SUMMARY KEYWORDS

industrial, constraint, hexagon, work, ai, data, conversation, types, built, talk, kevin, asset, solutions, asset management, pull, good, cloud, synergies, release, breaks

00:04

Welcome to the Industrial Talk podcast with Scott Mackenzie. Scott is a passionate industry professional dedicated to transferring cutting edge industry focused innovations and trends while highlighting the men and women who keep the world moving. So put on your hard hat, grab your work boots, and let's go.

00:21

All right greetings and salutations. Thank you very much for joining Industrial Talk. This platform right here is the number one industrial related podcast in the universe that celebrates industry professionals all around the world. You are bold, brave, you dare greatly you innovate, you solve problems, and therefore you are making the world a better place. We have a continuing conversation with a gentleman by the name of Kevin price. Hexagon is a company specifically hexagon asset lifecycle intelligence. It's a mouthful, but we just have a continuation of all of the conversations that we've had with Kevin here on Industrial Talk, let's get cracking. He never disappoints, paper and pencil time never disappoints, Kevin's Great, good friend. Massive, massive asset management, street cred. He's, he's amazing. All right, go out to Industrial Talk. We have a ton of content out there, ton of information, a ton of solutions to help you succeed. Of course, we have the interviews of all of the wonderful professionals that are, you know, solving problems. That's that's one, you have to be about education, you have to be about the the world of collaboration, find these Sherpas find these individuals. And that's what Industrial Talk is all about. Also, finally, it's an innovation game. It's, it's the technology, it's all of the stuff that's being put forth, and it's at a blistering pace, you need to keep current with all of that information as well. It's an exciting time to be in industry. And of course, I'm I'm all giddy with it just because I get to live the dream of constantly, constantly learning, constantly educating, it's exciting, I love it. We also have webcasts. If you want to be on a webcast, you just gotta let me know. And these are on demand webcasts. But they dive deeper into solving problems, the solutions, same sort of format, very conversational. But you know, you want to be on one, you just gotta let me know. It's easy peasy. It's just me. And then finally, we have a learning management system out there. We have the industrial Academy. And I dive deep primarily into marketing and sales from for industry. What are the tools and techniques I use? What are the tools and techniques other companies use? It is a great resource. And of course you get to rewatch it, do it again. The next one that I'm going to be launching is in the marketing segment. Of course, we have the leadership, but the marketing side is Google ads. But you get to follow my journey onto Google ads and, and opening doors that way. So that's all out on Industrial Talk. Alright, let's get on with the conversation. Kevin price is in the house. And I highly recommend that you grab a paper and pencil. And then you know, hit the pause button, rewind, do whatever is necessary to consume this information. They're doing mad stuff at hexagon. And it's exciting. It's exciting to see how far they've just it's I again, listen to me, I would stuttering and all giddy about it. There's just so much happening in the world of hexagon and the world of industry and how they're taking all this innovation to this technology to better companies to help them succeed. It's an exciting time. I I always get excited as you can tell. All right, let's get cracking with the conversation. Here's Kevin. Kevin price. Welcome back to Industrial Talk. Always a pleasure to have you on the number one industrial related podcast in the universe. How're you doing?

04:27

Doing great, man. Great to be here as always pretty good. broader Chat.

04:32

How's Clemson?

04:34

Clemson is outstanding. The only thing that really matters at the end of the year is not being in the playoffs. It's not beating the wrong tigers. It's going out to beat South Carolina games. And when you can do that, that's the only thing that really matters in the state.

04:49

One in 10 or whatever. As long as that one win is is the game. Yeah, it goes with that's pretty much every team They always have that one rivalry. That's, that was. That was a big game too. That was good.

05:05

It was it was down in Colombia, and it was that night. So those are always kind of rowdy outside the stadium and within. It was good.

05:12

Hey, do you think what's his name is going to the LSU quarterback gonna take the Heisman? Oh,

05:21

you never know. You never know. It depends on on. Sec. LSU LSU pays people pretty well out here so that you never know.

05:32

Jab, jab. Got me. Yeah. snuck that one in big time.

05:38

You know, if he did if he didn't have the problems County and then have to label every five yards, it'd be different issue. I mean, we just we, we can we do it every 10 instead of every five.

05:48

We're all about the math. Hey, now, for the listeners out there. Let's talk a little bit about EA Emelyn. Again, we've also had that conversation a number of times, but I'm more intrigued with the hexagon, the infusion of all that innovation, that technology, where do we stand with the current em and and just just the expanding of offers that that can exist? Can you sort of take us through that?

06:18

08:53

how does that work? Where I have, I have my EIM, my, you know, the system of record, and it's, it's collecting all the data. So you just sort of take the data set that you're interested in, go back emanate, whatever the period of time loaded into the constraint optimizer, that's right, and then run whatever scenarios or whatever it is that how it works. It

09:17

does. And there's some pre built ones that we do. pre built logarithms is what they are. And there's some pre built ones that we do for methodologies like I was mentioning, so Weibel, Laplace, F, mica, makedo, those types of things. But we also have the option to be able to write your own C, you can use that use Python scripts. In the market, you'll find especially when graduates come out of school, therefore more familiar with Python. And it's also easier language to write. It's not as verbose. There's a lot more things you could do with it. You can also pull from other datasets that may not be am the processes information. But then when you find whatever that algorithm result is, then it needs to be contextualized and brought back in eam so that you have a remediation event, you can then do a new work order, you can do an update to a work order, you can update to a piece of equipment, you can update a due date, you can modify preventive maintenance frequency, do something with that data, the idea is, you should be able to use the power of that technology and that platform and the power of the web, to pull this information and process it. And you shouldn't worry about running that locally. Because it can take up some power CPU power for doing so. So what we did is we developed our patented tool to be able to pull it out. And we call it a constraint optimizer. Because it does just that it optimizes the constraints that you feed it, which is the very basis for what artificial intelligence is built on. And

10:50

you pointed out that I could pull in other datasets from various other systems, whatever they might be, and to be able to, to to analyze that in context with all the other data you can And

11:06

an example would be weather conditions. If you have a fleet that goes out, you have guys that go out in in a hurricane event, as you and I live in the south, we know what that's all about, before the current game during the hurricane, and after the hurricane, you need to understand what those weather conditions are. So that you can be able to understand how to how to move your fleet around or how to be able to move your crews together and how to build that through. That's just one example of a data set. But there can be other data sets that you may pull from that can be OEM related. They can be existing expectations of what a sensor should be. But this is what you're finding and bear in back and forth.

11:45

But you bring up an interesting point about AI because that's, of course, everybody's an AI expert now as a result of chat GPT. So I've just drawn upon my stay at Holiday Inn. But there's just the beginning of the AI revolution when it comes to managing assets and conditions and all of that stuff. So is hexagon trying to sort of be that leader in that in that whatever AI world?

12:15

Well, yes and no, in my opinion, the reason why I'm saying yes is because it's absolutely critical for us to be able to have access to AI capabilities. And we do we already do actually in very different divisions. But no, in the sense that, by definition, we're an automation company that uses AR AR to automate our processes. We're not an AI company. So what we want to be able to do is be the experts and our target markets and our target verticals. And what we do. So doing AI for us is to an extent within our product platforms, like em to constrain optimizer. But we also have other divisions and other solutions like exalt that can pull this information as a service. So you can be able to subscribe to it from em to have it processed, and have it get a response and then send it back to em. So it's a great thing. Because when we were looking at the acquisition, as an example, if you remember, I was excited about this because it brought a new engineering grade level of capability. And it wasn't just business office type of stuff. And this is a perfect example of that. I can take elements from an engineering perspective. And I can apply artificial intelligence on top of them from an engineering perspective and be able to do something with that data set. It's not just going to some black hole that's retrofitted to try and do an industrial thing. It's something that's built upon the needs, the issues, and the things that we're trying to build out in our challenges. Does that make sense?

13:51

Make sense for me. I hear what you're saying. I just, it's just an interesting time because I I get me, I get overwhelmed by all of the changes that are happening. And, and I get overwhelmed by what are my my steps? Where do I need to go? What, what are the next things that I do that I need to do to make my business better? And it's just as I get inundated? I mean, the World Wide Web is is horrible at this, because you just you just get overwhelmed with a tsunami of options, right? And how do you how do I come to hexagon or or Kevin price and say, I need help? But I need help in such a way that I hear all this stuff. Where do I go? What do I Well, that's

14:52

our opportunity, right? Our opportunity to help you to help our markets to uniquely position ourselves is to be able to understand how to use this technology. To give you an optimization experience, to give you a way to be able to be competitively available, to give you a way to be able to solve a problem to reduce the risk to be able to understand, I think that's where we look at the challenges that we can be able to go after for your vertical. As an example, let's just look at some of the things we built into the core of the application, and then how that scales into the AI world. So one of the things we released it into 12.1 was something called dynamic checklists. And the dynamic checklist, what it does is we've always had checklists, and we built in ways to say if you select this particular checklist item, and your value for that checklist item is why then here's the next thing that you should do. But they weren't dynamic. And what I mean by that is, if you select this one, and this one in this one, and they have different variables that drives you to a different different directions, kind of like what risk based inspections would be. So what we had to do in the past is we had to build out very workflow intensive types of things that we use in our in our system, Teleflex SQL, to be able to identify if value, but it was very rigid, and then what it can do, and it was very intensive in terms of having to maintain it. It wasn't custom, but it was it was it was a little bit more difficult. So what we did is we added a dynamic checklist. So I have a list of 100 things that I checked. And if I find it on an asset, I'm owed on an asset, give me 100 different things that I check. And if I find on the on the on the second one, this particular value, that 100 list now turns into 20. And if I find the next value that that next check goes, it just drives me to not have to go through that whole process. So instead of managing by process, I managed by the exceptions that matter to me. And I build that into the workflow that I have. And we done that to the dynamic checklist capability of what we had in this release. And that's just around em. Now, let's scale it into AI. What if I took that data, and I identify this election points as constraints. And I push them to the cloud in large datasets, so monitors, sensors, vibration, heat, there could be a whole lot of different things that I would normally check with on a checklist, but maybe I'm feeding all this information through sensors that go through, I can't make sense of all that information. It's just a lot. There's a lot of environmental sensor moments, it's just a bunch of stuff. So if I were to send those to the cloud, and use intelligence on making a decision, when those datasets made sense, and then brought me to a place I wanted to be, that'd be great. So instead of doing it one by one, I can actually feed it to the cloud, I can use artificial intelligence to understand what when the heat, is this vibrations this age is this. And there's even a sound that makes sense, then I can be able to have a specific action to do,

18:01

how do you normalize that data to reflect, let's say, regional and all I mean, is still as you go ahead. How do you do

18:09

it? That's a great question. That's a great question. And I think that's, again, the opportunity. So for us, normalizing that data is tough, you need the experience that's there, either the person that's got the whiskers that's got the gray hair like you and I that's built in to be able to structure it in a good way to be able to make make those decisions possible. And then you got to be able to have a system that can go out and grab all that data, and then find that anomaly. And we do that through constraint optimizer that's built in. That's helpful in it works. But I just as we as we get more data from sensors, as we get more reactions from work processes, as we get more manual inspections that are built in just like you're saying, it's a tsunami of information, you got to make sense of it in some way. And we can structure those based on history based on experience, and institutionalize those in terms of those constraints. So if I know that there's a pitch going on in an engine, and there's a vibration in the heat and the time, maybe I know what's going on. So

19:14

as we take that same scenario, start pulling on that thread a little bit. I have a pump, XYZ pump, it's in the south, and it's hot. And then I have that same XYZ pump in the north, where it's a little cooler or whatever, less different, different patterns. And then I have information that is OEM specific about XYZ pop, right? And to be able to pull that all together so that me I'm having a hard time finding the proper technician, whatever it might be. I'm looking for something that gives me true clarity into the health of that asset. And the condition of that asset. specific to that area. It might get a little hot Hear and that's okay. Right? Yeah,

20:02

it could be the operator that's messing with it. It could also be humidity, right? Because I mentioned temperatures warm. But what if you're running in Barstow? What if I run it in other areas? But I mean, if I do that the humidity is greatly different. All right. So I mean, there also could be an area of the world that is difficult to get supplies to, it can be difficult to get people to. So if I know I'm running this asset, and I'm running it, all things being equal, based on the heat condition, the equipment, all these other things, I know it's going to fail, odds are with it, if it runs away, it's going to run it could fail within the next four or five days. But I can't get a technician in here until two weeks, well, maybe you should go ahead and do it, do a breakdown, do a replacement. Now, that can be interesting. That can be a good way to be able to make a decision is not necessarily unconditioned, but it may be unavailable resources. And you're

20:57

pulling in all that information.

20:59

Yeah, I mean, yeah, it is, we have lead time that's there for the parts, we have experience that we can identify based on the contractors that worked on it, I can be able to have ratings for them. So it's another constraint. It's just another variable that I have in that mix of how I manage when I managed and to what extent

21:20

how do you get to the point where I mean, it seems like you could tweak all day long on these constraints, you could tweak and then tweak some more. That's what these data analysts it's like, it's like an artist, right? When do you pull away? When do you say yes, there it is, in all its glory ready to go. So do that. I don't know

21:45

I, I did a webinar a few years back with a guy named Bob Zimmer from PepsiCo. Frito Lay it's probably still floating around somewhere but he pulled from trucks and those things in the webinar, you'll find it he he pulled from telemetry systems and monitoring telemetry systems on trucks can monitor a lot of stuff, not just about the engine, but about components on the trailer, put components that are on the driver everything. And his his worldview on on that problem was, I don't know whether or not I'll need it, just throw in the data lake, and then we'll figure it out later. Okay, so you just want to continue, you just take it, dump it in the data lake, and then we'll figure it out later. And he would get folks that were from OEMs, he would even get folks that were in local schools, to look at the data and figure it out, he would have folks that were engineering groups and firms that were dedicated to it, to find stuff. So to your point, you can start going and finding all kinds of stuff. Really. I mean, the question is, when, when it matters when it doesn't? And when does when I think you ever stop. But when you start to say, Okay, this is enough. I think it's all based on the risk. It's just like we always look at IT Asset Management is we have all these assets, which ones carry the most risk? And why? And if they break down, are they the most important? It gets back to this fundamental still? And is this worthwhile to monitor at this level? Am I getting the right kickback from it? Am I getting the right optimization from it? It's a really an industrial.

23:18

Let's take a plane, I want I want all the data in the world being analyzed on a plane? I do. Why not? Yeah. And because the risk is pretty substantial. And and I failure is not an option,

23:37

not an option at all. Not an option at all. I mean, that's you have, you know, typical trip triplication redundancy and figure that out of how assets are managed. Now, data is identified in a lot of a lot of those types of aircraft for that reason. But I mean, if a light goes, if a light bulb goes out in the lobby, do you really need to monitor the electrical? Is that I mean, that's, that's the fundamentals, I realized that that we know from Asset Management as you want, you can monitor that electrical system just as easy. You can monitor all kinds of stuff, you can put phase imbalances, you can be able to look at the frequency at which those light bulbs fail. You can look at how often when to shut it down. There's a lot of things to do. But is it worth it?

24:20

And again, yeah, it just again, you don't know. I mean, it requires conversations like this to say, yeah, that's pretty important. That's something new, I didn't know about that. And then we go ahead, we always

24:34

go and say if something breaks, what happens? What happens if something breaks and the CEO collision? So as an example, if you're again, light bulb breaks, it's okay if it's in your lobby. What if it's the light bulb above a Texas Hold'em tournament? No, that's, that's a bigger thing or whatever. It's a light bulb in the surgery room. Before you come in whether it was the HVAC filter in the surgery room, when it comes out, you're going in and you're seeing lead come out of the vent. That's the last thing you want to see. Yeah. So I mean, it may be the easiest piece of equipment. But it just goes back to the basics of what if something breaks, and alerts the CEO? What piece of equipment is that? I think that's where you start.

25:22

Yeah, but that CEO wants his AC or her

25:28

chair in the batter's the vote

25:30

of a sudden is a high priority asset.

25:34

I'll make sure that thing rock solid.

25:37

squeaking, I don't like it. No, that's a little off. That's yeah. That she's just gonna have to wait.

25:48

Well, yeah, that's something you want to advertise making that decision as an asset manager, you want to have that life for long?

25:57

Yeah, that's, that's a balance. No, we,

26:01

we actually did that though. One time in the workflow prioritization. So there was criticality, there was emergency, there's all this other stuff. And then the customer was like, Well, if the GM calls, it's like, everything drops. So we added another criticality just for that person, whenever that person goes, we put it through.

26:17

But then that talks about education that GM should have the, the wherewithal to understand that they're not that important. They're important, but not that important. The operations of the organization, let's do feature. Okay, there we go. We just solved that problem. And we got, we definitely whipped that horse into shape. What else is going on with em? Well,

26:45

the other things, we talked about the release, we talked about some of the major components, and then other things are more synergies. So one of the advantages that we had during the during the process of coming over here was all the other solutions, and how do we do integrations to them? And how do we build them out. And that's what we defined as the synergy. synergies for us are not only within our division of asset lifecycle intelligence, we call those cross segments because there's different operational segments solutions out there, Project solutions, design solutions, cybersecurity solutions, operational solutions, all that kind of stuff. But then there's also cross divisional. So I mentioned exalt earlier, that's a platform technology that's out there that we can integrate to. And what we're doing with each release is we're adding new synergies that are extending out to the solutions that we have built integrations, who's

27:40

who's responsible for, you know, tooting the horn about new synergies that are happening out there? Because let's say I'm a, I'm an EM, I'm a passionate EDM user, and then all of a sudden, and I don't know what I'm too busy doing my work, right. Of course, how do I know how do I get the latest and greatest? How do you go? One

28:02

of the things that was written up by both Gartner and ERC was to an asset management recommendation for CIOs, was to get in the conversation of your roadmap at your organization, your operational roadmap, with your vendors roadmap, so that we can be able to help educate? And those are things that we do we do workshops a lot. So we'll have both Value Engineering Services groups and sales groups and pre sales groups and those types of groups that sit down to say, hey, customer, XYZ, where are you? Where do you see yourself in two or three years, and well, we're going to be doing expansion and we're adding a new facility, or we're adding on a new support service, we're doing other things so that when that happens, we can say, hey, we actually have another organization or another division or another solution set that we can bring about. So as an example, we have knowledge management is always a big problem in our in our business. And what we mean by that is the the steps, we talked about inspections earlier, but there's different procedures. And there are different other types of tools that one has, when they go out to do these, these jobs. We have a solution specifically designed for that, that we that we bought called accelerator kms. And allows us to be able to maintain from a compliance perspective, what those inspections are and what those procedures are. So when you're when you're breaking down, midstream oil and gas valve or you're doing a valve resetting size, you have the right procedure that's built into it. Now that procedure may be applied to a normal inspection or work order or other things. But that procedure itself has to live. It has to be maintained as we brought in. So it's a natural integration to what we do. We also do other integrations with project performance tools for asset investment capital asset planning, and about every six months we have new synergies between the two. So when we look at the verticals that we target in our space, which are asset 10 of what we're finding is we can start to offer more audit grade, engineering grade types of things than we ever had before. Because we just have a lot more toys here, there's a lot more things that we could do now that we couldn't before. Wow.

30:14

See, it goes, it just adds, again, I'm looking for somebody to give me direction, I'm looking for that person that said, you know, come on in, I'm already using this, this powerful system, I see all of these integrations that are happening. And I see this constantly changing market landscapes of how I want to be able to operate within it and be competitive. And, you know, bottom line value, I'm looking for somebody, or team of somebody to say, this is where I see your business. This is where we think you need to go for these reasons. Right? Can I do it? Can

30:53

we do we do it with ourselves, we try to do it with it our events like hexagon live. We do symposiums I had to do three in Asia a few weeks back, we did it in Singapore, Korea, and Australia. We just did a hexagon live in Berlin, we just did another one in Latin America. So we try to use those events as opportunities. But I still think the bringing in your rep and bringing in the value engineering team to sit down with you through a work stream through a workshop is the best thing. And that's something that we don't we don't typically charge for we typically want to do that. Because it helps us understand what works in your environment. And we also have user boards and steering steering committees and things like that, that help us understand which ones apply to which vertical. But I just think the Open dialog and understanding what your problems are, are going to help us give you the best opportunity to optimize those or to reduce costs or whatever you're looking to do. And in turn, it gives us the opportunity just to spend the right effort in the right place. Just like you'd want to do tunes.

32:05

Yeah, it just makes complete sense. I see my dog in the background, by the way, just there she is. She's good. She's like She's protecting the house. That's what she she does. She's chicken. She's,

32:21

she does she on when you sit

32:24

right there, like, care. But that's See, that's really key. This is just important for, for me if I was a user of the EM platform, that there's new stuff happening, stuff that could truly help me be competitively advantaged, Ryan and I, and I just need to have that dialogue with the just to have that conversation because some of this stuff's pretty doggone advanced. Ai, I just I wanted to know about AI. What's going on with AI?

32:59

Well, everybody in their brother is talking about Yeah, in some way. What does it mean for us? I mean, the last thing I think you would want to hear from that type of workshop is me to come in and say what, what is that? You would freak out, you'd asked me to leave. And then we would look at our contract together, which was Bobby, what would happen. And we don't want that we want to come in, we want to we want to come and say, Hey, here's what we're doing for your industry. Here's what hexagon means to you. Here's what it means to us. Here's what it means to your vertical. And here's where we've had success. And we want to work from there. And we want to build it up from there. Because we have in that journey together. It's really a partnership. It's not it's not a one sided relationship in any stretch of imagination,

33:44

it has to be, you can't say say one and done, it's got to be that that collaborative conversation of because your system is powerful. It's a powerful tool. And when it's

33:54

when it's on the web to there's things you don't have to worry about. So what happens is our customers that move to the to the cloud, from on premise solutions, they actually increase their usage, because now they don't have to worry about adding new users, they don't have to worry about gathering data, they don't have to worry about integrating another system to it as much because of the data strain or because of the integration balancing that goes on. So they want to do more with it. And that's what happens, which is good. Because we want to we want to take a lot of that off your shoulders, and we want to give you the benefit and we'll have a writing system and that's become more of a business analysts and stuff. That's a whole

34:31

other conversation. I don't know why I would want to have on prem versus cloud. It just just the updates just happened magically and it's like hey, great. It's good. shut everything down.

34:44

Friends don't let friends build data centers that's what I read one time.

34:49

That's that's pretty niche II mean.

34:54

It is but you need to keep it you need to run it and it's what you do but um That's just an example. If we could take some of this off the cloud and give you back some budget, what would you use it with? And where would you go? And what could you do? If it could get you to phase two or three years implementation? What would that mean? But you can't have that without the dialogue? Yeah,

35:14

no, it's always gets down to that. All right, Kevin, how does he get a hold of you? Let's say this is great conversation, I want to know more, how do they get a hold of you? Oh,

35:22

go to hexagon.com. Look at the asset lifecycle Intelligence Division, or look at the other divisions that are out there. There's a ton of work that we posted. There's there's new in this release that Peter by now it's, he's one of our leads, and the portfolio strategy enablement team that he did. It's out there recorded, you can see what's out there, that's new. And we also have just a ton of other types of detail out there. So you'll see it on the resources side,

35:47

I gotta tell you hexagon live the one week, we're in Vegas, it's dazzling. There's a lot of really good Splash and Dash not dashed on Splash, and dazzle.

35:57

There is. But again, where does it apply? And what does it mean? It's like you get stuck like a deer in headlights just to look at some of the cool stuff and there is, but how do you interpret it? How do you how do you prioritize it? How do you opt to implement it? That's why we're here to help. All right,

36:14

you never disappoint. Thank you, Kevin. All right, we're gonna have all the contact information for Kevin out on Industrial Talk, once again, just type his name in Industrial Talk. And you'll get a slew of conversations because he's that good. All right, we're gonna be right back. You're

36:29

listening to the Industrial Talk Podcast Network.

36:39

It's like drinking from a firehose. Every time I talked to Kevin, there's just so much going on at hexagon, so much innovation happening in the world of asset management. It's an I believe, personally, and of course, it is tip of the iceberg. And to Kevin's point, how do you prioritize? What do you look at first, but gives you the biggest bang for the buck? Those those questions have to be answered by Sherpas like, like Kevin, big time. You need to be a part of Industrial Talk. You need to go out to Industrial Talk.com You need to be part of webcast, the learning management system. Be a part of the podcast. It's easy. You talk to me, go out to Industrial Talk, click let's connect. Boom, you're chatting with me. And let's solve problems together. You're the hero in this conversation. Be bold, be brave, dare greatly hang out with Kevin change the world. We're gonna have another great conversation shortly. So stay tuned.