Prepare for Your Future: Start Your Smart Manufacturing Journey

Welcome to the Industry 4.0 Club's fireside chat. The club's purpose is to engage the power of diverse worldwide talent to accelerate the global evolution to Industry 4.0. We accomplish this by hosting and supporting respectful, thoughtful, and interdisciplinary open conversations and creating quality content on wide-ranging Industry 4.0 topics to expand the reach of our community. My name is Mike Ungar, and I am the CEO of the Industry 4.0 Club. I have over 40 years of leadership and manufacturing experience, most of that with Michelin. Our broadcast today is a joint broadcast with the C4I4 Lab, otherwise known as the Center for Industry 4.0, based in Pune, India. My co-host today is Ankita Sharma. She is the marketing business lead for the lab. Ankita, would you please introduce yourself further and just tell us a bit more about the lab?

Yeah, sure. Hi, Mike. Hi, everyone. My name is Ankita Sharma. Hi, I'm a marketing business lead at C4I4Lab. We are based out of Pune. It's a premier institute developed by Ministry of Heavy Industries, Government of India, to spread the awareness about Industry 4.0 technologies in Indian manufacturing ecosystem. It's a premier hub which handholds the Indian manufacturing companies to start their digital transformation journey. We do a lot of training programs, we provide the Industry 4.0 solutions, and we also have an entire ecosystem of the hardware solutions as well. So basically, we are a go-to hub for all things Industry 4.0. Primarily, we are based out of Pune, but we got labs all across India, and more of it would be discussed later. Thank you.

Thanks, Ankita. So I would like to introduce our topic today, Prepare for the Future, Start Your Smart Manufacturing Journey. To help us with this discussion, we're joined today by Jim Wetzel, who's the co-founder of the NXGen Group. Jim was recently recognized as a winner of the 2025 Manufacturing Leadership Awards for his outstanding achievement in digital transformation leadership. He is an experienced technical leader with a demonstrated history of working in the consumer goods industry and smart manufacturing. He has a strong information technology background and is skilled in process engineering, process improvement, project management, automation and control, continuous improvement, and cross-functional team leadership. Welcome, Jim. Thanks for taking the time to join us.

Hello.

So Jim, just to get us going, I know I looked at your background and you spent over 3 decades with General Mills. That's, that's pretty, that's pretty amazing. A lot of folks don't spend that much time with one company. What motivated you to leave there and start the NXGen Group?

Thanks, Mike, and glad to be here. Great question. I'll put it this way. You know, when I look at digitalization and this journey towards digitalization, what I found was the marketplace was full of sellers and marketing folks and technology providers. And the voice of a manufacturer, someone that's been, you know, on the other side of the equation trying to improve performance, wasn't there. So I said— so I looked at where I was in my career and I went, how can I give back? And the idea was, I've got nothing to sell. I'm not selling a product. And so could I try to share my experience with small, medium, and large manufacturers and help them through this digital journey. Because one of the things that I found was when I started up the Smart Manufacturing Innovation Institute, most companies don't know how to start. There wasn't a process to start, there wasn't a way to start, and so a lot of companies started by listening to their suppliers and their vendors and say, well, here's a solution, why don't you start there? I said, well, just like you, Mike, being at Michelin for so many years, for me being at General Mills for so many years, I was the other side of the equation. I was sitting here with real manufacturing problems and trying to figure out how to start down that path. I formed NextGen Group to assist organizations in figuring out how they can go down that path.

That's great. Thank you for that. Along those lines then, I know you've done a lot of work to help plants get started with Industry 4.0. Your point about vendors and technology, we often hear about folks trying to find a technology to fix a problem. There's nothing wrong with just working on like solving one problem with, with one technology and just moving on from there.

Oh, there's all sorts of stuff wrong with that. So, so let's pick that apart just a little bit. So one of the things that I really try to work with my clients a lot on is this issue of picking technology before they picked a destination. You wouldn't do that in your personal life. You wouldn't say, I wonder if I should buy a bicycle, a motorcycle, a car, an airplane, a jet, a spaceship to go from point A to point B until you figured out what your destination was. What we find, I'll use a crazy example, If I decided, you know, I'm gonna, I'm gonna buy a bicycle, and then, then I later find out, oh, I'm gonna go to Hawaii. Well, I can't bike from the United States to Hawaii, or I can't bike from India to Hawaii. So you kind of go, well, I picked the technology without a destination in mind. And so I harp on this idea of, do you know where you're going? Another analogy is just because I know what my roof should look like when I'm building a house doesn't mean I put the roof on before I build the foundation. Many times we get super excited about our technology because it looks like it solves a problem for us. But without a destination in mind, you end up creating— and I have a nice slide for this for my career. An ecosystem of stuff. If you start off with a problem and then you go and find a solution to that problem, and let's assume you're 100% successful. Now what happens in your organization? Another problem comes up. You follow that same process. You look for a solution for that problem and then you solve that particular problem. Hopefully you're successful and hopefully you got the benefit from that solution to pay for the cost of implementing that solution. But you keep repeating that process of problem comes up, you go find a solution, the solution then is deployed. If you look back 5 years, 10 years, 15 years, 20 years of following that philosophy, you will have an ecosystem of stuff, all these point solutions that have databases, they have internet connectivity, they have configurations for defining what manufacturing looks like, and so on and so on. And then when you have enough stuff, put together, now you have technical debt where you can't move forward because the weight of your stuff is too heavy. It's a fragile ecosystem. Technology is changing at varying rates. A solution you put in 15 years ago might now be technically obsolete, so you need to refresh that. Well, if you refresh that, it breaks something else. When you don't have a common data foundation, you don't have a platform, you don't have an approach that can build on each other, you solve the problem of the day, which is okay, but how you solve that problem of the day just set the stage for a mess in the future. And so it's a fine line between saying, well, then don't start until you have the master plan figured out. Well, you do need to start getting momentum as you do this digital transformation and get quick wins and get your company motivated to see that this is going to be a successful journey. All I'm saying is you can't go in there blind. You don't build a house without a blueprint.

Right.

Without a master plan. Otherwise, you'd start off and you'd build up, you'd start off maybe with the kitchen, or you'd start off with a bathroom, or you'd start off with a bedroom, and then all of a sudden you wake up and go, well, I was trying— my house is not organized, my house is a mess.

Yeah.

And so a lot of those analogies apply to the shop floor and to manufacturing. And so this is not trivial to try to figure this out around what is my master plan, how does stuff— how do all the puzzle pieces fit together. But what I've seen in practice is when you do not have those discussions, if you immediately look at problem of the day and just try to solve that, you have set yourself up for this longer-term failure.

Right. So Jim, as you said, as you rightly said that, you know, the data is the foundation and data foundation has to be strong and relevant enough. So let's talk about that. The best of the manufacturers also, they were using the data previously as well. So what is the need to change? What is the change in approach that they have to follow now?

Well, a couple of things. First of all is, Is the data contextualized? You think about, to the previous question about can't I just start with a problem? What we're seeing is as you get more advanced, as you get to more difficult solutions that are more complex, you end up going sideways or horizontal across these solution sets. So as an example, instead of just having a control system for a unit operation, I might want to do the control based on what happened in maintenance and when was the last time I did some maintenance activity on this piece of equipment. I'm then going to change the set points and recipes and schedule and so on and so on. So what we're starting to see is that these point solutions are no longer robust enough to go sideways. When I have this raw piece of data, like a historian would on top of a PLC, where I would collect daytime and value. Say I'm getting a temperature or something like that. I can't just say that the data is good because I have the temperature. I need to have the context of that. The context would be on what line, on what piece of equipment, in what plant, along what environment, what's the rest of the activity going on around me, what crew, what shift, what person was working, all the rest of the story around this one single raw data point. All of a sudden becomes more and more important. And so the second part that I would say is, is a difference is trust. So we talk a lot as we think about digital transformation that we're going to put computerized systems that are going to digitize this and just act on this information. The further the data gets away from the shop floor, the further you have any human interaction with that data to tell you if it's right or wrong. So as an example, if I'm collecting a temperature from a piece of equipment and that temperature sensor is not calibrated— now, temperatures probably are— temperatures are usually pretty accurate, so it's probably a bad example. Let's say a load cell on a weight. Or something like that, or a counter, or, you know, any other kind of sensor that, that might have a bias or a variable to it. Who knows if that sensor is right? Most likely the person running the line. And the person running that line, let's assume for a second all positive intent, they will do the right thing and they will adjust the set point On that piece of equipment to get the right result all the time. As soon as I step away from the shop floor, and let's say as an example it is a temperature and I know that it's off by 5 degrees, like your oven, you know, everyone's oven in their house is all different, right? And so the digital readout in the oven might say it's 350 degrees Fahrenheit.

Hmm.

But when I put another thermometer in there, it says, no, the real temperature is 340 degrees. So what will a cook do? They'll turn it up by 15 degrees or 10 degrees to adjust that bias. So what does my data historian know? My data historian knows that no longer that date, time, and value is no longer 350, it is now 365. Because 365 is the temperature setting I need to have on my device to be able to get 350 as a real temperature. And so if I now put artificial intelligence on top of that, that data that's incorrect, it's now going to start making conclusions that are incorrect. And as you start removing yourself from the plant floor, the only one that knew that that data was wrong was the person that was right there. We've created a data stream that potentially is flawed. Not only do I need to know the context of that data, that's problem number 1. Problem number 2, I need to know the quality of that data and ensure that it's accurate because I'm now going to start acting on it remote from the shop floor. The cloud, the machine learning, the advanced algorithms won't have any of this knowledge.

Well, I think it, you know, Kim, thanks for sharing the, the perspective. I, I think back to the days when we would try to do design of experiments, I don't know, or would do them, try to do them, would do them. And we would try to control several variables, hoping that we would get to figure out what the combination of variables would, we'd learn from to be able to improve the process.

Mm-hmm.

And that was a very, time-intensive, very labor-intensive activity. And oh, by the way, you weren't sure you had all the right things you were controlling. And so it seems to me that today we have a lot more capability to be able to bring that together to try things much more quickly at lower cost. And it's what a wonderful evolution when you think about it. So I think that's your point about where we've been to where we are is quite interesting when you think about that.

Yeah, if you can trust your data, think all the wonderful things you can do with it. So design of experiments, we used to use EVOP in statistical design to be able to essentially figure out those parameters over time with minor adjustments and still make shippable product during all of that time and learn from our systems. Well, you can do that much faster in today's world than you ever could if you can assume that the data that you're looking at is usable.

I think that's one of the things that's a challenge today is there are many practitioners, if you will, who have been successful doing the techniques and skills that they've always used, right? The reality is, is that there's new ways to do things that are a lot more efficient. And just because you were successful before doesn't mean you shouldn't adopt these new techniques. I think that's one of our challenges.

Yeah, I have a tagline that I've used for years. You know, what got us here won't get us there. Right? So people have done a lot of great things to get to the point they are today. It's not sufficient. to get to where everybody needs to go in the future. And so how do you grow new muscle? How do you figure this out in a way that's leveraging technology that we've never had before?

So Jim, for a manufacturer who's starting out and who's actually shaping its conventional manufacturing into digital manufacturing, how can he take care of the data conceptualization and data quality? Can you give us a good example, a simple example where he can understand, okay, this is how maybe my approach should be like this to move forward?

Yeah, I would again start with this conversation around what is your destination? Where are you going? Before I'd have the conversation strictly about the data platform or the data model that's needed, I would say, so let's just first get squared away with what are you trying to accomplish? What are your business objectives? What are you trying to take? Where are you trying to take this manufacturer? So as an example, if you want to make to stock or if you want to make to order, are you going to have every piece that you make be unique and it's one of one, or are you going to do mass volume?

Right.

So what's your business model? What do you want to achieve? What kind of turnaround do you want to have? How responsive do you want to be to the customer? You know, when the customer says jump, you say how high, and you deliver on that immediately. Or if you say, no, wait a minute, these are the 5 things we make, Everything comes from inventory and everything comes from stock. And so once you kind of just understand the challenges and the design of that particular manufacturer— and oh, by the way, there's no single right answer. Every company can have their own recipe for how they want to run their business. This is their strategy, not my strategy deployed on them. It's their strategy.

Right.

Then you'd say, okay, so what must be true? What are the datasets that you need? How often does it need to come in? Is this information that is in microseconds that you need that kind of level of accuracy? Is this stuff that's coming in in minutes? Is it coming in in hours? It's coming in in days? Kind of what is the speed and accuracy of the information?

Yeah.

Then how does it need to be transformed? Questions around, can I act on raw data or does it need to be massaged and transformed into an insightful piece of information? Now all of a sudden I have to worry about not only just collecting the raw information, I have to worry about the transformations that I'm doing and the platform. Now I start getting into technical solutions, but I start worrying about the platform that can do the transformations for me. And then the kind of the third leg to that stool is understanding what my own internal resources are. So do I have the capacity and capability to do this in-house, or am I going to have to find a partner or a set of technology partners that can do this on my behalf, that I can trust, that I can extend my organization? So similar to your organization where you're trying to help these manufacturers, understand what is the art of the possible and how might they pick and what technologies might be available to them. Once you start doing that, then the question is, well, how do I do that 24/7? How do I keep my operations running? What's my job as the owner of a manufacturer, and what service can I hire to accomplish these tasks? So all that goes into the equation before you actually kind of pick something.

Right. Jim, you know, you've talked a lot about needing to know the destination, and maybe I'd like to kind of move into talking about the concept of smart manufacturing strategy or roadmap. I know you've worked with SESAME to develop an approach to putting one together. Is that something that really bore out of this idea of solving this problem about you got to know where you're going before you start, so to speak?

Yeah, Mike, so thanks for asking that. Yeah, it did. But even more basic than that was that when you— we started looking, this goes back to like 2020 and 2021 during the middle of COVID We started looking at success and failure of digital transformation projects. And depending on the— and this was US, these were US numbers, but 70 to 90% of the projects that were kicked off that were under the umbrella of digital transformation, digitalization of manufacturing, 70 to 90% failed to meet their objectives. And we— so then you go, well, why? What's behind all this? And 93% of the failures were due to process and people. 7% was due to technology failure. So the good news is, right, the products that are being offered in the marketplace from a technology standpoint do what they say they're going to do. So what are the major issues around people and process that are, that are causing these projects to be less valuable than what they're stated? And the number one, the number one problem was lack of alignment of these projects to, to their business objectives. So, and you see this a lot if you're talking with IT and OT professionals and you ask them the question, What business problem are you trying to solve? What's the objective of doing this? They'll give you a lot of technology speak. Oh, we need to put a database in because we need to do this. And, you know, we need to have single sign-on and federated this, and we need to go to the cloud and kind of go, I didn't ask you about the technology platform and why you chose that platform. I asked you the question of what do you— what business outcome are you achieving by implementing this solution? And usually no one can explain it. That's the root source of this. And so when the Smart Manufacturing Institute said, we see that too, how can we address this number one problem of lack of alignment to the technology progression we need to make with the business outcomes? We created this acceleration framework for developing a roadmap for the manufacturers, and we set technology aside at the beginning of this entire process. And what I do to shake people's heads up a little bit, I say, for the next couple weeks, just imagine this in our discussions. Imagine that all technology you ever needed in this world existed right now. You don't have to wait for it to be developed. It already exists. And point number 2 is imagine that it costs you nothing. It's all free. Oh, that'd be wonderful. And I'd say, and my hypothesis is this: you'll still screw it up. You'll still mess it up because you don't know what to do with it. If you look at all the things we can do with our with our iPhones and all the applications. There's millions of applications that we can pick from. In some cases, it made us one person way more efficient than another person. In other cases, people haven't figured out how to take advantage of it. As I think about our framework and our roadmap, as we're saying, let's align on the outcomes we're trying to achieve. And let's use the power of the organization to get behind us to get us there. Because here's what I found, and it's not everything— almost everything I'm going to say is common sense today. Digital transformation is a team sport. Unlike control and automation, as an example, Where you could go and put a robot in and it can move things from point A to point B and just do that as an engineering task. So the engineers are in full control of making that happen. Digital transformation is about aligning to the business outcomes you're trying to have.

Mm-hmm.

It's about aligning to the business model that your company wants to operate in. And it requires supply chain, IT, OT, HR, finance, engineering, all these organizations to be on the same page. And that's why if you don't have a plan, if you don't have alignment, you're going to be less than successful. So you still might, you still might be successful enough to pay the bill of the software license or the capital project that you did. But what I'm saying, when they're 70 to 90% of the folks have not been able to meet their business objectives, is because they haven't— the bar wasn't at the minimum level. The bar was that we're going to have this dramatic transformation in our organization. How come we didn't have that? You see the same write-ups around artificial intelligence. A lot of people are deploying artificial intelligence. It's not It's not setting a new high bar yet. I would argue that it's not because the technology is flawed, it's because all these other things we've talked about. Is my data good to act on? Are our use cases aligned with the business outcomes we're trying to achieve? Have I engaged and got alignment of all the players in my organization so I can row the boat with all of the players? instead of just a few. And what's been super interesting to me as I've worked with customers, a lot of the big companies have smart factory or smart manufacturing initiatives. They might have factory of the future. They might have a new plant that they're putting up and they want that new plant to be just, you know, the lighthouse for how we're doing it around here. And when I interview those teams that are the most intimately involved in smart manufacturing and Industry 4.0, they're not aligned on what they're trying to accomplish. So it's one thing to survey the entire company and see how misaligned they are, but now if I go to the subset of the people that are actually trying to do the work, you'd think they'd have the highest alignment possible. And they still don't. And we underestimate the power of having aligned people to the task.

So, Jim, you rightly said, and I think in our previous podcast also, we decoded the same thing, that 90% of the AI projects, they fail because of the non-alignment and they don't know what KPIs that they should be following. following up, or what kind of unrealistic expectations that they are having. So that's— so you have been also deploying the smart manufacturing roadmap, right? So you have been following some sort of approach. So what do you think, like, can you share some insights from that particular approach? And where do you see that approach, you know, going in future?

Yeah, so It's eye-opening for the companies that are using and leveraging the roadmap framework because we stop with defining the capabilities that are required. We don't get into the— we're not picking the solution. Technology, is only leveraged for showing the art of the possible. We are not picking which brand of technology, we're not, you know, we're not saying company ABC makes this widget or makes this software solution, that's what, that's what your company needs. Instead, we would talk about your, your organization needs a common data foundation with these attributes. and it needs to connect up with your ERP system with these attributes. Which brand you're using to do that, we stay away from that. What's been super interesting about deploying the framework and leveraging that is it gives the people the space to talk about what must be true and what capabilities must happen before they're talking about picking vendors. That is an extremely powerful conversation because you can have it with the plant manager, you can have it with operations leaders, you can have it with people on the plant floor. You're saying, this is just what we're trying to accomplish. Are you aligned with what we're trying to accomplish? If we did solve this problem, if we did create this future, what would it be worth? Again, here's another thing on the framework that is hard for people to get adjusted to from the start. A finance person, especially when we talk about Industry 4.0 technologies, might ask the question, what is the ROI? What is the return on this investment? We do not talk about the return on the investment in this framework. We talk about the value of creation. How much value am I creating through this digital transformation? Once we pick a solution, we'll be able to calculate an ROI. It's subtle, But it really changes the conversation when you say, no, no, no, no, no, don't worry about the ROI at this point. Let's worry about the value proposition. Because many times we worry about the return, you know, this cost-benefit thing. Let's talk about the benefit because the benefit side is going to drive almost an infinite amount of ability to pay. pay for these solutions because the benefit is so high. That's a huge gap. We do a fair amount of exercising with what we call a use case valuation and napkin math where we're calculating if we accomplish this, what would it be worth?

Well, that's great. Akito, go ahead.

No, that's a wonderful spin, Jim. I think this is more of a take for people like me who are in marketing, where we talk about what challenges are we addressing and what kind of ROIs we can bring once we do the investments. But I think you have given it a wonderful spin where we don't talk about the ROIs at first. We first talk about what value we can drive through these innovations and technological upgradations. And I think that is the right approach to follow where you can actually align your digital transformation or technology upgradation goals with your organization objectives. And I think that's a wonderful way and approach to take for this technology upgradation.

Yeah, it's different than what they normally deal with. It's usually ROI-based, and then all of a sudden people get into this conversation about I can't afford your solution, and so on and so on. And you go, wait a minute, let's talk about the benefit that's being derived here.

Well, that's great. We've been talking for the last 30 minutes or so with Jim Wetzel. He's the co-founder, the founder of the NextGen Group, with a great deal of experience in dealing with developing a strategy for smart manufacturing. So as we wrap up our conversation today, Jim, I wanted to Just ask you what your final thoughts are for our audience as they think about going forward with their own solutions and their own approach.

Yeah, so my final thoughts are that we live in a super exciting time. There's changes going on on all fronts, and I look at what advancements and how fast advancements are happening in technology around We can make things possible that you— in days or in weeks that would take years and decades to do in the past. So I'm super bullish that technology will enable us to do things dramatically different and dramatically better than we can ever imagine. My challenge is to upset the apple cart a bit and to say, if that's the case, if what I said is the case and people believe that, what future do you want to create for your organization? So most organizations don't have that difficult conversation. They just plow through a day at a time. Oh, we made it through today. I wonder what challenge we have tomorrow. We'll make it through tomorrow. And they muscle through Day at a time without thinking about what future do they want to create. And then from that statement of what future you want to create, back off of that and say, so then what must be true for us to achieve that? Because when you look back, you're going to be amazed at what you accomplished when you have that mindset of the future I want to create and what must be true.

Yeah.

Versus I'm in day-to-day and I'm just solving the problem of the day. I hope that today I solve this problem, tomorrow I solve that problem, and every day is a slugfest. As I think about final thoughts is, can you stretch yourself to think about the future that your organization and you as a leader in that organization want to create and use this incredible time that we're in right now where technology Enables those wishes and dreams to come true, and then overlay that in there, and then say what must be true for you, and then begin the journey to make that happen.

Well, that's great. Thank you, Jim. Really appreciate you taking time out of your schedule to join us to talk about how manufacturers should get started and really advance in their smart manufacturing journey. So thank you very much for that.

You're welcome.

The C4I4 Club and the Industry 4.0 Club would like to encourage you to take action. I mean, we've talked for the last 35 minutes about thinking about the future. We want you to take action about deploying Industry 4.0. You can do that by reading an article, attending a workshop, or as we've talked today, developing a smart manufacturing roadmap for your plant or your company, or encouraging others around you to do the same. We'd like to know what actions you're taking. And so please reach out to us also if you need support. Here's the web address for the C4I4 Club. I know, please reach out to them. They're available to help you on this journey as well as we'd love to hear from you with the EMC 4.0 Club. So here's our website as well. Reach out, let us know what you're doing. We'd love to hear from you and also understand what we can do to support you. With that, I want to thank Jim, Kita. Thank you guys so much for taking the time out of your schedules, and we look forward to hearing about everybody's success in the future.

Thank you. Thank you, Mike. Thank you, Jim.