Optimizing Concrete from Fleet to Finish

Podcast Transcript

Sarah McGuire:

Hello, Concrete Revolutionaries, and welcome to another episode of Building Better with AI. I’m your host, Sarah McGuire, and today we are going to be talking about the importance of getting data from fleet to finish, and monitoring the quality of what is happening in those ready-mix trucks.

Today we are joined by Chris Wurtz, the President at Digital Fleet, who brings over a decade of experience in engineering and technology. Chris holds a Bachelor’s of Science in Electrical and Electronics Engineering from South Dakota State University, and an MBA from the University of Minnesota Duluth.

Prior to joining Digital Fleet, Chris spent a lot of time in the OEM world, designing and developing vehicles. In his current position, he leads the company’s strategic initiatives, technology integrations, and overall growth in fleet management innovation for the concrete industry.

Chris, welcome to the podcast.

Chris Wurtz:

Thanks for having me, Sarah.

Sarah McGuire:

Chris, I wanted to have you on specifically for a couple of reasons. By now, if people are paying attention to what’s going on in Giatec’s world, they would know that we just launched a very exciting product called MixPilot, and we did a lot of our early work with you and the Digital Fleet team because we really look at you guys as the master of what’s going on in the Ready-Mix truck.

But the second reason, of course, is because, when we’re talking about building better with AI, and the importance of getting data to funnel into all of these algorithms, actually understanding what is happening in the truck and in transit is absolutely crucial. We look at concrete trucks like mini factories on wheels, which is a term that you coined, but we have now used quite extensively, because of how much is happening and how much variability is happening in that truck.

And learning from you and your full team at Digital Fleet during this process has been really cool. So we wanted to have you on to really talk about the importance of that data, as it pertains to optimizing and using that, eventually, for the purposes of AI. But first, we want our listeners to learn a little about you and learn a little bit about the Digital Fleet team. So, can you start by introducing yourself?

Chris Wurtz:

Awesome. I’ve been with Digital Fleet for maybe six and a half years right now, but prior to that I’ve always been in electronic vehicles. I worked for a motorcycle company right out of college. Ended up getting into this industry, I think in 2011, if I remember correctly, at McNeilus. So, I started off on the concrete mixer side.

Held a couple of roles there. Did a brief stint at Terex as well, in their paving division. And then joined Digital Fleet. Which it was great, super innovative company, kind of up and coming, really focused on just giving you data and getting a better look into your fleet overall. And since I’ve been here, I’ve got a great team, it’s really morphed into fleet wide management software, more so than just like a dot on a map.

I think most people think of us traditionally as a dot on a map, truck tracking type thing. But we do a whole lot more than that. It’s really evolved. It’s an amorphous blob of software, is how I describe it, generally. But it’s really fleet wide management to its fullest. And connecting your employees, and your trucks, and everything, and just giving you data so you can make better decisions as a producer.

Sarah McGuire:

I love that. And of course, we hear about telematics and fleet management in a lot of different industries, but this is very specific to concrete. And Digital Fleet is really specific to concrete as well. You were in this overarching industry before, but now you’re really into the nitty-gritty of concrete. Can you tell me what has challenged you the most about this particular industry? And what you enjoy the most when it comes to concrete specifically?

Chris Wurtz:

Yeah, you’ve heard me say this before, I think, ready-mix is a super interesting puzzle, and it’s one that’s changing every single day, depending on who you’re dealing with, and what equipment you’re using, and if your equipment breaks down or a person doesn’t show up for work.

The thing that makes ready-mix different than any other fleet I’ve ever worked on is you essentially have 90 minutes. I know that’s generality, but you have 90 minutes till the concrete hardens in the drum. There’s lots of things you can do to prevent that. Every mix is a little bit different. I’m not a concrete expert, per se. But that’s really the fascinating thing about this.

So when I was at McNeilus, I was also working on garbage trucks as well, which is very much, you go to the same spot every week, and you pick up a can, or pick up a bin, and it’s very regimented on what you do.

This, you don’t know what you’re getting yourself into by the hour with concrete, and that’s because you have 90 minutes before you have a 40,000 pound rock on the back of your truck. And so that whole environment creates a very different dynamic, and it creates a very different mindset, where at the end of the day, you just got to get the concrete off the truck, is really what it comes down to.

And so our technology, I think producers mindsets, combined with our technology, is really about optimizing that space or that part of the equation. And we got to get there, we got to deliver, we got to get a quality mix, it’s got to be right, we got to pour and we got to get back.

And you think of all the things that ready-mix producers do that are different, than let’s say an average fleet, and it’s all because of that. So that really is at the core of the biggest challenge, that all the other stuff, at least in my opinion and what I’ve seen in this industry, that 90 minutes is core to all the other things that happen and cause chaos throughout the producer’s day.

Sarah McGuire:

Yeah, you’ve eloquently put that. And we are going to keep coming back to that 90 minutes, I think, because one thing about this podcast is, even though we’re really talking about the nitty-gritty of the concrete side of things, and so a lot of our listeners are going to be from that space, this is a podcast for the overarching construction industry. And also, to give other people in that ecosystem an understanding of how complicated concrete can actually be.

And so, I do want to come back to all of those things in that 90 minutes that really happened there. But before I do, can you share from Digital Fleet’s perspective? Obviously, Digital Fleet is a newer company. You were founded in the last 10 years, so in that space that is newer. And Giatec, we’re in the same boat. We are kind of a newer company for this industry. But for customers that were not using Digital Fleet before and are now, what have been the biggest game changers for them overall? What is the biggest value that they’re really seeing in working with your team and your solutions?

Chris Wurtz:

Great question. So just to explain a little bit about what we do. So we do a lot of fleet tracking and what we call statusing, which is essentially, I hesitate to use the word predicting, but I’m going to, we’re predicting what the truck was doing at the time and the driver was doing at the time. And we have a variety of products that actually help you do that.

We have sensor solutions that go on the truck. We’re really truck guys, we’ve got a lot of truck guys on our team. So we do a lot of electronics that go on the truck that combine with our software. And so what we see more than anything with our customers is they’re gaining insight into what’s actually going on in the truck for the first time.

And there’s other great solutions out there that do similar things, but for us, we give a level of insight that I think they haven’t seen before. So most of the time, when they put our software on for the first time, it’s the first time they’ve actually seen some of the detail, and I’ll call it waste. So you’ll see some of the waste, point-blank, right in front of you, saying that you spent this amount of time doing this activity. And it could be something like, they’re idling in the yard for 30 minutes before they get a ticket. Or they’re on the job site washing their truck for 20 minutes. Or you give them a company goal of what we call wash, fuel, and park.

So basically, you’re ending your day, and we give a company goal of 30 minutes. And these drivers are taking 45 minutes or 50 minutes. And sometimes, that is actually acceptable, and that’s what… There was a situation that came up, the truck broke down, or whatever it is. But now you’re gaining insight into what’s actually going on to the fleet for the first time. And I think a lot of times that can be pretty eye-opening for producers.

Sarah McGuire:

Interesting. So you are giving a lot of data, and almost information, I guess, to these companies that have never really been able to see that before. Do they find that really overwhelming? How do you help them through that? And also, how do you help them through maybe a bit of an ego issue, where they realize, look at all of this stuff that was wasted, but they couldn’t have possibly have been tracking that without a system in place to do that. But yet, having to overcome that hurdle of, oh, look at this is now exposing the inefficiencies of my own job. How do you overcome that?

Chris Wurtz:

That’s actually a really good question. What I always try to stress more than anything, and I think this is our team and our approach to it is, just because it’s this way doesn’t mean you’re wrong. You were making decisions with the tools you had and the information that was in front of you. And it could be, you’re trying to adhere to company goals or whatever it is.

And so if let’s say you find out that, this is going to be a very specific example, if you find out that your washing time in the plant is way more than what you actually were thinking it was for a company goal. Let’s say your goal is you want to get out of the yard in 10 minutes after you get loaded, and you find out it’s 15 or 20. It’s not that it’s wrong, it’s that you at least know where you are. And so that’s the first step in making sure that you use that data correctly. And so that’s probably the biggest thing to just broadcast, is you’re not wrong. This doesn’t mean that you’re doing the wrong thing, or you’re failing as a plant manager, a dispatcher, or quite frankly even a driver. It just means for the first time you can actually see what you’re doing.

Sarah McGuire:

Interesting. I like that way of approaching it, especially, I mean, I ask that question selfishly, because we deal with that all the time on the quality control side. That is a very often uninvested portion of concrete companies. And somebody recently explained it very well to me, is that when we’re looking at quality control costs, it’s actually looked at overhead on most companies’ P&L statements, and it’s not really looked at to directly impact the profitability, which I find extremely contradictory, because of the fact that that department is literally in charge of maintaining the highest cost of the business, which is the cost of goods.

And so people really have a hard time investing in that part, so people end up having to do so much manually, and they’re relied on so heavily. So then when you plug in a system like ours, and immediately things and insights start popping up, and it’s dollars and cents everywhere, some people will go, “What if my guy in finance that doesn’t really understand how this works, what if he looks at this and goes, ‘How have you been leaving this kind of money on the table this whole time?'”

But the reality is, that without the tools there, with all this extra data and information that’s coming, and the lesser that we have all these people to actually process it. It’s just impossible for them to do that. But it is really hard to get through that.

Chris Wurtz:

I was going to say, there was one other question you had, was you give people a lot of data, and how do you actually do something with this? And I’m sure you guys face this, especially with the amount of data that you have.

I think one of the biggest challenges right now is, we plug into the engines, we plug into the trucks, we do tracking, we do all sorts of different things. And as a product development team, we do have a full-time designer on our staff, that actually spends his time in how do you actually utilize this data?

And you can have all the data in the world, and it’s not going to do you any good if you can’t interpret it and analyze it. So that’s a really big part of our product design process. And I’m sure you guys do the same thing. Its how do you make this data actually usable and actionable? Because if you don’t, it’s just data.

And so, we spend a lot of time with our customers trying to figure that out. How do we make it useful for you? We’re not dispatching trucks every day, but our customers are, so how do we take their feedback and actually make it so they can make decisions quickly? And instead of being more reactive software, we can be more proactive, by presenting that data in a way that actually makes sense to them.

Sarah McGuire:

So I think that’s a really good segue into talking about the real challenges, but also, I guess, the opportunities of working within that factory on wheels. I mean, we can both go into this together, but I’d love to put it to you first. Especially, for those that don’t understand everything that goes into concrete, can you describe in those 90 minutes, can you break down the key events that are happening at each one of those stages? And then we can talk through the challenges that happen at each of those stages, or where things can go wrong because we don’t have that data?

Chris Wurtz:

Yeah, sure. So I think just to explain the concept of the, we call it the mobile factory, the factory on wheels, we flip-flop on terminology there. The big thing on a ready-mix truck, and this is very prevalent in the United States, maybe not in the rest of the world, but most of the plants or the batch plants in the US are dry batch plants. And so what that means is that you’re actually mixing the material, and you’re creating the material once it gets loaded in the truck.

That’s not necessarily the exact same philosophy behind a central mix plant, but the philosophy still pans out. So when you’re actually producing it, you got to start analyzing and looking at the truck as an actual factory on wheels. So think of, if you are a non-ready-mix guy, think of somebody making a cake on the way to deliver it, is essentially what you’re doing.

So you’ve got the batter in the truck as it’s driving down the road, and you’re making cake. We just don’t bake it there. But that’s really the philosophy. So I come from a lot of automotive, I’ll call them automotive, that’s not necessarily the right term, but I come from a lot of factories. And the big thing on the factory side is, in every station, every time you touch the piece of equipment that you’re building, you’re trying to eliminate waste and variability.

And that’s the same philosophy a ready-mix producer should be taking on their truck. And the variability comes from a lot of things on the ready-mix side. It comes from the type of concrete, how it’s being poured, what job site it’s going to, how far away it is, did the right people show up, is the crew there. There’s all sorts of variability that gets injected. Where on the heavy hauling side, you do face some of the same variability.

If you’re in asphalt or anything like that, your asphalt can cool off. But the big thing that’s different from heavy hauling and a ready-mix, in my opinion, is that, I’ll call ready-mix is kind of point and spoke, where you just go out and you deliver concrete, you come back. Where, in heavy hauling, you may deliver a truckload of gravel, and that’s a drop-off location, but now it becomes a pickup location. And now that essentially becomes kind of a plant, where you’re getting the material and they drop.

So the variability on the heavy hauling side really comes just from the schedule and where the truck is going, because those trucks are so versatile. But going back to your original question now. So starting off in the plant, there’s the patching side, you get loaded into the truck, the truck loads, comes out, and starts washing out.

Now variability can happen from that, right? It could be, how did the truck get loaded? Did they put the right load in? How messy is the truck afterwards? What’s your process that occurs as the driver comes out? Does he have an external washout hose? Or is he using the water off his truck? And then you go to two job, and that’s the traditional things you would think of. Or you’re driving to the job, where you hit traffic, you got the wrong directions, whatever could be occurring there. You might even have printed the wrong ticket, who knows. Right?

And then it comes to the job site. And the job site is probably the biggest variable, I think, that occurs in the whole delivery process. Because it could be the actual contractor isn’t ready, or the contractor wants something different. They could be pumping the concrete, they could be doing a form, or whatever it is that they’re doing. And that’s where specifically we targeted on trying to get better insight to what’s going on in the job site. Because with what we hear and what we see, that’s where we see the most variability.

And that is dependent on the driver and the contractor. You may have customers that love to hold trucks, or don’t order the right amount, or whatever it is, and that can really drastically impact how that job site’s going to perform. I mean, think of a thousand yard pour, which is a big pour.You’re going to have a hundred different trucks showing up. And once one starts and is late, that just has a cascading effect for the rest of the trucks that are going on.

So we see a lot of variability in that, and I think that’s where we focus a lot of our time and our software, is trying to figure that piece of it out. Because it’s so variable and it’s somewhat subjective, and you just, up until now, people really haven’t had the insights to know what’s truly going on there. Because you could say, “Oh, I was delayed because of a slump test,” or “The load got rejected,” or you have leftovers, or the contractor wasn’t ready itself.

And there’s all this stuff that’s kind of out there, that I think up until the last few years, you kind of just had to say, “Oh, okay, I guess that’s what it is.” Right?

Sarah McGuire:

Right. I’m actually very interested in the fact that you would still consider that the job site is one of the most var… And I wouldn’t disagree with you that the job site holds a lot of variability, but for a company that has really been able to help master the insights that come from those 90 minutes, I’m surprised that you don’t think that it’s the in transit portion.

Maybe it’s not the most variable, but it’s the most crucial to get right, because otherwise, once you get to the job site, if you haven’t gotten that part right, opportunities go up exponentially of what could actually go wrong. And therefore, if you show up with a load that doesn’t have all the insights from what’s happened in transit, your factors are infinite, I guess, in terms of what could actually be the issue.

Chris Wurtz:

Well, what I would tell you is, yeah, there’s still a huge amount of variability on the in transit side. There’s still a pretty big unknown that we’re really excited to be talking to you guys specifically about, and working together with you.

But I think from our perspective, we focus a lot on time. And so, most ready-mix producers, I think if you look at the NRMCA average, I’m sure it’s changed over the years, but it’s usually around $1.75 a minute, is what a truck idles at. And so the big focus of our software, specifically, is that we’re trying to identify areas of time that are considered waste or variable. And so when we focus on that, we focus on time. And why I say that is, on the in transit side, it’s relatively predictable today, as far as the distance that you’re going, the traffic patterns that occur, the roads that are used to get there.

It’s somewhat predictable, and you can use… There’s all sorts of different mapping softwares out there to help with that. And so, when a truck says it’s about 15 minutes away, in general, it is about 15 minutes away. And the next piece of this for us, in working with you, is trying to figure out what’s going on in the actual drum itself.

And we have the peripherals around it to know the drum rotation, if the driver added water, and stuff like that. But I think, in particular, that’s why we’re super excited to be talking to you guys, because that’s going to definitely add a key piece to the puzzle on basically optimizing your delivery routes, is when we can start looking at MixPilot.

Sarah McGuire:

That’s fair. Can you give me an example? I want to ask about specifically when it comes to slump, or water add, or all the unknowns that happen in that. But putting that part aside for now, in terms of the, in general, insights that customers are getting from you guys that they weren’t able to get before, what are you finding is having the biggest impact? And can you also give an example of a time where your systems showed an opportunity, but just because they didn’t take it at the right time?

I guess I asked that question because, there is a lot of time when you present information to a customer with this new thing, but they haven’t quite mastered how to actually take it in a really, really strong way. So I’d love to hear a good example of that, and a not so good.

Chris Wurtz:

I think there’s a lot of different aspects of our software that helps identify where there’s problems and where you could have chances for improvement. And some of them are probably more effective than others. I would say, where I see the most impact on our side for the software is around timekeeping. And so what I specifically mean by that is driver timekeeping.

We do a lot of work on, I guess, essentially filling out driver’s timecards for them. And so, that could be all the way, that could be as detailed as fully costed timecards, to give you a simple example. Like, I deadheaded from plant one to plant two, so I charged that time to the plant two ticket, rather than the plant one ticket.

And so we get into a lot of details around that side of it. But I think one of the more effective things that we’ve done is, just by listening to our customers, is we have what we call exceptions on the employees’ timecards. And so you have a company goal of, I think I mentioned it earlier, like a 30-minute wash down time at the end of the day. Or you want your drivers to go through their vehicle inspection reports in 15 minutes and then have another 15 minutes to end their day and clean their truck, or whatever it is.

And we will see that quite often where a company comes in, they go, “Oh yeah, we’re really good, and it’s 20 minutes. “We’re putting our goal as 20 minutes.” And the first day, after all the drivers start logging out, it’s red everywhere. And so then they may adjust the goal, or may dig into it, or whatever it is. What we generally find, I’ll just say this as a big general thing, not a specific example like you requested, is if you present people the data, and it’s not the people that are actually making those decisions, and they’re not the ones who are trying to get people off the clock, or it’s the driver and he’s not seeing that he’s not performing to what the company’s standards are, in general, you’re going to have a tough time making any change.

But when you put that data in front of the people, and they have to approve it, or actually look at it, like if you’re approving an employee’s time card and you see that they took 45 minutes to end their day instead of 30, that’s where you really see the change start happening.

And so again, you can present people data all day long, and if it’s not in the moment, in their face, actionable, it’s going to be really hard to make changes. And so we could go into a lot of nitty-gritty examples there, but that’s really where we see it. And when we say that the biggest change we see, especially when people come into our timekeeping system, is when you start impacting people’s paychecks by what they’re doing and their actions to company goals, then things really start to happen. Until then, it’s just a nice thing to look at and say, “Oh, we weren’t very efficient today.”

Sarah McGuire:

No, I really like that, and it’s an unanticipated segue, kind of in talking about our sensors and what we’re doing together, right? Because one of the things that we noticed when we came into this market, and we brought out mixed optimization, key components that we needed, that we want that slump recorded from when it gets to the job site because we want to know what that was, to be able to mix for the next load, even if it’s not even for that specific load to control it once it gets there, but the next 10 that are being batched. Or to redesign the mix completely from the beginning, so that we just have no errors at all from the time that it goes.

So that’s one of the biggest ones. Also, return water, recycled concrete, volume, water added, anything, we want to be able to capture that. We’ve seen a lot of times where people look at our system, and they see all the dollars and cents that they can save, and then we’ll ask them, “Why are you not taking these optimizations?” And they say, “Well, I can’t do that because usually my driver always adds 10 or 20 gallons of water.”

We say, “Okay, that’s fair, but now you have the dollars and cents in front of you of exactly how much you can save if you go focus on fixing that problem. And suddenly, there’s motivation to do it. But if there’s no way to monitor that, well, how can you know? And for us, the most important thing when coming into this space was, there are systems out there that are gauging this. Or there are people that are doing manual tests when they’re recording them. But there’s no data that’s happening at the right time for the right people to actually take the action that’s needed.

And when we hear about people that have really invested in solutions to manage their quality in transit, I have been finding that the biggest benefit that people have been getting is more on data to back up that they were not the reason that a problem happened. It’s not to prevent the problem from the beginning.

And that, to me, that was one data point of saying, “Well, that’s because they’re not getting the data at the right time. And that’s why working with you guys to make sure that it’s presented to the driver at the right time, that the ticket information is coming through, that we’re getting all those insight about the customer, and maybe it’s the exact same mix happening, but different customers are having different water add practices, and it’s actually offsetting the performance of the mix. And now that’s something we can actually tie up if we want to go pursue that.” But without having all of that data coming together, it’s impossible to be able to even go down that path.

One example that we had, right out the gate, when we started doing this test with Maschmeyer, is they put the sensors onto their drum, and this was before we had proven the accuracy was as good as it is now, so they were just putting it on to test the hardware. Well, right away they were able to see that some of their drivers were having a huge RPM issue, going above 16 at certain points. And that’s going to completely offset the quality.

Now the load gets to the job site and there’s quality issues, or there’s something wrong, or it gets rejected, how are you possibly going to know that it’s because of the RPM going crazy? And so that was something that they were able to tie up right away.

But then we had another debate where we got on the call and they were looking at something where they said, “This load was rejected.” And then we go in and we say, “Well, our sensor is showing you that your slump was accurate. You were within spec.” And at first they didn’t believe us. And of course, we see that after the fact, but that’s, again, because we hadn’t done this at scale, we hadn’t proven the accuracy.

But if you don’t have the data at the right time, in the right place, for the right people, you’re just not going to get the value. You’re just not going to get it in the way that’s actually going to benefit everything from beginning to end. And that is still a big challenge of this industry, of all of these different systems, all this cool technology that’s coming out that can really make an impact. But if it’s not integrating together, and we don’t have free flow of data, and we don’t have any of that, we’re not going to be able to take full advantage of these things.

Chris Wurtz:

Absolutely. I think that’s the super exciting thing about working with you guys, right? So like I said earlier, this is really a missing piece into the puzzle that we haven’t had real insight into for a long time.

And so, you think about closing the loop between what goes into the truck, what is actually delivered, and then putting it back and adjusting your batch weights, or whatever it is that we’d be doing, it’s like, this is the first time that we’ve really been able to connect all the data together. And we’re firm believers in integration at Digital Fleet. I mean, that’s a huge aspect of what we do. And if we can get the data in front of the right people at the right time, that’s our belief. That’s how we’re going to make everything better for everybody.

And so what I think is really exciting about us right now, and working with you, is this is the first time that I’ve seen in this industry, that instead of being more reactive about things, we can really start becoming proactive about it. And we can actually go down, and we can see some of those things that are happening.

The RPM one’s a great example. You could see that the RPM is really high, and there’s things you can do and adjust for that, and you can see how that impacts your slump. So in theory, you could prevent water from being added, or different things that you could do to make sure that that load is actually going to be the correct slump, or the correct specs, when it’s delivered on time.

So I think this is really the first step in a pretty important journey to becoming more proactive in the ready-mix industry, and we’re finally having the tools, and I think the right pieces coming together, that we’re going to be able to do that.

Sarah McGuire:

Right, I agree. Now you’ve just said a very important word, proactive. Not very common word used in this industry, because… And this has been a challenge for us as well. People look at optimization and they’re like, “that’s very proactive, of like, yeah, I can go ahead and save all of those dollars and cents, but it’s not troubling me today, so why bother?” So can you give me an example of anything on the Digital Fleet side where people have been able to use your data proactively and really leverage it for the betterment of their business?

Chris Wurtz:

So it’s not quite data, but I’ll at least give you an example of a piece of hardware that we have. We have a water control valve that can go and be installed on a truck. And you have to get approval from the contractor that you’re able to add whatever gallons of water you want. So for a simple example, you go to the job site, the contractor says, “Add 15 gallons of water.” You say, “All right, cool. Need to approve this text message.” The driver sends a text message out, the contractor approves it, as soon as he approves it, the water’s added. Right?

And again, it’s a simple example. It’s not quite to the level of a proactiveness that I think that you and I are thinking here, and what we’re going to do going forward, but it’s a very simple example that goes out, it gets approval from the contractor, then the truck automatically adds the amount of water, not the driver, the truck will do it. And so that was approved for that exact amount.

So again, a little bit different than using data to become more proactive, I should say. But this is a way to prevent the driver from actually adding too much water, or whatever it is. I shouldn’t say, it’s not always negative on the driver. It should be that the contractor said that they only wanted five gallons, when in fact they actually requested 15 on the job site.

Sarah McGuire:

Yeah, that makes a lot of sense. And I think we are really only experiencing the cuff. But I do think, earlier you mentioned something that’s super important, is like, we can’t really be proactive about these things unless it’s brought in front of us. We have two examples in the Giatec SmartMix System. One is very reactive, in the sense that if there’s a low break that happens, we like to get those break reports into the system as quickly as possible from third parties or internal. So at the second we record a low break, it notifies everyone, and then we can actually be reactive about that, or proactive in informing the customer, or taking action before it comes to us in a way that now we’re under fire.

Whereas, we do have these optimizations as well that come up, and we actually disabled this. People were saying that these alerts that a new optimization is available was becoming, they’re too inundated with alerts, especially big companies. You don’t want to be alerted every five to 10 seconds every time a new break comes in and there’s an update there. But at least we have a dashboard where they can go and they can investigate opportunities to save money, that is not reactive. Because a customer is not calling them and saying, “I need this from you right away and I need you to react in this moment to fixing my problem.”

But we’re now able to look at it all in one consolidated place and actually start taking these insights. But it’s not data, it’s information. It’s been processed already. It’s being put in front of them. And I think that way of being able to amalgamate these insights, there are certain, the five to 10% of people that can go in and search for anything that they’re thinking of and find some information there. But for the majority of the industry, if we don’t put this information in front of them in a way that they can process it very easily, it’s just not going to happen.

This is too quick natured of an industry. We have too many other qualitative things that are happening on a regular basis that they need to go focus in on, that we can’t help them with on the software side because we’re not collecting data for it. So I think that’s the biggest challenge for developers of products like us, to figure out how can we make that proactive.

When I spoke to your CEO, Tim, I remember we were talking about just general benchmarks and things that we wanted to do together in the industry. And he was telling me you guys have an extremely, extremely low churn rate, extremely low. And the only times that you really have customers turning or not coming back to you is really because they’re not utilizing all of the data that they have in front of them. It’s just a lack of being able to use it, but yet it’s so, so low.

So what do you guys think that you’ve done really well in making sure that you’re developing things and insights for the industry that’s really working? Because this is a, it’s not broken, don’t fix it industry. So how have you guys been able to solve that on the product side?

Chris Wurtz:

Interesting question again. I think it’s really our approach to our customer base, more than anything. So we do really believe in customer support, just to say it outright. We understand, when a mechanic calls in that’s installing our hardware on their truck, that they may only have that truck for 30 minutes, or for whatever reason that truck’s in the shop today. So that time is absolutely crucial.

And so we really, really try to focus on customer support. I mean, like everybody, we’re not perfect. But that’s a big thing in this industry, is just making sure you’re available, and being able to help troubleshoot any problems, whether it’s a problem with our software, a problem with the implementation, or whatever it is. That’s really number one for us, is customer support.

From the product perspective, we have a super talented team, and we’ve done a really good job of listening to our customers, trying to walk in their boots, if you will, and then taking their ideas from what we’re hearing from other customers and trying to implement them.

So we subscribe to the agile philosophy of software development, and so we work in sprints. And right now we’re doing weekly sprints. That, obviously, could change, but it’s really trying to get rapid feedback from our customers. And there’s no limit to ideas. I mean, the great thing about ready-mix producers is they are always thinking, you can see the wheels are always turning whenever you talk to them.

And so it’s trying to basically corral their ideas into something that can benefit all, is the thought. Everybody kind of has the same idea that, “Hey, we want to tackle pre and post trip DVIRs. We want a more effective way to do vehicle inspection reports.” Or, “We want a better way to predict begin pour, end pour,” those types of things. And everybody has different needs and different philosophies on how to do that, but if we just keep listening to our customers, and trying to corral those ideas, it’s like herding cats too a little bit. But if we can corral those and make those adjustments, and make those changes, I think our team has been very good at that, for the most part, listening, understanding, interpreting, and a lot of times even exceeding what the customer’s expectations were.

So that’s really how we’ve approached it, is just continue to listen, bounce ideas off back and forth. It’s healthy debates, a lot of times, between us and our customers. Sometimes we can get to it, sometimes we can’t, depending on what we’ve got going on. But that’s really what’s helped us and drive. Like I said, our software’s an amorphous blob. It’s always changing. We’re always getting into something new. And it’s just because our customers keep coming up with different ideas, and they help us fine tune them and make them actually something pretty cool.

Sarah McGuire:

Yeah. Going back to that debating part with customers, do you have a time in your mind of where a customer’s really adamant about needing something specific to help them, but the solution that they were asking for was just not the easiest way for them to approach the situation? And kind of going back and forth on that.

Because we would say at Giatec, our customers know their problems inside and out. They can help us identify it, they can talk us through how they’re doing it. But they don’t always know the best way to get to a solution. And sometimes when we show them a better way, it’s like, “Wow.” But it takes time. So do you have an example of going back and forth on something like that, where you got to a point where you’re like, “See, this is why Digital Fleet is your solution provider, and you guys are the best problem identifier”?

Chris Wurtz:

I will say, it can go both ways. There can be times where we’re super stubborn about something, that we draw a line in the sand and say, “Oh yeah, this is how it’s going to work. And this is how it works,. Blah, blah, blah, and we’re not right. And then it can be the other way around, where you’re sitting there, and you’re like, “No, you guys…”

A simple one is automated timers on certain things, like statuses. When you look at that, you’re like, well, if you just have an automated timer, then you’re never going to know the actual data. So there’s a lot of different examples like that. We generally have pretty good healthy back and forth on what that looks like.

And maybe this isn’t specific enough for you, but I will say that there’s a lot of times where we might be stuck in a mindset that something has to work this way because that’s how we design the software. There’s also the other way, where somebody says, “I have to have it operate like this because of a company goal.” And so what ends up happening is that you try to meet in the middle somewhere with what that is, and you try to figure out what the best solution is.

Sometimes it turns out, like taking that outside perspective of not being a producer and not dispatching a truck every day or not being a driver every day. When you step into that conversation, and you show them, and you point out why that might be flawed, sometimes it works great and they’re all in.

And there might be other times where we get on the phone with developers or whoever it is, and we’re being stubborn about one way. And finally, at the end of the day, someone convinces us that, yeah, we need to make this change in order for you to use that software because that’s how your company works.

So there’s a lot of little specific examples that I don’t know if anybody would actually have a real reference to when I say them, but this is a thing that happens all the time within Digital Fleet. I mean, it’s an all day everyday type conversation we have with our customers.

Sarah McGuire:

Well, I think, especially on our side, we have so many people that have come from outside of concrete. I would say the people that are really customer facing, or engineers, or of course, we have concrete scientists that are helping with the research and development. But if they’re not on the engineering support or research side, we have most likely taken them from some other industry and we’ve taught them everything that they need to know about concrete, myself included.

So it’s easy to teach concrete, it’s not as easy to teach just skill, interpersonal, all sorts of things like that. And one of the things that’s so interesting is, I have now been doing this for almost, coming up on 10 years, or by the time this airs, I will have been at Giatec for a decade. So sometimes I’m a little blurred as well. But one of the things when we were looking at systems in this industry, when it comes to AI, AI needs to be able to learn off of as much data as possible. And a lot of these systems don’t have activity logs or snapshots in time.

You can’t go into a QC system and look at, what was my mix doing this time last year? Or what was this mix doing when I had this version of the material that was updated the following month because a new mill cert came in on my cement? We actually can’t see those differentiations. People who are going through the Type 1L shift, massive issue, because we can’t learn from the past.

And that was really detrimental to the AI side of things, because then it can’t learn from past performance and then continue evolving. And that was a huge issue for us. And when our developers were getting into these systems, they were like, “How is there no activity log? How is there no snapshot in time? That is like basic software.” But it just wasn’t needed before. And so then all of a sudden you start to add those things, and people go, “Wow, this is really going to help me, because before I was doing this workflow in these five steps, now I can do it in just two, because I have this activity log.”

Or when people make a mass change to a mix, maybe you’ve replaced a material in a new plant, and now you have to update that material across all of your mixes in that plant. What if you do it wrong, wrong proportion or something? Well, if there’s an activity log, and we can just go click one button and revert that change, I guarantee you people are much more willing to admit that they’ve made a mistake when there is a click of a button way to fix said mistake.

But when there’s not an easy way to change the mistake, nobody’s taking accountability for that. So it’s just little things like that, that when you bring people from outside the industry, as long as we can soak up everything from our users to learn what works for you, it’s really, really helpful when they trust us to come up with the right solution. But we do have to earn that trust. That is important as well.

Chris Wurtz:

Yeah, a hundred percent.

Sarah McGuire:

I think that’s a really good segue into talking about this whole early adopter program that we did with MixPilot, and bringing all these companies together, when we brought everyone together in June in Iowa. So we have these five companies within our program, and when we started working with them, two out of five we’re using Digital Fleet. And now it’s four out of five? Three out of five. Soon to be everyone, hopefully. We’ll see.

But the reason that we started working with you guys right out the gate is because you saw this as the missing puzzle piece. And I think we all identified, Giatec wants to be the master of quality. We are the quality solutions expert, from mixed optimization to monitoring institute concrete with our other sensors. We want to be focused on quality. We don’t want to get into statusing, or truck transit, or anything like that, because there are already masters in that area. We always want to work with the masters in every area.

And obviously, when we first shared this with you, over a year ago now, we told you what we were working on, in that we wanted to work with you guys, because one of the most important parts that we wanted to work on was the ability to get this data in front of people at the right time. And you guys were really owning the truck, and our data coming into the driver’s hands at the right time in the right place was very important.

What we heard loudly from companies, they don’t want a million different things in the cab. They don’t want a lot of hardware in there. So if there’s already a tablet existing, we wanted our data to be populated in there so it was very, very simple. And that is what we now have. That is now working very functionally for our customers.

What we also saw, and this was a big issue on our SmartMix side as well, is that we’ve really built these systems that work best when they plug into cloud dispatch systems, because the free flowing of data is easy. For companies like us, you guys are updating your software weekly, you’re doing weekly sprints. Giatec is doing bi-weekly sprints.

And so just like you guys, we’re sending release notes out every two weeks to our users, of this is the new feature, and this is how it works, and give it a test and let us know. There’s so much change happening so quickly. But the more integrations that we add on top of systems, if they’re not simple to maintain, and if they’re not cloud-based, it becomes extremely inundated.

You guys have all of that data available already, and the more that we can pull from you in that one integration, because you’re working with cloud technology, the easier it’s going to be for us to support the market.

So this was a huge motivation on our side to work with you guys. We came to you guys with this over a year ago. You saw it, you really liked what you saw. But then we sat in June, in Iowa, Dubuque, Iowa, and I think people were really excited. But you and other people saw what we were doing and had certain feedback right away. Now that is why we bring a whole group of people into that setting, because we had been engaging with you guys for months online. But until you really bring people into a room and get that dialogue going, you’re not going to get that brutal, honest feedback that you want. And we took almost all of it, and some of it immediately.

We heard that the slump measurements that we were getting were not happening quick enough. So within a week of coming back, we adjusted that.

We heard that there was a lot of data that was already coming from your system or other systems, that we had embedded into MixPilot, like GPS or statusing. We put that into MixPilot because we wanted people to use it as a standalone. But the reality is that most companies won’t do that.

And we don’t want to override data where it’s already given well, so we got a lot of feedback about that, so we worked with you to adjust for that. The other one that we got was the hardware. And even you came up to me, you said, “There’s no way that that is going to work.” You guys were looking at how, I don’t know, clean and pristine the hardware was. Come on.

Chris Wurtz:

Yeah, like I said, it had a long way to go. I was generally worried about how long it would last. I gave the hardware maybe a week or two in my mind. When you start getting hardened concrete on it, and honestly, stalactites that form off it, is maybe the right way to describe it, I have a lot of concern about that. I’ve been building, designing, helping build, been involved with electronics hardware now for over a decade, which is sad to say. But yeah, that made me very nervous, we’ll just say that much, when we first met.

Sarah McGuire:

And I think that’s fair because everyone felt that way. People looked at it, and everything was super wireless, and people were saying to us, like, “Why have you even bothered to make this wireless? What’s wrong with wires?” But the more hardware that you have, and the more wires hanging, the more opportunity there are for things to get cut, freeze, gunk, like you said, even concrete just getting on top of it. All of these things can cause it to go bad.

One of the other things that wasn’t prevalent to us in Canada, because we had been doing all of our testing, obviously, we do a lot of our testing with the local organization, Tomlinson, that we’ve worked with for years. Well, in Canada, we don’t have front discharge trucks. Well, front discharge only makes up about 20% of the fleet, so we were really working on that 80/20 rule.

But what we failed to understand, before engaging this group of companies, is that front discharge really exists among the group of people that consider themselves the highest quality. And those companies are going to be the ones that want to invest in something like this the most.

So we ended up having a very large group of front discharge users in our group by default, and we had a lot of concerns about whether or not we could create hardware for that. And we were able to, because we caught it early, because we engaged with people early on. So I think it really is a testament now. Where are we now, in February, sometimes when this rolls out? We now have completed all of our hardware validation tests. We were able to understand that the gateways that are sitting in the cab, making those as small as possible, which we’ve now done a redesign to make sure that they’re even smaller so they’re not blocking blind spots.

But the battery life lasted way longer than we expected. We’ve done this in minus-forty degrees Fahrenheit, all the way to a hundred plus Fahrenheit, and we were able to prove accuracy within a quarter of an inch. So we’re very excited about the fact that we were able to do this.

But had we just said, “We’ve perfected this hardware,” and not engage with these companies, and asked them to help us validate it, I mean, first of all, when we launched it, nobody would believe us. They’d look at this and they’d go, “Show me who used it.” And we are able to now say, “Here are the five companies that used it, and they’ll tell you themselves. And two of them have now ordered all of the stock that we can manage, and now we’re taking pre-orders for the rest.”

But we could not have done that without engaging you guys on the industry leader front. And we couldn’t have done that without engaging the customers. It’s just, that’s the only really way to be able to get your product to a hundred percent, I think, in this industry.

Chris Wurtz:

Yeah, and it’s two points. One, front discharge mixers are actually more regional specific than they are about quality of the mix. And so they live in certain areas of the country, the United States specifically, the Midwest, down to the Southeast, and then over into some of the mountain West states, is where they’re really prevalent. It’s an interesting mixer, because once they get into an industry, they tend to stay, which is always really interesting.

Because, again, customers like them because they don’t have to have a spotter, is really the selling point behind a front discharge mixer. But two, I think it is super important to get all that feedback. I think the biggest challenge that any developer faces, or any company that’s trying to make a product is, how dependent do you get on other companies? And so, it was really interesting in that room to talk about, because you said, you heard loud and clear that people don’t want repetitive software, repetitive development.

And I think that’s the great thing about what we’re doing, is we can focus on the in transit side, the statusing, all that stuff. You guys can focus on making the best sensor out there to predict slump. It’s really a good partnership at the end of the day.

It is a challenge though. In this industry, in this space, not everybody is always open with providing data, or sending data, or integrating. So if you can come together, we can make the best of both worlds, you don’t have to worry about having, where is your center of truth? Is it Giatec is the center of truth over here, and Digital Fleet is the center of truth over here? Well, why do they have conflicting data points? Or whatever can happen, because we may sample at a different rate than you, when it comes to things like GPS, and drum rotations, or whatever it is.

But when you can combine those, and you say, “You know what? You want to go and get the real details behind what happened on this mix?” You’re going to open up Giatec and you’re going to look at that. Everything else is going to be through Digital Fleet, or however you come together on it. That’s really a win for everybody. So it was fun to see how you guys adjusted and it was great feedback to get and being part of that.

Sarah McGuire:

I would be, because of the fact that this podcast is called Building Better with AI, I do want to wrap everything up with one main question about when it comes to artificial intelligence. And I think whole series is not really designed to talk about the nuances of AI. Frankly speaking, neither one of us are qualified to do that.

But what it is to do is to say, “Hey, artificial intelligence has the potential to be something super great in any industry, really. And how can we really leverage this?” And we’re talking about all of the other things in the AI space that we need to consider before we can even think about applying AI to solve a problem. And what we’re talking about here is the need for more data.

I’ve said that at ad nauseum on this podcast. Every episode, I’m sure it comes in, data, data, data. And it needs to be clean data, but we can use AI to actually help us clean our data. But there’s a lot of things that we need to do before. You were a little hesitant to talk too much about AI, and we talked about that offline together beforehand. But I assured you, I don’t expect you to be an expert in AI, because yeah, neither one of us are. But would you mind elaborating on why you wanted to be careful about how much you actually say about AI, and how that all pertains to the industry right now?

Chris Wurtz:

Number one, I’m not a developer. I’m an electrical engineer. But we have people on our team that are really good with this, and really technically sound and savvy with it that would be much better at speaking about it. So I think for me, I’m just being a little cautious about what I say. And I do really believe that AI in general right now is the wave of the future.

Everybody’s talking about it. There’s incredible programs from Google, and Chat GPT. And I think there’s a lot of people, including ourselves, that are using those programs to do things and enhance their own internal behavior. And I think producers, same way. I think everybody I talk to is out using ChatGPT and some of the other AI models that are out there. And I mean, they’re incredible. If you type anything in, and you could get a great marketing exploit very quickly.

So I think there’s a lot of people that are using it. And I think the challenge we have with it right now is that it almost seems limitless with what the potential could be for AI. And so I think this industry is still figuring out where AI is going to have a major, major impact into it. And the products, are figuring out right now where AI is really going to come into play from a product perspective.

Sarah McGuire:

I think that’s very fair, and I think companies like ours get a lot of questions about this because we are the technology leaders in this space right now. But being a technology leader doesn’t necessarily make you an expert in AI.

Now, maybe on the Giatec side it’s a little bit different, because we literally have a couple of products that wouldn’t exist without AI, that is really the foundation of them. And everything else that we’re building is really to power that up. But a lot of the time, I’m showing people what they can do with our system, and they are so excited about what AI can do. And I go, “Whoa, whoa, whoa. I’m not showing you anything with AI yet. I’m just putting everything together in a very basic algorithm and I’m putting it in front of you to actually show.”

And we have talked about the possibility of, with all of this data now free flowing back and forth, between Digital Fleet and MixPilot, and hopefully SmartMix, and bringing that all together, right now, there’s no AI being used in any of that.

Now, some of the data that MixPilot is generating will lead to optimizations, that is happening in SmartMix, that go beyond just optimizing for strength. Hopefully, we can get to a point where we’re optimizing for slump now, because we have accurate data on that. But as it stands today, none of this is AI. It’s just better data visibility, and free flowing, and compiling reports, and doing trend analysis, and all of these things that we can do in Excel. But now we’re doing it more intuitive, and we’re doing it at click of a button.

And sometimes the advancements around that are exciting enough. But when people are looking at all of these leaders to say, “Go do something with AI,” sometimes that’s not the answer to your problem. Or sometimes there’s a problem we’re solving in the middle before we actually get to the really cool solution that can happen.

And I think that’s the exciting thing that we’re super excited to work with Digital Fleet as we go on. Both of our companies are working with each other’s competitors. That’s the way that the space is. We’re both very adamant about, you got to work with everybody. We want to work with everyone. But we’ve talked about doing some really neat stuff, where if we put our technology together, we might be able to generate more when it’s coming together, as opposed to trying to operate in our individual silos. And I’m excited to see where that takes us.

But most importantly, I’m excited to see what our joint customers have to say about it. So I think, Chris, I want to leave it for, do you have any last comments to share before we close it out?

Chris Wurtz:

No, not really. I think this has been great. Like I said, I’m super excited about what this could do. I’ve wanted to do a lot with automated mix control for a long time, and I’m really excited to actually see some of this coming together. There’s a very clear path to, I think, the future with all the technology we’re talking about, our partnership, everything. And it’s really exciting.

I mean, I think the industry itself is ready for a change. You see it now. I think people are adopting technology faster than they ever have in this space. But I think there’s just a lot of things that are coming together. I mean, even going back to, let’s say the iPhone, that really changed people’s perspectives, and honestly, their demands from software and what it could be.

And now you’re seeing that in AI. And so I think there’s just a lot going on in the industry right now that’s coming together. And people are really ripe and ready to do something different, because they have the phone in their hands, they see ChatGPT, they see all this stuff that’s going on. And I think people are finally actually starting to utilize it, and make a change, and are ready to make a change, quite frankly.

So exciting times. I’ve mentioned this multiple times, it’s a key piece to the puzzle, and I’m really excited to see where this takes us.

Sarah McGuire:

Me too. Chris, thanks so much for joining us. For anyone who wants to learn more about Digital Fleet, we’ll have all of your contact details, your website, your LinkedIn, everything will be in the podcast description. Chris, I’m excited for the future and everything 2025 has to bring.

Chris Wurtz:

Thank you.