Using Real-Time Insights To Drive Manufacturing and Supply Chain Agility with Tom Ferrucci of Natco Home Group

In this episode, Tom Ferrucci, Chief Information Officer at Natco Home Group, shares how real-time data from the manufacturing floor drives operational efficiency. He explains how the convergence of operational and information technology enables better visibility, faster decision-making and greater supply chain resilience across dynamic, high-volume environments.

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Key Takeaways:

(02:26) The manufacturing floor holds valuable data that drives cost savings and efficiency gains.

(06:37) Real-time telemetry from machines replaces outdated manual tracking and offers deeper insights.

(07:46) RFID tags, originally for compliance, are now enhancing internal visibility in warehousing operations.

(10:17) Automated systems use sensor data to reduce scrap, speed up repairs and trigger maintenance workflows.

(11:37) Linking data to key metrics such as mean time to repair helps drive operational improvements.

(15:47) Applying data-driven strategies to the supply chain helps improve predictability in a volatile environment.

(21:00) Strong data governance and classification ensure fast-moving insights stay accurate and reliable.

(22:18) Manufacturing is data-rich and full of untapped potential when fully leveraged.

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Resources Mentioned:

Natco Home Group website

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Tom: [00:00:00] if we can penetrate into the manufacturing areas and get that data faster, make you know, that data information faster, it only helps in anything that we're trying to do. 

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Anthony: Welcome to Data Masters. Today we're gonna dive into the role of data in manufacturing with Tom Ferucci, chief Information Officer at Naco Home Group, one of the largest privately owned home furnishing manufacturers in the us. Tom has spent over 30 years in IT and digital transformation, implementing large [00:01:00] enterprise systems and using data to optimize manufacturing operations.

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At Nat o he oversees technology, strategy, infrastructure and data management, and he focuses on ensuring that information drives real impact on production efficiency. In this episode, we'll explain and explore how manufacturing as an industry leverages data differently, the convergence of operational and information technology, and how real-time analytics can help navigate supply chain volatility and industry disruptions.

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Welcome to Data Masters. Excited to have you here.

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Tom: Thank very much for having me on the data podcast. Looking forward to our discussion.

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Anthony: So I'm excited to dig into manufacturing as an industry 'cause it's something we haven't done before. Talked about and. Manufacturing is really the backbone of almost any economy. And it's [00:02:00] always been a really data rich environment. But historically that data was often very siloed and possibly.

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The role of data in manufacturing is a bit different than other industries. And maybe you can share a little bit, maybe at a high level, how you think about manufacturing as a, an industry, which is a bit different.

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Tom: I think you're right on, on some of those accounts and you know, it really is the backbone for a lot of. Of what we're doing here. But I think when you look at through manufacturing, when you penetrate down to the the manufacturing floor and you're looking at the warehousing, but especially in the manufacturing areas, the amount of data points that can be collected, can be captured, can be impactful.

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Can generate cost savings and efficiencies. And, there are so many areas that it can definitely touch. I think you have the opportunities are almost limitless on what you can do. And you really, need to take a look at, what is available to you. And, of course prioritizing that because I think there's a lot of areas that are impactful when you're taking a look at the [00:03:00] data that's coming off of a manufacturing floor. with people, with process with, we know the systems that are integrated on the floor, you mentioned really quickly, but it's an area that I've talked a lot about with many people is convergence between information technology, where we all sit up here, but then operating technology as it goes down to the floor.

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So there's many opportunities that can be tapped into.

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Anthony: So I haven't heard this distinction between operational technology and information technology before. Maybe let's be a little sort of precise and careful for listeners because they may not have that terminology in their mind as well. How do you define operational technology versus information technology?

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Tom: Yeah, so I, think information technology, I think we all, probably, everyone that's gonna be listening to this, is gonna have a pretty clear definition of the information technology, infrastructure, the applications, infrastructure, whether it's, on premise, cloud based, and how we manage our data.

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From, order to cash, procure to pay or whatever, data that you're managing. But when you're looking [00:04:00] at operating our operational technology, it really is, working potentially with a group of, manufacturing engineers who are designing the processes to make the manufacturing process more streamlined, more efficient. They're using a lot of tools that are somewhat similar to the tools that we're using. They're, programming PLCs or they're, going ahead and, putting sensors on equipment and all of that is, generating a lot of data. Right. But they're, I. Performing a lot of that in asylum or traditionally had done a lot of that in asylum and there are instances where you would, interact with each other, but it was used to be less and less.

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But I think there is a lot of convergence between the two. A lot of the equipment we see on the floor is all IP enabled, so you can. Talk to it over the network. And even more legacy equipment where the equipment is older and was built like that. There are things like sensors and, other devices that you can deploy to start aggregating the data. So the lines are much more blurred than they used to be. Less silo they used to [00:05:00] be. And I think a, lot of times that when you convers to it, it can become a very strong and powerful tool for transformation.

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Anthony: So to play that back to you what I hear you saying is that in manufacturing. Almost uniquely the active of production of actually doing the work of running the business is itself a data producing and data-driven activity. It is in contrast, perhaps to a bunch of other businesses which, they capture a lot of data in these IT systems, CRM systems, ERP systems, et cetera.

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I think the point you're making again, just tell me if I'm catching this, is that in increasingly in manufacturing, the manufacturing process itself is both driven by data, but also producing data. Is that fair?

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Tom: That's fair. And I think traditionally in the past where we had tapped into it as like barcode data collection systems or for labor tracking or some basic material movement, that's a lot of times where it [00:06:00] ended. But really now we can go much deeper into those areas and the data.

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That is being generated is enormous. Right? I think there's a lot of data that has the potential to be tapped into that we traditionally hadn't done as much of in the past or, in a very limited way.

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Anthony: so let's talk about that for a second. The amount of data is larger. There's more and more data maybe help bracket that for people. And maybe in the context of maybe some examples, real world examples of how you think about it. So, just how much data are we talking and what's the type and kind of that data, maybe even specifically to echo.

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Tom: Yeah, so I think, for us it's a lot of data, telemetry data almost, Hey, we have a piece of equipment that is producing X amount of. On a regular basis. And we are getting readings from that equipment to say, this is how fast we're running. This is how much, output is being generated. And that's great, that's good information. But we also know that there is information around downtimes and, why machine stop and things like [00:07:00] that. And a lot of times traditionally that may have been logged on a piece. Paper or it could have been stopped at the manufacturing engineering group and they may have done some analysis in a spreadsheet. And that would've been the end of it. Right. So, and that was pretty common. But now, when we look at this data we can say, Hey, let's integrate it into some of the other tools that we have. Available to us to analyze some of the data, whether it's a BI platform or obviously now, AI is a great tool for analyzing lots of data as long as you, have a good data governance and the classification of the data.

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Those foundational pieces need to be in place first as well. Right? We don't wanna be sharing anything proprietary with a language model that they shouldn't be. But, you know, assuming those pieces are in place I think that's the kind of thing that you can leverage. when you look at other areas like in warehousing, right? A lot of times if you're dealing with retail outlets, retail partners, they want to use certain tools like RFID tagging to, to move their material throughout the warehouse, which definitely is an issue with compliance. We would see, you know, certain [00:08:00] retailers saying that, Hey, when you know, the product comes to us, it has to have an RFID tag so we can identify every piece.

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So of course, you would typically comply with that requirement. There's also an opportunity there to be able to leverage that for your own purposes. Right. So, every time that, for us, whether it's a rug or a piece of home decor, a pillow or something like that is moving through the warehouse, we have the ability to know exactly where it is at any time down to a very granular level. And we're starting to do more and more of that. Again, those are opportunities that are available to us that we always try to, think of what are the opportunities that we're missing right now? So, you know, we're always coming up with ideas, right?

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Some are easier to execute on than others for sure.

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Anthony: So let's talk a little bit about that. How, because what I hear you saying is increasingly in manufacturing we're generating huge volumes of data. And you said this a few times about. Kind of under underlying, underline it a little bit. We're generating that data in real time. So we know, fairly, as you say, a fairly granular level up to the [00:09:00] second where raw materials are in the process where finished goods are in the process.

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Temperatures and vibrations and line efficiencies and movement and like, there's a lot of data being generated in real time. How do you even begin to imagine handling that volume of data in real time? Thinking about processing, acting on that information what are some of the techniques and strategies you've put in place for that?

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Tom: For me it really is following the business. Where are their pain points? What are they trying to measure? Where do they get the most leverage and with this information, right? So a perfect example in some of the cases that I've worked on in the past would be production scrap, right? I. So you are running a piece of equipment that is running perfectly fine, but then, there's something that may occur, and for us, if you're making rugs or in other, areas that I've, worked in a manufacturing, maybe there's a needle break so that something occurs where there's a defect in the material and you would typically have to wait for an operator to notice that the product was defective.[00:10:00] 

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Shut down the machine, open a ticket with the maintenance crew, and, have that repair done. Well, there are ways and. The interim between the time it occurred and the time that the operator noticed the problem. You may have generated a, many feet or pounds or whatever measurement you have of scrap. But if you have a sensor in place, or if you can tap into a PLC that can recognize certain types of breaks in a machine, you can immediately shut down the machine and you can build logic into some of that application to say, Hey, automatically open a ticket. In a, uh, CMMS system, a maintenance system for someone that would go and, maintain that equipment.

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So you're generating less scrap. your meantime to repair in theory is gonna be much quicker. And your production efficiency will go up. So those are pretty concrete examples of. Of ways that you can impact multiple areas, right? So I think and that, think about it, if you generate less scrap, you have to order less material in the long run.

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So inplex supply chain. So it really does, permeate, significant, areas [00:11:00] within the business. And that's one example that I worked on in the past that, we do think, you know, you have an impact there. There's, like I said, plenty of opportunities.

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That we're always trying to leverage. And again, and a lot of times your best friends in that area are the people that are running the equipment on the floor or the manufacturing engineering teams who are, either maintaining things offline or they, are trying to automate things themselves and. Maybe you're converging some of the data too. Like, Hey, within the technology side, manufacturing orders are tied to equipment, right? So let's try to, take these silos of information and make it even more powerful when you put all the pieces together.

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Anthony: So I think what's really interesting there is this idea of thinking about these business metrics that you'd mentioned mean time to repair or waste or raw materials or even supply chain inventory, things like that. I. These are not traditional data metrics, like things like that.

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They're really business metrics. And you're creating a tight linkage between [00:12:00] business operations and the kinds of data that you're drawing off the production line and then mapping kind of directly to those business metrics. 

Anthony: You made this point. Again, I'll kind of highlight it. That your business partners see great value in that.

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But I might suggest that the reason they see value in that is that you've tied the data [00:13:00] initiative directly to something they care deeply about. For example, meantime to repair, like how long a machine is out of production, like not available to work. Like, those are things that the business, for lack of a better term, like the business really cares about.

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And so making those data initiatives really connected deeply to, uh. the, in this example, the operational efficiency of that business that's a, great way to build momentum and excitement around data projects. Is that fair?

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Tom: Yeah, I think that's accurate. Right. And I think a great way to start is to, take a look at what the business thinks is important, but maybe something that would be a quicker win to prove your concept. Right. I think that's an area, maybe you can see the vision, the bigger project or the bigger. Approach to this would be a long term, but if you can get some smaller wins with impactful data that they hadn't really thought of analyzing in a way that you can analyze or the velocity of which you can analyze it. Right. So that's a big piece of it too. Right. I think within manufacturing, obviously we see a lot of the global change in the way things are being [00:14:00] manufacturing low cost areas versus, maybe domestic manufacturing.

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But if you can. Leverage velocity if you can make things faster, more efficient. Right? That's a good way to potentially offset, some of the other challenges that are faced. When you're looking global manufacturing.

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Anthony: So let's talk about that actually, because I think traditionally we've thought of manufacturing as a stable, reliable industry that everybody understands. And, maybe a little boring, if you might say it that way but it feels like, in the last few months, manufacturing in partic in particular domestic manufacturing has been anything.

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But there's been tremendous supply chain disruptions. There are tariffs that are on then off. There's a lot of global uncertainty. Things seem to be changing all the time. So we've talked a lot about how you use data to drive operational efficiency, in the production process, but. Cast your eye out a little bit.

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How have you been thinking about managing in what must feel like a much less predictable economic [00:15:00] environment?

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Tom: I think you're right. I think, we all love predictability, right? And when things become uncertain, uncertainty, it always makes people nervous, right? So I think the more that you can show that you have value in the data or you're making continuous improvement based on hard evidence on some of the data that.

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Maybe helps in some regard. The, obviously, the level of volatility or change is rapidly evolving, right? I think the pace of change we can all agree on is much more faster than we've all seen in the past. So, I think our ability to react faster, is impactful.

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I think, if we can penetrate into the manufacturing areas and get that data faster, make you know, that data information faster, it only helps in anything that we're trying to do. And a lot of what you're saying too, we still do import quite a bit as well.

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So I think that's an area, you know, supply chain. That's another area where we try to have some impact as well. So you're taking a look. It's obviously manufacturing domestically, we're really still trying to, take that a similar approach. In other areas, like I said, with [00:16:00] supply chain, I think trying to have better decisions more predictability that which is also another area of, somewhat volatile or somewhat volatility.

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And, there's obviously a lot occurring there as well that, every day you're watching and something seems to change.

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Anthony: Is there an opportunity to think we talked at the beginning. Data being in silos is part of the opportunity here. Kind of breaking those silos down and thinking about matching or mapping data that's occurring on a production process with more, broad trends in a market or supply chain like where you could be sourcing materials from.

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I'm curious about this linkage between really high velocity, real time, very local data, very, very much, literally in the machine to these broader systemic changes that are happening across markets or suppliers, or even thinking about it from a sales perspective, what's in demand.

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I mean, I'm sure the fashion industry think, trends and ideas change all the time. How do you [00:17:00] think about that linkage?

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Tom: we try to look beyond the transactional data or maybe the master data that we have in our system and try to really figure out the complimentary data for some of that. So I think the area that I can probably talk the most about would be with supply chain, right? So. Know, obviously we are managing a significant supply chain.

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Both, importing raw materials, some finished products from, various countries around the globe. And, probably up until 2020 things were somewhat predictable, somewhat stable. I think Covid, of course, I think I. Through a lot of volatility and a lack of stability. there's still a significant amount more than there ever was before, and we still manage them internally a lot of the same ways.

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We hope we're better at it. We hope our system's gotten a little bit stronger. But where I've, taken an approach here is to maybe looking at some of the complimentary data so we know what we're ordering from our suppliers. We know when we expect to get it in. We do have transportation management systems that manage the amount of time, say a. Shipment containers on the water for four weeks or six weeks, depending on, where it's gonna go. But let's take a look at some of the [00:18:00] supporting data around that. Things that we typically hadn't tracked before. You know, let's take a look at, GPS data that, gives us more insight into where the product is.

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The customer wants to know when the product is gonna hit their door, right? They don't really. Care about, some of the mechanics of it. But we can take a look at those kinds of data. We can take a look at data. We actually tapped into some open source weather data to see, what kind of impact that would have. Not just on shipping ocean container shipping, but also, with the trucking and how we're scheduling deliveries and how we manage that. And then we also tapped into some of the ocean ports and what kind of data they have there. A lot of times you will see, especially after covid, there's congestion around these ports.

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So, hey, my ship made it into port on April 1st, but by the way, you have eight days of congestion, so you just lost a week. So we try to converge this complimentary data into one pane of glass for my team to review, for our systems to evaluate, to hopefully make some better decisions on. Or, at a worst case is deliver news to the end customer that, hey, this [00:19:00] is what's gonna happen based on these factors. Where a lot of times that may have all been done separately in silos, maybe managed in a spreadsheet. We're really trying to converge it into a single piece of information, from the beginning to the end the best that we possibly can.

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Anthony: I think that's a great, example of thinking about kind of proactively bringing all kinds of sources to get data together. Getting that, I'll put words in your mouth, but a 360 view of the shipment 

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Tom: Well, it's funny, we, that's actually the term we use with some of the dashboarding. We do the, we call the, we call it, we have multiple, and it's called the 360 views because that's exactly the approach we take. That's in the exact terminology we use.

Anthony: Perfect. Yeah. Then that becomes a real competitive advantage. So, people wanna do business with you because they know, even if there's a hiccup, you are gonna be the one that's proactively reaching out and not being, yeah, trying to reverse engineer why something's late when it's three days late, you're telling them a week ahead to be prepared that there may be a disruption.

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That's again, powerful [00:20:00] differentiator based very much on your data strategy 

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Tom: We do hope. It's a competitive advantage too. I think that's, you said the key word right there. How do people think about us and when they think about naco, hopefully they're thinking, they see us trying to progress and move forward and, go beyond the traditional approach to data.

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Anthony: So in that spirit cast your eye forward where do you see the big opportunities for manufacturers in the future? And how would you like to see the data landscape evolve and the business evolve? And your answer must include the term AI or it's not buzzword complete. No, I'm cur I In all seriousness how do you think about the future of data in the manufacturing industry?

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Tom: Yeah. that's it. I mean, that, that's really the direction, everyone, at least trying to figure out how to take advantage of that. And same, we're in that same boat, we're definitely doing some things in that area that are, beyond proof of concept, but are still, I would consider them to be in, the. Infancy stage where we're really trying to, figure out the best approach to some of this and what tools to use. [00:21:00] And, again a lot of it goes back to the foundational work that we have done with data classification and data governance. Right? A lot of times, if you struggle in those areas. Maybe I'm gonna be more efficient in getting you the wrong answer, which is not what we want. Right. So, but I think you're right with the, the pace of change here is even faster than anything else. And just trying to keep up with that. what are we not uncovering?

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Or, what can these tools. Uncover for us that will, be that next leap with a competitive advantage or that next leap in how we manage the data. how we look at it, that we get insights and information and decisions being made on things that we thought were either too hard to get, we didn't think about getting, or, it was just something that didn't really hit our radar.

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Anthony: That's cool. Yeah, no I think that's exactly right. Well, Tom I appreciate you spending some time with us. Really interesting to think about how data can provide competitive advantage in the manufacturing industry. I love this idea of linking, I. Data about the business with data produced by the [00:22:00] business as it were, like the actual act of manufacturing.

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And it does sound like a really interesting challenge 'cause it's really a huge volume of data, but really can be a source of competitive advantage for you in the industry. And I think that's maybe not what people would've expected out of this industry.

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Tom: No manufacturing. You know, you're right. You know, maybe the, some people may think it's more mundane or you know not, but there really is. A lot of opportunity and it's actually, very interesting to, to be a part of this process. Obviously to see how things are made, which is always, you know, sometimes when you're a kid you look to see that kind of stuff, but what is behind that?

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And all the data that is behind that is amazing. It's It's a tidal away of data and you don't wanna draw and you don't wanna scoop it out with, just a little cup. You want to be able to, take that head to your advantage and that's the approach we're trying to take now.

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Anthony: Well, thanks for your time.

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Tom: Anthony, thank you for having me on.

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