Manufacturing’s Labor Gap: Can AI Agents Fill It?

Ed Ballina
Trust. Man, that is a that’s a tremendous commodity. And many times it’s in short supply, right? You’re asking people to change, you’re asking them to adapt. we’ve had this conversation about predictive maintenance since it began. Or, you know, even something as oil analysis, right? When that comes back. What do you do then? You get a recommendation. I don’t know if it’s right. I mean, hey, I’ve been doing this job for 30 years, and this thing is telling me to go check X.

When I think why is the problem, right? How does trust again a really powerful commodity? How does that get built between the AI agent and your organization? Because they, you know, as human beings, we don’t like change much. And now you’re gonna have an oversight telling you what to do. You may be a 15-year veteran.

Hello there. I’m Ed Ballina.

Alvaro Cuba
Hello guys, Alvaro Cuba here.

Ed Ballina
Well, welcome to our latest Manufacturing Meetup podcast. This is a show where we sit back, Alvaro and I chit-chat about our experiences in the plants, maybe share a few notes with you that you might be able to take away and get some benefit out of. So, welcome to manufacturing meetup.

Alvaro Cuba
Great to have you guys. So what’s about with the hat, Ed?

Ed Ballina
Well, the hats, you know what? I know it’s after the fourth of July, but I did want to showcase this beautiful painting I have in here celebrating our two hundred and fifty. So I thought it’d be appropriate to also wear this new hat, America, the land of the free. And we also know it’s because of the Braves. So a big shout out to our servicemen and women out there that keep us safe. So that’s the story about my hat. You, my friend, are barely recovering from fever.

Alvaro Cuba
Yeah, I’m still recovering, but it was an amazing experience. The World Cup, I was lucky to be in Miami and be able to attend seven games. So we got probably the best game with 10 goals and some got to see most of the best teams in the World Cup. So it was quite an experience.

And I have all my all my remembrance and my catch up for the World Cup.

Ed Ballina
How exciting that must have been. I I I heard it say that you tried to tackle Massey at some point in time from an autograph. Is that true or is that just false false information?

Alvaro Cuba
I got to do that with Messi, Ronaldo, Mbappé and a couple others.

Ed Ballina
A couple others.

Alvaro Cuba
yeah, but hard to get guys. So,

Ed Ballina
Yes, yes. Awesome.

Alvaro Cuba
let’s go. What do we have for today?

Ed Ballina
So manufacturing is being asked to we always get asked to do more with less, but it feels like in this current environment, right, we’ve got rising input costs, the labor shortage we’re experiencing, you know, the whole situation in the Middle East with crude oil. Man, it feels like there’s a lot going on. And I heard a really interesting piece of information that just adds to this. And that is we have seen manufacturing growth in this country for the last seven quarters in a row.

The last quarter was over four and a half percent. That is huge. No matter how you feel about, you know, tariffs and all that, it appears that we are re reshoring manufacturing. Alvar and I have talked about this for a while. And while that is terrific news, right? The flip side of that is Alvar and I were talking about the fact that we have a million openings in many job openings in manufacturing every year and we can only fill half.

If this trend continues, that gap, folks, is only going to get wider. So we’ve been talking about AI and how AI can help bridge the gap. and you know, we continue to believe that that is part of the solve here. but there’s also this kind of perception problem: AI is going to take my jobs, right? Now it’s being confounded with data centers. It’s a very hot topic. but I think at the end of the day, AI is going to create a ton of opportunities for us. And manufacturing is coming back to America, folks. So we need capability. We need more workers in our factories making the goods that we use here in America. So let’s get the elephant out of the room. Okay. Is AI going to cost me my job? Am I going to have a problem, you know, making it meet and all that? we’ve got somebody that I think will speak to that. And Alvaro, you have the floor now.

Alvaro Cuba
Well, you, as you say, we really need more people in manufacturing and, and, prepared and, and trained and well, technology is here to help us, but, we have a very special guest, guest today, Jared Pfeiffer. and, he has bachelor degree in science and industrial, technology, which is very relevant. for this. And he has been in both sides of the aisle. No, first in the plants, like you guys, he’s been in maintenance, scheduling, planning, corporate reliability, everything that we need. And he also managed a plant with Bayern, with CropScience. Then now he’s working for Augury and he’s working with manufacturers trying to get the real value out of technology. So I’m sure he’s going to help us to unpack this and what it means for everyone of you guys. But before we start, please hit the subscribe button so you don’t miss the conversation. And then you can also chime in and let us know. And with that, let’s get started.

Ed Ballina
Let’s do it.

Alvaro Cuba
So welcome, Jared. It’s a pleasure to have you on the show.

Jared Pfeiffer
Absolutely.

Ed Ballina
So it’s great to great to have you know folks that have that shop floor shop floor experience, right, that we all talk about. it’s one thing to sometimes pontificate about these things, but we all know the rubber meets the road on the shop floor and you bring a lot of credibility to our conversation. So

Alvaro Cuba
And that side, Ed, but also the other side, because now Jared is also in the technology side, helping the manufacturers to get sense of this new technology. it’s a perfect person to match both and tell us what’s going on out there and how to manage it.

Ed Ballina
Yeah, you went from the customer to the supplier. so that’s an interesting switch. So before we get deeper into the episode, like give us one you know, we want to talk about AI, and and it seems like agents are the ones that are really driving a lot of the growth. Tell us a little bit about a specific AI agent that you used that worked. because I think there’s a lot of folks that have this textbook idea of what an AI agent looks like and what happens on the shop floor. And you can tell us what the reality is. So Jared

Jared Pfeiffer
Absolutely appreciate it, Ed. I like to start off kind of with you, just for those that may not be totally aware of just what AI agent and kind of sourcing that back. What I like to hit on or talk about is thinking of the difference between like a GPS and a personal assistant, where you see a a GPS is gonna tell you, hey, turn left, turn right, and then you have the the personal assistant that knows your schedule and knows your preferences and where you’re coming from and they they book that whole trip for you. And what I like to tie that to is an alert that comes out being that GPS it tells you that something happened, but then the AI agent incorporates that into a personal assistant where it knows what happened, it sees that alert, it pulls up the history on whatever asset we’re alerting on, for example, and it tells you, hey, this is probably what it is based off of previous experience, previous history, the services, a job plan or the procedure behind what we’re working on. Then it can go even go as far as checking if the parts in stock and can kick the work order off in some instances. and to your point, I saw this firsthand when I was a planner scheduler and kind of s tying these two together, where we get a work order in and we spend hours searching for information, whether it be with what’s going on with this specific issue, do we have enough details? And then we just spend all that time tracking things down. And then I tie that to the agent where the agent is able to compress all of that research and information that we’re looking for into minutes, whereas we’d be spending a lot of time tracking that down on our own.

Alvaro Cuba
So now that we have more clarity about what agents are, let’s talk a little bit about why is a hot topic and how we got there. So Jared, in your years in manufacturing, some years ago, AI was not there yet and less so the agents. So when you were in the floor in maintenance, what were the big issues? What was breaking down?

Jared Pfeiffer
No, that’s a good question, Alvaro. I think you guys hit on it too, in the opening of talking about the the gap with open roles in manufacturing. wasn’t necessarily a data gap that we had, it was a capacity gap. The problem was we didn’t have enough people with the time and expertise to to do something with the data that we had. So you’d have a reliability engineer that was is covering multiple areas or even a process engineer in some cases that I was working with, they were getting pulled into meetings, they were doing PMs, they were writing reports, the data was all sitting there and but the analysis wasn’t happening. So that’s that’s the capacity gap because and it only gets worse when that RE or that person retires and you spend time trying to backfill that. So it created a much larger issue as that capacity gap grew, being able to manage the data and then actually do something with that data.

Alvaro Cuba
And you’re talking about this translation situation, no? So you have this, but you have now how to act upon upon that. So why is that?

Jared Pfeiffer
I think when I we talk about translation issues, I think it’s we have someone who’s very skilled and I’ll I’ll take vibration for example from a reliability engineer or a vibration analyst. A vibration reading doesn’t mean anything on its own, it’s just a number, but it takes someone who’s seen that pattern multiple hundreds of times and look at it and say, Hey, that’s an inner race issue on this bearing. We and based off of the data we have in the CMS system, last time this happened on this asset, we’ve got about three weeks to do something with that. And that goes into that’s not being a data problem. It’s just translating the data that we do have into something that’s actionable. And that’s a right now, it’s the person that’s doing that. And mo most likely in what I’ve seen is they’re already stretched too thin and or they’re on their way out. And when that we all I think we all know when that person leaves, that knowledge walks out the door with them because we haven’t done a good job of translating that or h storing that historical data. But the agent doesn’t replace that person. It carries their pattern recognition forward and it allows them to do more with the capacity they do have.

Ed Ballina
That’s such a great example because right now you can’t you can’t watch a news feed without hearing about kind of the the experience and talent and capacity drain that is happening in the workforce, right? So people with a lot of experience are choosing to retire, and there is a gap in knowledge between the people that are coming in behind them, right? And how do you capture that?

There’s very there’s there’s some companies that are good at this. If you’re a year away from retirement, you’re like a head mechanic, you can they’ll put that person on a special assignment to mentor and train, leave that capability behind. Very few companies do that, to be honest. And this is where I can really come and help bridge that gap. so let’s talk about what what it actually might look like on the shop floor, right? Because I think one of the problems that we have inherently is What are agents, right? Like we barely have a an idea of what AI is, this big thing that sits in the cloud and gets all this information, right? But now we’re asking people to understand what agents are. And I think it helps if we kind of clear the air a little bit and identify what they are. So here’s an scenario that I think our manufacturing audience will recognize very well. You got a vibration sensor on a piece of equipment.

It fires off that something is about to fail. Now, to be honest, I’m not talking about the really bad systems that are just idiot lights that say, ooh, vibration. Okay, well, in what piece of equipment? How do you know impact that? Right. So but somebody gets an alert, maybe they investigate, maybe they don’t, maybe they follow up later, they don’t have the time. So even though the data, to your point, is J being generated. We like to use this term, nothing happens until you close the last yard in the real physical world, right? Not virtual. Somebody’s got to put a wrench on it or tighten a bolt. So give us an idea of how the agent works in the loop here.

Jared Pfeiffer
Yeah, absolutely. And to your point, you you hit it nail on the head. So today a sensor fires and somebody gets an alert based off of that, and maybe they act on it, maybe they don’t, because they’ve got multiple things going on tying back to that capacity issue. With an agent in the mix and kind of looping that in, the same moment could look like the agent picks up the fault, it cross-references it against the asset’s history and all similar assets that are in the database and it identifies the probable cause based off that historical information.

It pulls up that procedure like I’d mentioned. It e it could even go as far as checking your parts inventory for what’s needed based off of the failure mode. It flags it. And in some cases, depending on integration, you can initiate the work order. So what used to take those two, three, four, or five hours of back and forth between a technician, a supervisor, reliable engineer, planner, happens in minutes. And that human is still in the loop, so they’re the ones that are making that call. But the legwork that took them multiple hours to track down and involved multiple people. is already done for them closing that view loop.

Ed Ballina
And the thing if I just g add one other piece to this that really blows my mind, then in some s cases the AI will order the parts for you and have coming before you really need the shutdown, but it’ll build that into this into into the whole f process flow, which is amazing. So sorry, Alvaro, you were ready. No.

Alvaro Cuba
In reality, it can go even farther than that. If the machine is going to change its uptime, can even reschedule the production. they can do a lot of things. But one thing, Jared, we hear from plant managers is, my God, this is new technology.

And our experiences or our feelings, one more thing to do, one more system to log in on top of everything that we already have. because nothing is given away and this is coming on. Is that a risk with the agents? And if you think it could be how to avoid it.

Jared Pfeiffer
Absolutely. And I think that’s a totally legitimate concern, Alvaro. And I honestly I think I think we’ve all been a part of this and folks listening, we’ve tried to roll out a new digital transformation era or a new program. And I think that’s actually the most common reason rollouts fail. because if something an agent in this case becomes another dashboard to log into or another system to check before they can actually do their job, people are gonna stop using it inside of six months, and I’ve seen that happen. the ones I’ve seen be successful, and I where this is where this applies are the ones that stick are the ones that show up where the technicians and the team and the planners and the operations team already live. So that’s in the CMS system, that’s in the RP, that’s in an MES, it’s in the work order, it’s in the inbox they’re already looking at. So the goal isn’t to add to another layer or add on top of what they’re doing. It’s adding to what they’re already working on. So it’s to make that layer they’re already working on smarter. And that’s the change management piece that of the work that matters more than the actual technology itself is Don’t make it in addition to how they work, but make it a part of how they work.

Ed Ballina
Absolutely.

Alvaro Cuba
And I think that’s a great point because that’s the main difference between this new technology and the previous technology. This goes in straight to do things that otherwise you would have to do. So if you stick to that very fast, you start to need to do less things, which gives you more time to think and do other things. Just something related to that, Jared.

Many plants don’t have reliability engineers. They don’t have the budget, especially mid-size, small-size plants. What an agent can mean for that kind of plants.

Jared Pfeiffer
Honestly, I think this is where agents are gonna matter most, speaking from experience and what I’ve seen as well. to your point, especially on small and mid-sized plants, they can’t justify a full time reliable engineer or that head count because the salary doesn’t really pencil out. so what happens is that they the plants and the teams either make a decision that that work doesn’t get done or it gets piled on to someone who’s already running maintenance. So it could be a maintenance manager, a supervisor on the off side it could be something else and and they’re wearing multiple hats.

So that agent gives that plant level pattern recognition from an RE standpoint or otherwise and helps supplement that team and gives those recommendations without adding that additional headcount costs. So it’s it’s not replacing a person, but it’s filling that gap where there was never going to be a person to begin with in the first place. That now that the plant used to fly blind, it now has visibility because they’re able to add that expertise using that tool.

Ed Ballina
Absolutely. I this was one of my struggles. when I I as I came into the beverage industry, I learned about vibration analysis thirty some years ago in paper making and I swore by my vibration text, I saw the opportunity beverage. Our beverage plants are small.

Alvaro Cuba
Now the guys with the sensor and running around the plant, one guy in the entire plant.

Ed Ballina
Yeah. These z they in in in this facility, we actually had three vibration techs and they had all come out of the US submarine service. and they were the ones that used it and when those folks came to your office and gave the report and said, Hey Ed, you did not question. It was like, Okay, how much time do I have? Can I shut down right now, type of thing? But when I came into Beverage, you know, that was a facility of a thousand people, right?

You come into beverage, our plants tend to be small. To your point, we couldn’t justify it was hard to justify a vibration tech. so when I first encountered Augury, to me, that was wow, here’s the solution to the problem I’ve had all along. I trust the technology, I love it. I can’t afford to have a full time person. Augury, just to point one out, solve that problem for me. So I I’ve lived essentially what what you what you had mentioned. but how does You know, an alert tells you that something happened, right? But going back to the comment about idiot lights, right? When your check engine light comes on your car, right? Is there an ODM s thing that you can plug in so it’ll tell exactly what it is? you know, can the AI agent tell you something that the alert, the flashing light can’t?

Jared Pfeiffer 
Absolutely. I think to your point too, where that alert tells you something essentially across the threshold and then you add the AI layer on top of it, that’s that piece where it says an agent tells you exactly what it probably is, why it’s happening based on the at the history of that asset or that that line. It gets to know that piece of equipment, how serious it is relative to similar patterns and seeing, and then even providing recommended action and what happens if you don’t act. So a lot of times we fall short of, hey, here’s what’s going on, here’s where you need to take action, but what happens if I don’t act? And it provides that context. And then to your point, it’s the difference between your check engine light coming on and your mechanic saying, Hey, it’s your O two sensor. It’s been training for three weeks. Here’s what it costs if you wait and here’s what it costs if you fix it. So it’s that same data, but it’s completely different decision making experience.

Ed Ballina
Great example. That’s a great example.

Alvaro Cuba
It’s a lot that agents, and you are giving us great examples that can do, no? So they can replace people I cannot get because I don’t have the budget, or they can fill in for people I don’t have. It was talking about we have half a million people and it can become much more as a gap in manufacturing. There is also the other part, which is the knowledge part. Imagine we have 30 year old veteran that is retiring, a lot of retirees right now, and they have 30 years of knowledge and they are working out the door. So can agent help in that situation as well and capture somehow that knowledge and help internally or that’s a wishful thing?

Jared Pfeiffer
Absolutely not I I think I would answer it as partially because I think it’s important to be honest about what partially means here too. You can’t fully replicate the relationship between a person that had a specific relationship with a machine. I mean, going into a subcontext like he used to have a maintenance mechanic to your point that was a thirty year veteran. His morning coffee route was to check in with the operator, he’d sit his coffee down on a gearbox and he could tell by the the ripples on his coffee if there was an issue on the gearbox before they had vibration. Yes. But

Alvaro Cuba
We have seen it, we have seen it.

Ed Ballina
Sorry about the guy with the ear muffs and a broomstick?

Jared Pfeiffer
Do you ever see that? but what you can encode is that pattern recognition. So it’s those thousands of examples that veteran you mentioned used to make those judgment calls. But when an agent sees a fault pattern, it’s drawing on all those similar faults across every similar every similar asset it’s ever seen, including the ones that veteran diagnosed. So the knowledge is no longer locked in that one person’s head. It’s it’s distributed, whether it’s a new tech, a new hire, a different site, everybody gets access to it.

Ed Ballina
That’s right. That’s yeah, lots l lots of moving parts, right, that have to almost perfectly mesh for this thing to to to work. But once it does, it’s almost like magic, right? Things that would take us ungodly amounts of time. Now we can do and I I’ve become an addict. It was Alvaro’s fault, and then Sarah followed using AI. And I had to adopt and now I’ve become an addict. I can’t pull the chat GPT or Claude needle out of my arm. It’s very, very cool. but hey, let’s let’s talk you know, in in our chats, we always try to boil things down to whiff them, what’s in it for me, right? Do we want to talk about what this means to you as a supply chain and manufacturing professional, right? On our plants and our shop floors. So trust. Man, that is a, That’s a tremendous commodity. And many times it’s in short supply, right? You’re asking people to change. You’re asking them to adapt. we’ve had this conversation about predictive maintenance since it began, or you know, even something as oil analysis, right? When that comes back. What do you do then? You get a recommendation. I don’t know if it’s right. I mean, hey, I’ve been doing this job for 30 years, and this thing is telling me to go check X when I think Y is the problem, right?

How does trust, again, a really powerful commodity, how does that get built between the AI agent and your organization? Because they, you know, as human beings, we don’t like change much. And now you’re gonna have an oversight telling you what to do. You may be a 15 year veteran. I’m like, I know my stuff. Maybe not so much, Jared.

Jared Pfeiffer
No, absolutely. And I think Ed to your point, it comes back to what we all kind of learn as we grow through our career is trust with verify. I mean it’s it’s it should start small and the the first time an agent flags something, or first time anything flags something, a good technician, a good individual is gonna go verify it themselves to your point they’ve had history. and that’s exactly what they should do. And I think what builds that trust is when they go verify it and it’s correct, but then it happens again and again, and after three or four accurate calls.

The team stops second guessing it about every single one. so I think that’s where it starts is those individual small verification steps that they take along the way.

Ed Ballina
that’s great. Yeah. ‘Cause we all know that one “oh boy” wipes out ten “ayya boy”. you know, you wanna make sure you minimize those “oh boy” moments.

Alvaro Cuba
Yes, no, I was going to ask Jared a related question. It’s a new technology and we have seen this tech rollout. Everyone excited, it goes in at the beginning, we are all cheer. And then three years later, nobody’s using it or is using 20 % of the time. and that it has that bad feeling in the mouth. If this, can this happen with agents you think? And if so, how to prevent it?

Jared Pfeiffer
Absolutely. And I think, Alvaro, to your point, it we’ve seen it happen in the past and it I think it can happen anywhere. I think it relates back to potentially bad impl implementation and it usually looks like someone gets excited, whether it’s incorporated at at the plant level, they go by the technology, they do the install, they hand it to the team, say, Hey, here you go. But they what they didn’t do is go through that change management process of, Hey, do we have a champion? Do we bring him into the conversation? Do we do any training? Do we integrate to their current workflows that we talked about earlier?

And then to your point, six months later nobody’s using it because it became extra work instead of less. I think the way you avoid that is you find your champion on the plant floor first, not in and not to upset anybody, but not in IT, not in the C suite, but you’re finding the person that’s being impacted. And you find in this case, you find that reliability person or the maintenance manager who sees the problem it solves. You make them successful with it and then let them pull it through the organization. Because technology doesn’t drive adoption, people do.

Ed Ballina
Absolutely. it there’s there’s there’s a lot of integration needs to happen. but we mentioned kind of the elephant in the room before. what do you say to the maintenance tech that’s sitting here and thinking, hmm, is this gonna take my job away? I mean, if I teach it everything, maintenance folks, at least in my experience with him, they jealously guard their knowledge.

They’ll be willing to share with you once they respect you and they trust you, right? but that’s kind of their currency, right? Thirty years of experience, man, that’s worth something. I’m not just gonna give that away to somebody who’s gonna waste it. So what do you say to that maintenance technician on the floor as they’re looking at AI agents?

Jared Pfeiffer
Absolut absolutely, that’s a question. and I’ll I’ll be honest with you, I think it starts back with I think how we all learn to build those relationships and it’s just being honest. I’d be honest with I’m gonna hopefully they listen here as well. But some tasks are gonna change. I mean, re whether it’s repetitive data gathering, manual alert triaging, basic scheduling, PMs, yeah, agents will do more of some of that building, but here’s the thing that you guys already touched on the very beginning. We have tons of open jobs in manufacturing right now that nobody’s spilling.

So the problem isn’t we have too many people. It’s nowhere near enough. So that agent doesn’t replace that maintenance technician, but it’s giving that technician the tools to do the job of more people in the same amount of time frame without overwhelming them. So I and I think that the plants that are thriving five years from now won’t be the ones that cut the most headcount. It’s going to be the ones who whose people have the best tools and are using the headcount they do have to drive better adoption and and engagement.

Ed Ballina
And it drives retention, right? It drives people staying at the job and and having more rewarding role.

Alvaro Cuba 
Yeah, we are seeing it in every plant, you know, so everyone is so busy and there is a lot of burnout and all that. So everything that helps just to take that out is not only going to help, but make their lives much easier and much more comfortable. instead of replacing them. Jared, if I’m a maintenance manager,

And I’m listening to this podcast. And I’m thinking, okay, I got it. This is going to come to me no matter what. So I need to get ready. What would you say how this person has to be ready? Not from a technology perspective, but from people and culture.

Jared Pfeiffer
Absolutely. I think you hit it on the head is before they rush out to buy anything because hey, my boss says we need to start looking at this or do this, you start that cultural shift. And also that means having an honest conversation with your team. And in this instance where we’re talking about reliability, for example, where is it breaking down? Not to blame anybody, but to sit to name the the capacity gap that we talked about earlier together. And we start normalizing the idea that, hey, the data should inform our decisions, even if it’s imperfect data.

And then how do we build that habit of reviewing alerts as a team, talking through issues on why we acted or didn’t? Because what we talked about earlier, when an agent does show up, it’s not introducing a new behavior. It’s just accelerating the one that the team has already built. And I think the plants that struggle most with adoption are gonna be the ones where data was always someone else’s job and they haven’t taken that into a part of how they’re they’re working together.

Ed Ballina
So I have at least on this topic, one last question for you. are we talking about stuff that’s gonna happen here in the next five years or is it happening already on the shop floor? And I think I have a sneaking suspicion what your answer is gonna be.

Jared Pfeiffer
Absolutely. it’s already happened to your point Ed. I work with manufacturers right now where agents are already running in production, not in a pilot, not in a proof of concept. It’s on live critical equipment. and and the technology is coming to your point, it’s already here. And I the question isn’t whether to adopt it. It’s how quickly you can get your team ready to work with it to that cultural aspect we talked about earlier.

Ed Ballina
Think that’s a that’s a great answer and Avvaro you to use one of your terms, folks, if you’re out there this is coming your way and you can either adopt adapt or you can adapt by dying. I think you said that now. You’re gonna you gotta change one way or another, right?

Alvaro Cuba
Yes. not only for you, not only for you, think about your people and what we talked. No, this is the way that you can make easier the life for your people and have them doing quality stuff versus repetitive stuff. coming to the end, Jared, we always try to give some very concrete, very tangible takeaways to our audience. So after we all discussed, if someone is driving to the plant right now and listening this, what are the three things that they should remember? Not only remember, but when they go to the plant, what are the three things that they should start thinking how I do this?

Jared Pfeiffer
Absolutely. That’s a good question. I I was thinking about that as we were talking through, but I would say problem is capacity, not technology. So like we talked about, plants have more data that they can act on and the gap isn’t sensors, it’s people with the time and expertise to turn those signals into decisions and then agents help fill that gap. secondly, I think agents work best when they become invisible or technology works best when they become invisible. The ones that stick aren’t gonna be the new systems that we were logging into, but it’s woven into a how the plant already works.

And that’s that change management job we talked about. and I think thirdly is to Ed’s point, it’s already happening. The five year conversation is over. The question now is how fast can you build that culture to get the most out of it?

Alvaro Cuba
Thank you, Jared.

Ed Ballina
Great takeaways.

Alvaro Cuba
some words of wisdom from your side.

Ed Ballina
hey, I gotta dig deep for this one because it’s been a lot shared already, by you guys. I think the one point that I would latch on to that you made Jared is this concept of you have to integrate it with the work if you add and listen, every initiative I’ve ever been a part of or have been having pitched, they’re like, this isn’t extra work. This is just you change how you do the work. Sometimes that’s true, sometimes it’s not. It’s extra work, right? But here, make it as simple as possible, right? To your point, if I have to log on to four different dashboards to get this information, I’m not doing it. You know, eventually it’ll collapse from its own weight. So keep it as simple. Start small. You don’t have to drain the pond in one day. so and if nothing else, always have a vi a bias for action. Okay. An 80% baked plan is better than a hundred percent developed plan that never get executed. So Yeah.

Alvaro Cuba
Thank you. Three points from my side. One is technology is different these days than before. So Jared was telling us, don’t worry about technology. Even more so with this technology, which is easy, cheap, fast to implement. So they will come and come fast when you are ready internally. To be ready, what Ed said, no? So start small.

Look for your leaders, the people that like change, people that are curious and start small with them and they will show the rest, the value. When it happened to me, when these guys are going to be working easy and better, the others want a copy and then you create a mass. And one final thought is, in this case, technology.

helps the process as well. It’s not a different thing. It’s part because it teaches you, it gives you advice and is one step ahead of you. So you can also use technology to help yourself in the process of implementation. Thank you very much, Jarrett. I think this is a so relevant topic and

It’s happening right now and with all what’s happening in manufacturing, everyone that jumps into this, I think can get a big help. And I think you help us for our audience to understand why it’s so important, how to implement it. So thank you so much for that and great having you in the show.

Jared Pfeiffer
Likewise. Thanks for having me on.

Ed Ballina
it’s been awesome. you really bring that here’s how it’s done, right? perspective, which I think is great. let’s get out of the hypothetical and let’s talk about how this actually works. So great examples. your perspective was was phenomenal. Thank you.

Alvaro Cuba
Thank you very much. And friends, that’s the wrap up for today’s show. Thank you so much for. Yeah, it is. Thank you so much for listening, for following us. If you like it, please share with your friends. Continue watching and let us know what you think. And if you like this episode, watch YouTube, please like it. If you are listening in iTunes, please leave us a review. More important, share it and join the meetup.

Ed Ballina
For sure. we hope you want to keep this conversation going. And if you do, you know where to go. You can email us at mmu at orgury.com. we’ll also have links in the show notes for this episode. And check out some of our previous stuff. I went back looking through some of our other podcasts. Pretty interesting. I think the one with that we did on the VP tour is is is in the top ten for sure. I think that was lots of fun and that was our last one. So with that.

See you next time, friends.