Bridging the Skills Gap: AI-Powered Knowledge Transfer in Manufacturing

Hey, everybody.

Hi.

Yeah, welcome to AI in Manufacturing/ChatGPT episode 4. Is it episode 4, 3, 5? Feels like they're going pretty quickly here. I wanted to welcome you all for joining. As you guys know, this is part of Industry 4.0 Club. Before jumping into this conversation today, I wanted to explain the purpose of the series. You know, as a club, our mission is to accelerate adoption of Industry 4.0. And if done properly, this is something that should be good for manufacturers, you know, improve profits, good for consumers, you know, you'll get stuff faster, and also good for folks on the shop floor. And so our goal really is to pull folks together from, you know, different parts of the solution here, you know, from people who are helping to promote the value of Industry 4.0, from influencers to trade shows to people who actually provide technology and services, and most importantly, practitioners. You know, at the end of the day, you know, the stories that come from those who are working on the shop floors, who are implementing this stuff, Those are the stories that we wanna elevate because that's the reality of, of, you know, what, what's happening. And I think in some cases the purpose of this show is to sort of break through some of the, the jargon and stuff that's out there with regards to what could be happening, the theory, you know, what, what sounds good and really get to the core of, you know, what people are doing. And for ChatGPT in particular, there's some pretty neat capability that I wanna explore on this call in particular. But like to do it in a way that is practitioner-centric. Really, when I say practitioners, there's people who are plant managers deploying tech in manufacturers, really for those who are there. And the way to accelerate adoption is really through behavior change, not through simply communicating. And ultimately, to change behavior, people first need to understand. So for today's conversation, wanted to dive into some of the areas that ChatGPT could help us with? In particular, you know, how can it help us capture knowledge from folks in our plants that have years and years of experience, where in some cases that knowledge, you know, once they leave the shop floor and retire after 30 years, you know, that disappears? This is one application. And then how can we also use this knowledge to train folks who are starting, you know, starting now, which is another huge challenge? So today I'll be chatting— our co-hosts are Mike Ungar and Katharine Brierley. Katherine and Mike together both fit into the practitioner category. They are manufacturers, manufacturing leaders who spent 25 years plus in industry, continuous improvement, digital transformation, really, you know, people first, driving improvements and knowing how to connect technology. So, you know, I'm super excited to chat with Mike and Katherine, with you both today. I You know, you guys have combined, was it 50 years? I'm not gonna say who has more or less of that.

That sounds awesome. Yeah, thanks, Martin.

It's quite a long time. That's a little bit scary.

Oh yeah, it's a long time.

But at some point, I think, you know, that's in manufacturing, you benefit from years of experience. You know, I think it's something, the more things that you've crashed into and experienced, that collection of knowledge is super valuable. And it's the difference between, you know, a production supervisor being able to go through a day full of chaos and be calm compared to a production supervisor fresh out of school kind of running from one thing to the next. My experience in manufacturing was, you know, the first 4 or 5 years of my career where I was that production supervisor running in circles, doing the wrong thing more often than not. So I think I'll— I hope to contribute to this conversation, but the depth of knowledge really is from from, you know, my early days on the shop floor and then, you know, since then spending most of my time on the dark side, on the provider side, providing tech here. Before we jump into it, just wanna remind for those who are listening that, you know, not only do we produce content on LinkedIn Live, we are also at live events having similar conversations. We're at the Manufacturing X.O conference Southeast On April 26th and 27th in Greenville. Mike's there and Catherine, you both are there, is that right? Yeah.

It's gonna be just the 26th, Mark.

Just the 26th. Okay, 26th in Greenville. Eastech in Springfield on May 16th and 18th. Tech Exchange in Chicago on the 22nd and 23rd. And then IME East on June 13th to 15th. And Catherine and myself, along with a few other previous guests are gonna be on stage talking about ChatGPT and, you know, some real ways that we can use it. So let's get into it. Thanks for, you know, listening through my monologue there. So first off, you know, how big of a problem is it to retain the knowledge of our senior staff? And is this a problem getting worse? Like, you know, is this a big enough problem to even warrant us chatting about it?

Well, it's a huge issue. Martin, so thanks for that long introduction, by the way. It's a huge issue. You know, uh, baby boomers are retiring. You know, when, when I worked at Michelin with our equipment, it took us, uh, it took several months to train a maintenance troubleshooter. And so we've got troubleshooters on the shop floor today that have been with the company for, for decades. When they walk out the door, I mean, there's things that they do to repair the equipment that only happen Once every year or 2. And so when you end up, uh, how do you train them on those sorts of once-off sort of activities? Well, it's experiential. And it would be wonderful today if there was a, you know, we'll talk about this, to capture that knowledge before it walked out the door. So it's a big deal. Um, and then the other part that goes with it obviously is getting interest in the young folks coming in the door to actually want to learn, learn from them as well.

Well, But what's the perspective of sort of the older maintenance tech who, you know, knows what's wrong by that, you know, by the sound of the machine? Like, what are they thinking? Like, how do they view this issue?

You know, I think for them, they, they aren't as worried about it. Obviously, they're close to retiring, so they don't, they don't, they don't— some of them are interested in passing on their knowledge. Others of them sort of just They just don't know how they become unconsciously competent. They don't really even know how to tell somebody when something's not right. They just know it's not right.

Yeah. I think from my experience, our senior operators that historically we as managers have believed that what is what is in their head and the way they run a production line is the best way. And historically, this problem that we have here, this transfer of knowledge from our senior operators to the new generation that's coming in, at least our plants at Cooley Group, has been looked at it has been looked at as a, as a huge issue. Um, but now that we have more information about our process, and now that we have, have turned sort of analog information into digital information, we have real-time information now. We are seeing that there's There's more to it than what's in our senior operators' heads as the best way. There's actually, you know, that's actually not the best way with, you know, with this new information. So, you know, I think that, you know, now that we have this visibility, we have the visibility as managers, our operators have the same visibility. everyone in the operation is seeing that there are some things about our process that we've never known, that, you know, that we're just learning, that, you know, these insights are residing in these datasets once we figure out how, you know, how, how to leverage these datasets. And there is, you know, I've seen a level of, of maybe, you know, some, some intimidation, you know, that, that this information is You know, there's a little bit of that going on with some of our senior operators.

So, Amelia, I'd love to dive into this because, you know, one of the things that shocked me when I hit the shop floor was how different the reality of the shop floor was compared to what it looked like through the data. And what, you know, what it made me realize is that the data, and this was, you know, 20 years ago or 15, 20 years ago, that the data, you know, only, you know, scratched the surface of what was actually happening on the shop floor. And then even sort of the narrative of the maintenance techs not, you know, not quite understanding it to the right level. I'm wondering if it's like, you know, if there's actually sort of a lot of truth on both sides, because I think, you know, as from, you know, going through engineering school, I can— I was completely on the side of go to the data, you know, instincts have no place when it comes to problem solving. The data will tell us how to solve that problem. And I think my view after having spent several years on the shop floor is like, you know, there's a combination of facts and knowledge and, you know, magic here. And I think I'm curious, you know, where that line is. And I'm curious for the, you know, for your folks at the Cooley Group just to get into the minds of your senior maintenance techs? Like, are— like, how do they view this problem? And I guess, you know, what do they think the solution is in particular with regards to capturing the right way to do something?

You know, I think it's— and I think you just alluded to this— there's going to end up being a combination when it comes to our senior operators or our maintenance techs, the guys and the gals that have, you know, 30-plus years of experience. When it comes to that transfer of knowledge from a manufacturing management standpoint, there are things that— there are, you know, parts of the job that, you know, will need to be that experience will need to be transferred using classical methods. Whether it be something like, okay, how to start up a line after it's been down for a changeover. The physical steps that need to take place for that piece of work, that knowledge or that experience will probably be transferred in the classic way. Hands-on, on-the-job training. These are the steps. that, that, you know, you, you go through, watch me, you know, watch me do it. Now let's do it together. Now I will watch you, you know, that, that classic on-the-job.

Yeah.

But then there are going to be other aspects of, um, the, the knowledge that it takes to be able to run a certain product that I believe will come more from a digital type tool or some piece of augmented intelligence that we will be able to find in our data, that part of the job will transition over to this new way of thinking in terms of experience or knowledge transfer.

You bring up a great point because when we used to, because a lot of these guys, as I mentioned, were unconsciously competent. And so we used to have to have somebody watch them do their work, watch them do their repairs, for example, in order for them to be able to say, okay, so what did you just do there and why?

Yeah.

Okay. And they would write it down and capture it and we would become— they needed help to actually be able to train somebody else. Well, and then, but the problem is that we would try to document that on paper and get lost. Now, if we could take that information, that data, they still may be unconsciously competent, but then translate that into an AI model. Now we have a chance of really having it become something historical and a record that will now— we can use to more quickly help new folks coming in and also maybe even get more predictive with AI. So I think there's some real opportunity here to do things differently going forward.

So maybe just to keep it back at what's been done so far, because in some ways, a lot of the talk about AI in manufacturing is just talk at this point here. Like, you know, the— I would say that the vast majority and what we're talking about here today is not stuff based on deep experience. So, you know, some of the most powerful things that have come out are, you know, came out a few weeks ago. So I guess what are the things that we've done successfully in the past? And I would say like, let's keep the definition of success. I'm curious about the definition of success from the perspective of the, you know, maintenance leads, you know, and balance that with, you know, success seen at a corporate level. So what are the things you mentioned— documentation— what are the things that you guys have, you both have seen work well with regards to knowledge transfer from, from our older employees?

You know, I'll just maybe jump into the start one. It's just job shadowing. In other words, you know, we bring a new guy person in, we put them with someone who's a veteran. And they shadowed them. They basically were on the same crew, 2 of them together, and were together for months on end. I mean, it seems time intensive, but it works.

Yeah, it does. And that's definitely been our approach at Cooley Group for years and years. But I do see that changing. That's been our main strategy is shadowing, on-the-job training. But when we have a senior operator who is what we call like a lead operator, right? You know, that's the person who is, you know, setting up our line and, you know, ultimately responsible at the production level of, you know, how that line runs. You know, if there is someone shadowing him or her, it's, it's going to take— it's, it's going to take a long time potentially. I mean, if this is a, you know, is it— this is a high skill level type of position, uh, you know, lots of different products, lots of different scenarios, you know, things run differently every that, you know, that shadowing situation has for us taken a long time. You know, it could take up to a year for, you know, that person who is shadowing to, to gain that knowledge. What, you know, we have found that, you know, moving into, uh, a, a data-driven, uh, environment, even for our operators, um, you know, what, what can we do with these digital tools that we are building with them That, that will speed up that process. And what we have started with is, okay, what, you know, how can we do better at, you know, setting the expectation so that when an operator goes home at the end of the day, he or she knows without, you know, us telling him or her whether or not they were successful? And, you know, that is done in, you know, in, in real time in a, in a digital format, you know, in the form of operational metrics at the production level.

So I think, you know, highlighting almost both the things that we've done for decades as well as some new advancements. So, you know, the idea that we shadow new folks coming in, document the process, these are things that we've been able to do for a long time. And just as you were talking about, you know, the process for learning something, I'm sure many of us have You know, tried to learn something from YouTube and seen how, how easy it is. Like, I play piano and guitar and just sort of transitioning from how I used to do it to just trying to like, how are they playing this? To seeing a video where they show exactly how somebody was playing something. So this, I think there's some neat tech out there like, you know, Tulip and Polka and Dozuki that kind of have taken this idea that you can learn from video and help these new folks learn the process. One of the things that is a challenge with these videos is that you can't really interact with it. It's not like standing next to that. And I think that the, you know, for many of us, there's a point at which during the learning process where you want to ask a question, ask why. And I think that's one of the interesting things with ChatGPT-like technology that's complementary to the things that, you know, some of the tech that we brought in here is this ability to not only interact with people during the learning process, but also learn from the interactions. So one of the things that is sort of pretty exciting about, you know, these sort of chatbots, and I'll sort of like, I'll group ChatGPT into a chatbot, you know, chatting with an instruction manual is interesting, but what's even more interesting if you're looking to actually get people to the point where they understand a process is to monitor the nature of those chats and to even see like, are you having a hard time picking up the process? And that feedback, you know, the ability to actually harness not only the insights of the trainer but the insights of the trainee, that's really exciting because I think, you know, right now we've sort of taken the approach in manufacturing and in many other areas where we kind of create like a one-way, one-size-fits-all approach to training. And then some take a week, some take a year, like you were alluding to earlier, Catherine. But how do we get it so new folks who are coming in the plant— and now it's like they're coming in all the time because people aren't staying for 40 years, they're staying for 3 years, for 4 years— how do you ramp them up quickly? And I think, you know, it's exciting to potentially have the ability to, you know, tailor, you know, learning to the style of somebody. And I haven't seen— have you guys seen anything like that yet, or is that still— That's one of the things I see as a potential for the ChatGPT. So I'm curious to get your thoughts here. And sort of, this is more of an open-ended question where I didn't— I wouldn't—

Yeah, so I've seen it more with, you know, when you work with somebody, listen, so I guess we'll separate 2 things here. From a personal development perspective, I've seen very much people chart their own path. And use whatever tools they want to, whether that's YouTube or, as we'll see with ChatGPT, or whatever they would like to do to develop themselves. I haven't seen it though so much when it comes to like learning the job on the shop floor. And I think the question is, how do you make that transition to where, you know, the individual has a, uh, if you will, a self-guided training plan? And, you know, certain dates that they're required to be competent at certain levels when they're There's somebody there to help them, but basically they're driving the train rather than the plant driving the learning process. I think that's an interesting evolution to think about.

There are definitely products out there that's utilizing this technology on the manufacturing plant floor. I haven't actually seen it used yet, but definitely seen demos, you know, some of these trade shows that, that you go to and, you know, products that, that kind of come your way, you know, when, when you are a practitioner. Um, so yeah, I mean, it's, it's coming. It, it all depends on, you know, what, what the fit is, you know, what, what's the— what is the problem that, that we're trying to solve here? For, you know, with some sort of chatbot-type feature or ChatGPT, you know, going out and pulling information from, you know, from the end, you know, everything that's available that has anything to do with what you're working on or what, you know, your operators are working on or the problem that they're trying to solve. But yeah, I mean, there is, you know, if you've got an operator out there on, you know, on night shift and let's say he or she is newly trained, so, you know, they're out there, you know, on their own and they run into an issue, you know, the level of accessibility to information or another person as a support person that they can have immediately is just— I mean, it sounds like it's a perfect fit for these types of problems that arise in manufacturing.

Yeah, I wonder, and maybe just looping in, I think, Mike, you said something that was pretty kind of funny, but shows the way we roll in manufacturing, how, you know, there's all this stuff that we've learned to do in our personal lives and somehow it's not on the shop floor. Why is it that manufacturing plants are stuck in the '80s? And I would say that there's like, I don't know how many plants that if we walked into and took a snapshot of like 1987 and 2023, how different they'd be. You know, there is a reluctance to bring in tech, but yeah, I think this idea that, you know, especially for manufacturing organizations that over the last decade has really thinned out the middle management and engineering layer, you know, what happens is we have folks who are less experienced now put in a position where they kind of need to figure it out for themselves. And I think that is like risky in many ways. You know, one of the things here kind of getting into, I wanted to chat about some of the risks about deploying this stuff or even experimenting with it. And there's, of course, the the classic risk of don't share confidential information with, you know, a piece of software that you just found on the internet that seems exciting. Just don't do that. And I think, you know, there's— I'm sure there's been other shows about how you shouldn't do that, and I think we can skip over that. But I think one of the aspects of this that I'm curious to get your thoughts on is sort of related to the 8th waste in manufacturing, which is the waste of human capacity or capability or ingenuity. And, you know, one of the things that could happen, you know, as you start to bring in technology that, you know, in some ways can do your homework for you, and I guess in a manufacturing context, you know, there is a world where folks kind of turn their brain off, which is sort of counter to, say, Toyota, where, you know, you're not there to work, you're there to think. I'm curious, you know, how about, you know, your thoughts on the risk of Effectively creating an organization of folks who are not thinking, just moving from one thing to the next, leaning on this crutch of AI to, you know, answer questions. So I'm curious to both of you, like, what— how big of a risk is this? And, you know, what should we do about it?

You know, I think when you're dealing with, you know, large pieces of equipment and, you know, potentially running a process that can create a lot of scrap, which can be very expensive. We're talking about if our operators or if we're making decisions based on information that's not— may not be correct. Yeah, the risk is huge. So we need to be careful going into this. If we put this type of tool in our operators' hands and this is this is your, this is your support system. You know, we have to, we have to be, be careful with the, with the accuracy and, you know, quality of the, of the information that's coming in. You know, when we start talking about AI and modeling processes, I mean, I've never seen a process model that's 100% accurate. I mean, you know, we're doing good with probably 80% accuracy. You know, that, that's the information that AI is telling us to run by. So 20% of the time, it could not work. So yeah, I mean, we have to be careful.

But maybe just follow-up question. So how do we make sure that what happens on the shop floor isn't the equivalent of our kids handing in their homework using ChatGPT? Like, how do we make sure that that doesn't become the new reality?

A lot of that just comes through coaching and conversation so that people understand it's only a tool to help them succeed, not a crutch. And I think you just have to have that conversation. And that goes back to coaching. We talked about, or in the shop floor, you're always out, should be out there coaching and looking at how to make things better. And so if you're really, as a leader, engaged with your team on the floor, I think it's going to happen. I think it's that level of engagement to say, show me what you did, how'd you do it? I remember, and this isn't a new problem, right? We had gone through, I remember a situation where one time on the shop floor, we had a process for finding the root cause of a problem. And the person followed the process to the end and then went ahead and took the last step of the process. And we said, well, why did you do that? Because it didn't appear that it was needed. And it was just because they had learned the steps, but they hadn't thought about, do I really need to do the steps? In other words, it didn't quite apply. So I think that same thought process, that same sort of understanding and coaching and show me has to be going on whether you're using technology or not using technology. And that's how you overcome that.

Yeah, it's really a double-edged sword. Like, you know, we want to empower our our operators on the production floor with as many tools as we can so that they can make the right decision. But, you know, something like this may put them off on an island somewhere where, you know, they're, you know, they're just using that automated information coming in to make decisions without the, you know, full collaboration in some sort of, you know, coaching situation, like Mike was just saying, you know, we still have to have a level of team collaboration there when it comes to, you know, proper decision-making.

I think, like, ultimately, what we're, we're not trying to necessarily replace problem-solving or replace, you know, creativity. And I, when I was you know, early in my career as an engineer, I was working in the blinds industry and we had these bamboo shades. I don't know if you guys know bamboo shades, kind of like—

Yeah.

And then as we were building them, you needed to put a grommet in the bamboo to basically let the strings run through that would pull up the blind or the shade. And we were built— we were going through the process and then— and we had this process where to punch the grommet, you had to put a whole bunch of glue And then you'd punch through the glue, because the idea here is that if you punch a grommet in bamboo, you want to make sure it's all glued together so that, you know, it doesn't fall apart. And then at some point, we're sort of looking at this process, and it takes a long time because you put the glue on and then they're trying to dry it. And then somebody in the group said, like, hold on a second, do we really need that glue? And, and we saw that, that's, that's a good idea. So we tried it out, and apparently you don't need the glue. You can just punch that grommet right into the bamboo shade And that saved like 5 minutes in the process. So if you think about, you know, the— if we are just mindless— mindlessly following the process, we're going to follow that process that involves you putting some hot glue, you know, trying to wait for, well, 5 minutes of process time, you stick in the grommet, where, you know, at some point we want to make sure that, you know, folks on the shop floor, all folks are dialed into like looking for opportunities. And I think that there's an important thing here where as we run out these, deploy these new technologies, we want to make sure that, you know, AI is assisting us and we aren't AI's assistants. And I think that if you don't pay attention, you're going to end up as the assistant, you know, the, the, the, the human assistant to the AI that's going to be doing stuff that's probably, you know, that's okay but not what it could be. Sorry.

We used, yeah, we used to, it's, you're, when you describe that process, it, you know, having deployed lean, it's the same thing about waste elimination. The more waste elimination you The more waste you eliminate, the more you see. And the idea here, I think, with the technology is to get you past the initial things that we know so that you can then think about and delve into what we don't know yet. And that's, I think that's kind of what you're saying, where it doesn't become, I'm done, but it gets you more quickly to the point, excuse me, where you're now going into the real problem solving, to the real creative side of what you need to be doing on the floor.

Mm-hmm. Yeah, you know, AI, I guess, officially stands for artificial intelligence, but, you know, it may be sort of better to call it augmented intelligence, right? It's, it's, it's, it's, it's just, you know, that, that piece of information that we've just, you know, we've just never known that's, you know, you know, kind of been sitting there in our data. And it's just a You know, extension of what, you know, we or operators or management team knows about our process.

It'd be interesting to have these conversations to get rid of all terms, AI, augmented intelligence. At some point, if we were speaking to a maintenance tech with 30 years experience, you know, ultimately what they're interested in is something that makes their job easier. You know, I know of senior folks where they kind of want to retire. And the plant manager won't let them retire because they just know that nobody's coming. So I think, you know, for us to really, you know, if, you know, as a club and just as a sort of like an industry in general, really finding ways to sort of move beyond the terms here and ultimately capture the imagination of folks on the shop floor. But I— So I guess as we sort of move to wrap things up here, you know, I'm I'm curious to get your thoughts on what manufacturers should be doing now. And Kathryn, I know you just went to Hannover Messe, the trade show, and Mike, you're kind of conference hopping. You know, from my perspective, it seems like, you know, given the capability of this technology, everybody is trying to figure out how to use it. And, you know, it definitely warrants Some attention. I think one of the challenges with this kind of technology is that it has so much capability that's kind of directionless, where if you take capability and apply it in a useless direction, you're just going to create more waste and distraction. So it's kind of a— it's a strange challenge because usually things are so complicated that they move slowly, and that's different from this here. So I guess from a practical— from a, you know, practitioners to practitioners, practitioners, um, you know, Catherine and Mike, let me start with Catherine. Like, what, what should people be doing now so that they don't get left behind?

Yeah, you know, I think the— at least, at least for us at Cooley Group and the, the position that, that I have, it is so important to understand what, what is out there. It is so important to be as educated as possible as to, you know, the, the technologies that are on the horizon, you know, this ChatGPT and digital twins and, you know, uh, the, the industrial metaverse. And, you know, what, what does that even mean? And, you know, how can, how can that, how will that ever be applied to, you know, to, to, to your organization? It's just, it's some pretty cool stuff. And, you know, it's just to, just to, just to understand, just to, just to be aware and to, and to stay on top of how those those technologies are progressing, and especially when they start to be utilized and, you know, some of these, these real use cases come about. And other than that, it's just, it's really, um, you know, listing out the problems that, that you want to solve within your organization and finding that best fit. You, you have to understand your fit. And, you know, a, a piece of technology for, you know, a, a huge company with, you know, awesome budgets is totally different than a small to medium-sized, you know, company like the one that I work for. So there's, you know, there's all kinds of things to consider there, but it's really understanding what problems you want to solve and prioritize those.

And I think just mentioning the size of the company, there's always going to be a difference between what the big firms do with the huge budgets. And in some ways, you know, as I sort of said off the top here, our mission is to accelerate adoption of Industry 4. And in some ways, the way to create momentum is really amongst the small and mid-sized companies. The one that doesn't have 50 engineers at each plant trying to optimize a process where it's, you know, a supervisor, a couple of engineers, plant manager, and a whole bunch of folks on the shop floor. I think that's, that's really where, you know, how we're going to accelerate things. Mike, Mike, what are your thoughts?

Yeah, you know, Katherine, you hit it on the head. There are 2 things I think that are really important. One is you know, focus on solving your problems and bringing in and then challenging yourself to go find the tools, maybe new tools that you wouldn't have considered before to help you do that. But always start with your problem, because if you focus on the technology as you're going in, you're going to end up perhaps not getting the value from it and then really hurting the understanding of the potential. But along with that, and I guess kind of the side Yeah, and I want to come back to your point about large companies and smaller ones. If you're large enough to have a little lab where people can tinker with the technology to learn what its capability is, you know, so that when you need to use it, you understand how to use it, that's great. But if you're a small to mid-sized company, you need to get, in my opinion, a group of vendors, if you will, or providers who can help you with that. In other words, they can they can take you into another client, or they can take you someplace where you can see how it's been applied. So you understand when you have a problem similar, how to pull the tool in. So I think there's kind of both elements. Don't try to solve something, don't try to use the technology for technology's sake, solve a problem, but then also spend some time learning enough about it so that you can actually know when the right time is to apply it.

I wonder with the— so the word Problem, I think we— that one is really critical. And in some ways, in order to solve a problem, it needs to be fully understood. And one of the ways to both get better at solving problems and better at creating engagement is to go to our front lines and, and ask them, ask them about their process, ask them about their experience. So you get to the point where the problem is fully understood and agreed to by all levels of the organization. And I think this is kind of a step sometimes as organizations where we, we maybe skip over truly and deeply understanding the problem before experimenting with different ways to solve it. You know, digging deep into the problem, in particular by chatting with folks on the shop floor, is something that will facilitate solving the problem with or without technology. But also, if there's an agreement about what the problem is, Then, you know, it's not a big lift to then agree to how to begin to solve it. And I think that those are some of the things that are often easier in smaller organizations where, you know, there's less distance between, say, the plant manager and a new operator just starting on the packaging line or packing line, you know, this week. So as we sort of wind down, you know, our chat today, I want to throw, you know, Katherine, to you first, then Mike. Final thoughts, you know, is there anything that we should have spoke about that we shouldn't? And maybe also like, you know, things that we should explore in the future. Final thoughts, Catherine?

You know, I think you just kind of hit on a really important thing that I truly believe in, is the, you know, all these technologies that we talk about will get us to the next level. There are definitely some that Cooley Group are, you know, we're getting serious about We're trying to figure out creatively how to do these things that we know we need in order to get to the next level. But, you know, these technologies will never replace our people. You know, that's not what, you know, what we want to use these technologies for anyway. But, you know, just solving problems, it's always going to be a marriage between these new technologies and our people, whether they be at the operator level or, you know, middle management or executives when it comes to solving problems. It's gonna be both. And, you know, using both of those strategies will make us way better than what we are now.

And I think bringing in new technologies is something that manufacturers are actually quite good at. I think the only difference here is that, you know, some of the ones that have come through recently have come with a whole bunch of marketing dollars, which make it sound like this big, massive thing that's different from from. But manufacturers, like, new stuff is rolling in all the time. Sometimes it's not as exciting as this stuff. So in some ways, I think manufacturers are good at change. They're good at integrating new technology and process and people. I think that's, that's an asset. Mike?

Yeah, I was gonna— you mentioned the word change. I was gonna really— that's where I was gonna speak to you for a second. You know, being exposed to something new can be scary. And if you're not familiar with it, You know, like, how's— like, Kevin, you mentioned, uh, you know, we're not going to be replacing operators with the technology per se. Bring the technology in, uh, and get people comfortable with it. Let them understand that it isn't, you know, that it's friendly and that there's something that they can take advantage of. I think there's a lot of that involved there. And involve them early. Involve each person who's affected by it early in the process, and you'll break down a lot of barriers and resistance to change. So I think the biggest thing for me is You know, the word technology can be scary, especially if you don't live in that space. The reality is bring it in close to you so you, you become comfortable with it, understand how it can help you, and then that'll make it a lot easier to move forward and, and take advantage of it.

I think that goes back to our people, process, tools mantra at Industry 4.0 Club, where, you know, you start with people, doesn't need tech. You, you lock down your process, that doesn't need any tech. And then at some point If your folks on the shop floor, you know, believe that improvement is necessary, believe that improvement's possible, are willing to take actions to drive improvements, work on the process, and then at some point, you know, it'll be obvious when you should bring in tools. The kinds of tools that are coming right now are pretty neat and are actually way more intuitive to use than a lot of the stuff that, you know, they've been using for years, which is kind of what is pretty exciting. So, you know, I wanna, you know, Mike, Katherine, thanks for chatting with me today. It's great to have, you know, 2/3 practitioners. I think I considered myself half-half practitioner, but it's been a while since I've worked on the shop floor. And thanks to all who have listened and for spending time with us today. So we'll be continuing this ChatGPT/AI manufacturing series kind of indefinitely, or I guess until you guys on LinkedIn complain about it, you know, it's sort of— outliving its welcome. And again, we'll bring in representatives from those who are promoting Industry 4.0, for those who are providing to Industry 4.0, but most importantly, those who are actually practitioners who are working at deploying this stuff, who understand the experience of folks who are actually trying to continuously improve with technology. We also host conversations on Tuesday and Wednesday with other topics alternating around the themes of women in manufacturing, future of manufacturing, and fireside chats with industry leaders. If you want to know what we're up to, follow our LinkedIn page. You can also check out our new, our snazzy new potentially AI-built website, www.industry40club.com. Yeah, and drop us a line, send us a note on LinkedIn. You know, we're happy to chat. We're excited about anybody who is as excited as we are about some of these changes. Um, thanks all, and Mike, you can push the button now that does the countdown.

Thanks, Martin.

Thank you.