example, into a master badge.

And that combines a number of skills into something that allows someone to have a job description kind of certification. So, for example, our badges will combine into a master badge for an operator. And so it's not just someone who knows CAD. They know CAD. They know how to run a machine. They know how to troubleshoot a machine.

TROND: So we touched a little bit on how these things can be taught. But is this a very practical type of teaching that you are engaged in? I mean, Fab Labs, so they are physically present, or was that kind of in the old, pre-COVID era?

SARAH: Well, yes, we were typically physically present with COVID. This past summer, I spent a lot of time piloting more online programs. And so, for our design classes, we can still have people online. And our interns 3D-print their designs, and then they can look at them via photography or video, if it's a functional design, and see how the design needs to be iterated to the next step. Because, as you know, it never comes out right the first time; it takes a number of iterations before it works.

And we just recently, this week, actually completed an agreement with MatterHackers, who are a distributor of tabletop 3D printers, to bundle their 3D printers with our badges. And so someone can then have a printer at home. And so, if you have a family and you're trying to educate a number of children, it's actually a pretty economical proposition.

And they offer two printers that are under $1,000 for people who are, for example, wanting to upskill and change careers. They also offer the Ultimaker 3D printer that we use pretty heavily in our lab. And it's a higher level with added expense. But if you're looking at a career change, it's certainly cheaper than going back to college [laughs] instead.

TROND: So I'm curious about the impact. I know that you started out this endeavor interviewing some 200 U.S. manufacturers to see that there was...I think you told me there was like a paradigm shift needed really to bring back well-paying, engaging manufacturing careers back to middle-class Americans. And that's again, I guess, pointing to this new-collar workforce. What has the impact been?

I mean, I'm sitting here, and I see you have the book, too, but you generously gave me this. So I've been browsing some of the impacts and some of the description of what you have been achieving over the past few years. What has the impact been? How many people have you been able to train? And what happened to the people who were trained?

SARAH: We've only been doing it a couple of years. And in our pilot, we probably have trained 2,3,400 people, something on that. And it's been a mix of people who come to us. Because we teach project-based learning, we can have classes that have varying levels of experience. So we have people who are PhDs from the Los Alamos National Lab who drive the 45 minutes over to us, and they're typically upskilling. They're typically engineers who went to school before 3D printing was in the curriculum. And they are adding that to their existing work.

But we get such a wide range of people from artists. We're an artist colony here. And we get jewelers, and sculptors, and a wide range of people who have never done anything technical but are looking to automate their processes. And so my necklace is the Taos Pueblo. And it was designed by a woman...and her story is in the book.

So I should add that the book you're referring to has augmented reality links to the stories of people. And she just was determined. She, I think, has never graduated from high school and is an immigrant to the United States. And she just was determined to learn this. And she worked with us, and now she designs in CAD, and we 3D-print the molds. And her husband has a casting company, and then he has it cast in sterling.

TROND: I find that fascinating, Sarah because you said...so it goes from people who haven't completed high school to kind of not so recent PhDs. That is a fascinating range. And it brings, I guess, this idea of the difficulty level of contemporary technologies isn't necessarily what it was years ago. It's not like these technologies take years to learn, necessarily at the level where you can actually apply them in your hobbies or in the workplace.

Why is that, do you think? Have we gotten better at developing technologies? Or have companies gotten better to tweak them, or have we gotten faster at learning them? Or is the discrepancy...like, this could be surprising for a lot of people that it's not that hard to take a course and apply it right afterwards.

SARAH: Learning anything comes down to are you interested? It comes down to your level of motivation and determination. A couple of things, I think the programs, the technical programs, and the machines have become much easier. When I started in the laser business, every time that I wanted to make a hole, I would have to redesign the optical train. And so I'd have to do all the math, so I'd have to do all the advanced math. I would have to put it together on my bench, and hopefully, it worked, and tweak it until I got the size hole I needed in the material I needed.

Today, there's autofocus. It's just like your camera. You press a button; you dial in the size hole you want, and away you go. And it's interesting because many of the newer employees at our company Potomac Photonics really don't have the technical understanding that I developed because they just press the button. But it moves much faster, and we have more throughput; we have a greater consistency. So the machines have definitely improved tremendously in recent years.

But I also think that people are more used to dealing with technology. It's very rare to run into somebody who doesn't have email or somebody who isn't surfing the web to find information. And for the young people, they're digital natives. So they don't even know what it's like not to have a digital option. I think that a number of things have come together to make that feasible.

TROND: Sarah, let me ask you then this hard question. I mean, it's a big promise to say that you can save the middle class essentially. Is it that easy? Is it just taking one or two courses with this kind of Fab Lab-type approach, and you're all set? Can you literally take someone who feels...or maybe are laid off or feels at least not skilled really for the jobs they had, the jobs they want, and you can really turn them into highly employable in a matter of one course? Has that really happened?

SARAH: In one course or one digital badge, it is possible to get some jobs, but it probably takes a combination of courses in order to have the right skill set because it's typically not one skill you need. It's typically a combination of skills. So to run the 3D printers, for example, you need CAD design. You need to understand design for 3D printing. And then you have to understand how to run the machines and fix them when they break.

So it's probably still a more focused and condensed process. So you could do our master badge, which comprises five or six badges, and get a job in six months for about $2,000. With one class, you could get a job part-time and continue the other badges and be paying for school while you're working in a field that is paying a substantial increase over working at McDonald's.

TROND: So give me a sense. So this is happening, in your case, in Santa Fe, New Mexico. Where do we go from here? Is this going on anywhere else? What are the numbers? How many people are being trained this way? How many people could be trained this way? How easy is the approach you're taking to integrate and scale up? And is it happening anywhere else?

SARAH: Our non-profit, which is the organization that issues the badges, has, right now, I think, 12 or 13 members, and they were part of our pilot, and they are all over the country. So in my team, Lemelson, the Fab Lab in El Paso, the Fab Lab in Tulsa, MakerspaceCT in Hartford, Connecticut.

And so we have a group that just started this year was when I started the scaling after, I was really pretty confident that it was going to work. If it worked in Santa Fe, which is a small town and in a very rural, very poor state, I really thought if I could make it work here, we could make it work anywhere because there are a lot of challenges in our state.

So we started scaling this year, and each of our pilot sites is probably putting through their first cohort of 4, 5, or 6 badges, and they each have about 10 in that first cohort. We have a lot of requests for people to join our group and start issuing the badges. I've really come to see the success of our online program. And so, our online program is instructor-led at this point. And I'm working to create a self-directed program that people could do online with a tabletop printer at home. But we will still continue to scale the New Collar Network that actually disseminates the badges.

And I really see enormous interest. As you know, college enrollment has been declining for the last ten years. There has been an 11% decline in college enrollment. And people are looking for alternatives. And I think that I've had requests from school systems. I had a request from a school system back East that has 45,000 students that they want to get badges. We have had a request from a school system in the Midwest where they get a lot of teachers who are getting 3D printers, and they don't know what to do with them. And they'd like for us to train the teachers.

So I really see a huge opportunity. And these tools that we're using are not just being used in manufacturing. One of the people that we worked with on the HR side in research was Walmart. And their big worry is now they're putting in these janitorial robots. And their big dilemma is who's going to program them, and who is going to fix the robots when they're not working? And it's everywhere. It's not just am I going to get a job at that manufacturing company? It's also your local retail store.

TROND: Fantastic. This is very inspiring. I thank you so much for sharing this with us. And I hope that others are listening to this and either join a course like that or get engaged in the Fab Lab type Network and start training others. So thanks again for sharing this.

SARAH: Oh, it's a pleasure. It's a real mission, I think. [laughs]

TROND: Sounds like it. Have a wonderful rest of your day.

SARAH: Thank you.

TROND: You have just listened to Episode 3 of the Augmented Podcast with host Trond Arne Undheim. The topic was Reimagining Workforce Training. Our guest was Sarah Boisvert, Founder, and CEO of Fab Lab Hub and the non-profit New Collar Network.

In this conversation, we talked about reimagining workforce training, industry 4.0, and what you mean by new-collar jobs and Fab Labs; what skills are needed? How can they be taught, and how can the credentials be recognized? What has the impact been, and where do we go from here?

My takeaway is that reimagining workforce training is more needed than ever before. The good news is that training new generations of workers might be simpler than it seems. Practical skills in robotics, 3D scanning, digital fabrication, even AR and VR can be taught through experiential learning in weeks and months, not in years. Micro certifications can be given out electronically, and the impact on workers' lives can be profound. Thanks for listening.

If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. Augmented — the industry 4.0 podcast.

Special Guest: Sarah Boisvert.