Integrating Systems & Processes for Smart Manufacturing: IME West Preview

Hello and welcome to the Industry 4.0 Club's fireside chat. My name is Mike Yost. one of the co-founders of both Bennett AI and the Industry 4.0 Club. I'll be one of your hosts today along with Scott Phillips, another co-founder of the Industry 4.0 Club. We are joined here today by Konrad Leyva, who's the Vice President of Ecosystem and Workforce Education at SESAME, which is the US's National Institute on Smart Manufacturing. We're gonna discuss connecting systems and processes. And we will get into that in just a minute. But first of all, if you are new to the Industry 4.0 Club, we'd like to welcome you. We're glad that you are here with us. Our purpose is to engage the power of our diverse worldwide talent to accelerate the global evolution to Industry 4.0, and we need voices like yours. We accomplish this by hosting and supporting respectful, thoughtful, and interdisciplinary open conversations and by creating quality content on wide-ranging Industry 4.0 topics so we can expand the reach of our community. So we're glad that you decided to join us here today for this fireside chat with Konrad. Konrad's gonna be sharing his perspective on the value and the approach to connecting systems and processes, and it's a prelude to him speaking on this topic in 2 weeks at the IME West Expo in Southern California. So my co-host today is Scott Phillips, again, a Co-founder of Industry 4.0 Club and the guy I want to be when I grow up. And so, Scott, I would ask you to please introduce yourself, and if you could set the stage for Konrad and for the upcoming IME West Expo, that would be great.

Great, thanks, Mike. Appreciate it. Scott Phillips here, like Mike, co-founder of the Industry 4.0 Club. Been involved for the last year or so, really enjoyed it. And the funnest part is doing these live interviews. I've met Konrad before, heard him speak many times. He's always engaging, engaging and interesting and brings a lot of personal experience, so I'm looking forward to it. When I'm not working with the I4O Club, what I do is I manage the Industry 4.0 Accelerator for the state of Michigan. We recruit Industry 4.0 technologies to Michigan to help our manufacturers. And then I have an assessment tool called I4Score where I help manufacturers do Industry 4.0 assessments. So that's me, and with that, I'm going to turn it over to Konrad to do a brief introduction of himself.

Hi, and I'm Conrad Leyva, VP of Ecosystem and Workforce Development at SESMI. SESMI, the Smart Manufacturing Institute, is a public-private partnership and consortia funded by the US government to accelerate adoption of smart manufacturing technologies. I'm an industrial engineer working in manufacturing systems. Over 30 years working on smart manufacturing over 10 years. That's my brief introduction.

Great. Well, thanks, Conrad. I really enjoy it. For anybody listening, feel free as we go along here to enter any questions you have for Conrad in the chat box. If we get any questions, we'll pop them on the screen and Make sure we address them. So feel free to post comments at any time. So Conrad, this is my first time getting to co-opt the show. I'm I'm here in Michigan, and anytime I can go west in January, it's great. This show that's coming up is called IME West. It's interesting because it's actually five different shows kind of packed together in Anaheim, California from February seventh to the ninth. The I four O Club is partnering with the event organizer to manage the one of the 5 shows called ITX West. And of course, you're one of the session speakers at ITX, or at the ATX show. So I think you're speaking on Wednesday, if I remember right.

On the 8th.

On the 8th, correct. Yeah. So anyways, the goal here was to ask you to, you know, tease us a little bit, give us an overview of your topic on that day. What are you going to be speaking about? And I'm sure Mike and I will have some questions, and hopefully we'll get some questions from the audience.

Thank you, Scott. Well, I'm going to be talking about smart manufacturing, not just being about machines and systems. That smart manufacturing is really about how we empower the team with highly connected business processes across the organization. I mean, I will discuss how connected things Connected systems and connected teams come together to deliver highly connected work processes in manufacturing organizations.

Well, that's fantastic setup. I think as we get started with that, and again, we don't want to give the whole presentation here. Folks can come see you in Anaheim in a couple of weeks. watch the show afterwards, however they want to do that. But if we in this conversation sort of break it up into those groups you talked about, right? You talked about connected things, you talked about connected systems, connected teams, and then the value and the work of connecting all of them through processes. So maybe we start by just giving us, like, drill into what is a connected thing? How do you frame that up? What do you put in that bucket?

Yeah. Well, so we've been talking about the Internet of Things, right? And that expression has become popular. And I hope everybody knows about it, right? But what are we talking about is really data, that at the center of smart manufacturing is data. And not just data, but real-time data. We Because managers want to be able to view, analyze, and act upon time-sensitive information with greater speed and accuracy. So we want to be information-driven decision-making, you know, so we want to have that information ready to make better decisions. And it can't be information that's 3 days old, or, or you get that manager That manager that looks at this, like, oh, caramba, we need to do something about this right away. And by the time they go over there to solve the problem, they find out that somebody's already been working on it. And it just creates a lot of inefficiency. That's just one kind of inefficiency. And, and the other important thing about this and why now is because it's become very affordable. You know, I mean, sensors have become very pervasive, right? I mean, just some on our smartphones, right? We got like 6 sensors in there or something that are helping us with navigation apps, with health monitoring apps. And the same thing is happening on the shop floor, where, where we got all the— we can sensorize around machines and and monitor the health of a machine, monitor the health of the whole process. And we can do that with sensors that are a few hundred dollars. And with middleware and edge gateways that are around $3,000 allow us to get that data into our systems. And, and all kinds of data, right? Data like That environmental, about the running conditions that really let us tackle all kinds of problems.

So I was on a webcast not long ago and moderating a panel and a question came in. There was a supply chain leader from the University of Washington, actually, their healthcare system. And question was asked of him of talking about things you're talking about, having access to data. There's sensors all over the place. There's Data flying around like crazy. And he said, how can— the question was, how can you not know where everything is in your supply chain when I can order a t-shirt from some guy with a business, you know, running a t-shirt shop out of his basement, and he can tell me down to the minute when I'm gonna get the delivery of my t-shirt? So obviously this is all very simple, right? There's— it's really easy to take all the data and make sense out of it. But no, maybe address the issue of just because we have the data, right? How do you, how do you go about making use of the data?

Well, first of all, for your example, that guy that just gave him that answer right away did not necessarily have all the real actual data at his fingertips. So let's demystify some of that, right? That a lot of those decisions are still being made on like, I know that on the average, it takes me to this lead time to get you this product. And a lot of things in that kind of lead time and averages, time to get you things, is driving a lot of processes today. And what we're trying to get is to where it's more real time. You might have a standard lead time of a day, 2 days to do something, but what is it taking today? Because, you know, You know, 25% of your staff, maybe there was a flu running through there, and things are clogged up somewhere, or your machine went down. And, and maybe that typical lead time is not true today as it was yesterday. So, so really having that real-time level of data, it's not as typical as you might think.

Gotcha, gotcha. It might be a little harder also to get the information off the shop floor, right? The type of environment that we're dealing with, right? And so—

This is Scott. Just to build upon Mike's question a little bit, Conrad, you mentioned sensors, then the price of sensors and becoming ubiquitous. Given Sesame, I imagine you're dealing with a lot of small-medium manufacturers across the US who have legacy equipment that may or may not be internet-enabled. And so can you talk about that? How often is just getting censored up and getting connected— is that an issue 20% of the time or 80% of the time for the quote-unquote average small, small-medium?

Yeah, I mean, I mean, so, but most of our projects that we're working on I would say 80% of the projects we're working on right now at Sesame have to do with small-medium manufacturers. And it has to do with even large manufacturers putting sensors and collecting data around legacy machines. So it's very rare that we're asking people that the premise is buy a new machine that has all kinds of new native sensors and native data capabilities to to get to smart manufacturing, where what we're doing is usually putting a layer of sensors on top of existing machines to do not— to measure environmental conditions. Yes, you know, around a process, because those can affect the process, but also even inside of machines to, to, to add that capability. So we can We can add sensors that are looking at position of things inside of these NC machines and help with some of the guidance. We can add vibration, proximity sensors, pressure sensors, temperature sensors. And a lot of this can be done not just to help the machine process, but also to help the people processes around the machine.

Scott, go ahead, Mike.

No, no, go ahead, Scott, that's fine.

Well, just because you mentioned the people processes and earlier you mentioned connected teams and connected systems and people, I think, or teams. In our work here at the Accelerator, we see a lot of what I would say connected worker technologies, digital work instructions. That seems to be a Big growing category, augmented tools and things like that. Is that something you're seeing too from a connectivity? As we tend to think about machines and materials and all that, but what's going on with connected worker?

I think it's really in the last 5 years really exploded. I think there was, if I look 10 years back, a lot of the activity was around Let's put in big automation, big machines, and, and, and there was really less— let's automate more with an intention of lowering the labor force. And I think now there's more of an impetus on smart manufacturing being about helping, helping connect people, connect the human to the equation. Not— so not only among each other, but in all the orchestration that needs to be done for processes in handovers of data from people to people in different departments, but also connecting people to the machines and the systems. Basically equipping people with better tools to do their jobs. It's not just about trying to replace people, but it's about, about making— helping people do more work, be more flexible, learn new jobs faster, And be more productive, agile, and safe in the work environment.

So does that come back then to your first point about connected things and all these sensors and stuff being able to give all the data they produce, being able to be more useful for the workers themselves?

Yeah, yeah. I mean, so what are we doing with this data? We— if to make the data really achieve that high value from the data, we need to get that data in front of people. So as soon as possible, I mean, that could be on the screen they have next to them. That could be if it's a roaming kind of employee, right on their phone. If— is it through some of these fancy headsets with Heads-up displays. Is it projected on the workbench? Hints. Is it that monitor on the hallway telling us that we're on schedule? All these different ways that we're putting information in front of the person for the job. Is it the best way? Hands-free way? To help him do that job, get the right information to him at the right time, right? We say those things, but it's the, the most important thing is to get that information to the people. And, and people are realizing much higher productivity levels and usefulness from that kind of thing.

Comrade, I got a question from the audience here. What are the challenges you see when applying all 3, safety, education, and performance with the connected worker?

Challenges. Well, I think maybe there still is a little resistance, like, are you helping me or are you The overlord overseeing me and, and trying to use this as a, as a new whip, you know, to, to get me to work harder, uh, versus it should be more about helping me work smarter. You know, there's the people that tend to see a machine and have even some of the mechanical safety features like, okay, the first thing I'm going to do is get rid of this guard here. It's getting in my way of doing this job quicker. I think some of that with the digital assistance tool has also been a challenge. A lot of these data-driven processes, some of the challenges, and we see it on the education side, are soft skills. Do folks have some of the troubleshooting, the communication skills?

Yes.

Are they open to to asking for help when they need it. Even if you make remote assistance available, are they shy about it? Do they feel empowered or do they feel it's going to be viewed negatively on them? The cultural impact of this should not be underestimated.

Excellent. Thank you, Conrad. I just want to take a quick second and reset the room here in case folks have joined us since we started. This is the Fireside chat for the Industry 4.0 Club. Mike Yost and Scott Philip here tapping into the wisdom of Konrad Leber, the VP of Ecosystem and Workforce Development at SESAME, talking about connecting systems and processes. So far we've been talking about the things, we've been talking about the people, the teams, haven't really talked about the systems. Maybe talk a little bit about that and how it plays in with what you've been discussing so far.

Yeah.

Well, I have to mention, right, that the convergence of OT, operational technology, or automation technology and information technology, it's a big overall theme in smart manufacturing. I mean, so we had this technology to run machines in the shop, was really evolved separately in their own networks, with different people working on it than, than the systems that evolved in the, in the office, and the IT team maintained for all the office processes. And, but to really realize this vision of having that data available to everybody, we really need to connect these things. And they've been becoming easier to connect because some of those IT ways to communicate and connect are making its way into the plant to where now we can work more in common networks and share and that the data can flow easier. I'm not saying it's all— we've figured it all out. There's still a lot of issues with interoperability. And, and but there's a lot of things available now. Another thing available is for scalability, as cloud and edge computing continue to get better. So we're, we've been now for several years getting used to cloud, to infrastructure as a service, software as a service, and, and all the ease of, especially for smaller manufacturers, of having someone else maintain these systems, build the redundancy, the backup, provide all those kinds of services along with providing the solutions. And but there were things that traditionally we couldn't do in the cloud, we had to do on-premise. But, and all that is getting more packaged into commercial applications that are becoming affordable to small manufacturing. And under the umbrella of edge computing, there's a lot we can do. to handle things that need— that are— they need— have late, more tight latency requirements. We can do them on-premise. But we can do it in a way that we're prepping that data, filtering, and aggregating, and sending into the cloud what we need to do for other kinds of enterprise analytics.

So just a quick question to stick with the systems piece for a minute. Are you seeing nowadays, and are people that are considering Industry 4.0, when you talk about systems, is it, you know, a big all-encompassing monolithic system that runs everything from, you know, beginning of the order to the end of the order and, you know, all that kind of stuff, like a big bang holistic system, or are you talking more— solving a problem, solutions to, you know, improve quality or help us with our maintenance or help us, you know, improve efficiency or throughput. Is there— I guess, what's your vision of what— how do you define systems from a scale?

Yeah, yeah. I mean, many organizations for years have been trying to say, let's standardize on a single vendor, you know, let's get that ERP system to do everything for us. And it sounds appealing, right? Have a single, single solution. And let's keep adding more functionality and ask that vendor to keep adding more and more functionality to that. And then we don't need to worry about integrating multiple systems and things like that, right? So that was that. I think now there's been a shift. And we realize that these monolithic, huge systems become locked in systems, it's really, really hard to change. Every, all the rules are hardcoded, all the, so that it doesn't lend itself to an evolution with. So as the business evolves, you want to keep changing processes, you want to be more agile. So there's a shift to more modular solutions and breaking down some of those, those things that actually have been evolving best, best practices, you know, the folks focus on quality have their quality solutions, the folks focus on maintenance have their maintenance solutions, the folks focus on inventory have their inventory solutions. And when you try to bring all that together, you end up with average solutions that, that do a lot of things, but maybe not the best in one category or the rest, or maybe not the best for this industry or that industry. Because the other thing is, folks specialize in different industries, right?

Sure.

So I think that the push is for more modular solutions. Now, how do you make all this work? Then you need to worry about interoperability, about how, how does the data exchange— let's spend more time on how we connect things. Making them easier to connect to share information back and forth. And then we can have best-in-class functions in, in the organization. And one of those best-in-class functions, for example, could be different kinds of analytics that you could apply in parallel or on the side, really, with data from all these different kinds of— and even bringing data from all these kinds of systems together.

Yeah, and Scott, maybe we— I don't want to steal all the questions, we can circle back on this, but I think to your point, one of the things when I talk about SESAME and the work that Conrad and his teams do is they're working on things that a lot of us don't know need to be worked on, right? So you're talking about, you know, data connections or connectivity and storage and contextualization and interoperability. There's all sorts of stuff Right, that you mentioned smartphones earlier. There's a whole bunch of really powerful things that are happening, and I am completely oblivious to them, right? And I don't have to know because smart people worked on making sure that the apps work together and all that other kind of stuff. So, Scott, I don't mean to redirect things, but I think at some point, you know, Conrad could talk about SESAME itself and what the group does and how it ties in with the things that we're talking about here as well. I don't know if now is the time for that or not.

No, I think so. But maybe just before, this is from Catherine, a question from the audience, a comment, I think, taking us back to the conversation about connected worker 10 minutes or so. Maybe Conrad, we just take a look at this and then we'll do a gear shift like Mike said. Kathy's talking about her experience as a frontline worker and In the issue of fighting the stigma that you're turning a person into a mindless robot who just acts without being able to use— I think you were starting to allude to that earlier.

Yeah. Thank you, Katherine. I don't know if more of a statement than a question. That's a good summary of what I was talking about. The one issue she brings up is, and it has to do a lot with, with how people are designing these applications to help the worker. So you can, you can design things and then tell the worker follow exactly that every time. Or you can provide looser guidance, you know, and more on-demand help. This is where The, the voice interfaces have become more sophisticated, the AI behind them. We've heard the buzz, or some people might have heard the buzz about chat AI and stuff, how it's evolving. But the thing is, you rarely get the same situation all the time, or maybe 80% of the time things are working normally, and you are Maybe working a little more mindless. But when it really matters, when that 20%, 10% of issues happen, that, that's when you're going to need the person there to— and you're going to have to get that data to the right person of why, why is this right now acting a little differently? Why is it trending in a bad way? I need to now drill down into that, what could be causing this. And this is where my expertise is really gonna come to bear. And if things are that, that easy to automate, then they should be automated. We want people using their heads, their mind, not turning into, into robots. So the idea of this assistant and the digital assistant is not about turning people into robots. We can just turn robots into robots. It's about empowering people to handle the hard things that we cannot automate and program.

Well, and that comes— I mean, you started early on, Konrad, talking about culture, right? And, you know, I think that's a really strong point that culturally, if your organization is business or data-driven, And you can convince folks that you're doing this because you're trying to make the job more rewarding, not turn them into robots. And that's stuff that people can work on now before you're hooking up plants and before you're hooking up smart sensors or selling some big vision of those sorts of things. And you probably see a lot of that within Sesame too, right?

Yeah, it's just like you see there's always a A dark side to some things. I mean, our smartphones, right? People can say some people are getting addicted to them and they just, you know, hey, the phone made a sound, I better go look and see what happens. And that versus— but the phone can be a great tool, right? And you can use it as a tool and use it when you need it and not become a slave to it. So it's I think that's true with a lot of these things, the culture side of that, of how you use these tools, how you approach them, it is important.

Excellent.

Well, good.

Let's circle back and maybe just one more quick reset here at the top of the hour here. We're about halfway through the maximum time we've asked Konrad to give us here today. Konrad is giving us a preview of a conversation he's going to lead at the IME West. Expo in 2 weeks on connecting systems and processes. And he is the Vice President of Ecosystem and Workforce Development at SESAME. And Scott Phillips and I are trying to pick his brain and learn what we can about connected things, connected systems, connected teams, and then the value and the work to connect the processes there. So I brought up a minute ago about SESAME itself, and I think for those that may not know, SESAME. If you could maybe just take a few minutes and talk about the institute itself and the type of work that you do. You mentioned projects that you have running and things like that. Maybe frame up what SESAME is and how people could learn more about SESAME.

SESAME is a consortia and we have over 160 members right now. It continues to grow. All creating an ecosystem that is really pushing for interoperability, for education. What we did is we've gone out and identified certain gaps, challenges, and then we moved in gaps and challenges in implementing smart manufacturing. Why have we been a little stuck in this Industry 3.0 era. And even though we've been talking about Industry 4.0 for a while, we seem to be stuck and not make it proliferate at scale. So, and, and, and so we have focused, we've built a roadmap around some of the main issues, and we've been tackling it and still got a few more few years to go. But interoperability was one of those big challenges. that things have not become as plug and play as we would like. I mean, the starting point was very diverse, all the way from, from diverse networking protocols, never mind, to different ways to handle data semantics. And, and, and now we're really pushing for more standardized, open ways to share that data. and, and become interoperable and pushing for information models. So for a while, there was a, just a big push, put data however you have it, stick it in the, in the data lake. And, and you'll be able to do wonderful things with it later. And people have realized maybe, maybe we need to put a little more thought into how we structure information, How do we make sure that data has the context we need to do the proper insights and analysis of data and get more value out of the data and do it in a way that you're solving an immediate problem? Let's suppose you went at this with a machine maintenance lens on and you created the data you needed for that problem. But you can later, later, the next team can come and say, By the way, I need 70% of that data. I need to add another little bit of data to that to handle quality, to handle quality in that process. You can do it in a way that these information models are evolving to really be shared across the organization, and we can leverage the same data instead of keep adding data silos. That's a big issue we've been tackling. The other thing is to make sure that we connect all that data to AI, to logics, to more of this great technology that has been evolving with AI, to, to really package the AI models for different industry, different problems. So we can, for example, save more energy, become more energy efficient by making the process more efficient. So it uses less energy in the first place. I mean, so the DOE, Department of Energy, is behind the institute. And we— the DOE has several initiatives. Some are to promote different energy sources that are more green than others. Some are to promote carbon capture technologies that help you after the fact. And smart manufacturing is what helps you save more energy in the first place by being more efficient. So that, that's one of the things we're doing. And then the other big issue that's on the top of every manufacturer is having a skilled workforce. So another thing SESAME is working on has been working with educators to update their curriculum and add more of these smart manufacturing skills that bring a lot of the legacy that was happening in engineering, with all the AI skills that was happening in automation kind of professions, and in IT and data science side, creating curriculum that crosses those old educational silos and creates more of these new skills that are needed to bring all these things together.

So, so who, who can join CESMI? Like, what are your— what does your membership look like? And then how would people find out more about that?

Well, we, we have— well, uh, so CESMI, C-E-S-M-I-I dot org. So it's org, not a com. This is a, a, a, a, uh, consortia, uh, government-funded institute. So is that cessme.org. I invite everybody to find out more about it. We have a new membership model that really makes it very practical for smaller manufacturers to join. And a lot of the, the, it's more important for us that members engage and work on projects. So we're even letting you put a lot of the membership fee right into projects and And to get started right away. So we lowered the bar, we made it very easy to engage in the ecosystem. And the ecosystem includes manufacturers, of course, are the main one. But a lot of the ecosystem that supports manufacturers, technology vendors, academia, all working together.

Excellent. I see Scott put The website domain name up there, cesmi.org, C-E-S-M-I-I. Excellent, thank you for that, Scott.

Yeah, Conrad, what's— when you say a project, so if I were a manufacturer and I joined CESMI, what would be the nature of a project? Like, what— can you give an example?

Oh, so we have the traditional R&D projects that are creating new ways to sensorize machines, new AI algorithms to handle specific problems that that have a big impact on manufacturers. But right now we have a lot of projects that is— we want to prove out and have all these success stories with small manufacturers that they have a project and they can have a specific problem to solve that solves— makes a big impact and it can be generalized. The methodology can be generalized and packaged And we have a platform at SESAME that we use to demonstrate how you can do a lot of these practices and to verify interoperability and to enable apps to be developed to solve specific problems and make them available in a marketplace. So all of those ingredients also there. So if a manufacturer has a problem to solve, that they think the solution is not readily available, but it's a problem that impacts a whole sector of industry, and especially if it improves competitiveness, efficiency, and saves energy, then that's a recipe for a project at SESAME. Or if you're an educator that's adding smart manufacturing skills to your curriculum or wants want resources and you need new— I mean, some of the projects right now are creating new simulators and datasets that can be used for education with some of these example problems.

Conrad, I think you— well, I know your main operations are out in California, but can you talk about— you've got some innovation satellites and things you're spreading out around the country to be able to touch more geography, I think. Is that the— is that the SMICS?

Maybe?

Yeah. So we have— we're up to 7 innovation centers and several satellites on them. So it's important for manufacturers to see a lot of this technology and how it connects and how, how easy or hard it is to connect different processes, see these— this AI in action. So we have established relationships with organizations and that have assets, industrial assets and demonstration centers. And we've been adding smart manufacturing on top of that. And they all have a mission to make this available to showcase how all this technology works together. So that's also if you go to SESAME and look at ecosystem, These regional demonstration centers are a very important part of the equation.

Excellent. I'm sorry, Scott. I was just saying one of the big things we wanted to talk— we've talked a lot about things and systems and a little bit about people and the teams, but we haven't really talked about process. I think we would be remiss to not discuss that because I think if somebody— one of the things that I always look for, Konrad, is There are ways to tell people to like go and do even before they're, you know, twiddling bits and, you know, collecting data and stuff like that, right? So, and I think that the focus on process and the value of doing that is a takeaway that people can learn from. So why don't you lay it on everybody, like your perspective on the value of process and how it relates with all the things that you've been talking about?

Well, So the word process itself is interesting. So, and I see the word process used at a couple of different levels. So one is you have, you know, a grinding process, a thermal process, a cook oven process, or you, and you need, want to optimize that process and that process that runs within a piece of equipment. And we do a lot of projects at that level. But then the higher-level process of manufacturing a product, that also needs to be optimized. Important to smart manufacturing is not just that local process that needs to be optimized, but how also the whole value chain process that starts within the factory and station to station to station and how that is flowing, how material flows in the organization, how we minimize waste. This is where lean and smart manufacturing intersect and a lot of these goals of efficiency. But making sure, for example, traditional lean was very focused on a lot of the physical processes, but maybe not the digital. But now we're making sure the digital lean practices all are also creating that efficiency. And it becomes even more interested— interesting when we look at those traditional departmental silos we've had, right? And we want to connect, but we've had human resources doing their own thing to manage people and skills and qualifications. We have The maintenance folks managing asset management and making sure the machine is up and running, you know, and reducing downtime. We've got quality folks focused on getting the best product out the door. We have the scheduling folks, right, trying to optimize the scheduling of production orders. Well, you can imagine that if each one of those are working separately, right, it's not as effective and efficient to get that all other big level of efficiency we want, we want all of these coordinated together. They all have to be an efficient machine together. That has been traditionally working in separate silos, separate information silos, system silos. And now a big part of smart manufacturing is, let's not just locally optimize, but let's optimize The entire process that too. So we get the best product at, at this, you know, shortest amount of time out the door. And we always know where everything is, all where all the material is, where, where constraints are, that we are solving those problems as we go. That's a mouthful there. I mean, there's a lot of detail to cover there. And it continues to be a challenge. But if we standardize the way that these functions— data collection, job dispatch, resource management, material management, quality management, performance management— all, all the— how they work together and work on those interfaces and standardize them in our organization, Then those solutions become more plug and play, these processes become more plug and play. And the future, a smart manufacturing future, has worked out all these kinks in the process, not only within the factory, but into the supply chain is the next level. Right?

Right, right. Yeah, I think, I think for years, you know, we've focused on particular processes and hardcoded solutions to those processes and created silos to where that process is, you know, that piece of the process, right? That piece of the overall work process or business process is optimized or working well. But like you just articulated, it's owned by one department. Maybe it's owned by one person, right? And that person leaves someday and you're stuck with the system.

Yeah.

you can't support and things like that. So this push towards Industry 4.0 and interoperability and more data flexibility and availability and things like that should allow manufacturers to continue to solve problems at a local level, but part of a larger—

And at the presentation at ATXL, I'm going to try to dive into some examples. This is really a challenging topic to handle in 45 minutes, but I'm, I'm gonna try to make sure, uh, and you guys coming, keep me, keep me legit, make sure I, I get into very specific examples I want to share.

Well, Scott will be there for sure. So, um, yeah, is there anything, anything you wanted to talk about with regard to the show at all, Scott? I'm not sure.

Uh, no, no, just like I said earlier at the beginning, it's— I've never been to the show and this is my first chance, and I happen to be moderating the session that, uh, Conrad's going to be speaking on. So I, I'll be right there and with the hook and the cue cards and anything I can do to help you. So I'm really, I'm really looking forward to it.

I'll give you, I'll give you my hook. You can use it. You got to pull Conrad off.

Yeah.

Yeah. Well, I mean, to me, this has been a great conversation. And again, we didn't want to go through everything that's going to be covered in the event in 2 weeks. But I don't know if either of you have another area that you want to drill into here. there are several things I took notes on I'd love to talk about, but we'd be here all day. Conrad and I are used to speaking to each other, and that's not what we want to do here. Scott, is there any other particular line of questioning that you wanted to bring up?

I just wanted to go back to ask Conrad a little bit about something he said at the very beginning. He talked about information-driven decisions and data-driven. I hear the word data-driven cultures and things like that. I don't want to put words in your mouth, but would you say that if somebody was saying, why should I pursue Industry 4.0 roadmap and all these opportunities? Would you say kind of the destination is the idea of a data-driven culture trying to get an organization, or am I— would you say it differently?

It's, it's one of the ingredients. It's one of the ingredients. But I think also, I don't want to I want to also emphasize that we want to be— that, that, that makes it sound like I just want to have better reports and metrics. And there is a pattern of— in that problem, it's important. It's a good foundational thing. We need to have good metrics, dashboards, stuff, but we need to also move into the proactive side of things. to get more proactive, more predictive, prescriptive to where we actually start using data. We're looking at some of the regular decisions can be automated. So we get to where we are also, those dashboards should be more exception-based, should bring to people's attention more of the unusual situations that are happening. And If we can have the algorithms handle the routine situations and keep the factory, the beat running on that 50% of regular things, that's also a goal of smart manufacturing, to be more proactive, more semi-autonomous, not fully autonomous. And it's not We're not going to automate everything, but focus people on where the people skills are really needed.

Thank you.

And I think we've talked for decades, right? I mean, the original value proposition of manufacturing execution systems was aligning production with demand, right? And being more responsive and agile to market realities and plant floor realities, right? And so we've been pursuing that in a lot of different ways with a lot of different technologies and approaches and such. But I think where we are today with the technologies and with the education and the stuff that you're doing at the institute, the research, the rest of it that comes together, you know, we should be able to do that in ways that do not make you beholden to whatever beast of a system you created. and have to support, you know, forever, but rather have the technology in support of the business. Right?

Yeah, just keep adding more and more intelligence, more automation, more tools that are assisting the people. So to a whole new level, not, not just showing the metrics, but actually putting a little more intelligence into what is this What should I do next? I mean, have I learned, you know, if we've learned things from, from the last few years, what do we usually do when this pattern happens? Just bring it up, up for me. Help me make my decision. Why, why do I need to be doing the same decision from scratch, you know, and as we bring new people into the organization, so this becomes really important when there's a high turnover, and we're Moving to a whole new people. Have we captured a lot of that experience in these algorithms, in these, in these systems, knowledge systems? Have we captured a lot of that knowledge that is exiting the workforce because they're, they're ready to retire, right? Are the new folks going to make all the same mistakes? Are they going to have to reinvent the wheel again? That's That's happening in a lot of places. But, but we have technologies that can help capture that knowledge into, into algorithms, into, into predictive and prescriptive methods. And wherever we can, that will give us a lot more power for the future.

Excellent.

Appreciate that. Scott, are there any other lines of question you want to pursue?

Or— No, I'm like you, Mike. I could sit and ask 1,000 questions, but I'll wait till I see Conrad in a couple weeks.

Well, excellent. So I mean, I guess I would encourage everybody to attend the event if you can and hear that, support both the Industry 4.0 Club and SESAME and Conrad in their work. I think, Scott, you're going to be moderating that, that day that Conrad's speaking. Is that accurate?

Yeah, and I think we're going to try to do a couple LinkedIn Live streams, the I4O Club, from the event too.

Excellent. Excellent. I guess maybe just one takeaway for me, and maybe Scott, if you want to share, and then Conrad, you can give us final thoughts here. I would encourage people to get to know who Conrad is and what he has to offer individually and through Sesame. He's working— SESAME is working on defining really the definitions that we can all work toward of actually defining what is going on in this space. So there's definitions around what smart manufacturing is, how they look at it, you know, first principles of smart manufacturing. One of the things Conrad and I talk about a lot is that this is an and story. We have to be able to solve this problem and the next problem and the connect with the supply chains and be environmentally responsible and all those sorts of things. So there's an absolute ton of thought leadership that Conrad and his team have available, and I would strongly recommend that you see him in person, see him online, and as Scott said, continue to plug in here with us for LinkedIn Live events, LinkedIn Audio events, and things like that. So Scott, you have thoughts as far as a wrap-up takeaway here?

No, I just echo what you said, Mike. What I like about what Konrad does is this— from my experience, this topic of Industry 4.0 is so big and so complex potentially and wrought with jargon and hype to some degree. And what I like about what SESAME is doing is they're creating a vocabulary and definitions and understandable frameworks that, you know, the mass of manufacturiers can understand. So that's really needed, and I think that's They're doing a good job. That's excellent. Yep.

Appreciate that. Conrad, how about you? How about parting thoughts, words of wisdom from Conrad?

So, so my final thought is that putting all this technology in place is great, but it will not make a big impact unless you go into it with that big impact in mind at first. So Be intentional on the why you're putting technology in place, why you're collecting data, what metrics you want to drive your business for better behaviors, what data you need then to calculate those metrics, to do the diagnostics you need. So be very intentional in your technology projects. And always Tie it to the business strategy. I think that's the biggest takeaway. I hope to see you guys at the conference. I know I'm going to see you, Scott, so I'm talking to the audience here. I encourage you to find out more about SESAME, and we shared that sesame.org is up there, and find out more about what we're doing because it's It's an open organization that we definitely want more people joining this ecosystem with this mission to get smart manufacturing accelerated.

And it's taxpayer financed, right? So we're all already investing to some level, so you might as well show up and take advantage and learn. So I'd just like to say thank you, Conrad. Thank you, Scott. Encourage everybody to Go to the Industry 4.0 Club page on LinkedIn, join, follow. We host these types of conversations 3 to 4 times per week. Monday's focus is women in manufacturing. Tuesdays are the future of manufacturing. Wednesdays we have this fireside chat. And then on Thursday we focus on people, process, and culture. So you can find out about that at our LinkedIn page, at our website, industry4oclub.com.

Great.

So that— I'd like to wish you a good day. Thank you, everybody.

Thank you.

Thanks for having me.