Finding speed in Digital Transformation with Joe Gibbs Racing – Transcript
Kate Eby: Hello and welcome to The Industry Forward Podcast where we explore key trends, transformative technologies and real-world innovations that are reshaping fields from aerospace, industrial machinery and semiconductors to pharmaceuticals and beyond. I’m Kate Eby, and I’ll be your host for today’s episode. Today, we’re diving into how the comprehensive Digital Twin, advanced simulation and AI-driven optimization are revolutionizing race car design, enabling teams to build a new car nearly every week with almost no margin for error.
Kate Eby: This approach isn’t just transforming racing. It offers a preview of where manufacturing and automotive industries could be heading in the next three to five years with compressed development cycles, rapid adaptions to constant rule changes and the relentless pursuit of performance. To explore this, our guests are Mark Bringle and Jon Rittle from Joe Gibbs Racing, one of NASCAR’s top teams, where missing a design or production deadline by just one day could mean missing a race. Their experience brings unique insights for any manufacturing or automotive leader facing intense timelines and competitive pressures. So, Mark, Jon, welcome. I would love for you to go ahead and introduce yourselves. Maybe we can start with you, Mark.
Mark Bringle: Hey, I’m Mark Bringle, and I’ve been with Joe Gibbs Racing for 30 years. I was the first engineer hired for the team back in the mid-90s. I’ve worked with, I think, every driver in the garage to this point, starting with Bobby Labonte back in the day. So, yeah, thanks for having me.
Kate Eby: All right. Jon?
Jon Rittle: Hi, I am Jon Rittle. I’ve been working with Joe Gibbs Racing for 25 years, working in various groups along the way, including engine development, vehicle design and most recently the aerodynamics group. I set up complete race car models for evaluation and CFD.
Kate Eby: Great, welcome to you both. Before we dive in, one of the questions I’ve been asking people who appear on our podcast is about their favorite sci-fi reference. With the automotive side, though, I usually ask about how, everyone who’s building the future, who’s in engineering and technology fields, usually has a favorite sci-fi reference. It’s either inspirational or cautionary or a combination of both. But given that you guys are on the racetrack, I’ve got to ask, what’s your favorite automotive racing movie?
Mark Bringle: It’s got to be Talladega Nights.
Kate Eby: All right.
Mark Bringle: Has to be.
Kate Eby: Is it the southern connection?
Mark Bringle: Absolutely.
Kate Eby: Perfect.
Jon Rittle: Yeah, Days of Thunder.
Kate Eby: Any particular reason or just the best one out there?
Jon Rittle: Nah, just the best one out there.
Kate Eby: All right. We’re going to go ahead and dive right in. Today, Joe Gibbs Racing uses a comprehensive Digital Twin across design, simulation, manufacturing and validation. What decisions does that Digital Twin enable you to make with confidence today? And then how do you expect that to change over the next three to five years?
Mark Bringle: Well, the reason we went with Siemens to begin with is when I first started, we had one engineer. And so, as we started adding to the personnel and as the company grew, we went from one team, which was the #18 Interstate Batteries team with Bobby Labonte. We added Tony Stewart. Then we added the #11 team with Denny Hamlin. So, the company started growing, and having individual silos of information was not acceptable for daily use anymore. So, we reached out to Siemens, and they have seamless integration between all platforms. So manufacturing, design, FEA, CFD and simulation could all talk to each other at that time without a lot of translations taking place across the board.
Jon Rittle: Yeah, we rely greatly on our Digital Twin in our aerodynamics program. I myself prepare complete car models, produce using NX for each track type, use them to feed Simcenter Star-CCM+ software, which is used to simulate the tunnel and, you know, track aero conditions. We’ve established a level of confidence such that, you know, the resulting information can be used to make tunnel or sometimes race day decisions. As we fine-tune our processes, I hope to see our confidence growing steadily every year.
Kate Eby: And how has your use evolved over time, right? You know, from when you started to where you are today and maybe where you’re planning to go in the future? And I ask this because we recently did a survey with one of our industry analyst groups, and it was really interesting to see how far along people actually were versus the way, we talk about the comprehensive Digital Twin and we kind of talk about the journey, but where people are on the journey varies greatly from those who already are, thinking about next levels and those who are just starting out. So, I’m just curious about how your journey evolved with bringing in software.
Mark Bringle: Yeah, as far as mine goes, I think first, anytime you have a group of people that have worked together longer, we have some really long tender there at Joe Gibbs Racing. I’ve been here 30 years. Jon’s been here 25. So, whenever you build a team and you start developing the skill sets, your team just works better together because you’ve worked longer together. I’ll say this on behalf of Joe; you win with people. So, that’s our whole thing; we find the right people, we embed them into the process and we’re in it for the long haul. So yeah, the longer they’re together, the better the team becomes, we found.
Jon Rittle: Yeah, I second that. Yeah, the continuity with the Siemens products and our requirements and what we need just seem to have gone hand in hand throughout the years. Yeah.
Kate Eby: One of the key areas where you’re using a lot of software is in simulation, right? You’re using that a lot before you make the physical parts. But how reliable is it, or how much are you depending on simulation when it comes to guiding those decisions that you’re making on a weekly basis when it comes to a race?
Mark Bringle: Yeah, so NASCAR is ever evolving. Four years ago, we went to, what’s the next gen car, and it’s basically, the supply chain is driven by NASCAR, similar to other leagues that we currently experience. So, ours kind of did a 180 from being heavily in design and low-end simulation to now, we’re more into simulation and lighter in design because of the supply chain requirements given to us by NASCAR. So, simulation is huge now, to the point where we have sim rooms there in the building where our drivers come in and schedule their time.
Mark Bringle: They spend time on the track that they’re going to that weekend, so I think they can get some seat time. And then the accuracy of it is within one-tenth of a second of the actual lap around a track that they’re driving that weekend. So, Jon’s group has done an outstanding job on every part that is measured that goes into that sim package. Every detail that brings it all the way down to one-tenth of a second.
Jon Rittle: Our cars are constantly scanned. You know, they just, for repeatability, history and also for myself to replicate these models.
Kate Eby: A tenth of a second. That is impressive. Switching gears a bit, pun intended, you use AI-driven optimization to explore large design spaces today. In the next few years, where do you see AI moving from exploration to recommendation or decision support for engineers?
Jon Rittle: Yeah, with AI, we’re definitely, I believe, at the tip of the iceberg. We’re just myself, just in using NX, AI capabilities are embedded in NX, I use AI to create journals to make my job move along quicker or extract information. The aerodynamics engineers, they use AI quite a bit for dissemination of information. We have information coming from all different directions, from CFD using Simcenter STAR-CCM+, from the wind tunnel, from various track tests and yeah. To go in there and extract the quality information, put it all together and come up with something that’s beneficial towards constructing our cars is great.
Kate Eby: So how much is AI supporting your recommendations or decisions around design that your engineers are making?
Jon Rittle: I think it just gets us to decisions quicker. Like I said, it’s just very helpful in disseminating all the information and putting it in one place.
Mark Bringle: I think we’ve heard through this conference that it’s garbage in, garbage out. So that’s the number one thing is managing all the data. We use Teamcenter quite heavily. We have some other silos of data internally that our group, we have code writers and a lot of things that we’re doing internally, but just managing that data, making sure that you got good data going in and then having the ability to analyze the data when it comes out using AI. I think years of experience with crew chiefs and engineers and mechanics on the floor, if something was to come out that wasn’t accurate, it would be picked up really, really quick. So yeah.
Kate Eby: And I think that leads right into my follow-up. As we’re continuing to see increase in use of AI as well as advancements in AI capability, what are the engineering decisions that you see always staying human?
Mark Bringle: I think in our sport, the live data that we get after a race, when a car comes back from a race, there’s a lot of things that are evaluated in the car. All that is recorded and it goes into a build sheet for the next time that we go to that racetrack. So, where you end at one race is where you begin at another. So that’s information that has weather relations in it. I don’t know if you watched the Nashville race this weekend, but we had three cars that were on the last lap fighting for the win. So, every race, every situation is unique to the race itself and the conditions that it’s being run under.
Kate Eby: I did not catch the race this weekend, but once upon a time, I used to actually follow NASCAR racing pretty closely. But now I got a two-year-old. So instead of NASCAR, it’s Miss Rachel. All right, so today you correlate wind tunnel and track data back into your digital models. How does that feedback loop shape how aggressively you innovate now?
Jon Rittle: How aggressively we innovate. When you’re racing week to week and you discover something, either at the wind tunnel or a test, and you have to have the information out for that weekend, you just make it happen. And AI, a tremendous tool for getting there, just to get us the numbers that we need to see to make those decisions.
Mark Bringle: I would say in the 30 years I’ve been with Joe Gibbs Racing, we haven’t been late with a product yet.
Kate Eby: That’s good, right?
Mark Bringle: Well, you can’t call the track and say, can we race Monday?
Kate Eby: Exactly. Well, and kind of building on that, with all of that feedback loop and the data that you’re already using, the AI, et cetera, you’re already getting within one-tenth of a second, right? You talked about that. So, if you’re already that accurate, what is that near real-time correlation look like a few years from now?
Jon Rittle: A few years from now, that will be interesting to see. We, I expect it to, I expect AI… Yeah, it’s really hard to say because you don’t know what three or five years from now is going to look like, but.
Mark Bringle: Yeah, I refer to it as back when I got my first bag phone. And I had no idea at that time what cell phones would do to society and how we live. I think that’s why with AI, we’re right at the threshold of something incredible. And I don’t think any of us realize just what we have at our fingertips right now.
Jon Rittle: Yeah, exactly.
Kate Eby: No argument here, but NASCAR rules, costs and timelines keep getting tighter. How did the Digital Twin and AI help you compete inside those constraints today? And what’s going to be more critical as you move into the future seasons?
Jon Rittle: Yeah, each year, NASCAR limits our tunnel time. Right now, it’s about 130 hours per team per year. And I mean, it was about double that the previous year. So, it gets reduced quite a bit every season. They also limit our CFD simulation runs to about 100 runs per month. So, we also have to work within that. The confidence in our results that we do get are extremely important. We can’t afford to waste either tunnel or simulation time. And I see AI definitely as a major tool in helping us move forward.
Kate Eby: So, everything we’re talking about here, the technologies that you guys are using, they don’t just change cars, they change teams. And I think you talked about this a little bit earlier on, just the impact of how being a team helped you to adopt these technologies more quickly. How, in turn, has simulation, the Digital Twi and AI, how is that shaping your team or impacting your team? Has it changed the way your engineers work significantly?
Mark Bringle: We’re winning.
Kate Eby: I mean, mic drop moment right there. Let’s just stop the recording. That’s all we need to hear.
Jon Rittle: Yeah, that’s the main goal.
Mark Bringle: No, so yeah, each year, it totally reinvents itself. And through the engineering efforts, through the personnel that we hire, through the talent that we’re able to attract, it’s ever changing, I mean, that’s what I love about it is it’s never the same from not only day to day, but week to week, every year is a totally different business model.
Jon Rittle: Yeah, and like Mark said earlier, it’s all won with people and having the right people in place that embrace new technologies like AI, not just embrace it, but share the information that they learn along the way with the rest of the group. Yeah, that’s key.
Mark Bringle: And this is one thing that’s unique with Joe Gibbs Racing; is if you ever come to visit us, which you always have an open invitation, bring your two-year-old, but we’re one team for cars. So, everybody there wears JGR apparel, and everybody works on all the cars. All engineering supports all the cars. So, what that does is it gives you four times the talent pool, because a lot of teams have individuals just for that team, and they don’t share across the owners’ teams. And some do, some don’t. But we feel like we got four times the talent pool because we’re one team for cars.
Kate Eby: Well, with four times the talent pool, no wonder you’re so successful at the track. Unfortunately, we’re about out of time. As we wrap up, what really stands out from this conversation is that racing is the ultimate proving grounds for innovation. When you’re building for race day, there’s no room for disconnected data, slow decisions or uncertainty in the process. Your teams have to connect design, simulation, manufacturing, validation and real-world performance in a continuous loop, then use those insights to make better decisions faster.
Kate Eby: And while the racetrack may be an extreme environment, those same pressures are showing up across automotive and manufacturing. Tighter timelines, greater complexity, shifting requirements and of course, the need to get it right the first time. That’s where the comprehensive Digital Twin, advanced simulation and industrial AI become more than technology investments. They become a way to turn complexity into clarity, move with speed and confidence and keep improving every cycle.
Kate Eby: Mark, Jon, thank you so much for joining us and sharing how your team is using digital tools to compete at the highest level. We wish you the best of luck on the track. I’d also like to extend a thank you to all of our listeners for tuning in. We hope this conversation gave you a glimpse into how the lessons learned on the track can help shape what comes next across industry. I’m Kate Eby, and we’ll see you next time on The Industry Forward Podcast.
To learn more, visit Siemens.com/motorsports.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
