Could We Really Make Anything, Anywhere, Anytime? A Deep Dive into Distributed Manufacturing - Dr. Charles Johnson-Bey, ERVA Co-Principal Investigator

Distributed manufacturing allows goods to be produced closer to where they’re needed — but enabling that future requires a complete rethink of infrastructure, systems, and workforce development.

In this episode of The TechEd Podcast, Dr. Charles Johnson-Bey joins host Matt Kirchner for a deep dive into Engineering the Future of Distributed Manufacturing — the new national report from the Engineering Research Visioning Alliance (ERVA). Charles, a former professor and recently retired Senior Vice President at Booz Allen Hamilton, brings decades of experience in defense, systems engineering, and academia to this conversation.

Together, they break down ERVA’s five priority areas for enabling distributed manufacturing: modular and reconfigurable infrastructure, digital design tools, edge-to-cloud data systems, workforce education, and new performance metrics. Charles also shares how these priorities came from input across industry, academia, and government — and how they’ll guide research, funding, and education in the years ahead.

Listen to learn:

•  What distributed manufacturing actually looks like in practice — and why it matters now
• Why “digital twins + AI” are critical for linking design, production, and data-driven decision-making
• The essential role of public infrastructure in enabling connectivity and access for all communities
• Why proximity to advanced tools like a digital twin or a cyber-physical testbed is essential for scaling distributed manufacturing

3 Big Takeaways from this Episode:
1. Distributed manufacturing is a modular approach to resilient, tech-enabled production. Charles defines distributed manufacturing as a system where production assets can be easily moved, reconfigured, and localized closer to the point of need. He describes how smaller, agile, and digitally connected systems—like reconfigurable machines and regional testbeds—enable manufacturing to respond to disruptions, like the ones exposed during COVID-19.

2. The workforce of the future needs digital fluency—and systems thinking. Students must be prepared not only to operate new technologies, but to understand how those technologies interact within broader systems. Charles highlights the importance of human-machine teaming, digital twins, and cyber-physical testbeds, and calls for education that helps learners “fall in love with the rigor” of complex technical work.

3. America’s manufacturing strategy must include small and mid-sized firms. Charles points out that most manufacturers in the U.S. are small to mid-sized, yet lack access to advanced infrastructure and scalable tools. He argues that national strategies must focus on democratizing technology—making AI, automation, and data systems affordable and available to all levels of the manufacturing sector.

Resources in this Episode:

• Read the ERVA Report: Engineering the Future of Distributed Manufacturing
• Learn more about ERVA
• Follow Charles on LinkedIn

Visit the episode page for more!

Instagram - Facebook - YouTube - TikTok - Twitter - LinkedIn

Transcript

Matt Kirchner: 0:00

Matt? Well, it's another week and another episode of the number one podcast in STEM and technical education. My name is Matt Kirkner. I am the host of the TechEd podcast. We're going to have a lot of fun today talking about some really cool research in the world of manufacturing that has been done released in a report we are going to talk with the CO principal investigator of that report. It is Dr Charles Johnson Bay. He is the Irva co principal investigator, Senior Vice President of Booz Allen Hamilton recently retired, as I understand it. And so Charles, let's start out with just welcoming you to the TechEd podcast. So awesome to have you with us.

Charles Johnson-Bey: 0:46

Man, it is such a pleasure for me to be here. I'm an educator at heart. You know, the former professor at Morgan State, this is a great place for me to be. And so just really happy to be with you. Yeah,

Matt Kirchner: 0:58

we're happy to have you. We're having so much fun. And you know, our audience, as you suggest, includes a lot of educators, and they're going to love hearing about the work that you've done. Certainly at the university level, we have educators as well from Technical and Community Colleges, k 12 industrial trainers and lots and lots of people that are interested in STEM and tech ed in general. I know they're going to really enjoy this episode. Charles and like I said, it's awesome to have you here. I mentioned in the intro that you recently retired from Booz Allen Hamilton, I think our listeners probably know one of the more iconic consulting firms on the planet. So great career in consulting, but your career wasn't limited to that. You spent time in engineering and in leadership roles at places like Lockheed Martin, Motorola corporate research labs, and as you just suggested, you're a professor at Morgan State University. What a great background, what a wide variety of experiences you've had. Charles, looking back at that, talk about some of those common threads on that journey, and then how it ultimately led to your work with Irva, which, by the way, Irva is the engineering research visioning Alliance. So talk about that a

Charles Johnson-Bey: 1:59

bit. Yeah, thanks. So honestly, it's one more name I have to say in there from my education, and that's the Baltimore Polytechnic Institute, or poly. Poly is an engineering High School. It's the engineering high school in Baltimore. That and my oldest sister sort of got me on this road. I say my oldest sister, because she would make me do math problems over the summertime. Yeah, so I wouldn't embarrass her, as the little brother coming into school, she was like, you're gonna know these problems. You're gonna do this over the summer. So that was fun or not, but I do like math now. So you know, we get something from it, for sure. But I will say that I've really have been interested on just how things work, but not just the theory of it, but just the practice as well. Like, how do you demonstrate things? How do you bring things so that from paper into realism that just hints right there to manufacturing. And so just going through school, I think what one of the things that I focused on, and even in my PhD, was, you know, looking at the hardware and how do you design something that, you know, on paper and math sort of works out. But then how do these electrons and all this stuff make it work? I thought it was magic, and I wanted to learn magic, to be honest, Matt, so that's sort of how I got on my way.

Matt Kirchner: 3:14

I love that. You know, we just had our last episode was with Jordan Ellenberg, who wrote one of Bill Gates his 10 favorite books. And Jordan talked about that. Talked about that. Talked about, if you understand mathematics, it's almost like understanding magic. You understand engineering, it's almost like understanding magic. And by the way, the fourth PhD, as I understand it, or at least close to that of your family. So that's incredibly good genes that are going on at the Johnson Bay household. Obviously, I could tell you, Charles, I have a younger brother, I made him do a lot of crazy things over the course of the summers that we had growing up together, none of them involved doing math problems. So credit to your sister for challenging you that way. And obviously created a brilliant brother, and one that, by the way, as we mentioned in the intro, co principal investigator on this engineering research visioning and Alliance, or Irva report, helping to guide national conversations about all the great things happening across society and what the role of an engineer and the discipline of engineering in general can be around all of those challenges taking place across our society. So what drew you personally this topic, you call it distributed manufacturing. Why is that topic interesting to you? And maybe just a little bit on what your definition of distributed manufacturing is?

Charles Johnson-Bey: 4:26

Yeah, thanks. So I know originally we had talked about maybe democratizing of manufacturing, but that's such an essay T word, right? Nothing wrong with SAT words, but we thought that distributing would be something that was just a little a better language, right? Better term for that. And so what really we were talking about was, how can anybody, anywhere at any time, build and manufacture something that they need, right? And one of the things when we were talking about this was, right during covid. Right? And if we learn nothing during covid, we learned about supply chain. Like, everybody started talking about supply chain, oh, we can't get this because we can't get it now. And so we were, like, from a national standpoint, that's something that's gotta be addressed,

Matt Kirchner: 5:15

right? Yeah, no question. I mean, you think about the whole idea of supply chain, and you know, the way we say it on the TechEd podcast is when you could get what you wanted, anytime you wanted, at a price that seemed fair. Who cared about supply chain, right? Nobody was in thinking about, I was going Amazon or wherever, and I order it, and it shows up in my front door, not too much to worry about. And then all of a sudden, to your point, this hiccup called covid, and we've got all these ships stuck on docks, and we can't get product in. People can't get materials, and all of a sudden, whatever that thing is that you just took for granted doesn't show up at your house. And it's like, oh, I guess this does come from somewhere. I guess somebody does make this. I guess it does matter where this gets made. It's interesting that definition of distributed manufacturing, if I looked at at a standard as a standalone word, I'd be thinking, well, what is this like? Supply chain distribution thing? Or what is it? And it feels more, and you can tell me, if I'm getting this right, it feels more like the idea is, how do we make manufacturing available to everybody, whether that's on the supply chain side, whether it's, you know, people interested in getting into the discipline of manufacturing is, you know, I made my living there for 25 years and still spend a lot of time in and around manufacturers. Am I? Am I getting close in terms of how you're thinking about this. Yeah,

Charles Johnson-Bey: 6:22

you are. It's two points I want to make there. One, if we look back a little bit at the history of the United States, right, where the manufacturing boom happened from about 1940 till about 1979 or 80 we were booming in the United States, and that was an area where folks could get a job, get into the middle class, make a good living, all of that in this country. But now, over time, that has declined right since the since about 8182 forward. And so we're going, you know, we're sort of in this thing of coming back. And so we've got things now today, like the chips act and things like that. That's looking at manufacturing in the United States and bringing it back. And so again, we thought this was an important point for Irva to look at, because the thing I really like about Irva, and again, it's the engineering research visioning Alliance. It's sponsored by the National Science Foundation. We don't lobby, so we don't tell National Science Foundation, sort of what they can fund and do that, or Congress, or anybody. So we really convened sort of about 50 of the best minds in a given area from across the nation to come together to look at global challenges that have national responsibility, and what can engineering do to help move us forward. And so just that kind of convening takes is an art in itself. I will tell you that. That is, you bring 50 people in that all have opinions and all of that, and getting them to talk with one voice is, I think the thing that Irva, I think we do pretty well, right? I think in so part of this distributed manufacturing was to bring us together to talk about that, and not just your area of research, but how do we as a nation face that? What are some of the questions that we've got to answer? And then the second part, though I want to hit on a little bit, is innovation. And I certainly have a different take on innovation. You can't talk about innovation unless you talk about your constraints. Because, you know, if you mentioned, if you've got everything you need, at the price you want and all that, why do I have to be innovative? Right? I've got everything, but if I don't have the resources, if I don't have the material, if I don't have those things, I really got to get innovative and think about, how do we do this? And so that's why we really looked at an extremely challenging problem of, how can we manufacture anywhere, anytime, right? And then we also talked about the waste and all that. So it's all those other side pieces that come along with this

Matt Kirchner: 8:56

absolutely well, you know, as you're saying that, first of all, I won't tell you how offended I am that you brought together the 50 greatest minds in engineering and manufacturing, and you didn't invite Matt Kirkner, the host of the TechEd podcast. I'll get over it, but

Charles Johnson-Bey: 9:10

email it said you said you were busy. I must have

Matt Kirchner: 9:13

been number 51 is my guess. I'm just guessing I was the last guy to get cut before you got to the top 50. I'm kidding. Of course, that sounds like an amazing group of people. The other thing that I really think is amazing and interesting when we start talking about constraints in manufacturing, and that you brought that up, I mean, to me, my life, my manufacturing life, changed 30 years ago when I read the book The goal by gold rat, he wrote the theory of constraints as well. And it's not luck, but just a phenomenal author in the area of manufacturing and looking at constraints and manufacturing processes. And I think you're exactly right. And you know, anything continuous improvement, improving yield, improving cycle times, improving quality. I mean, any of those things come back to looking at what your constraint is in a process which doesn't necessarily, by the way, have to be limited to the manufacturing process itself. So you. Your point, it might be a constraint of, how do we get materials, or what materials are we using, or proximity to suppliers, proximity to customers, all these different constraints that we can find you talk about waste, believe it or not, I made my kids, who are now 25 and 23 when they were about eight years old. I made them probably 10 and eight, something like that, memorize the Seven Deadly Wastes. And to this day, each of them can tell you the seven deadly waste in manufacturing, of course, that was brought to us by Taichi Ohno of the Toyota Production System. But identifying those constraints, identifying those wastes, you and I are 100% on the same page. Talk about the report in which you know, if you say we're putting these things on the table, we're talking about waste. We're talking about constraints. We're talking about how we need to be able to manufacture anything anywhere. I agree with you. I think that's where the economy is going. Where the manufacturing economy is going is with with processes and manufacturing getting closer and closer to the point of consumption, for a whole bunch of reasons. We'll get into but, but let's just kind of sit up here at 30,000 feet and tell us about the report engineering. The future of distributed manufacturing, by the way, is the report's name, and we'll be sure and link that up in the show notes as well for our audience. But go ahead, what are you up to in that report? Charles

Charles Johnson-Bey: 11:08

Matt, as you know, manufacturing is such any topic is just a big topic when you're looking at on a national scale. So one of the challenges, not just for the manufacturing but also the other visioning events that we put on is we need to have something that's big enough that will have national impact, but focused enough that we can get people around to talk about it and have and focus on what are those key questions. And so in this case, we came up with sort of the three big grand challenges to talk about. One is the material supply chain. So how do we design the next generation materials for manufacturing that enables synthesis and non polluting, recyclable and renewable materials? Because, again, you don't want to do something good, but then you got something bad on the back end. So we wanted to be very mindful of that. Next is tools and processes. So how do we design new systems based on adaptive control with closed loop? How can we have sort of like some swarming manufacturing that enable multiple small tools to impact what we're doing and then create sort of this open source hubs, right? So, though people can understand, these are the processes that are going on. This is how you can improve things. And then the third thing. So again, first is materials and supply chains. The second one is tools and processes. The third thing, and it's, you know, all these are equal, but I think this one might be more equal than the other two. Sounded

Matt Kirchner: 12:37

like George Orwell in Animal Farm, right? All animals are equal, but some are more equal than others. Okay, go ahead, thank you.

Charles Johnson-Bey: 12:42

See, it's just like that, right? Good book, by the way. It is good reference. It's just data and quality assurance. And I think this is actually the thing that's really going to be the driver going forward. Sort of, how do we share data? Because right now, people are like this, right? I don't want to share my data, or do I trust the data that's being shared with me? And I think those that's extremely important. And how do we get to some model based, you know, certifications and things like that?

Matt Kirchner: 13:13

For sure, I certainly agree with those three tenets as being incredibly important in the world of manufacturing, obviously, materials and sustainable materials. We'll talk about that a little bit more. No question, the tools we use important. You mentioned a term. Most of those terms you mentioned, I'm familiar with. Swarming is one that I'll admit, I'm not sure I'm totally familiar with. Can you share a little bit more about what you mean when you talk about swarming as a tool?

Charles Johnson-Bey: 13:36

That's a good, good question. We've got references for that, so we'll share those too, so folks can look it up. But really swarm engineering is, how do we use multiple small tools like subtractive, additive form, join those kind of things to help solve a problem? Right? So we've got these things all coming together, all these I'm going to call it different disciplines within manufacturing to help solve a problem. And it's really hard, as you know, for small and medium size, which most of the businesses in the United States are, it's probably, what, just over a half a million of them. But you know, they're all small and medium, so it's hard to do that. So how do we get them to work together to solve a problem?

Matt Kirchner: 14:18

It's fascinating, if you don't mind me invoking a comment from, actually, one of Booz Allen's competitors, McKinsey, we had their Global Director of Strategy on aztage party a year or so ago. He talks about, actually wrote a book about it. But he says, you know, 70% of innovation in the United States of America happens in small to medium sized manufacturers. And I think you know, of all r, d, of all innovation, 70% of that innovation is happening inside of small and medium sized manufacturers. So I think you make a really interesting point when we talk about those size businesses where there's tremendous amounts of opportunity to innovate, but a lot of times those companies. And you can tell me if I'm on the right track here, if I'm a machining company, I know machining, if I'm a fabric. Education company I know welding and wire bending, and you start to think about, there is probably never any one best way to manufacture a product, but there may be an optimal process to use. And maybe it's additive, maybe it's subtractive, maybe it's metal fab, maybe it's casting. I mean, there's all these different manufacturing operations that we could utilize. So a little bit this idea of swarming is, let's pull subject matter experts from various disciplines together to say, Let's exchange some ideas, and let's see if we can't optimize this process with our collective knowledge. Is that kind of what you mean by swarming?

Charles Johnson-Bey: 15:32

Yep, absolutely. And the thing that we're going to need for that, and something that's going to help enable that, are the business models, right? So we really have to look at, what are our current business models? What are the new business models that we need? And again, that hits sort of the constraints, right? Because right now, people have their business model, their investment, what they do, and it's like, I make my money this way, right? Or I make an impact. Because not all of you're in business to make money, but you're also most folks in business to make an impact, right? For sure, in the area 100% so how do I make a better impact and reap the rewards of that? So I think that's also gonna come from focusing in on this area, in this way,

Matt Kirchner: 16:10

you know, running several businesses that are focused on securing the American Dream for the next generation of STEM and workforce talent. We know all about what it's like to run a business to make an impact. And I think you're right, and that surprises some people, sometimes too, where, you know, if you haven't spent time in and around manufacturing in general, or business in general, manufacturing specifically, you know, sometimes people from the outside looking in will be like, well, those guys are just all about making money, forgetting about the fact that you have you know, almost every manufacturer that I know cares deeply about their people. They care about their customers. You know, their teammates and their teammates and their team members, their customers, their suppliers, the greater environment. I don't just mean that environmentally and sustainably, but that's important too. But just in general, the community that they're in, and I think that gets lost sometimes, is that, you know, manufacturing specifically, can have a huge impact in a lot of different ways, not the least of which, of course, is continuing to improve design, making products available to consumers at a lower price, doing that sustainably. I also, by the way, will agree wholeheartedly with that third point, and the more equal than others on the data side, and I think that it's becoming even more equal than others as time goes by, and we see the incredible role that data can play. If we can capture it, we can acquire it and then we can analyze it in a way that provides usable and actionable outcomes, really, really important. So I want to dive a little bit deeper in our next question. Charles, on that first topic you mentioned, with regard to materials, I've probably no less than a dozen times on this podcast, talked about a book that I read within the last several months called Genesis. It was written by Henry Kissinger, co authored by Eric Schmidt and Craig Mundie. There's a whole section in there about in the age of AI, and the whole book is around artificial intelligence in the age of AI, how important materials are going to be, how much innovation we're going to see in the future of materials, that being able to use a large language model and an AI agent and understanding different material properties, we're going to have new materials that nobody's ever thought of that are going to be stronger, they're going to be lighter in weight, in some cases, they're going to be smarter, they're going to be more environmentally sustainable. Certainly, that's a topic that really, really fascinates me, and I think is going to be front and center here more so than it's ever been in the field of manufacturing. Talk for a bit about why material engineering is such a linchpin in your mind for distributed manufacturing. You mentioned sustainable materials, but, but why is that important? And you know, let's dream a little bit about some of the new opportunities that we may unlock in the

Charles Johnson-Bey: 18:31

future. When we talk about materials, we're not even talking about just the material itself, but we talk about housing material developed. Where do you get it? Where does it come from? Like, if it's got to come from a far place, that's something that gets fed into the equation, right, of the business and how we do it, and then once it's used, what happens to the waste? Right? If we get a waste of that that is harmful, then we're going to have to deal with that, right? And it's a whole bunch of regulations and all that circular and that. But if we could develop materials where we can use the waste, right, if we have it such that we can look at materials like bio based feed stocks that can be reused, we look at novel alloys and composites, things that can replace the difficult to obtain materials, and the output, sort of the waste product of that feeds into another part of the ecosystem, right? I think that's the thing that is going to help propel this thing forward and even generate ideas that people haven't thought of, right? Oh, now we can do this, and we didn't know this. And again, when we talk about the small to medium sized businesses that are all in manufacturing, and where are these businesses? They're in neighborhoods, right? They're in cities like yours and mine, and where people are talking and so that just helps the ecosystem of the city get better, right? And that just helps, again, the United. And it states get better. And, you know, not to say on motherhood or apple pie, but man, we're all in this thing together. And if we can do some things, I think that's I think this helps everybody, to be honest,

Matt Kirchner: 20:10

you and I are 100% aligned in that regard. And solid manufacturing economies, solid outputs, doing it sustainably. All of that plays into a stronger community, more opportunities for everybody. We're so we're 100% aligned on that particular topic. You know, you think about this whole field of materials and what we're going to be able to do now by analyzing data and engineering at scale and quickly, and then some of the outputs. You know, I think there's an old school example that just came back to me on Friday night as we record this, on a Monday morning, I was walking with my wife in our neighborhood, and I smelled chocolate, and not just chocolate in general, but like a really, really familiar smell of chocolate. I'm like, Man, that smells good. And I'm like, is somebody baking cookies or whatever? And then we walk by this that are one of our neighbors about half a mile away from us, just re landscaped our front yard, and what they used for their mulch was cocoa bean shells, believe it or not, and there are companies that would make cocoa, but then they had these shells that were left over, and they created this. I mean, it's a really simple example, but it's a byproduct of rather than just putting those shells in a landfill or putting them out somewhere to rot away, they actually repurposed them. Created another revenue stream, right? Because they're selling that now to somebody that's going to use that in their yard. And so we that's like a really simple idea, right? Everybody knows what a cocoa bean looks like, and everybody knows that it comes in, or that, you know, it's grown and has a shell, repurposing those shells. And then you start thinking about, to your point, getting into some of the way more complex scientific considerations about, how could we repurpose something into a clean biofuel, or what have you. So it's not just thinking about the manufacturing process itself and making sure that the process is sustainable, that the materials are sustainable that end up in that product. But maybe there's a byproduct of that that is also has another purpose, right? It plays on itself for creating another revenue stream, or for that matter, once that product, whatever it is has reached the end of its useful life, then what happens through it, and is there an opportunity to repurpose it? Am I? Am I getting that right? Is that what you're

Charles Johnson-Bey: 22:06

thinking? That's absolutely right. And I do think that AI is something that can help us right. This is Charles Johnson Bay's opinion. I believe that the technical folks who are developing these tools and technologies, I think we have to do a better job of explaining what they can do and what they can't do and what's possible and what's not possible. And I think in the AI space, we aren't doing a good job of that, you know, I'm a sci fi guy, right? It's a whole bunch of sci fi movies out here and, you know, and people are thinking, AI is going to do this that the other you know? And I'm like, Yeah, we got to do a good job of saying this is a tool that people are using. It's not going to take over. I want to qualify this. Yeah, it's not the AI that's going to take over people's jobs. It's the people who know how to use AI and know how to use it. Those are the things that are going to take over the jobs, because they know how to use this new tool to how to do it. So it's not the tool itself that's going to take over. Agree, it's not. I think that if we understand what it can do, if we understand that this thing can go through a lot of iterations and look through the ecosystem of a material from start to finish and then help us jump start, sort of how we can use it. Then I think, yeah, we're in pretty decent shape there. I think that's a good place for research. No

Matt Kirchner: 23:29

question, that's a good place for research. You know, we talk quite often. In fact, I'll steal a line from a former guest on the podcast by the name of Dr rich Barnhouse, who's the president of Waukesha County Technical College in Southeast Wisconsin, who told me once, and I use it over and over and over again, will AI take your job? And the answer to that question is no, but somebody using AI might. And so the whole idea is that, you know, AI doesn't need to be a threat. If two things, I think you make a good point about, let's recognize that there's applications where it's gonna work, and in the end, it should be a problem solving tool. It's not any different than any other lean tool we use in manufacturing. It's a tool that we can use to get to a greater answer, to get to a greater purpose or a greater result. But it really does speak to this whole idea of upskilling the next generation of young people, and really people of all ages around the applications of artificial intelligence understanding, what I like to call the edge to cloud continuum, that it's not just what chat GPT is doing, or how you could use Cloud or meta AI or perplexity, or whatever platform you're using. I use all of them to answer a question or to solve a problem or to figure out a recipe, or all those different things that we use generative AI for. But also, how are we gathering data on the edge? How are we acquiring that data? How are we analyzing it on the edge? Where does it go? What kind of a control system are we using? I know I'm going down the rabbit hole here of control systems, and I think I read in something that that's an interest of yours as well. We could probably do a whole conversation on lateral logic and PID control, but we'll spare the audience that conversation

Charles Johnson-Bey: 24:59

I got. Patent in that area. Yeah, really,

Matt Kirchner: 25:03

what's your patent

Charles Johnson-Bey: 25:04

on? That's why I like you, man, because, like, look, hey, man, let's talk about it.

Matt Kirchner: 25:07

Yeah, you brought it up.

Charles Johnson-Bey: 25:11

Yeah, this is work that I did when I was at Lockheed Martin, and we were working with the Navy. When you're on a ship, right? It's a lot of systems, right? You got you right, machinery, plant, control, monitoring system, it's a lot of stuff happening, and you only have so many people, right. And so the patent is on, how do you continue to maintain control and situational awareness on a ship that size, that's minimally personed, huh? Right? So we are agnostic on the hardware. So regard there are certain things that are fundamental to big systems like this, and so we came up with a way to be able to maintain that control and keep things moving without a lot of people.

Matt Kirchner: 25:54

Fascinating. I toured a few years ago, Charles an aircraft carrier. You worked at Lockheed Martin. You spent a lot of time around that kind of technology. Kind of technology. And I hadn't, I'm a lifelong Mariner, but not somebody that ever worked on that aircraft carrier, a large military vessel, and they had, like, a whole machining shop on the aircraft carrier. I mean, like, literally, yeah, I mean, and you're looking at me, like, Yeah, no kidding, but yeah, I was just shocked by that, that they've got, you know, all but you think about it. I mean, you're deployed, you know, in the Middle East and the South China Sea. I mean, wherever it is that you're you're doing your thing, and all of a sudden you've got to be able to fix a problem, repair a part, what have you. They had their own machining center sitting right there. That was, was really, really fascinating. It's kind of like the ultimate in putting manufacturing and production as close as you can to the point of use, right? It was really right on the on the aircraft carrier. Which kind of leads into my next topic in question, which is your whole idea of doing exactly the same thing. You know, we started a little bit while ago talking about localized supply chains and about the appreciation everybody got for having manufacturing proximate to where they were consuming that product. Actually did an episode of the podcast, it's been probably, I don't know, 10 months or so ago with Barbara humpton, who's the CEO of Siemens, USA, and we talked about this, her belief, and my belief, that we are in the middle of this trend where we are going to see manufacturing getting closer and closer and closer to where we're consuming the product. It takes out all the supply chain risk, takes out the transportation costs, takes out all the inventory risk. We also are able to be a lot more responsive to the needs of local consumers and so on. Lots more variety, lots more choices. Is that the way you're seeing it? Why do we need to enable more of this agile and localized manufacturing? And then to your other point about data, what role is data going to play in that process.

Charles Johnson-Bey: 27:41

Great questions. And I believe Siemens is they're a big conglomerate. They're old. They've been around a while. I really like their CEO and their CTO. I think they have a very good vision, because what they're doing, they're pivoting, right? They're pivoting in this space and looking at how AI can help them with manufacturing. They've got a big global r, d, I think their North American hub is in Princeton, New Jersey, so that's a company that is really doing things among others. But I think that getting to the point you just brought up a real it's a really beautiful point where I think that the bringing things closer together, right? That's getting right back to, I guess, the title of the report, right? Because, from a distribution standpoint, how can we do it? How do we bring things closer together so that we can do that? Things that sound potentially impossible today, in the future, they won't be but also, when bringing, also the matter of the ship that you talked about, that's all constraints, right? So we're in a ship route, in the notion we need this stuff. How do we do it? Oh, well, let's bring us a little, small manufacturing place, right here, on ship, right? In order to do that, it can't have a huge power draw, right? It can't vibrate, but so much because you don't necessarily want to be seen at certain times, so you don't want to put signatures out there. Just getting into the wireless thing, another I led a vision event on wireless comms, which is a whole nother subject that's fascinating, but it's certain things that you've got to take into account and to get up. That's why I like engineering, because you've got to figure out all these problems, these real world problems, to make it happen, right? So those are really good examples of that. And now getting down to data, I want to talk about data from a couple of standpoints. The first one, there's certainly sort of in situ data that still needs while you are manufacturing, while this thing is in process. I think there's research to be done in those areas of, how do you collect that data? Right? How do you help a machine or the automation get better while it's. Going instead of waiting to the end. There's a lot of stuff that we can do in digital twinning, in computer and math and things like that. But ultimately, we got to get down to a thing that I always like to say, something you got to hit with a hammer, right? So how do you make that so that it has the right ting when you hit it with the hammer that you want it to while it's being processed. So I think it's a lot of research that can be done. And we talked about that in a report, you know, just an in situ data like, how do you gather the data? What are the type of sensors that we need? How do we communicate that data? Is there a way in which we can commonly sort of collect and identify that data so that we all know what we're talking about while we're doing

Matt Kirchner: 30:44

it. I think that's exactly where it's going. Charles is, you think about the evolution of manufacturing, and even in my lifetime, you know, I spent my 25 years, whatever it was in the manufacturing world, starting kind of early to mid 1990s or so. And even back then, you know, he started out, and you kind of knew you had a problem when a problem when a customer complained about it, right? And then somebody said, Well, okay, what if we could catch that in final inspection and before it ever leaves the plant? And so we started inspecting things at the at the receiving or at the shipping dock, or maybe we had a department called inspection where things went, and we'd, you know, we do samples and whatever, and not to say that we're not still doing that, because certainly we are, and there's and that's important. But then we backed up and we said, well, what if we could do in process inspection? So rather than having this thing go through five different operations and then to final inspection to find out that we made a mistake in operation two a week ago and kept adding value to a product that we couldn't ultimately sell to a customer, how about in process inspection? And now we're in this age where we're able to pull data in real time, right? I mean, with smart sensors and devices, they've got embedded intelligence. They can communicate with each other, proximity sensors, light sensors, ultrasonic sensors, smart RFID, smart barcode, smart on light smart. I mean, we've got all this ability to pull data in real time and then create a large language model out of that data. You're right. There's a ton of research to be done there so that we can using data as opposed to an inspection process. And I should have had your computer vision too, is having a huge impact on that. And then we can use data to identify an anomaly in our process. And then let's get to the point where we're not identifying an anomaly in the process. Let's model it in a digital twin to your point, and let's find that anomaly before we ever build the machine or before we ever produce the part, and then fix the problem before we ever have it. Is that kind of what you're thinking?

Charles Johnson-Bey: 32:31

Yes, Matt, you're a perfect spokesperson. That is the flow, right? Because it all makes sense. And we start looking at these ecosystems based on the need and the constraints and what we have. And again, these are problems that we need to focus on. And one of the things that I'll talk about is in the report we bring out here, what are some of the critical questions within a five to 10 year standpoint, within the 10 to 20 year standpoint and then a 20 plus year standpoint. Because our challenge a lot of times, this is quoting one of the matrix movies. People think five minutes in front of their face, right? It's like you just gave them the bridge, right? People think two minutes, five minutes in front of their face. We need people to think out 20 years and say, and it's hard, right? Yeah, it is hard. But when you think about sort of how quickly technology moves, what are some of the things 20 years from now that we need to be looking at? And then we can back that up into the five to 1010, to 20, that way. So then we can start saying, Okay, this is really novel. This is really bold. These are things. These are areas to go. And when you lay out, sort of the flow, as you just eloquently did, that's the thought process of an urban visioning event. I

Matt Kirchner: 33:50

love that you're looking out into the future. I mean, is there something you know, take us forward 20 years? Is there something that kind of pops into your head that says, This is the world of manufacturing in the year? What would that be 2045, so 100 years after the end of World War Two, what does manufacturing look like? Yeah, make sense.

Charles Johnson-Bey: 34:06

So yeah, actually, I'm gonna name one thing from each of our thing, if I can. Yeah, go for it. So for materials, one of the things that we talk about in the 20 plus years is develop AI algorithms for material substitution, addition or replacement of other materials based on Property Performance, trade offs from shared adaptable knowledge and sustainability to prevent waste, so no waste. Yeah, right. And then algorithms should take into account regional availability to optimize options that account for local materials variability. So looking within your space, what do you have? What can you use so that you have no waste? You manufacture, you got something that you hit with a hammer and it's no waste? Yep, beautiful. That's one. Let's talk about tools so standardized. As raw materials and processes, tool heads and robot lineup and establish a global supply for the standard items. So what are some standard items that we need? And then we'll just line these things up. So we've got a lot of automation. We've got some robotics, right? So you gotta need to manufacture the robotics that we have, very much like your logo, right? And all that. And then we distribute, right? The autonomous general purpose factories capable of handling the product life cycle from cradle to grave, right? So being able to do that 20 years from now is like a vision. So what research do we need to do to make that happen?

Matt Kirchner: 35:37

That's so cool. Let's go a little bit deeper on that too. Charles, you know, you think about this whole idea of tools, and we and we've talked about data and manufacturing. We've talked extensively about materials, you know, the equipment we use in manufacturing. I toured my friends, brand new to him, he just bought a manufacturing company within the last year or so, and I hadn't been there, so we, I did a tour about two weeks ago. He had a machine in that plant that was built in the 1950s right? And it was still just cranking product away. I mean, literally, this thing is now 75 years old. I remember when I was running for Rockwell Automation, a great automation company, by the way, we gave a shout out to Siemens, and I ran a Rockwell spin off for 10 years, you know, number of years ago, and we had a piece of equipment in that plant, believe it or not, that was pre World War Two that was still producing parts, was still generating revenue. It's just just crazy and it, but it was, you know, it's built for one purpose, and that was the purpose that it had served in that case, for, you know, 6070, years. So now here we are, and you're starting to talk about machines that are in you just kind of teed it up in your last discussion about having an autonomous factory, a Mobile Factory, versatile factory, machines that are, quote, small, agile and reconfigurable, which really kind of fascinates me. Most of the manufacturing equipment we have today are built. It's built for a single purpose, or at least it might be like a machining center. It's built to machine product. It might be a punch price that's built to fabricate product. What do you mean by small, agile and reconfigurable machines, and what does that look like for the future of manufacturing? Yeah,

Charles Johnson-Bey: 37:09

so I think all these topics are, like, just great research areas. I'm like a kid in a candy store. It's like, Oh man, that looks that looks good. This looks good, right? All that. But one thing I want to say quickly about just the, what I'm gonna call the older, maybe older technology, just from a date standpoint, yeah, man, just because it's new, or just because it was thought of or built in 2025 doesn't make it better. You and I both see machines that are working and continuing to work. What brings to mind, like this micro controller called the PDP 11 that NASA used for space applications

Matt Kirchner: 37:44

for years, right? Yeah, we still see slick five hundreds in manufacturing, right?

Charles Johnson-Bey: 37:48

That's right, that's old school stuff. So getting on hardware. I'm a electrical engineering education. We have a thing where you can have a common something that is specifically built for a given purpose, right? And then we had chips where we would call them field programmable, right, where you could put them in the systems, and if you wanted it to change, then you could, you know, sort of change some code which changed the configuration, which gave you a new system. I think manufacturing is going along a very analogous path, right where you have machines. Some are originally thought of for a given problem, but when you look at how quickly technology is changing today, or how quickly we are getting smarter at the things that we can do. Actually. This ties back a little bit to the example I was saying when you've got machines that are looking at, sort of the data while things are happening, and then be able to address and change itself. Yep, I think that's where we're going. So sort of a field programmable manufacturing process, and I think that's probably first heard on me. So I'm a, I'm going to trademark that, but I think that's whatever manufacturing, yeah, real programmable manufacturing. SPM, for short, that's right. I think that's where we're going, honestly. And I think there's certainly a need from that. I think that now manufacturing, you got different flavors, because you've got things like bio manufacturing we're looking at like bio materials, I think that probably is an area where that field programmable manufacturing is gonna work, right? As you're learning about the biomes, the proteins and things like that, I think that's when you look at the constraints and the need. I think healthcare gets better if we do that better. So I think the pull to get sort of that field, programmability, into manufacturing, in that space, right, it has a bigger need. So I think my opinion is that's where we would see it pop

Matt Kirchner: 39:57

fascinating. You know, first of all, you. At that, that patent on situational awareness, now you can have both a patent and a copyright on FPM that, there you go. Yeah, field programmable manufacturing, I like another one of the topics that I just want to dive into with you here quickly, Charles and again, it speaks back to the report and some of the recommendations and items that you considered is this idea of common data standards. I should say so the ability to commonize standardized data. Talk about why that's important, and then the other one is number five, with sector wide connectivity. What's going to need to be done in order for us to not just have that common data standard, but also have connectivity across sectors, whether that's technological connectivity or just connectivity in terms of people and talent.

Charles Johnson-Bey: 40:43

Yeah. So certainly, when we look at how manufacturing is going, and as I talked about collecting data and having some standards on what data we're collecting, what does it mean, the format, all that that's important. And I'm going to talk about my friend daroda, who is the PI, the lead pi in Irva, her and I attended a manufacturing conference actually was leading up to this visioning event, and one of the things that they talked about, that they talked about in that conference, was how future manufacturers aren't going to be the ones that have 2030, years of experience, but we're but we're going to need these people to keep moving things forward in the challenges that we have, both as a country and globally. So having a standard data, how do we do it? What do we collect? And all that that is important, right? It's important for decision making, because manufacturers must collect that decision useful data, while also protecting against cyber attacks and stuff like that. So so trusting the data, and also when I believe the resiliency of the data is what I like to call it, because if you assume that that it's bad actors or folks in there trying to futz with your data or something like that, you need to make sure that you have the right data so that you can use and then also trust to me, is a timeframe, right? So I can I trust you over this time. So certainly, a lot of research that's going to go into that, but I think that the current and the next generation operations are going to require that sort of effective, standardized methods for storage management and sharing that data as we need to. Yeah,

Matt Kirchner: 42:23

there's no question that that's going to be important, the whole idea of trust. And I think it's fascinating that you bring that up, Charles, over a period of time, trusting data, trusting that your data is going to be safe, recognizing and understanding what technology is available to keep the stuff that we want to keep to ourselves, to ourselves, and what we're willing to let out of our plants. You know, back in the day when everybody was air gapped and there was no way for data to get out of an individual facility, that was one situation now where we have machines, sensors, control systems, networks that are all sharing data, talking all over, making sure that we protect that secret sauce that may be the magic behind a specific manufacturing operation, keep that secret, but also have data that we can share across platforms and across people. It's going to be really, really important, not just for large companies, but probably even more important for smaller companies. You know, we've talked a lot conceptually about some of the recommendations in the report. If I'm a small to medium sized business, you know, what should I be thinking in terms of making this vision a reality?

Charles Johnson-Bey: 43:24

And this report, honestly, is for the small and medium sized businesses, to be honest. I mean, that was the focus. So I think that a lot of the research areas and all that are for those small businesses, because that's the heart, right? If we don't get it right at the small and medium sized businesses, then we're not going to get it right. And us, manufacturing is such at a critical juncture right now, and we won't maintain or have global leadership if we don't get that part right. I don't think we need to convince the small and medium sized manufacturers that this is important. They know it's important. They know the service that they bring. But what I think we need to do is we need the more general public to say, Hey, this is important to me, and I need to understand it so it's so important to me that there's things that I can do, right? I can do at home, like, you know, the advent of 3d printers and things like that. Like, I have one in my little secret laboratory in my basement, right? That my kids brought for me, but I love it, right? So I've got one of these things. So things like that have made things that were impossible when you and I were younger possible today. And so I think that looking at re energizing the manufacturing, especially when we look at the use of AI again, another tool. Let's start bringing these tools together and saying, How can we do it? And how can small and medium sized businesses help solve some larger problems by working together? And I think this whole idea of data and trust and working together and business models, I think that. That's the thing that's gonna make it work, just to inspire the folks to do that, both academic, industrial, government, all that. I think this is a prime time and that's why this particular podcast is so right on time and so critical, because we need this. And now's the time for us to start focusing on it.

Matt Kirchner: 45:20

Now is the time to start focusing, indeed, the next episode, assuming that we get together again, Charles, we're going to do on location from your secret laboratory, just so, you know. So I think the biggest, yeah, the biggest news that we we've learned a ton of great news. But Dr, Charles Johnson Bay has the secret laboratory

Charles Johnson-Bey: 45:38

complete with the printer and a foosball table. So bring your

Matt Kirchner: 45:41

Foosball game. It's perfect. I actually got game when it comes to foosball. We could have some fun with that. Yeah, absolutely, in fact, I hit the ball so hard that sometimes I break those little guys. But the beauty of it is that if you get a 3d printer, we can just print

Charles Johnson-Bey: 45:54

another one. So we can print them off. We can print them up. Some good health insurance. It'd be great.

Matt Kirchner: 45:58

Exactly. Sounds like fun. You know, you know, you mentioned the importance of, you know, small to medium sized businesses, industrial employers, government, academia coming together. Let's what's your message for academia? So, I'm an educator. What do we need to be doing to create this next generation of engineers and technicians for the way that manufacturing is going to change?

Charles Johnson-Bey: 46:17

Oh, so part of this is we have to think about education differently, right? Yeah, that's one.

Matt Kirchner: 46:27

So as a former professor, I love it when I hear you say that, or maybe you're still teaching, or you did teach then, then how do we need to change it?

Charles Johnson-Bey: 46:34

So I do a lot of mentoring, as you would imagine. But the other thing that I'm really proud of is that I recently joined the board of Project Lead the way. Okay? When Project Lead the Way pulls together STEM education, curriculum and such hands on which I like, as I told you in the beginning, that's what I like, right? This one theory and practice. So this brings together the theory and practice. They're in school districts across the United States. Another thing I think that educators can do is get out of the students way, right, because they are really interested in things that they're interested in, and let's let them be interested in them, and then show them the tools. Here's a tool you can use without presupposing what they're gonna do with the tools. Here's some tools, and just have at it, right and be amazed at what they do. I think things like FIRST Robotics that's growing. I think is fantastic. I used to run an underwater robotics competition when I was a professor, actually, in Baltimore City. So I would train the teachers in Baltimore City. We held a very big regional event, and then the winners of that were going to the national event, and winners from Baltimore City was invited to the third science fair that President Obama went on, and I was able to accompany them. How cool is that? Yeah, man, it's really cool. So what I think for educators is become familiar with all the there's a lot of tools that access out there. And maybe we change, I'm speaking real time here. Maybe we change sort of science fairs, right? Maybe science fairs go from, I'm gonna show my age here, from baking soda and vinegar and volcanoes in the solar system. There was a student, high school student, who was designing a apparatus for her sister's shoe, right? I think one leg. She had a issue with her feet and all that. So she was looking at, how do I design an apparatus so that she can walk better? And that was that part of the curriculum. I talked to her about it. She had her little design, I said little design, but she had her design all that, but that's something that she can manufacture, right? Three print things like that. I think we will be amazed at what the young kids, the younger people can do

Matt Kirchner: 49:03

no question, yeah, you forgot about the salt, though. I think that's what we use to make the little volcano that you put the vinegar and the baking soda in to create that eruption. I remember that. Remember that very well. I did that project myself. You know, we have come a long way, and have a ways to go, but to your right, to the extent that we can have a student go from problem identification to ideation to, you know, coming up with some solutions, to actually manifesting those solutions, in this case, a real world example, like that girl whose sister had the issue that she was trying to help her get over. That's a that's a perfect, perfect example. You mentioned, you know, some great organizations, first, robotics being one of many I've been involved in a project called discover AI over the course of the last year, which is kind of hitting the same target that you're talking about, in terms of different experiences for different students. Get out of their way. Let you know everybody's going to learn at their own pace. We need to guide the student through their learning journey, as opposed to just dictating what that is. Everybody learns differently, matching the delivery to the learning style. Really important, and then letting students choose their area of interest to learn different concepts. I think that's the future of education. You and I agree 100% on that, as we're kind of wrapping up and closing out on our time here with Dr Charles Johnson Bay, I wanted to pose two last questions to you, one of them still related to education, Charles and that is, we all have our own education journey, obviously, with a couple letters like doctor in front of your name, yours went pretty deep, and you're a pretty smart individual, no question about it. But do you have a couple thoughts in addition to what you've already shared with us about education that might surprise our audience a bit?

Charles Johnson-Bey: 50:35

Thanks for the kudos and the very kind words there so kids and people, they all learn, right? They all learn. And I think that as an educator, we need to give them the space and opportunity to learn and to fail, quote, unquote, right, to try something and get it wrong, and then try it again and maybe get that wrong, and then try again, and then maybe you get it right, and maybe you talk to your friend. Hey, I got this friend, Matt. Let me give Matt a call and let me, let me bring Matt over to the table and let me talk to him. I think, from a very togetherness standpoint, I think one of the things that part of our challenges is to make sure that people know how to communicate with each other, right over a problem, right? Here's a problem. Let's talk about how we are going to solve this problem. What's your idea? Because we have a lot of folks today, you know, just with the phone, and they're looking at the phone, or they're they're not ready to go online to try to find this answer. How about let's just sort of think about it and talk about it and try something. I think that's another part of education that we can accentuate. Going forward. We

Matt Kirchner: 51:55

learn as much from failing and trying again and figuring out what works and what doesn't work, and over the course of that process and reaching out to people, having a lot of different individuals that can influence our thinking and show us a different way of doing things. That's the way so many of us learn. That's certainly the way that I learned. I've learned a ton from you so far. Charles on the TechEd podcast, we got time for just one last question. It's one we love to pose to every guest that we have here on the podcast. We're going to take you back a couple years, maybe a little bit more, before Booz Allen, before Lockheed Martin, before Motorola corporate research labs and so on, before you were spending your time at Morgan State University, go back to when you were 15 years old. You're a sophomore in high school. And if you could tell that young man one thing, give him one piece of advice, what would you tell him?

Charles Johnson-Bey: 52:38

So I was 15, I was at Poly probably getting ready for our big rivalry football game, and which we won, by the way. Again, what position I played, defensive end, okay, high school, nice and outside linebacker at Hopkins. So go hop that's the connections. But actually, this is getting to my answer is that the friendships and connections that I made when I was 15 are still friends and connections that I have today, and it's so much more valuable. So what I would tell my 15 year old self is to just maintain those friendships, also being older, maybe sit a little bit with the person who might be sitting by themselves, right, just to have a talk with them to see what's going on, to be that ear. You know, my mom would always say, you never know when someone just needs somebody to talk to or share with. I think I would tell myself to share more of that. I think that I would also tell myself that it's gonna be okay. I think when I was 15, that I, you know, I had some insecurities, all that, I think I would tell myself that it's gonna be okay to just keep driving. It's gonna be fine.

Matt Kirchner: 53:51

All really, really good advice I still have. If I made a list of my 10 Best Friends, there's probably four of them on the list, give or take, that were buddies of mine when we were kids, when we were, you know, in some cases, going all the way back to early grade school. And you know those friends, and they know you in ways that nobody else does. And so, yeah, maintaining those friendships, and it does take work. You know, every once while people will say how you still friend? Well, you got to work at it. You know, you text them, you call them, you get together. You know, you can go years, in some cases without seeing someone in person sometimes, but you just maintain that connection. And then when you do get back together, you pick up like it was yesterday. It's just absolutely, absolutely fascinating how that worked. Yeah, it works really well. That's the family you pick right, right, exactly, right. Yeah, you're stuck with your mom and dad. You're stuck with that sister that made you do math problems, or, in my case, the brother that I had bossed around when we were kids. You know, the second part of your answer about spending a little bit of time. You know, certainly I would have done things differently as a brother when I was growing up. You just maybe don't know any better, or what have you. And you know, as a friend to your friends, and then maybe as a friend of those people that you don't necessarily know as well. You never know when that kid sitting alone at lunch might need an ear to listen to or to listen to them, or, at a minimum, they don't need one more problem with some. My picking on them. So you know, to some Be careful about that side of it. Then finally, that whole idea of it's going to be okay. And it's amazing how many people tell me that that look, if I could go back in time and just tell myself, I don't know how this is all going to work out, but somehow it's going to work out. And just take a little bit of that anxiety off of there. Things have certainly worked out in your case, Charles, really, really fun to have you here on the TechEd podcast. You've got a great background. You're doing some amazing work at Irva, doing some great volunteer work in the world of STEM and technical education. And really appreciate you taking some time for us.

Charles Johnson-Bey: 55:31

Thanks. It's been my pleasure, Matt. I'd love to come back talk about some other stuff. So keep me on your list. Yeah.

Matt Kirchner: 55:36

Well, you're not coming back. I'm coming to that secret laboratory. So that's the way we're going to do it. But until then, certainly thank Dr Charles Johnson, Bey, the CO principal investigator from Irva, former senior vice president of blues, Allen Hamilton, now retired. Really, really fascinating conversation about the present and future of manufacturing, whether it's materials, whether it's process, whether it is supply chain, whether it is equipment, there's going to be some great things happening in manufacturing. One thing I can promise you is the TechEd podcast will be here to keep you up to date the whole way through. We will also be here with the best show notes in the business. You can find those at TechEd podcast.com/johnson Bay. This episode, show notes will be at TechEd podcast.com/j O, H, N, S, O, N, hyphen, B, E, y, when you're done there, check us out on social media. We are all over Instagram. You will find us on Facebook. You can track us down on Tick, tock, of course, on LinkedIn, wherever you go for social you will find the TechEd podcast, and you will find me right back here in the studio. One week from today, can't wait to talk to you next week. Until then, I'm Matt Kirkner, thanks for being with us.

Unknown: 56:49

You.