Episode Transcript
[00:00:00] Speaker A: What if the biggest barrier to smarter manufacturing isn't AI or automation, but bad maintenance data? In this episode, we dig into why so many teams are still stuck in reactive mode, how to move up the maintenance maturity curve, and why technician friendly tools matter in a tight labor market.
We'll also explore how better CMMS data can cut downtime, improve planning, preserve tribal knowledge, and make AI useful on the plant floor.
Welcome to Ctrl Alt Manufacture.
Hello, hello, hello everybody. Welcome back to the Control Alt Manufacturing podcast, Resetting and Rethinking Manufacturing where we're going to be exploring some of the people, the technologies, the strategies that are driving the digital transformation of manufacturing. With me, I'm Gary Cohen and Stephanie Neal.
[00:00:57] Speaker B: I'm Stephanie Neal. I thought you were gonna call me Thing two.
[00:01:01] Speaker A: No, I only do that once. Well, I may do it again.
[00:01:03] Speaker B: You've done it twice, but.
[00:01:05] Speaker A: Okay, well I won't then, then I'll have to retire that one. But this is Stephanie.
[00:01:10] Speaker B: Hello.
[00:01:11] Speaker A: How you doing today?
[00:01:12] Speaker B: Good, I'm good. How are you?
[00:01:14] Speaker A: I am doing well. We got a good one today because we have, well we have, I wanna say two Rosses. Although it's a first name and a last name. We have Paul Ross of Limble Ross Ferguson of RBC Bearings. We're gonna talk about smarter Maintenance Data Technician first.
Practical AI can help manufacturers move, like I said in the intro, from reactive kind of firefighting to more proactive, reliable operations.
But before we get into that, Stephanie, we're gonna do a get to know Stephanie on this one.
You said on a previous podcast that you are a foodie. I know you just did a whole vacation based around food.
Do you have a favorite restaurant or type of food that you go to over and over again? I wonder because my partner's a foodie and she like we'll do, you know the, the menus. It's now the words escaping me now that we're on, on camera here, the course menus where you get like seven little courses and really fancy stuff. Are you into that kind of food or you like it a little bit more casual?
[00:02:19] Speaker B: I love Italian and we have like a really good authentic Italian restaurant near me and it's called Mother Anna's and a little plug for Mother Anna's in the Boston area. But like they homemade fettuccine and like just, it's just mouth watering. Like I just, I love it and it's like walking into your, your nana's kitchen and, and having that, that comfort meal. So I, I love that I can't have that A lot, because, yeah, a lot of comfort. But guess what?
Yesterday. So we're recording this on a Monday. So yesterday, Sunday, I Dec.
Sunday, dinner's back. And so my adult children, they're all moved out, they're married. And I said, hey, you know, come over, I'm gonna make spaghetti and meatballs. And I made a Tuscan white bean soup. And it was amazing, if I do say so myself.
[00:03:18] Speaker A: So next time I'm in Boston on a Sunday, I'll be there. No, that sounds really good. Yeah, Italian food, you really can't, kind of can't go wrong with.
That's sort of how I feel too. Like I, I like the tasting menu. That's the word I was looking for earlier, by the by. But, but yeah, like just a good meal with whatever Italian food. I mean, my daughter could eat Italian food every day of the week and would be the happiest person in the world.
Yeah. So let's, let's, let's turn the corner here, segue and talk a little bit about our topic of the day. I mean, I think Stephanie, obviously in the era we're in now with digital transformation, a lot of manufacturers, everybody's excited about AI. Automation is impacting just about everybody.
And we talk about this a lot in different ways, but a lot of operations are still kind of dealing with the same maintenance issues, like reactive work, incomplete records, not knowing what to do with all the data they're collecting, disconnected systems. We talked about this on a recent podcast and we've talked about it before, kind of tribal knowledge that exists, too much tribal knowledge.
And that can end up being a problem because if you don't have that right, foundation technology can't save you. All the higher level technology in the world won't really deliver value if you don't have those foundations in place.
[00:04:45] Speaker B: Yeah. And I think the bigger problem is a lot of this information is still captured with pen and paper, so it's not even digitized. So then how do you manage that and how do you get that into the system?
I mean, I really think that's still happening. I mean, our guests today can probably confirm or deny that it is, but I think that it is. And I think people, it's just stuff gets overlooked. And I'll give you another, like just real world example. Today there's a knock on my door and there's a guy trying to sell me something, right. But he's like, look at your window sills and look at your door and like the wood is like starting to rot and I'm like huh, I guess it is. I never really noticed that. But I think that's just like any sort of maintenance type of thing. A lot of things get overlooked because you're rushing to get to the next best thing and do all this digital transformation stuff, all this cool stuff that we're talking about, but you still just have to keep managing maintenance and operations and it's a critical part of the whole puzzle.
[00:05:48] Speaker A: Yeah, and I think that's exactly the point. So the question kind of becomes how do you build, like you said, these systems and processes in that actually help people do their jobs better while also creating, I don't say clean data but cleaner data and you know, helping improve decision making across the business. So that's the kind of stuff we're going to be talking about today.
We've got really great guests today. We have Paul Ross who is a Chief Marketing Officer at Limble where he leads global marketing and strategy and execution for the company's modern maintenance and asset management platform serving more than 3,500 customers worldwide. He's responsible for brand strategy, demand generation, product marketing and go to market initiatives that drive growth and customer adoption. We also have a customer of his, Ross Ferguson, who's a facilities manager at RBC Bearings. Ross began his career as a maintenance technician before joining Sargent Aerospace and Defense which is part of RBC Bearings, a manufacturer serving the aerospace, marine and industrial markets. He was recently promoted to facilities manager following the success implementation of a new CMMS system which he completed in just eight months, well ahead of the original two year timeline which of course we are going to ask him about.
Paul Ross, come on in.
[00:07:07] Speaker B: Welcome.
[00:07:08] Speaker C: Hey guys. Hello gentlemen.
[00:07:10] Speaker A: How you guys doing today?
[00:07:12] Speaker D: I'm doing well down here. How about you guys?
[00:07:14] Speaker A: I think it's unfair because Ross is the only one who's in real heat right now. He's in Arizona and we're all dealing with technically spring in places that aren't quite as warm.
[00:07:25] Speaker D: Yes, absolutely. I'm making sure the maintenance on our H VAC systems is up to date because we're going to need it this summer.
[00:07:33] Speaker B: Excellent.
Well, thank you both for joining us. And I guess before we even start we should try to define some things right because we throw around these terms and we just want to make sure everybody knows what we're talking about. So Paul, I'm going to start with you and just for our listeners who are may know or not know the term cmms, could you just give us sort of that definition but like the bigger picture, how does a modern CMMS fit into a manufacturer's broader digital transformation journey?
[00:08:04] Speaker C: Yeah, that's a really good question, Stephanie. I think CMMS basically stands for Computerized Maintenance Management System, which is a lot of words. But ultimately I think if you're right in terms of looking at a bigger picture, what these systems have evolved from is just managing work orders for maintenance or capturing those things things to being a maintenance and asset management platform. So all of this data, which was a key thing you guys were talking about in your introductions around data getting into these systems and then being used for decision making across the organization, that's really what these platforms are designed to do.
And I think it's a really good point that you guys were raising around.
The reality is a lot of organizations are still using pen and paper, maybe even Excel or Google sheets to kind of manage a lot of this stuff. And so this transition to moving from like break fix, how do I react to something going wrong in our production line, to being able to kind of build that preventative maintenance model, to being able to then get into things like condition based, you know, using sensors, those types of things, that's really the direction that the kind of whole space is moving. Is these things becoming a little bit more strategic and helping customers move along or helping manufacturers move along those that maturity model. And then the second thing that we're seeing is this, that data being then used for really big decision making like asset management, like when do I invest in a new circuit printer, when do I change out the conveyor belt on these things?
And that's where a lot of the data and a lot of the kind of work that folks like Ross are doing then becomes a strategic thing rather than just being perceived as just how do I manage my work orders.
[00:09:47] Speaker B: Yeah, no, that's really important, the strategic side of it. And just like understanding, like you said, when is it time to change things? So just to follow up on that, Paul Limble talks a lot about the five phases of maintenance maturity. Can you walk us through that model and explain what it takes for the company?
[00:10:04] Speaker C: Yeah, it's like the five phases are really, it moves from being reactive, which is that kind of break fix model. Something's broken, send a maintenance tech to go fix it.
And then we have preventative, where we're actually Putting in place PMs, preventative maintenance schedules for I need to lube the bearings on this machine every 200 hours of runtime, having that as a kind of scheduled thing so that somebody's being pushed in that direction. And then we get into things like Condition based, where we're introducing sensors, so like vibration sensors on a conveyor belt as an example of something like that, they're then being used to kind of make those decisions. So we're going out of tolerance. We need to take action and have that flagged. And then we get into the more advanced things like predictive. So when do I actually predict that I need to be able to do this based on all of my data and then get prescriptive where we're kind of like starting to get very closed loop in how we actually go about doing these things. So it's all very heavily driven by the capturing and the structuring of data from everyone from the technician on the shop floor all the way up to how the procurement teams operate, all of those sorts of things. So as we can talk about, the reality is that most organizations are very early in that maturity model and that has a big impact on things like uptime, how much. If we're being reactive, it means that our systems, our workflows, our machines are not up and running and producing and that has a big impact. So we can talk more detail about what does that transition look like and what we see in the market where people are today.
[00:11:41] Speaker A: Ross, I want to go to you here. So you guys have started using Limble as a product. When you joined RBC Bearings, what were the biggest pain points that you saw with kind of the legacy maintenance system? And how did you know it was time to do something about it and make a change?
[00:11:57] Speaker D: Yeah, absolutely.
My first project at Sargent was to replace our existing EAM or equipments or equipment asset management system.
So we already knew off the bat that we needed something.
And the biggest pain points with the old system that we had were it was unsupported, where if we had any issues with it, we couldn't go to the website to get any information or any guidance.
It was old too. It looked like it came straight out of the dot com era.
So it wasn't very easy to use in terms of that.
And on top of that, it was slow. You know, there were. I would see instances where managers would come in, in the morning, start a report and go get a cup of coffee because it would take, you know, up to 15, 30 minutes to just to generate a single report to then, you know, take into a meeting or show to leadership like that. So it was again unsupported. It was old and it was very slow.
[00:13:03] Speaker A: I assume you evaluated multiple platforms before landing on Limble.
What stood out during that process? When you looked at all the options out there and what were the main pieces that you were looking for?
[00:13:15] Speaker D: Yeah, absolutely. I think the two things that stood out to me the most when I was evaluating a lot of different systems was the ease of use and the support.
For me, with my perspective, you know, I was bringing a new emerging technology to a group of, you know, professionals who don't always use technology in their day to day operations. So for me that seemed like a big step, you know, sometimes could be a too big of a bite to kind of swallow. So I wanted to make sure that I found something that was easy to use, that was something that wasn't going to take them months to conquer and to learn. It was something that they could have a quick training session and just hit the ground running right off the bat.
And then also on top of that, with support, considering our old system was unsupported and moving to a new system, there's a lot of questions for customers.
How are we going to get all of our old data in? How are RPM's going to be set up? You know, there were a lot of lingering questions that I wanted to make sure. How am I going to be supported in the process as we are implementing it and getting set up at our site?
So for me, those were kind of two of the biggest considerations that I was looking for when I was looking at getting a new system for us there at rbc.
[00:14:38] Speaker A: One quick follow up on that. It's probably more for Paul than Ross, but feel free to both answer if you have an answer to this.
Ross just mentioned, you know, you're dealing with people on the plant floor, the factory floor, dealing with legacy systems. These are people who might have been working there for decades. How much pushback are you getting from people about bringing this new technology in and how aware are you of trying to make it accessible for them so you can kind of handhold them into these new technologies?
[00:15:09] Speaker C: That's a great question. I'm sure, Rush, you could talk to the individual folks at your organization, positives and negatives, I'm sure, in terms of rolling out. But it's like actually, it's actually one of the most important aspects of how we think about things like the. If you think about the. Just purely from an organizational perspective, if people aren't using the tool like Ross talked about, like it's an hour to wait for a report. So that's great. I'm just going to work this out myself and get somewhere. Like people don't trust the data.
We did a survey recently which showed that if we weren't capturing the data, like people weren't using the system to capture those things.
Trust from other parts of the organization, like finance, like procurement, like whoever is responsible, drops down to 4% level of confidence.
If people have got a complete view of it, it goes up almost 12 times that in terms of the thing. So if you have a set of technicians who had a hard to use thing that isn't allowing them to actually do their jobs effectively, they're just going to work their way around it, they're going to get the job done, but you're not going to get any of that information. And so you end up with just a platform that nobody's using and then you just don't get that data. So like we just launched a new version of our mobile application which is how the technicians use the system.
They're literally on the shop floor scanning QR codes, entering information as they go through their day and deal with their work.
Like that application has to be designed specifically for those users, not just the replication of your app on a phone like you would do with a lot of other things. It has to meet those needs. And so we think about that constantly, whether it's for that, you know, the app or the AI tools that we build or anything else, it's really got to be about how you get that usage and make it easy for them to build into what they're doing.
[00:16:59] Speaker B: And I don't. Before I switch directions and ask another question, Ross, I didn't know if you wanted to hop in with your own sort of experience in this area and getting people to adopt this stuff.
[00:17:10] Speaker D: Sure, yeah, yeah. I certainly faced some reluctance from the technicians when I was getting ready to implement Limble. You know, they had used this system for over 10 years, the previous one, and they had gotten, you know, comfortable with it and how to use it, despite how slow it was.
And so, yeah, you know, kind of bringing a new technology, something new for them to learn.
There were a lot of people that were worried about, hey, how much, how much more difficult is my job going to be now that I have to learn this new technology on top of, you know, doing the regular kind of routines in my, in operations, within my job itself.
But yeah, a lot of the things that Paul said hit it right on the, right on the head.
[00:17:53] Speaker B: So. And you did mention the ease of use and the support. And in the intro, Gary talked about the fact that you led this implementation in just eight months instead of the original two year timeline. So what do, what did you do to accelerate adoption and make the rollout successful so quickly?
[00:18:12] Speaker D: Yeah, absolutely. I came up with a project plan, you know, and with a CMMS and all the data that resides in a system like that, I really tried to kind of break it up into phases and really kind of make it more digestible of a project to implement. So for me, the initial implementation kind of looked like, how do I export all the historical data from our old system? How do I clean that up, import it into Limble so we retain all that. We don't lose that by coming over to a new system.
And then I focused on what I called the meat and potatoes of the system. So for me, the, the original goal to get us up and running was just bring over assets and bring over preventative maintenance templates and then also set up our work request system.
That way it allowed us to just kind of get the ball rolling right off the bat. You know, we weren't going to get bogged down with, oh, we have to set up vendors, we have to set up parts, we have to set up all this stuff and make the system super valuable for us.
I felt like it was, we were gaining more value by using it off the bat, you know, kind of running into issues as we're using the system and try to identify those and correct them as we're going and just essentially gain value by using the system. Like I said, from day one, you know, and, and from there we can, I was able to break it up to where we were implementing different features of the system in different timelines. And that made it a lot easier for me to do, but also it made it a lot easier for our team to use as well, just because I wasn't flooding them with all this information all at once. It was, you know, kind of broken down into little pieces.
[00:19:57] Speaker B: And it's probably important right now, especially as maintenance teams are struggling with labor shortages, technician burnout.
So having that right technology was, is that enabling the reduction in paperwork and capturing all this tribal knowledge and helping the technicians focus more on solving the problems instead of working on administrative tasks?
[00:20:23] Speaker D: Absolutely. Yes. Yeah. You knocked on two of the main things that we've gotten out of implementing Limble was, you know, we, I, I got our guys tablets. So instead of them going and printing off work orders every day, you know, hundreds of pages of paper are being saved because they can take their tablet, they can go complete their work on the go.
And, and it, it solved it just made a lot less administrative work, a lot less time them sitting at the computer and working on tasks like that.
So, yeah, it's been really great for us.
[00:21:00] Speaker A: And Paul, I want to ask you, Limble talks online, and I've heard you say before about purposeful AI.
What does that mean in practice? And where do you see AI creating the most value in maintenance today without just becoming another buzzword? Like, we know everybody is talking about AI now, but how do we make this purposeful?
[00:21:20] Speaker C: Well, I think it goes back to where we're having that value. So we are implementing capabilities like the ability for you to plug in your AI chat into our data, like something called an MCP server. It's like, used by big organizations when you want to do it. But this purposeful AI stuff is much more about, I guess, what Ross was talking about, which is like that person on the shop floor getting value and being able to do things quicker.
Give you an example of that. We just recently rolled out a ability to take a picture again using your phone, your tablet, as Ross was saying of the asset tag, like that metal label with all of the information around the manufacturer, and use that to add that asset directly into our system automatically.
So she's using AI to go, okay, this is this particular piece of H vac, as you know, Ross was talking about, in terms of the example, this is the model.
And then we can create, automatically use that to create preventative maintenance schedules. So we're not like, you know, it's not like a tech is sitting there trying to use a chat function or trying to work out those things, something we will enable, but it's about making it very purposeful to that person's job. So I've got to deal with adding 200 new assets because we don't have them in our asset hierarchy right now. Oh, cool. I can just take the picture of each of those elements in the machines, and that gets automatically added to my system. So is that much more purposeful in terms of, like, the person who's at the other end of that?
[00:22:55] Speaker A: Got it. I also wanted to ask you a little bit. You guys have had a new. Limble has had a new survey come out, and we went through the data, and it points to some persistent issues like, you know, reactive work, incomplete maintenance histories, disconnected systems, low trust in asset data.
Why are those problems still so common? And what separates organizations that are making real progress from those that are still kind of stuck?
[00:23:19] Speaker C: Yeah, it's a really good chat. We set out really just to understand the state of things in our customer base. There's been lots of discussion around that maturity model I talked about. We can get into that in detail around how people are still very early in that process. But we were trying to understand like, okay, what's actually driving this. And the key things that came out of that were this issue of data not being brought into the systems for then it to be used for these other pieces of decision. Because, you know, as Russ talked about, having paper work orders means that the data is going to have to be added into the system later or not at all. Most likely not at all. So we see very large organizations, even some of the biggest manufacturers in the world, running their systems on paper and pencil, which is wild.
But what it is is that it's just that this data hasn't been viewed as mission critical, hasn't been viewed as a major thing. It's about cost center, like, how many people do we have to hire for maintenance teams or those other things? And so the ways that the organizations that are making those transitions are looking at things where they're moving from this reactive state into this preventative state as a starting point, if we look at the entirety of our data set. So one of the things we did is we took the results from the survey and then looked at every conversation we've had with a manufacturer or other organization over the last two years and assessed where they were in this. And what we found was that overall, 75% of organizations are in that reactive state.
In manufacturing, it's a lot better, actually. It's closer to only about a third of organizations and about another 50% who were in that preventative model or moving into that preventative model where they're building preventive maintenance schedules that Ross was talking about.
And what that's doing is that's getting all that data into their system that allows them to make these bigger decisions. So if you go back to my point around like a 12x improvement in the trust and data, the procurement team trusts that they know when they need to acquire a new piece of equipment versus, you know, the operations team say this thing is starting to wear out. Where's the data that proves that? Where's the things that show that? So that's the difference that we're seeing. So I think the first thing is that recognizing that getting data into the system is an incredibly important point here.
But that's about as we were talking about, making it easy for people to do that, making it part of their workflow, rather than somebody being given the task of going, capturing those 200 assets that we were talking about.
[00:25:55] Speaker A: Was there anything else in that survey that, that that really stood out to you? A Data point that you guys weren't expecting.
[00:26:02] Speaker C: I think, I think that the biggest thing was that lack of trust. That was the biggest, biggest aspect of it. I think when we then took that and start to look at our, the reality of where people were in their adoption or in that in their journey, I think it still remains just shocking how many organizations are early in that process and really don't have the data that they're doing.
And you think about it in the context of right now, Stephanie, you were kind of talking around this issue, which is we have a kind of a generational gap occurring in the maintenance techs and their tribal knowledge just disappearing out the door with them as people are moving on. But then we have people who were earlier in their careers, like Ross coming in and saying, okay, let's capture that information.
Like that makes a huge difference to us being successful as an industry with this.
Because ultimately this is about uptime.
Are we producing?
[00:27:00] Speaker B: Right.
[00:27:00] Speaker C: And that's where you're really, really, really getting the biggest benefit.
[00:27:04] Speaker B: But it's interesting though, because Paul, you did mention that, like, the maintenance departments are often considered a cost center. Right. If we can get in front of it, can it be more of a strategic profit center for an organization?
[00:27:17] Speaker C: It's funny, I was at smrp. I don't know if you're familiar with that conference. It's reliability engineering basically for producers.
And there was a really great presenter was talking about exactly this, which is that the barrier to being strategic is literally access to the data and people being afraid of how am I going to get, how do I justify that if I don't invest in people or equipment, that this is going to be a problem because it switches. The effort is to switch it from an, from a cost to an investment.
And they were talking about, they produce consumer food materials and they were talking about the. If I take my 12 plants and I look at what the maximum standard production for a day is and then I apply that to each of the lines and I get an understanding. I can now quantify the impact of one of those lines being done.
It seems very simple, but that's a real challenge for reliability engineering from operations leaders to be able to go through that process. And so having the data really makes a huge difference to be able to do that.
[00:28:33] Speaker B: So, Ross, you did this, you did the implementation. So what changed operational at RBC Bearings after you did this? Whether it was the downtime visibility or more analytics or maybe just team morale, like what did change after this limbo system was put into place?
[00:28:52] Speaker D: Yeah, yeah. I would say right off the bat, morale was probably one of the biggest ones.
Not just from my technicians, actually. Surprisingly, a lot of the morale boost came from our internal customers. So the people that are, you know, submitting work requests, you know, similar to what you guys were talking about, there was such an erosion of trust in the old system that we had, especially in terms of submitting work requests, where it was like, this takes too long. I don't want to do it.
Are they actually seeing it? Is it getting reviewed or. I've never got any communication from that.
So, you know, the morale from being able to just be seen and heard, you know, hey, I saw your request. We're going to fix that on Monday.
A lot of people just walking around the plant, once they hear like, hey, that's the limbo guy. You know, they come up and they thank me. You know, it's like I feel like a hero sometimes.
[00:29:43] Speaker B: They're like, you're the limbo guy now. That's your limbo.
[00:29:47] Speaker D: Limbo guy. I probably have the title.
[00:29:49] Speaker C: Well, it's funny, like, our kind of mission statement is empowering the humans who keep the world's assets running.
And that's a key kind of component of how we think about this, is that, like, you know, those. It's these humans that are doing it by how much technology we have. It really comes down to these humans having that reaction. And it's so great to hear people thanking you for a change, I'm sure, versus their experience, historically.
[00:30:12] Speaker D: Yeah, yeah. It was a bit surprising at first. I didn't really know how to.
But I mean, it's a great sign. You know, it means people are using it. It means people are enjoying using it. It's not a hurdle for them to get their things done.
But also the. I think transparency is probably the biggest.
One of the biggest gains we've seen as well, is just being able to show leadership. Hey, we had, you know, 90% uptime last month. You know, last fiscal year, it was this much. This is how much it increased from this year to last year.
Just being able to provide all of that data, all of that information to allow them to make better decisions. You know, that's probably been the biggest thing.
And even taking that to our internal customers too, where, you know, one of my projects was to set up digital signage. And with that, I, you know, made sure to put our limble dashboards up there so people could see, you know, hey, these are our open jobs. This is what we've done. This is how many hours we've done.
So I think transparency, just not only showing leadership, but also just showing the rest of the organization, you know, hey, this is what we're working on. This is what we're doing for you guys. This is how we're ensuring that we're keeping, you know, your equipment up and keeping, you know, production running.
I think it's been a great aspect to implementing a system like Limble.
[00:31:32] Speaker A: You guys have both been really generous with your time. We'll wrap things up here. But I actually have a question for both of you.
So looking ahead, as platforms like this begin to spread to more sites and as maintenance teams adopt more mobile and AI driven tools, what advice would each of you give to manufacturers that want to modernize maintenance without overwhelming their teams? Which is something we've been talking about a lot today. Ross, we'll start with you and then, Paul, we'll give you the last word.
[00:31:59] Speaker D: Sure.
Yeah, that's a great question. I would, I would probably just say it's not as daunting as you think it is, you know, if you, if you approach the project step by step and just know that it's accomplishable.
I think that kind of helped my mindset, you know, here at rbc and one of our slogans is make the challenging a reality. And, you know, I think that's where I kind of came from with this, where it was like, hey, we can, we can do this, you know, make it digestible, kind of what I mentioned earlier.
But, you know, it can seem like a big task, but if you just try to focus on doing it little by little, it'll go by a lot easier, I think, than trying to take it all in one whole piece.
[00:32:45] Speaker A: You guys both have good slogans.
All right, Paul, how about you?
[00:32:51] Speaker C: Well, I'll add another one. I have more of a personal one, which is like making the complex simple.
And I think, and making it simple and reliable. And that's kind of where what Ross was talking about in a lot of ways.
The thing that really strikes me is that in manufacturing, we are the home of the ERP.
And ERPs are complex, costly, difficult to deploy, like all of these things. And I think that's what people are used to in our space.
But what Ross was talking about, I think is the answer to your question, which is how do we just start this process of moving forward and digitizing this singular workflow in a simple way that just builds and builds and builds, because the aggregation of that data that we were talking about, that's where the strategic value is. While we're getting this human value that Ross was talking about. So I think that that's really what I would say is Ross's recommendation of. Let's start with the kind of like something that was very complex, make it simple and get that moving is the path towards this. Because if, if we're looking at even like a third of our manufacturing base being at that kind of highly reactive space, there's a lot of money and a lot of time being wasted there.
And so the opportunity for us to move up that process and get there is just. Is absolutely huge for us.
[00:34:09] Speaker A: Fantastic. Really good stuff. I also want to point out if either of you are looking for a second career, I think the Ross and Ross personal injury law fir firm is kind of a no brainer. Oh, there we go.
[00:34:20] Speaker C: Think about it.
[00:34:22] Speaker D: Let me write that down.
[00:34:24] Speaker A: No takers. All right.
[00:34:25] Speaker C: No, on the law side, I don't think.
[00:34:27] Speaker A: No, no. We really, really appreciate you guys being on really terrific information and a fun conversation. So I appreciate your time.
[00:34:35] Speaker B: Thank you.
[00:34:36] Speaker C: Thanks, guys. Really appreciate it.
[00:34:37] Speaker D: Yeah.
[00:34:37] Speaker A: All right. Thank you, gentlemen.
All right, Stephanie, There we go.
Good slogans all through that one. And I do think they should start. They just need a Ross. And it doesn't have to be a law firm, but some sort of Ross and Ross. I think inevitable.
[00:34:55] Speaker B: It's definitely catchy. I think they would be successful in anything that they do with the Ross and Ross moniker.
[00:35:02] Speaker A: One of the things that I think has been interesting and it's actually really shown itself in the last few podcasts we've done is we're talking about AI a lot. Everyone is talking about AI a lot.
And we keep coming back to this. The fear of AI is like it's going to take our jobs and it's going to.
But the, the point that everybody we've talked to keeps making is the human point behind it. You know what I mean? That like it really is, you need to get adoption. There's still people involved in all of this stuff. We end up talking about the human side of it more than the technology side in a lot of these discussions. So I think that has been an interesting sort of thing that I've noted in the last few podcasts we've done is I'm like, we're going to do a tech podcast. And then as we talk, a lot of it is about, you know, making sure that the people on the plant floor, the people in the C suite, understand the technology and are using the technology and that it works for them and it's not about replacing them. It's about complementing them in ways that works for them.
Yeah.
[00:36:07] Speaker B: And you know, when Paul was talking about the survey, I think that what really jumped out to me and it is a human nature type thing, this lack of trust. Right. So lack of data equals lack of trust. And if the CEO doesn't trust what's happening on the plant floor, then we've got a big problem. So you gotta connect the dots there. The technology can help. The people have to be in place and the information has to be reliable, like we talked about. And then that's where you get the trust. And then you can move from this reactive to proactive, from cost to profit.
[00:36:42] Speaker A: Right, Right. I mean, you and I as content producers, we're screwed. AI is going to take our job, but they're going to be fine.
[00:36:50] Speaker B: I'm going to join the Ross and Ross legal firm.
[00:36:53] Speaker A: That's exactly right. So on that positive note, let's wrap things up.
If anybody's looking for employees when Stephanie and I get ousted by AI, we're both good, reliable people.
But thanks so much for listening to yet another Control Alt Manufacturing podcast. Always say it at the end. If you like what you're hearing, please like, please follow. Please subscribe. It helps out the podcast. Also check out our other suite of engineering sites with WTWH Media. Whether that's Control Engineering or Design World or any consulting specifying engineer, plant engineering, there's a ton look under our umbrella. If you're looking for engineering content, we've got it for you.
[00:37:32] Speaker B: Yeah. And connect with us on LinkedIn and also send us a DM. Let us know if there's a topic that you want us to talk about, if there's a person that you want us to speak with. We're always open and looking forward to continue to explore this topic.
[00:37:48] Speaker C: Absolutely.
[00:37:49] Speaker A: I think that's all we got for today.
[00:37:50] Speaker B: That's all we got for today. So I'm gonna close with have a wicked good day.
[00:37:57] Speaker A: I love the Boston. Bye, everybody.
[00:38:00] Speaker B: Bye.
