AI-Powered Predictive Maintenance Success

Ed Ballina (00:00)

Hello fellow manufacturing peeps. Hi, I am Ed Ballina and this is Manufacturing Meet Up.

 

Alvaro Cuba (00:09)

Hello guys, Alvaro Cuba here. You are in the plant, Ed.

 

Ed Ballina (00:14)

I, you can’t tell by my background cause I’m in the hotel, yes, I was in the plant, deep in soda, making adjustments, making the world better for all sort of production. How, how crazy is that? You know, but.

 

Alvaro Cuba (00:26)

That’s great. You do a fantastic job gaining these guys’ efficiency and service and a lot of things. So keep it at it.

 

Ed Ballina (00:34)

I certainly, certainly will. Well, as I mentioned before, welcome to Manufacturing Meet Up Podcast. This is our second year. Woohoo! Yeah, we finished off the year and we’re kicking off this year with a lot of guests, which is a lot of fun. So this is the show where we kick back on our downtime. We talk about what’s happening on the shop floor.

 

Ric Wojcik (00:47)

Yes, nice

 

Ed Ballina (01:02)

We talk about efficiency and waste and the environment. So, welcome to MMU.

 

Alvaro Cuba (01:19)

Well folks, today we have a very exciting show. We are very happy. We have a special guest. But before going into that, this is going to be about AI. And I just wanted to recap a little bit. For sure, you have been in the news, hearing all what is happening on AI. These two first months has been crazy.

 

New Announcements by President Trump about 500 billion investment in infrastructure, data, Brookes, OpenAI, Waymo, all these companies investing six, seven, $10 billion each. No, and I’m sure you also notice in your day to day. Right now, Google.

 

Search has AI embedded. ChatGPT, most of you probably already have and are enjoying it. Even the iPhone now, just buy the iPhone and it is there. And not only in the US, but in international Alibaba and for sure you read about DeepSeek and how cheap they can do AI abroad.

 

That’s on the supply side. So a lot of money being put on AI. On the demand side, it also a lot of activity. Just a couple numbers for you to have in mind. 70 % of the manufacturers today in the US are investing in AI. And it’s suspected that by the end of next year, so.

 

year and half away, 93 % adoption of manufacturers. So almost everyone will be on AI. And more important because yeah, you can go on AI, but what that means, just I read couple surveys, one the Institute of Supply Management, the other Accenture.

 

They mentioned on their service 50%, 60 % of people that are investing in AI and smart manufacturing are reporting.

 

more profit than they expected when they went into the projects. No, and basically it’s innovation, productivity, efficiency, service. So a lot of activity in both sides. And we have been talking in the show about how to go in a concrete way. And that’s why we are pleased to have one concrete example today with us.

 

that will share with you what happened in a real example, a very success example, and that now is expanding and taking it to the next way. So Ed, I’ll let you introduce our guest.

 

Ed Ballina (04:34)

Absolutely. You know, I can’t, I can, but I can’t add a ton to this AI conversation, except to tell you that last week I was in Austin, Texas, speaking to a group from Standard Industries on AI, predictive maintenance, et cetera. And what’s coming our way is incredible. And we’ll have to save that for another episode. But as I sat enjoying an adult beverage in Austin, I counted no less than 10 self-driven

 

vehicles go by. Okay, the first time one of them passed by, I seen it, it’s at this, what is this car with this thing on top? And then I look a little closer and there’s nobody driving. There’s a woman sitting in the passenger side as this vehicle is navigating the streets of Austin. And I thought, well, that’s kind of odd. And then I looked and within literally within half an hour, I must have counted 15 of them. I mean, real tangible examples of where this is going on. So anyway, enough on us because

 

really we’re here to talk about Ric Wojcik, who’s a senior manufacturing engineering manager for Fiberon, and who was also one of our spotlight award winners. Alvaro and I had the opportunity to co-host that event at the end of the year. It was Augury’s first kind of customer award celebration, and out of hundreds of customers, Fiberon showed up on top. And this person you’re going to meet?

 

Was a huge part of making it happen. So Alvaro, let’s talk to our buddy Ric Wojcik, another MMU guy.

 

Ric Wojcik (06:10)

Thank

 

Alvaro Cuba (06:11)

Well, welcome, Ric. We are so happy to have you in the show. Yeah, I know our pleasure. I think our audience will really appreciate it because one thing is you talk about this and the other you have a very concrete example and very successful as you did. But before we go into the project itself, how was the life

 

Ric Wojcik (06:17)

Thank you for having me.

 

Alvaro Cuba (06:41)

in the plant? How was the life of the operators and the mechanics before you implemented AI in your plant and predictive maintenance?

 

Ric Wojcik (06:52)

Well, you know, when it comes to maintenance, you know, our objective always is to keep uptime at the front at the front of the the front of the need in the facility. And, you know, for the size of our facility and the size of our maintenance group, it’s always a challenge being on top of everything from a reliability perspective, looking at motors, looking at gearboxes, stuff that does take maintenance, stuff that does take that attention.

 

We probably did not do as good a job at it as we should have, and thus you have failures that you really don’t want. You know, failures are one thing, failures at two o’clock in the morning on a Saturday or a whole nother deal. And those can be frustrating for everybody, for maintenance, as well as the facility having to deal with it. So yeah, that was something that we worked through. I’ve been here about two years now. So that’s something that we dealt with.

 

We’ve seen some good change since Augury came on board with us.

 

Ed Ballina (07:56)

That’s terrific. I mean, certainly from having spent some time talking to you guys and reading your story, it really is very compelling. So talk to us a little bit about how you sold this in, right? So when you find out you’re going to be a pilot for this predictive maintenance, know, something from headquarters and we’re here to help, right?

 

How did you sell that to your organization? I copied a term from somebody a long time ago. It’s WIIFM, right? It’s what is in it for me. And I think about that when I try to do something to the work, something new, because we want to do what’s right and get one for the Gipper, and all that other good stuff, right? Be altruistic. But deep down in our core, there is always that question, what’s in it for me?

 

Ric Wojcik (08:49)

Wow, that’s a great question. Yeah, I think the challenge is, I think everybody, including myself, I’ve seen systems similar and they promise big results and big returns, but we don’t always see them. Right. So I won’t, I’d be fooling you if I told you I was all in when I first heard about it. There was definitely some apprehension in my part because I knew it was going to be a large expense going into it. I wanted to see us get the bang for the buck.

 

After talking with everybody in your group and the several meetings that we had, I could see a difference. I could hear the difference, understanding what support we were going to have from Augury, from the reliability assistance to the day-to-day monitoring to the way I describe it. It’s kind of like having another person on your maintenance crew that you don’t have to feed, right? It is having your reliability person

 

that can interpret the data that they’re seeing on a daily basis. Give us a heads up on when something is in trouble, when it may fail, what we need to do moving forward so that we can keep it on track. And so the sale for me was all about minimizing downtime. If we could show that being more proactive in the business and on the equipment on those particular devices, we’re going to reduce the unplanned downtime

 

that’s associated with it. And that was the biggest sale. The other challenge too was convincing folks that, we’ve got a system now that is going to work with us. We have to trust the system. And that’s something we’ve done. You have to trust it. You don’t want to put it in and then ask yourself, really? Do we really need to do that? Is it really a problem? So you put it in, you trust it, you move forward. And I think in a lot of cases, most of the cases we’ve seen, it’s absolutely true.

 

So I think the sale was in what we’ve seen, what we’ve been able to present and demonstrate to everybody, whether it’s upper management or folks on the floor or technicians in the maintenance shop. Trust it and work with it.

 

Ed Ballina (11:01)

And such a key point because a system that cries wolf all the time for us manufacturing folks, you give that two chances. After that, it’s the idiot light that nobody pays attention to. So false positives, right? If you’re not in the 95 to 98, 99 % reliability for accuracy, my God, it doesn’t take one, ah, you know what, to

 

just lose complete faith in the system.

 

Ric Wojcik (11:33)

Absolutely correct. Absolutely correct.

 

Alvaro Cuba (11:36)

Ric, tell us, for sure you have several, but share with us one success story.

 

Ric Wojcik (11:45)

Wow, we were just talking about this yesterday.

 

Alvaro Cuba (11:48)

Yeah,

 

well, one that you when you say this at the end, say, “oh, it worth the investment.” Yeah.

 

Ric Wojcik (11:56)

So, and I’ll give you one, we’ve had multiples, but one that came up recently. Because of what we do here, uptime, obviously in any manufacturing facility is important, but uptime is crucial for us, the way our process runs. And we had a condition on one of our large extruder motor and gearboxes, on one of our process lines that was beginning to show some signs of failure. As it does with…

 

with the Augury system. We had some responses back from the reliability tech to say, check these components, see what you’ve got going on, and then get back at us so that we can continue to monitor. Well, in this situation, it turned out that we had a bad setup. The motor and the gearbox had issues over time. And it wasn’t that we didn’t have to change it out. This system doesn’t mean

 

that everything’s going to be saved, right? It’s not what it’s about. It is about being able to plan it, know about it far enough in advance, make the plans and work through those plans on a planned basis instead of an unplanned basis. On this particular situation, this motor and gearbox needed to be replaced. We knew we had a shutdown coming up, the plant was going to be shut down, okay? And so we were able to plan the motor, the gearbox,

 

the alignment, the startup, everything during a shutdown. Now that process took eight or excuse me, 10 to 12 hours, but we had zero downtime on the line because the plant was already shut. Had that failed in the middle of the night or on a weekend, we wouldn’t have been able to respond so fast. And that’s, think that’s the biggest bang for the buck when we talk about Augury, and what we can do with it, getting that heads up, being able to plan, do your due diligence upfront. And, and even if

 

You have to change it out. You’ve done it in a controlled situation rather than that panic mode of, we’ve got to get the line up and running. So that’s been a big help. And that’s typical of what we’ve seen.

 

Alvaro Cuba (14:03)

That’s very, is clearly a success story and you did it all of the equipment at the same time, which also helped you a lot. One success story on the people side. Wow. Something that is represented a win for your guys.

 

Ric Wojcik (14:28)

Well, I think I think the wins come from the proof in the pudding. Right. So it’s like I said at the beginning, we have to trust. We have to trust what we’re being told from the system. You mentioned crying wolf, you cry wolf too many times. People start paying attention to it. And I think the wins come from demonstrating that this is what we saw. This is what we’ve been told to try, told to do.

 

We followed through with those recommendations and we’ve seen the improvements in our program. We’ve seen the improvements in our equipment. And for me, that’s the people side of it, right? They start trusting it. They start buying into it. We see issues and we say, hey, we’ve got to fix this. They may look at it because we get really good reports from Augury what we have to look at. They buy into that one. They say, oh, by the way, we need to look at this and this as well. So they see it. They believe it.

 

And now they’re starting to follow more and more with it and paying a lot more attention to it. So that’s the people win. You know, of course you’ve got upper management that they want to see those results as well. So we’re able to demonstrate that, show them some of the savings that we’re beginning to see along the way, either from planning downtime so that we’re not doing things, you know, unexpectedly or avoiding costs along the way. Hey, had we not done this, we could have possibly paid for this expense. And so we’re starting to see some of that on both sides.

 

Alvaro Cuba (15:54)

And I’m sure that less stress for the people, they can think more instead of reacting. Absolutely. And obviously safety and quality also impacts them.

 

Ric Wojcik (16:08)

Yep. Yep. And I’ll tell you, it’s a two way street. I mean, we’ve learned some, we’ve learned a lot from Augury. Um, I think the group of folks that we work with at Augury are learning from us as well, because everything’s not just black and white, right? I’m sure. So we have situations that maybe they haven’t experienced and, we get to kind of teach them as well as them turning around and helping us as well. So it’s been a two way street all the way through.

 

Ed Ballina (16:33)

You know, and it’s that kind of attitude, right, that yields to kind of wins that you guys were able to post, right? Embracing the change, being willing to be part of the journey and teaching each other, right? Learning from each other and you end up with a much better product at the end. And, when you talk about the impact on people, hey, look, nobody likes to run a crappy line, right? So if implementation of these things means

 

I don’t have to pick up pieces of bearing that blew up in the shaft, wade through all kinds of waste product because the line just started puking forth. That makes for a crappy day.

 

Ric Wojcik (17:17)

really

 

Alvaro Cuba (17:19)

We all have those kind of lines.

 

Ric Wojcik (17:22)

Yes.

 

Ed Ballina (17:23)

It

 

does. Nobody likes to run a crappy line. Hey great story, right? I was, you know, we’re thrilled to hear, you know, hear some of the details. But now that you’ve been at this for a little while, what’s next? I mean, obviously more of the same, right? But I mean, you guys thinking about process health? Wow. You know, there’s so much that you can launch on this platform, right? Yeah.

 

Ric Wojcik (17:50)

You know there is and it’s you you say what you ask what’s next and and and I look at where we’ve been so far so as the pilot facility just just in Fiberon we started out with 40 machines okay and of course now Augury is Fortune Brands wide so all of our facilities and Fortune Brands is utilizing Augury instrumentation but just for 1NL, for the facility that I

 

reside in, I’ve actually I’m over all three facilities here in New London, but the facility here at 1NL, We went from 40 machines and we’re now up to 180 machines that are covered so we cover everything in the facility and the benefit of that was we went from just production and process equipment now, we’re also monitoring all of our I call it my ancillary equipment everything that feeds

 

material handling and and so we’re now able to get a much larger view on everything that’s happening in the facility than we’ve ever been able to on a much more regular basis. Again, it’s like having that extra reliability tech on hand that and it’s you know, it’s not a body. So we talked about what’s next. I I personally think we’re still in the mode of learning everything we’ve got, right? How do we manage it? What do we do with it? You know, how do we, how do we take

 

the action on the items we find and respond to them in a more effective way. I think we’re still there with that. We’re still moving forward with it. We’ve got another facility down the street that is our recycle plant. They have 72 machines covered and they’re in the process of learning the systems as well and using them, but they’re all getting used to, are we 100 %? Absolutely not. We miss stuff.

 

And it’s all about that growth, right? It’s looking at it, understanding how we manage the time, how we manage our resources to get it done. And we’re getting better and better at it. And I think that’s the excitement for us. We’re making time to follow up on what needs to be followed up on. Like I said at the beginning, if you’re not going to use it, we’ve just wasted a lot of money. So you’ve got to make time for the repairs and the resolution to take place.

 

We’ve got to have the folks who are going to let us have the equipment and take it down and do what we need to do with it so we can move forward with it. I think that’s the best way I can answer that question. I think we’re still, even though we’ve got all this stuff going on, we’re still in the mode of let’s get better at what we already have.

 

Ed Ballina (20:31)

No, and look, that is kind of a typical migration if you think about it, right? So you start off with machine health. And what do most of us do as manufacturers? Hey, man, just give me my top, you know, my top 25 % most critical pieces of equipment, right? Because those are the ones that are going to kill me. But let’s be real. We all know that our lines may run well, but if the water treatment pump fails, right, and you can’t get rid of your waste, you’re shutting the whole plant down, right?

 

So moving from what are considered core processes, very critical, right, to ancillary systems is a natural progression. You’re also helping to drive this whole mentality of predictability, right? We can now control ourselves. And, you know, eventually as you run out of runway on the machine health, process health is right behind it to give you that total production health.

 

you know, nirvana that we’re all searching for.

 

Ric Wojcik (21:31)

And that’s exactly what we’ve done Ed. I mean we you know our initial equipment was was processed and when we went to the ancillary stuff or the the the raw material delivery systems those are systems that will shut the entire plant down. Right? So we could control a production line but when you take half the facility down because you can’t feed material in that’s a whole nother animal and a whole nother set up.

 

A whole other set of liabilities that we have to be careful with. Yeah, and so it affects, and when that happens, not only do we affect production and process, but we affect people. And we’ve got to avoid doing that. So everybody comes into play with that when we’re faced with it.

 

Alvaro Cuba (22:17)

Ric, one last question. On the lesson learned, something that was a challenge, something unexpected, something that you can share with our audience. If you are going into this, watch out with this, watch out with just a couple of lessons learned.

 

Ric Wojcik (22:42)

And I think the biggest thing is, well, I won’t say the biggest thing. Be prepared up front. One thing that Augury does is really emphasize preparation moving into the program. Understand what you want to cover. I think you want to ensure that you are getting the critical pieces under the system.

 

Again, things that will shut you down for the day, things that will shut you down for a couple days. Understanding what that is. And I think we did a lot of due diligence up front. That’s how we got to 180 Machines. We did a lot of due diligence up front to understand this is what we want to cover. It wasn’t just a knee jerk, a haphazard, what do you think we ought to do? We took time and thought about it. We did it as a group to know where we were heading with this process, knowing it was going to be a long-term program.

 

So that was one thing going in, I would tell everybody, just don’t put yourself in a silo, open up the folks around you, find out what’s important to everybody. And the second part that I would say moving into was develop a good plan for follow-up. You can get lost. You can get lost in the systems. Develop a good plan for the follow-up and how you’re going to move forward once you start receiving the information that you receive.

 

Because keep in mind, if you haven’t had Augury from the beginning, you’re going to hook all these sensors up and all these nodes. And then all of a sudden, you’re going to find out you have problems you never knew you had. And so that was a thing. And we weren’t inundated by them, but we had things that you would never know. And we said it going in. Oh my gosh, what’s this going to be like when I hit the on switch?

 

because we’re going to find things that we never would have expected. So be prepared for that. Don’t be disappointed that, oh my gosh, we’re doing a wrong job. It’s not. This is what it’s all about. But then you spend your time playing catch up. Get the things that the reliability to your suggestion you do go through that and you’ll have some good, you’ll have some bad. Take them both. But those are the things to be prepared with when you go into it because it’s not going to be hit the switch and lights are on and everything’s golden. You’re going to find, but here’s the key. You want to find those things.

 

That’s what it’s all about. You want somebody to tell you if you don’t want it, you shouldn’t do the system. You want to find out what’s going on with it. So those are the two. I think those are the two things that were challenging for getting to that point and, you know, being able to follow up on a timely manner and responding back to Augury with what you find and how things are moving along with your, with your program.

 

Alvaro Cuba (25:24)

That’s terrific. It’s so great to have you talking about the really concrete example of putting AI and moving towards smart manufacturing and with real lessons and real results in the plant floor. So thank you so much for that, Ric. Ed, anything else with Ric?

 

Ric Wojcik (25:46)

You’re very welcome.

 

Ed Ballina (25:50)

No, I think you guys have covered it all. Again, Ric amazing, hearing your story several months ago, even better, hearing it live from you today and, these details. I mean, it’s, it’s obvious, you guys have a great grasp on this, where you’re at, what you need to go forward, a good culture in place. Man, I, I think you guys are to be tough to beat for the Spotlight Awards in 2025.

 

Ric Wojcik (26:27)

Absolutely.

 

Alvaro Cuba (26:27)

Thank

 

you so much and keep it up.

 

Ric Wojcik (26:31)

Thank you guys, appreciate you having me.

 

Alvaro Cuba (26:33)

Yes, no, thank you. And with that, I think we are coming to the end of this second episode of the second season. Always great to be with my co-host Ed. And that would be the wrap up for today, guys. And please help us to keep growing up this manufacturing meetup.

 

If you watch us in YouTube, please like us. If you’re listening in iTunes, write a review, but more important, share with your pals and come enjoy with us and we’ll continue the discussion.

 

Ed Ballina (27:20)

Awesome. And hey, not to peel back the curtain, but we have more guests where this guy came from. Okay. And you have to admit he was pretty engaging and a lot of fun and better than just hearing Alvaro and I just babble on, you know, senselessly. 25 minutes, right? So more to come. And if you like this, you want to keep the conversation going, you can email us at mmu@augury.com.

 

You can also find us at the Endpoint. It’s a free online community for manufacturing supply chain peeps like you and I. That’s endpoint.augury.com. We also have links in the show notes for this episode. So see you next time from your manufacturing meetup buddies.