Podcast: The Real Benefits of Data Tracking in Machine Shops

In this episode of Great Question: A Manufacturing Podcast, IndustryWeek's Dennis Scimeca talks with Mike Payne, president and owner of Hill Manufacturing & Fabrication, about how he uses data to drive shop-floor performance. Payne explains why he pulls every possible metric from his machines, how that information shapes his decisions, and what it reveals about actual setup and production time—insights that were impossible to see back when managers relied on rough time estimates that often became self-fulfilling. Despite his deep focus on data, Payne also emphasizes that strong relationships and reliable execution still matter more than the technology itself, giving listeners a clear look at how modern manufacturers balance analytics with people-centered operations.

Below is an excerpt from the podcast:

DS: Hello, everyone. My name is Dennis Scimeca, Senior Editor for Technology at Industry Week, and my editor-in-chief, Robert Schoenberger, has allowed me yet again to hijack the production pulse tonight. We're talking with Mike Payne, who's the president and owner of Hill Manufacturing and Fabrication. We ran into each other a couple of months ago at a conference, and Mike told me this, I thought, fascinating story about how he's wiring his plants up for data capture and what he's doing with the information. So I wanted to bring that story to you. Before we go any further, Mike, would you like to introduce yourself to the audience?

MP: Sure. First of all, thanks for having me. I've enjoyed—I think this is, what, maybe our third time we've talked?—and I've enjoyed it every time. So thanks for having me on.
Yeah, I'm Mike Payne with Hill Manufacturing and Fabrication, which is really kind of an umbrella for five shops that we have all in the Northeast Oklahoma area at this time, looking to expand that maybe a little bit. I'm also the host of a couple of podcasts, Making Chips and Buy the Numbers. So Making Chips is a more generalized manufacturing podcast where we try to equip and inspire manufacturing leaders. And then my By the Numbers podcast is spelled B-U-Y because a lot of my background is in finance and accounting. So it's all things finance, accounting, data-driven decisions—that type of stuff.

DS: As I mentioned, Mike is not just a podcast host; he has networking new plants down to a science, which is why I want to speak to him about the how—and, more importantly, the why—of capturing data. So, Mike, when we first met, you were talking to me about, I believe, the very first plant that you purchased, in 2018, and you were saying how you didn't have any data capture at that plant when you first started. And when you first turned on machine monitoring—when you got the plant wired up—you said you were running 30% spindle utilization. And when they began capturing and analyzing data, that changed to the 50% to 60% range. Now, to what do you attribute—that’s like double—to what do you attribute that change just by looking at data?

MP: Yeah, I think—I mean, everybody's heard their entire careers: what's measured matters, right? And when we weren't tracking it, it didn’t matter. When we talk about not having data capture on the front end, I mean, I guess we did have data capture; it just was garbage in, garbage out, right? It was a paper-based system where we’d give an operator at the machine, like, “Here's this job you're running. It should take eight minutes a part.” And remarkably, how long do you think it took? Eight minutes a part, right? Because they just filled in the blanks: “Well, I was here eight hours and I got 64 parts,” or whatever. It might have taken them eight and a half; it might have taken them seven, but there was no tie to that data. It was just handwritten data.

So our first step into capturing data was implementing a good ERP system, where we were really capturing the time spent on essentially everything, right? We’ll log time even against Kaizen events—how much time are we spending on Kaizen, how much on training, how much on an actual job, whether that's setup time, runtime, first-article time. That gives you an early look. And I would say even with just that, things bumped—but probably not a ton from an efficiency standpoint—because you were capturing better time. You were getting more accurate data. Some were high, some were low on a paper-based system. You know, they level out. 

But then the missing piece—you mentioned the machine monitoring. Once we get that, and it's tied to our ERP system, it now knows what we expect to happen based on history, estimates, all that type of stuff. But now you're getting what really happened. And you throw that data up on a screen, and naturally things just get better.

Part of that is the old Hawthorne effect, right? If I see how I'm doing, I'm going to do better. I mean, if you played an entire football game without the scoreboard on, some people might know the score, some might not. But if I see I'm down by three with two minutes left, I’m going to act differently than if I have no idea.

Same thing in a machine shop. Someone’s looking up there, and our machine monitoring gives you a grade—this machine is running at an A, a B, a C, whatever. And just the natural competitiveness in a human being—most people want to perform at an A. We all want to be successful. So I think just that natural effect created that immediate bump from, say, 30% to 50%. Then, when you can start dialing in why you're not at 60, 70, 80—and you have the data to make good decisions—you can start seeing those improvements.

DS: So this is the first plant—excuse me—between 2018 and 2021. That was how long it took to get the systems and processes in place for this first plant. Can you talk about what was happening in those three years? Is there a need to break that down? What was the journey before you got the data and were really able to start rolling with it?

MP: If we back all the way up to my history, 30 years ago when I came out of college, I started a software company, and I actually did shop-floor data collection. So I've been around data collection my entire career. And here I am—I come in, I buy this shop. I can see the data in the financials, but I can't see the data on the floor and what's affecting those numbers. So yeah, I just immediately start looking for opportunities to get more information so that I can weigh options.

Like I mentioned, the first step for us in 2018 was getting into an ERP system that allowed us to start collecting a lot of that data. Then from there, we just started drilling down and going, okay, if we've got this data, here’s where we're still lacking some data, right? The ERP maybe gave us today's performance on a job. Machine monitoring gives us this minute’s performance on a job, right? So where I used to know how we did today, now I know the first time we run a part how we're doing on that.

So when you look at that three-year period, it started with collecting some data, deciding what data was important, and then improving that data. It was an evolution over that three-year period to get there. And of course, we had COVID punch us in the mouth in the middle of that. It changed a lot—although in some ways I would say it changed us for the better, right? Because we had a serious blow to our revenue, but it gave us time to work on ourselves too.

We made it through that without having to do any layoffs or anything like that. So what we did is we were able to use the same labor we had to make ourselves bigger, stronger, and faster. And a lot of that was through better data collection. Even where I talk about an ERP system—being able to track Kaizen events, right? How much time are we spending on improving ourselves? Are we spending enough? Are we spending too much? Things like that.

Or the money and time and effort you spend on having a tool room manager—do we see that in a reduction of setup times? Those types of things. That data gives us what we need to make decisions.

About the Podcast
Great Question: A Manufacturing Podcast offers news and information for the people who make, store and move things and those who manage and maintain the facilities where that work gets done. Manufacturers from chemical producers to automakers to machine shops can listen for critical insights into the technologies, economic conditions and best practices that can influence how to best run facilities to reach operational excellence.

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