Beyond Asset Performance: Unlocking Iot Data for Strategic OEM Insights

Welcome to the Michigan Folklore and Art 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 experience in both manufacturing and leadership Most of that at Michelin. I'm joined today by my co-host, Mike Yost. Mike, would you mind introducing yourself, please?

Not at all, Mike. Thank you. Welcome, everybody. As Mike said, my name is Mike Yost. I'm the volunteer chairman of the Industry 4.0 Club, one of the co-founders, and Mike's co-host here on these fireside chats. I've been in industry a little over 35 years, with the last 15 on the industrial nonprofit side. And really excited to have you here with us today and also to be joined by a good friend and respected industry voice, Yen Pengel. Yen is the product leader of digital solutions at Ingersoll Rand, and he will be presenting at the MD&M South event in Charlotte, North Carolina on April 23rd. The title of Yen's presentation is Beyond Asset Performance: Unlocking IoT Data for Strategic OEM Insights. We've known Yen for a long time. We'll talk more about that through the course of the next 20 to 30 minutes or so, but just by way of background, let you know that Yen's been in the technology segment of the industrial manufacturing space for more than 20 years. He's been making software that enables optimization, integration, and insights into the production and manufacturing supply supply chain. Yen has held a variety of positions in global product leadership, portfolio leadership, global product strategy, and global business management over different periods of time in his career. Like me and Mike, Yen's also one of the co-founders of this club. He's a board member of the Industry 4.0 Club. He's an advisor with ARC's Digital Transformation Council leadership team, and he's generally active as an influencer on Industry 4.0 and IIoT on LinkedIn. So speaking of LinkedIn, if you are joining us here today and you have a question for Yen, you want to contribute some thoughts, please go ahead and do so in the comment section during the broadcast here. So Yen, welcome. Thanks for joining us.

Thank you very much. It's been a while since I've been in on one of these.

Yeah, it has been. It's great to have you.

Really?

Yeah, we're glad you could join us. So let's jump right in. Lint, tell us a little bit. So you've— I know you've had a long career in the digital space and working with IoT. What was the most pivotal decision or inflection point for you that shaped your trajectory and maybe something that you learned from that and how it shaped what you're doing today?

Well, yeah, I started my career in software engineering, electrical engineering with major in software engineering when I was in school, right. And I probably thought I was going to be developing software for the rest of my career. But I guess fairly early in my career, I started to gravitate towards defining, designing the solutions as opposed to just programming it, working with customers, understanding their needs. Understand the problem they're trying to solve. And, uh, initially I, I was, um, you know, also then the developer providing that solution that I was working with them on. But over time I started becoming more focused on product management and, uh, and, and has really not been coding any, any, uh, important software, so to speak, at least for the last 20 years or so, or more than that. Um, what I learned is that, um, Well, I learned a lot, first of all. I mean, product management is something that most people are just thrown into, or at least in the past were just thrown into, um, and without too much guidance, and they kind of find their own space. But, uh, I've had the, at least the benefit and opportunity to get a lot of structured training in different, uh, product management philosophies. Um, and, um, at the end of the day, they're, they're very similar. Um, you know, you manage all stages of life cycle of a product. Um, you are trying to be with users, customers in mind, looking at understanding their daily problems, finding sustainable solutions. Um, and so, uh, one key thing I've learned, uh, from transitioning to the one that's trying to, to solve the problem to the one that's identifying the problem is, you know, we really want to look for problems, not solutions. Uh, you want to find the problem that you want to solve as opposed to looking at solutions that are in search of a problem. And you see that a lot right now, especially with generative AI. There's a lot of these edicts coming down. We got to do more of this generative AI. But in reality, you sort of figure out what are the problems you're actually trying to solve and figure out what the right solution is to solve it.

Well, we will, we will not take offense, Jan, that you didn't identify the pivotal career moment as when you met me and Mike and the club. So we will not take offense to that, but truly appreciate it.

It's up there, it's up there.

Yeah, I'm sure it is, I'm sure it is. Well, excellent. You know, you're gonna be talking at the event here in April, you know, talking about unlocking IoT data. And, you know, there are a lot of companies that collect a lot of data, but they struggle to act on it, right? So can you just share what, in your view, separates organizations that can, truly take advantage and convert IoT data into measurable benefits, as opposed to those that are just creating dashboards that go unused or like you just mentioned, keep chasing problems without really understanding what they should be focused on.

Yeah, that's a very good point. I think a lot of the equipment manufacturers, they develop these IoT solutions initially because it's become table stakes in their industry, right? It's something customers just expect. that the machines are online and they can go on and see what the, what the state of the current machine is. And then it gets to the point where they provide some information about if there's a situation, there's an alarm or something, that the customer can see that. Um, and, and at Ingalls Orion, we, we didn't start there. We, we started basically internal and said we have, we have a very, very, um, Uh, very, I would say, unique service offering, which it's called the Package Care. What it means is that when a customer signs up for that, we take full ownership of their equipment. That means that with whatever happens to it, we will fix it at our, our cost and expense, even to the point of replacing the unit. Um, so if, if a customer buys a machine and they continue to, to be on that service whenever the unit is time to replace it, we will replace it because that's based on, on the, on the offering that we have. So whatever we can do to reduce our cost is critical. And so when we started this IoT was really to be able to connect to the equipment that we are supporting and be able to first of all get information about what, how it's doing and if there's an issue, but also when there is an issue, be able to go online and see what is the issue. So when we get on site, we had to— we have the right part, we can fix it right away and get the customer up and going with their equipment. So as an example, um, before we applied IoT, um, we were— we had about 65% of what we call trips when we go out on a trip to fix a machine. 65% what we call, uh, um, one trip. That means that we go on there, we fix it, and we go back and we're done. And then 35% was more than one trip. That means that we go there, we don't have the right part, we misunderstood something or whatever, and sometimes we even have to do 3 trips or more. Um, when we applied IoT, after the first year we saw that we were at 90% was one trip or less.

Huh.

So 10% was more than one trip. And of the 90%, half of those, 50% of those were zero trip. We were able to go online and diagnose that there's some kind of behavior, some situation that we've seen before, and we would call up the customer and say, are you doing this, or is this going on? And they say, oh yeah, that's right, just stop doing that, or change that, or do whatever. Um, or if the filter is clogged, go change the filter, right?

Right.

So we don't even have to go on. I mean, that's a huge benefit for us. Um, and so that was, that was where we started. And just after that We've made that information available to our customers in a customer portal and gave them a way to get SMS and email notification when there was an issue with their equipment.

So the IoT data was the foundation that'll let you add— put these value-add services and drive those types of benefits.

Absolutely. So for the last 3 years now, we've then started developing what we call insights using machine learning. we have the benefit of, you know, versus an individual customer will have maybe 3 to 5 of our machines, uh, that might even be connected, and they'll be connected even internally, and they could do some analysis on those machines. But we have the majority, we have like thousands of machines that are connected. So what the machine learning we can build on all those machines are just that much more powerful than what your customer can do. And so we've been been developing these insights over the last 2 years, providing real value to our customers.

It begs the question, I think, a little bit about this data. And it's interesting, you talk about you're providing it for your clients. Um, do your clients ever ask you to provide it to their customers or their suppliers as well? And can you talk some about the risks of that with respect to cybersecurity and Privacy and ownership.

So, um, so our customers, um, so typically the equipment they have is, is, is a, a, to a certain point, a utility, right? It's, uh, okay, right, compressed air, compressed air, for example. So they have compressors in their equipment and providing it as that, as a utility, uh, so, so there's not too much data to their customers because Their customers are cutting their end product production, and I think that for most OEMs is the case, right? Is that our equipment is not part of their offering; it is the outcome of using our equipment that's part of their offering. So I haven't seen that, but but but but it is a good point talking about cybersecurity because that's a big big thing with any IoT. application is cybersecurity. And I think what we've— what we're finding is that most of the vendors or manufacturers, equipment manufacturers, have found that in the past, when we were doing, you know, traditional automation, we would want to get access to the equipment, and we would do that through the customer's network, get a VPN or something. And, and when you then provide equipment with IoT on it, That's just not viable. Most customers will have an issue with us using their network to get data in and out, become a potential gateway into their system. So most, most vendors today, they're using cellular network. That means they're basically connecting directly to the equipment outside the customer's network and basically air-gapping that information from the customer's network.

And so if any—

if anyone got into one piece of equipment, they still couldn't use that to transition into a customer network. Of course, we also need to do everything we can, you know, best practices for cybersecurity, make sure our system is secured and everything. And we're definitely doing that. When you talk about privacy, The typical privacy today is the individual, uh, person's privacy issues around the data that is being collected about them. And, and we don't do any of that. But, but there is a related kind of area where it's a— it's more like an IP thing where customers—

Yeah.

Have information on their equipment that they feel is proprietary to them. So like recipes, or what are they currently producing, what products are they running. Et cetera. And so that's definitely something that OEMs that are providing actual production equipment is taking care of and identifying how that is handled. We don't because we're just— we're utility, so we can't see what recipe a customer is running or what product they're producing. We can just see they're using compressed air.

Yeah, how do you— you know, along those same lines though, you must be protecting your clients' data from from other competitors and things, or is that not a concern?

It— for us, it's not as much a concern, but we are definitely protecting the data. But, but it's not as much a concern because the, the information we're pulling are, are— it's the same as if you're looking to your, your, uh, utility provider, your, like, you know, electricity, for example, right? Um, there's not as much data about view that they can see from the information about how much electricity you're using. Um, there's not that much privacy information around that.

Okay.

But other vendors definitely has to make sure that they're taking care of handling how they're doing. But what we're doing in general, when we're talking about our machine learning, we're, we're basically, uh, um, what do you call it, uh, uh, we're basically taking the customer's information. When we're doing machine learning across, you know, 1,000 similar compressors, we're not looking at who the customers are. We're just looking at how the machines is running. Sometimes we pull in the industry so we can see what the industry and the area of the country, but the actual customer is not relevant in our machine learning. And we're basically just looking at the machines as a serial number and pulling that data but not using the customer as a information around that at all.

Well, yeah, I just want to remind everybody, if you're with us online and you have questions or comments, we're talking with Yan Pingel here ahead of his presentation at MD&M South in Charlotte coming up in April. He'll be talking about moving beyond asset performance and unlocking IoT data for strategic OEM insights. And so please feel free to throw questions or comments into the into the chat here, into the comments section, and we'll throw them up on the screen and talk about it here. Yeah, and I wanted to just sort of stick with that for a second because I think it's really, really interesting. And I guess maybe less of a specific question for your business and a little bit more panned out. What you're talking about doing, I don't think people really recognize the fact that the business value of being able to connect into assets no matter where they are around the world, to be able to add value-add services like you're talking about, right? And improve the customer experience and provide more services and value back to the OEM. And that's written into the core tenets of Industry 4.0. Like as the German government looked at, you know, how they could leverage technology years and years ago, that's sort of a core value prop that was built into there. And so I think the point that Mike is talking about also, the, you know, it gets to privacy concerns and things like that, I think is very real. So, you know, I guess I'm just wondering from a broader perspective, if— do you see these conversations? Are people more accepting of the fact that they're going to buy equipment or buy assets and there's likely to be this type of technology capabilities on it? Are they more accepting of that? Are you seeing people push back? Because I know for some time there was a lot of pushback of people like, hey, anything that happens in our plant, our data, you know, you stay out. Just broadly speaking, even if it's beyond your role at Ingersoll Rand, are you seeing people be more receptive?

I mean, absolutely. Yeah, definitely. I, I see them being more receptive to, to this type of technology, um, unless it's specific industries like, um, like, uh, power plants, um, you know, nuclear, nuclear plants, uh, some, some level of pharmaceutical, uh, it's definitely still, you know, very, very protective, and that makes a ton of sense. Uh, but in general 5 years ago, we had to really, for some, at least some of our like large key accounts had to go in and explain exactly our technology. So they, and to the IT department so they can understand what we are doing. And most of them, we still, you know, got approved. And then there were some that says, you know, using cellular, we just don't do that. You can't do that here. And we have to kind of turn things off. Right. And we're seeing now that most of them are understanding that there's just huge value to what we can do if we can get the data and help them out. We've had, we have had some of our large customers ask, you know, you're doing all this great machine learning and insights, can we get that and then deploy that locally? And we said, well, first of all, of course, a lot of it's proprietary because that's, that's our value add, right? But at the same time, even if we did so, the whole point is you can do that on 3 pieces of equipment, we can do that on 1,000. There's just You can get something out of what exactly what your machines are doing, but we can see things much earlier because we can see the trends and the patterns of 1,000 machines and be able to identify something that they just are not.

So it brings up a really interesting point, and I don't know if we have a lot of time to dive too deep into this, that point. So you have 3 compressors in one area and you have 1,000 more broadly, you're learning across all of those. Like, what's— and again, not specific to your company necessarily, I don't want you to give away any secret sauce or anything like that— but like, what are the general product leadership perspectives on, hey, we're capturing, we're learning a lot across the performance of our 1,000 products that are out there, we're going to turn that back into something either sellable as a service or greater insights. Is the value of it more likely to come back to the OEM or to end up making its way to the customers as that data is normalized and understood?

I think it's definitely both. So if you think about Most industrial manufacturers of equipment, they're in a business where their machine is not really distinguishable from all their competitors.

Sure.

Yeah, it's almost like anyone you choose, they're very similar. They come up with the same technologies about the same time, etc. Um, and so the differentiator is something like this, uh, and, and we're seeing right now, you can just imagine with the the current economic times we're in, um, most manufacturers are looking at, okay, where should I focus and where should I defocus for them, you know, get, get through this economy in a good space. And, and this IoT area on the data area and the, the machine learning and the insights, that's an area that at least from our perspective we're putting a lot of focus on. We have over 50 insights on our equipment, different, different things that we can identify. Um, and, and, and each of those can be just a behavioral insight, you know, tell the customer don't do this anymore and, and you will, you will save money. And then there's what we call service insights, where there's something you need to come and do service, the machine is going to break within the next 30 days or something. Uh, and then there's what we call sales insights, where more we can see that they're, they're just not, uh, the machines are not, uh, performing effectively enough. And, and by replacing a 20-year-old machine with a new, much, much more, uh, efficient machine, you're going to get a return on investment in less than a year, and now you're going to save this much every year after that. So those are kind of like the 3 different things that we're working on, uh, and we have now, as I said, over 50 of those that we are deploying globally, uh, against our portfolio.

Yeah, it's exciting what this unlocks, the opportunities that, uh, Absolutely.

One of the things that I'm going to be also talking about is the sustainable— sustainability aspect of this. I mean, most sustainability, uh, kind of, uh, initiatives from a company are all about their factories, trying to make them more efficient and less, uh, um, less polluting and, and all that kind of stuff, right?

Right.

Um, if you look at especially the, the equipment that I'm working on right now is, is air compressors. This is the most ineffective production equipment in manufacturing today, is an air compressor, if you look at how much energy and how much you get out of it. So whatever we can do to make that more effective and use less energy, um, that transitions out to our entire customer base. And so, so that's a key element of where you can actually take, take that and, and, and claim at least some effective effect on sustainability that you are providing, not just within yourself but out to your customers. Um, there are tons of those insights that we have. We're not only, uh, providing the customer with it, whether do this differently and you're going to be better, but we actually trying to quantify this with how much energy you're going to save, how much money you're going to save, how much less polluting are you going to do with if you solve this problem. Um, and so now we're— we've been doing this well, and, and then the latest one we're doing now is actually installing software on site that is directly manipulating the equipment to be more efficient, uh, um, based on, you know, looking at the current demand within the factory and then figuring out, okay, should I run these 2 compressors or these 2 or whatever? So systems like that that can make sure that that you're providing the production need that you have, but at the lowest cost and lowest energy.

It's pretty cool. And Mike, I don't mean to keep asking questions up here in a second. Just this weekend, I was driving and sitting at a red light by myself with nothing coming. And I was like, boy, I can't wait for the day when these traffic lights are smart and they have AI built into them. And they're like, all right, They can let me go because there's nobody coming. And I just think about the progression of the equipment, just like you said, as we learn together as an industry that we can shut, you know, things down over here, we can run things differently, you know, that it's— that, you know, there are, you know, things we can do from an operational perspective that are just good best practices, right? And that the systems will get smarter and smarter and help us behave that way.

So.

Just popped into my head there.

Well, you know, that's great, Mike. We've learned that you respect the laws, but we'd also like to learn here, um, from Yen. Yen, for folks that attend your presentation, what do you hope they learn or take away from your presentation at MD&M South?

Yeah, maybe, maybe I'll probably go back to where we started, right? Um, there's been talk a lot about the value of IoT, um, or lack of value of those projects that's been started. Many of those products have been run for years and once they were deployed, nothing much changed. I think this is the thing that customers need or anybody needs to understand is that this IoT data can be a goldmine for all kinds of different initiatives within your company and start looking at what can I use this data for? How can I benefit either the company or can we benefit the customers? And it can be a huge differentiator for your, your product and your brand. Um, and especially where today many industries and equipment spaces, there's so much competition, uh, and the equipment is very similar. And so that's an area that, uh, that people can differentiate their offering and their brand. I think that's, that's one of the things that I want to, want to provide, uh, uh, information about and understanding of.

Very good. No, that's great. You think about all the possibilities. I mean, you've shared some today that are unique to your company in particular, but it goes much beyond that in terms of what's out there and what you possibly can do with to help drive business and sustainability, as you described. So as we reach the end here of our time with Yan, maybe, Mike, you'll share a final thought, and then Yan as well. Before we wrap up, Mike, your thoughts?

Yeah, sure. You know, as we talked about throughout the course here, like, I think this is— we're seeing the realization and the maturity of the industry and the vision and such. I remember back in 2013 when a report came out from Cisco about the value of the Internet of Everything, and it pointed to manufacturing as being the largest potential value area because of all of the equipment and pumps and motors and compressors and all those things and all the data we could get out of that and how long it has taken for people to understand how to actually take advantage of that. And so to hear Yan talk about his business, how it aligns with really the original charter of Industry 4.0 and the vision of how you can do the types of performance management, move beyond that and get into to insights and things like that. I think it's really an impressive and maybe under-the-radar success story that this stuff is happening because we hear so much noise and every day the technology discussion goes faster and faster. But here are folks that are actually doing these things. I'd encourage everybody to go to the event and meet Yen and certainly follow him online. if you can't make the event. But I think it's been well worth your while. So thank you, Jan, appreciate you sharing.

Absolutely.

And Jan, how about you? Final thought for our audience before we wrap up?

Yeah, maybe go back to the last point here, you know, talking about the goldmine of data. I, you know, try to look at, you know, even maybe weird ways that you think that data can help you. I mean, I was talking about 5 years ago how marketing, for example, with, with the right anonymization of the data, they could— they might be able to see when an industry is, is, is, uh, going up and, or when it's, uh, you know, uh, going down. You can see a lot of different things if you're connected to, like, like we are. We're connected to pretty much any manufacturing industry in the world, and, and that data could just be amazing for, for not just the typical R&D and service and sales, but other areas of your company. It's just a gold mine of information that for the most part is, is not utilized.

Well, thank you very much. That's perfect. So, Jan, thank you so much for joining us today to give you— give us a preview of the presentation you'll be giving at MD&M South. That'll be happening on April 23rd at the Charlotte Convention Center in Charlotte, North Carolina. We hope you decide to join in. and the Industry 4.0 Club at the event. If you haven't already, here's the website. We hope you go there, register for the event. It's really, really a great opportunity to hear Yan, other speakers, and also visit some of the exhibitors that will be at the event. Please take a moment, register, and we'll see you in Charlotte. With that, I want to just mention that the club, the Industry 4.0 Club, would like to encourage you to take some action. to advance the deployment of Industry 4.0. So read an article, attend a workshop or a presentation like Jens, or maybe even start a smart manufacturing roadmap. We'd love to hear from you what you're doing. Drop us a note on our website at www.industry4oclub.com or our LinkedIn page so that we can hear from you and hear what you're doing to help advance the deployment of Industry 4.0. We also host conversations like this weekly. So if you have something you'd like to share with our audience, we'd love to host you. Reach out again on our website or LinkedIn page and let us know of your interest and we'll connect and have you on a show in the future. With that, Yen, thank you so much. Mike, thanks as well.

Thanks for having me.

We want to wish everybody a good rest of the day and a good rest of the week. Take care, everybody.