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SUMMARY KEYWORDS

data analytics, AI solutions, customer obsession, advanced analytics, generative AI, decision intelligence, actionable insights, IoT integration, predictive maintenance, energy management, HVAC optimization, connected ecosystem, data storytelling, proactive maintenance, data platform

00:00

Scott. 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

00:21

all right once again, industrial professional. Thank you very much for joining Industrial Talk and thank you for your continued support of this platform that celebrates you each and every day, because you know you're bold, you're brave, you dare greatly, you innovate, you're collaborating, you're solving problems each and every day. You're making the world a better place. That's why we celebrate you on the number one industrial related podcast in the universe, the universe, I tell you, it's all backed up by data. Speaking of data, we're going to be talking about data in this particular conversation, but we are broadcasting on site. Accrent Insights. Is the event we are in San Antonio, Texas, and it is a collection of individuals that are truly passionate about solving problems. It is all here. They are absolutely fantastic. Again, I said data. We have Charlie Boyle in the hot seat, VP of advanced solutions. They say that, right? You did. I got it. And we're going to be talking about, well, data analytics and AI and, you know, it's a paper and pencil conversation. Let's get cracking. All right. You having a good conversation? Awesome. It's good. It's great. It is cool. This is a good were you here last year?

01:33

I was and I think last year was interesting, because you had a different, diverse group of clientele and maybe more variety of sessions, but now it's more focused on certain topics. Very, very structured this year. Love it. It's

01:49

amazing. That's the that's the sense I get. But what I do get is, without a doubt, customer is king, oh yeah, in this

01:58

customer obsession, one of the Florida FBS. It

02:02

is absolutely and it it shows all right, before we get into the conversation, give us a little background on who Charlie is sure. So

02:10

Charlie Boyle, VP and product line, General Manager of advanced solutions for accru. It started with the company about two years ago as the VP of data strategy. Person, yeah, new person as the head of data strategy, I got to interact with across the different FAL operating companies, so facilities and asset life cycle group. I've been in advanced analytics and data science for about 25 years, most recently, the chief data scientist at Honeywell and most recently, Chief analytics officer at a telematics company based in Rome. So this is, this is great to be back in back home side.

02:50

You've seen a lot of changes I have,

02:52

and I've been lucky to see a lot of different interesting analytic use cases. Just, you know, the use of AI in a different capacity and different industries and different verticals solving different problems. And that's what I love about the discipline. Quite honestly, what

03:07

happened all of a sudden one day, you know, we were talking about AI, we were talking about, yeah, getting data and collecting data, and it's that's important stuff. There's gold in the data and and then all of a sudden, boom, chat GPT comes on a thing, and everybody's thinking, oh my gosh, it's here. What has changed so that it enabled all of this stuff? And then all of a sudden, this plethora, this tsunami of use cases coming out, and like, oh my gosh, it's just the most amazing things. What changed? I

03:41

think it's it has a lot to do with the computing power of the machines that we use today, right? I mean, and the plethora of data that's being captured across a number of different industry verticals, in different markets, just data is king, as you said earlier. And quite honestly, the more data that's out there, you're starting to need solutions, automated solutions, to sift through it. Quite honestly, the human can't do it anymore. And what you're using generative AI and some of the newer AI tools is really to do data storytelling, right? I talk about decision intelligence, right? How do you take data, go to insights, and then generate action, because I want actionable analytics. I don't want to just have diagnostic solutions that tell me that there's a problem. I need someone. I need a solution that tells me how to solve the problem.

04:31

Yeah, but it's just amazing. I think, I think we're going to have, how do you deal with the amount necessary? I go off on a tangent because I just, I, I think about it, but I have no answers, and so I'm going to pick your brain. So you get these server farms, right, and you get this gen AI, and you're just constantly generating data, just and not to mention all of the stuff that these, these, you. These companies are, are building, and everything goes into the cloud. I think the continent of Australia was going to be a server farm.

05:10

Got to be Scott to be, I don't see put it down in Antarctica. It'll never, it'll never stay cool. They'll say cool exactly. You never have to worry about fires. You never have to worry about the data melting.

05:21

No, I'm just, I'm dumbfounded, nice,

05:25

tremendous amount of information out there, and it really is. I look at generative AI as not just content generation, but it's that ability to decipher information from, you know, actionable need of the data set, right? So I always talk about the difference between informational and actionable, and there's informational alerts that I get from my system, and then there are alarms that I need to actually take action and prevent something from a disaster from occurring.

05:57

Okay, so let's, let's delve into it, sure. So what we have is we have, all of a sudden, this, this and crew, and you are collecting data, you're you've got data coming in and with clients are looking for ways of making their business more efficient, more actionable information, greater Insights. And yeah, that is a that is a real good use case for a an AI type of solution, because I just, I can't do it, sure, but everybody's telling me I need to have all this data so the internet doesn't lie. So where does that fit into Accrent, what do we what? How are you thinking through that? So

06:43

the mission at accruing is really to unify the built environment. And you heard, you heard Richard and Brooke during the keynote speak to the fact that we are about the connected ecosystem. We can't have the connected ecosystem without IoT, and that's the industrial internet of things, and that's where advanced solutions are really focused. So my area of focus is to manage the data platform for Accrent across all the product suites. So that means all of the reporting, all of the business intelligence, and essentially our insights generation, or insights as a service engine. And then the other aspect is our observed product, which is situated around IoT, and it's our IoT analytics platform, and we connect sensors to different assets, and that telemetry data is being streamed into our data warehouse. We're doing calculations, we're doing we're applying different predictive models and set and basically solving business challenges for our customers.

07:42

Why is that important? What's going on? And I have my idea, but I want to hear from you,

07:47

yeah, I think it's a high total cost of ownership for certain assets, right? You think about the retail space, you've got the ability to have these large refrigeration cases going down overnight and food spoilage. And I think that's amazing. Yeah. I mean, we have customers who have come back and tell told us that, you know, just saving a catastrophic event on one refrigerator case will save 50, $60,000 in food spoilage, because, you know, you need to be proactive in maintaining these assets. They're expensive, right?

08:19

They don't have the bandwidth either. No, they don't have the bandwidth of being able to sort of say, Okay, we have a full time maintenance individual doing, you know, the diagnostic, and we're wandering around and

08:29

what think about the optimization scenario, right? Like, if I've got six or seven different ones going down at once, and I need more than one technician to come out and at different times of the day and different days of the week. I mean, it's a coordination nightmare.

08:42

Oh, yeah, absolutely. So with all that said, I know that everybody's been talking about this connected environment, this and how are we how are you looking at looking at the data, looking at AI, looking at at all these connected solutions. How are you evaluating what use case is getting elevated to the we

09:06

have a really interesting model at fordive, where we follow what we call the dream process. So we take it through a few different stages of looking at whether it's a problem worth solving, doing some customer validation, and then executing on what we call a business model validation for most of the data science and AI solutions that we're developing, we're actually partnering with Ford and their Data Science Center of Excellence. They call it the fort fort, yes, so and I have one of my data scientists here from the fort, and he and I have been presenting on our anomaly detection algorithm. But what we do in that partnership is they are our accelerator. They are building rapid prototypes, MVPs, minimum viable products that we can then demonstrate to our clients and check and validate if there's any value in it to them. Is it a solution that there were work? You know, they're willing to. Spend on, or is that a nice to have? And that's the distinction that we really need to make every day when we're evaluating these solutions, because, quite honestly, sometimes it doesn't require an AI based solution. Everybody wants to jump on the AI bandwagon. I guess I

10:14

agree. I just, for me personally, I don't want to see how the sausage is made. I certainly want to just be able to say, here's my business. I have the solutions that that are in place. I'm getting the data that I need to make, the decisions that I need to make to make this a more resilient business, sure efficient business, managing what I need to manage, and it's sort of pushing that information to me. I don't have to go search for that information.

10:39

Yeah. I mean, it's the distinction between being proactive and react. Yeah, right. And our customers don't want to react, no? Because once they have to react, that means they've, they've lost right? There's an inefficiency in that situation, in that workflow. They want to be more proactive. And if we can give them the lead time in order to be proactive, and the insight to go resolve their, you know, resolve the issue, or from a maintenance perspective, or, you know, from a reporting perspective, they're going to, they're going to generate so much value from that.

11:10

With all that said, and you're, you're in the trenches, you've seen a lot of changes happening. You see a lot of things happening going forward. Put your future hat on. What do you see happening with you know, everybody talks about collecting data, but what do you really see from a use case perspective,

11:27

three or four primary use cases at accruing I mean, we're looking at accruing space intelligence, the occupancy analytics, right? That's huge. Predictive maintenance is a top of mind because our asset management solutions are driving all of this telemetry data we're learning about different assets every day, the nuances of those assets, we're leveraging generative AI to really assist an aging technician population. I don't know if you've heard this silver tsunami. Yeah, the silver tsunami is hitting us, and quite honestly, it's hitting those maintenance technicians that are going and retiring, and the new knowledge base is not being transferred over to the younger gene. Having that conversation.

12:11

I was with the consulting firm, and we were having that same conversation. These planners are going to be retiring anytime soon. How do you just pull that information out of the head? We just didn't have the capability,

12:21

yeah, and now we have like, they can document their notes, they can document their knowledge base. We can put it into a generative AI solution, and a junior technician can go out and look at an asset that's being, you know, need, that requires maintenance, and they can pull it up and ask it a natural language question, and just get the answer right in front of them.

12:41

13:15

is exactly the use case that we would be trying to solve for. And quite honestly, it's pretty simple, right? You take the OEM, the original equipment manufacturer training manuals, you take the maintenance technician manuals, and you take all of the notes that those aged technicians have been pumping into our CMMS platforms for years, and you allow and you train the generative AI model to basically collate all of that data and generate an answer when it's connected to a specific anomaly or a specific incident that's been or event that's been generated. See,

13:52

it would seem to me, too, for accuracy with these Gen AI solutions. Now you got the chat GPT, and it goes and scours the World Wide Web and then comes up with, no, that's not the answer I'm looking for. I said it this way. I didn't say it that way. Why am I getting that? You know, really odd, odd response. But if you, if you encapsulate the Gen AI into a certain environment that I'm pulling that information from, like you said, CMMS, other sources of data, it's a single source of truth, yeah, so I I get it.

14:33

And quite honestly, the training of those of those models isn't all that difficult. When you have that single source of

14:39

truth, yeah, it's coming from you. This is like, yeah, yes, I think it's easy coming from that perspective. No, sure. So that's that's interesting. So with that, what other items? What. What other data analytics outside of AI, what give us another paint, another picture out there that I'm collecting, I got my devices collecting data. Give us another How about

15:10

energy management? All we keep hearing about is the cost of energy is skyrocketing for all of our customers. How about we utilize those sensors to do auditing on your energy bills. How about we compare what the what the energy is coming off of the meters and the sub meters to your energy bills. And then you can go back to your energy provider and say, hey, look, I didn't use this much, and we're saving five to 8% across the board for many of our retail clients, really? Yeah, and it's not just an auditing situation, it's, it's also a controls automation, right? I can reduce the set point if it's cooler outside. I could raise it up if it's, you know, it's 98 degrees out here. I mean, yeah, I'm glad that it's cooler in this

15:56

I'm glad this facility works. Because Have you, have you been walking around? It's like, why am I doing out here? People are melting out I know. Why am I walking around in this i and then you start sounding like a whiny little baby, because I'm crying. I'm going to anyone like this heat. It's kind of any

16:13

problem. Well, it's interesting. The HVAC use case is one that's near and dear to my heart, because it's how I started in the industry. Yeah, right, when I was at Honeywell, that was our primary use case, and I learned so much about how the HVACs in certain institutions think about, like, your big box retailers, you know, some of your corporate real estate, there's not just one HVAC unit on the top of this hotel. There's like 50.

16:36

You see this cooling tower, yeah. Speaking of right there, right there, all right. Now, for the listeners out there, as you wrap this up, how do they get a hold of you? They're saying, God, this guy knows his stuff. I'm

16:53

on LinkedIn. I'm on LinkedIn. Did we have that conversation before? I think we did. He's on LinkedIn. I like it

17:00

now. That was a great conversation. Yeah, you got a lot of pepper.

17:07

All right, passionate about the

17:08

job. Yeah, man, you can tell all right, listeners, we're gonna wrap it up on the other side. We're gonna have all the contact information for our friend Charlie out there and reach out. He wants to collaborate, at least again, you need to educate, you need to collaborate and you need to innovate each and every day. Make that a priority. That's the three legs to the stool that I always talk about. All right, thank you very much for joining. We, once again, are broadcasting from Accrent insights here in San Antonio, Texas. It's a must attend event. Wonderful people solving great problems. So stay tuned. We will be right back. You're

17:43

listening to the Industrial Talk Podcast Network.

17:53

Again, if you haven't heard yet, enough data. Data, you can't experience all this wonderful, innovative stuff without the collection of data and then being able to take that data in such a way that it makes sense. And that's an AI solution there, you know. So I get the tsunami of data. It's coming in, it's coming in, it's coming in. But how do you sort of sift through it so that you get tactical insights? Yep, Charles Boyle, he knows it. Port of is the company the event was accruing insight. And don't, don't be left behind. You need to collect data. You need to do it. All right, Industrial Talk is here for you. You industrial professional, you have a podcast, you have technology. You want to tell your story. Go out to Industrial Talk.com talk to me. You won't be disappointed. All right, be bold, be brave, dear. Greatly. Hang out with Charleston. You're going to change the world. So we're going to have another great conversation shortly.