Ep 35: The Road Ahead: Pioneering the Future of Mobility with Goodyear SVP of Global Operations and CTO Chris Helsel

‍Evan: Hi there, and welcome to Enterprise Software Innovators, a show where top tech executives share how they innovate at scale. In each episode, enterprise CIOs share how they've applied exciting new technologies, and what they've learned along the way. I'm Evan Reiser, the CEO and founder of Abnormal Security.

Saam: I'm Saam Motamedi, a general partner at Greylock Partners.

 Evan: Today on the show, we’re bringing you a conversation with Chris Helsel, SVP of Global Operations and CTO of The Goodyear Tire & Rubber Company.

Goodyear is a leading vehicle equipment manufacturer known for producing tires and rubber products for diverse applications, including cars, airplanes, and industrial equipment around the world.

In this conversation, Chris shares his thoughts on the vital role of technology at Goodyear, important blueprints for successfully leveraging AI, and how Goodyear tire sensors are enabling the future of self-driving vehicles. 

‍Evan: Well, first of all, Chris, thank yoU.S.O much for taking the time to join us today. I know Saam and I were really looking forward to this episode. Maybe to kick us off. Uh, do you mind giving our audience a bit of background about your career and the current role you have at Goodyear? 

Chris: So I have two degrees in mechanical engineering and my background is primarily in simulation. So I got involved in the mid 1980s. Uh, as long ago as that was, it was actually on punch cards doing computer design and technology called finite element analysis. And at that time I was going through college and I was able to do that work for the petrochemical industry in what ultimately became British Petroleum and their final purchase of a company out of Cleveland, Ohio, which was one of the original Standard Oils.

And then from there I took that same technology in the nuclear industry, aircraft industry, and ultimately joined Goodyear in 1996, so that's my technology background. And Goodyear then brought those same tools first to race tires. So we were at that time competing in Formula One racing, all kinds of open wheel and NASCAR, which we still support NASCAR today, are a proud partner.

And, you know, it was, it was all about how do you tune for that high performance. You know, make those cars go faster and in particular, how do they handle better in corners? And, uh, after doing that for a few years, they said, why don't yoU.S.Ee if we could use these tools in order to design consumer tires and commercial tires, and pretty soon it grew into, well, geez, you can lead technology. Why don't you lead some of our product areas? And, you know, the next thing you know, I'm leading our innovation worldwide, in terms of the traditional tire, uh, technology. 

And then I had an opportunity to run our retread business, which is a great sustainability story in and of itself. And then in, uh, 2017 fall of 2017, I became our chief technology officer. But then even, uh, in March of 2022, we added to that global operations and my responsibility, so it's kind of a unique combination of technology and operations. 

Evan: That's amazing. I appreciate yoU.S.Haring some of the history there. It's a, it has like a lot of responsibility. It's such a large organization. Um, I, I feel I must really ask this question, but I feel like I'd be remiss if not, um, for, if there, if there's anyone in our audience, maybe hasn't heard of Goodyear or doesn't really fully appreciate, you know, kind of all the products and services you guys provide. Do you mind kind of giving a brief overview of kind of what you guys do?

Chris: So we're really, really excited to be able to share in August of this year, we had our 125th anniversary. So just a lot of companies haven't ever achieved that. And of course, it's the Goodyear Tire and Rubber Company, and primarily it's all about those tires. And so, you know, we'll make, manufacture. More than 150 million tires a year, and those tires are used in all kinds of applications.

Consumer, which many, many folks readily recognize, but also commercial trucks. Whether those are things like cement mixers or those 18 wheelers, we do off highway tires, which includes aircraft, where we're like the largest provider of aircraft tires in the world, and of course still even racing tires. So, so really we do a lot there.

And I would say part of my technology role is, as much as it is all those tires and development of those tires and technology that goes into them, including the processing capabilities, we're just as proud to talk about our beyond tires, which could be things people don't know about us, which is how we're bringing things like the Internet of Things to tires by putting sensors in those tires and making it so that those tires now can detect things, specifically with that contact with the road, were the only thing that touches the road, and managing that ,contact with the road is a job that we uniquely do and now we're doing in a digital way, in addition to the way we've done for 125 years with chemistry and mechanics. 

Evan: The world's getting much more complicated. There's a lot more data than there probably was for your business 20 years ago. And if you think about some of the fleets you help supply and protect right through safety and reliability, I imagine there's like a lot more data that you're, you have to kind of gather that either goes into the development process, and so, you know, how has, how has some of the technology kind of infrastructure change, right? As part of your development compared to maybe 30 years ago where the primary focus was on, you know, material science verses, you know, kind of data collection, real time, you know, feedback loops. 

Chris: So maybe to be illustrated, I'll go to some of the work we've had to do in terms of just getting capability to do work in the cloud.

So when we think of the tire intelligence, maybe I could describe a little bit of what, what that is and how it works with one of the people we work with, which is one of the different ways we work. We established a corporate venture fund with that corporate venture fund we've been able to connect with some of these startups are really trying to drive new mobility, and by working with them we're really developing solutions side by side, right. Helping them to scale faster and hopefully at the same time, better prepare ourself for what's coming. So one of those is Gattic. 

And so with Gattic, we've done a lot of work around their autonomous driving system in terms of trying to give them a real world, real time friction prediction. Underneath those tires at any given moment while they're driving around, and Gaddick is, of course, for those who might not know, they're doing like middle mile delivery. They started with Walmart, Georgia Pacific. They're doing some work with grocery chains in Canada, and so they're not only driving on really nice roads that are all dry. It's 80 degrees outside and sunny. They were in Toronto, Canada, driving around in the snow. And, you know, that's where I think there's a real value for something like what we're doing with tire intelligence is if we could unlock some of those use cases sooner, of course, that enables them to scale and get to market quicker. Because you can't say, well, I can only have my autonomous, you know, trucks delivering on Sunday days. That's not gonna support a full scale rollout. 

So how I connect this then to the data is, our solution has a sensor in the tire. It's about the size of a quarter, about two quarters thick. It measures temperature, pressure, acceleration, ID. It has a small chip and a battery. So we take that information off there, combine it with some other vehicle information we're pulling off, passing it through Telematics, up to the cloud.

So that whole cloud architecture, you know, the Goodyear Mobility Cloud, we've called it, has had to be built out in order to consume this data, combine it then with other information we have, run our algorithms, and pass the information back down to the vehicle. And what we do in this most advanced case is we combine that with real time.

Localized weather, which can be provided up to or, excuse me, to refine down to about 100 m. And we're giving them a probabilistic prediction real time of what that friction potential is under the tire. That might not mean a lot to you to a lot of people, but what that means is a whole lot to people are trying to do vehicle dynamics and control vehicle at any given time, which is what you're doing when you're giving instructions to a robotic system on how to steer and drive.

And so they now know, based upon that friction prediction, how fast should they try to take a corner? And so it's really changed how we have to handle data, the capabilities we have in terms of transmitting data, understanding latency in terms of being able to pass data around. Those are all new things for us.

And what we do in the cloud, what we do on the edge. That's all completely been developed, but it's pulled off of that use case that we have to try to, again, give better information what's happening at that road interface, right? That's our core job to be done. I'll keep going back to. 

Saam: So you've given some excellent examples of how technology drives the end products. Um, and in the end, you know, experiences that you're delivering. I wanted to look at the other side of the coin, which is how you actually run Goodyear as a company, and how you run the organization, operationally. You mentioned kind of your roles expansion as well. 

What are some of the ways that you're using technology to improve Goodyear itself, and the operations of the business?

Chris: Yeah, so maybe I'll start that one with, uh, Describe a little bit what's my responsibility there? It's called a senior VP global operation. So what I have under me there is some of the traditional things you think of; quality, manufacturing, supply chain, Procurement safety. So these are these are things we do there.

We do go to market with three regions. So the daily SNOP processes run in each of those regions, so they decide exactly what they're going to run in each of those plants. My group is really around their processes, their capabilities, equipment standards, you know, the way the work gets done. And so we're really pulling the new technology in and coming up with how you redo that work, re engineer that work.

So a couple examples of that that I might bring up would be quality. So quality is something that, you know, oftentimes people manufacture product. At the end, they run it through a go-no-go check and they say, yes, it's good or no, it's not. That's your worst case quality. You want to move upstream and inspect your quality in at the source of the process and in tires, there's actually factories within factories.

We actually bring in all the raw materials. We mix it all. So almost think of it as we make the batter, right? We make the, the, the, those rubber materials by doing the mixing. We then extrude them. Imagine your Play Doh fuzzy pumper, um, where we push them into shapes that we want to have, that then get applied to, imagine a drum.

So we start giving the tire its cylindrical form. Then ultimately that thing after it's all those parts have been assembled in that cylindrical form gets moved over to a press where it's put under heat, pressure, temperature for a period of time and it's baked or cured, right? So out, out at the end pops bread.

And it's, it's really interesting in that, coming from a lot of industries that were steel and aluminum manufacturing, there is elements of that in tire making, but there's a lot more baking, quite honestly. You mix the material more and more and more, you're going to get different properties. Just like if you overmix the bread, right? Dough. 

So, some of the things we're doing is to really understand measuring back in the process. So, putting sensors on that extruder where, you know, or your fuzzy pumper there. What are the feed rates? What are the pressures? Any changes in the that head pressure and trying to relate those then to your end quality so that now we understand what's those key factors that if we could control better, we're going to get better yield at the end. And ultimately, maybe we don't even need to do just that final finish check because we know if we better control those parameters, we don't have to control on the end. 

So these things are significantly driving higher yields for us. Because a lot of, in particular, new vehicles have some pretty discerning limits on like how much vibration you're allowed to produce from the tire, things like that.

So, so those are ways in which we're seeing big impacts in operations by using these types of technologies and data. But, you know, you're seeing them in supply chain planning. Traceability. You know, to things like, especially during the times of when ocean shipments were very unreliable, just a few, not very long ago, on really knowing at any given time where any one of our shipments of rubber are so we know when to plan it, how big our inventories need to be.

And of course, all these things relate back to how you run the businesses, how much cash you got tied up in your inventory, you know, lot sizes, et cetera, et cetera. So, so definitely these same concepts of bring technology leverage data is allowing us to run our business on a daily basis, both a little bit more efficiently and also, also with a, um, hopefully reduced commitment to cash.

Evan: One thing that's a common theme in the show, right, especially in the last, you know, couple months is AI, right? And it's hardly hard to have a conversation in 2023, right? With anyone in technology that doesn't involve AI to some extent. And so, um, I'd love to hear your thoughts on where you think we are in that hype cycle, right? And how a company like Goodyear approaches their thinking around, you know, where AI can play a role or should play a role and where it doesn't make sense or what is kind of a fool's errand or maybe too much hype. 

Chris: So let me use a couple good examples, I think, in our technology area, and that's in for every tire, it's nuanced. But if you had tires stacked up next to each other, they actually have different shapes to the outside contours. And that shape actually dictates a lot of performance because you can impact the shape then of that contact with the road significantly with those shapes.

So we do a lot of simulation to optimize those shapes. And as much as we love to go do our first physics simulation, you know what's even better is we've done thousands of those already today. Um, why don't we just train some AI on that in order to give us the first prediction of what that mold shape should look like.

And so we've done that and now all of a sudden you're able to hone in on that in half the time. The first iterations, first time right. Not every time, but oftentimes, as long as you're in a pretty well defined design space. Um, and it's making our people all that much more efficient. And in particular, new engineers who don't have all that background to say, Well, you know what, I got this result so I really need to change the curve, you know, to make it look this way. The A. I. Basically tells them right away what to go do. So I almost think of it is, you know, it's the Tony Stark. When you put on the Iron Man suit, it makes you kind of human augmentation. I think that's the way to think about it.

You make your engineers, you know, have this suit on that gives them superpowers, right? And, uh, brings him immediately up to speed, shortens your training cycles. Uh, so, that's that's big. And especially, you know, new engineers. You could turn, you could turn up to half of them in their first five years, so you're constantly retraining.

So if you shorten that training cycle to make them effective, that's a big, big impact, right? 

So that's just one really good example, and we use that kind of tool all the time now. The second thing is using it in order to improve our demand planning. So I'll go to my operations site, where we see about, you know, 10, 15 percent better demand forecast, on at the individual SKU level than people do with traditional statistical regressions. And once again, you know, there's huge impacts in that because now we know, don't build so many of these tires, build more of these tires, and now we have more tires in the right place, which of course is better for the cash, but it's also better customer service. So we see those numbers pull through as well. 

So I think, you know, those use cases are definitely there. It's tough, though, to, you know, get those two sides who understand the problem that planning has to happen in the way it's done today and combined with those who really understand the technology and that it can be done different.

As soon as you get past that, then it becomes the human change initiative. So, you know, we're early. But I definitely think the types of numbers people talk about are achievable. The question is, it's probably not in one day. 

Evan: Yeah, I think that's fair. And I think if you look at just major technologies, even the cloud, right? It took, I know we're still kind of like, still figuring out how to fully apply that towards kind of business models and products and services, business operations, right? So, you know, we're kind of in like, it just, I know, machine learning has been around for a long time. It does feel like we're still in the very early days here. So I think it's the right, the right long term view. 

And Chris also really appreciated, um, that was a great example about how, how you're using some of these generative technologies in ways that are not kind of the common use case. I think everyone is thinking about, hey, how do I do chat support, right? Um, or how do I do, you know, digital art, but that was a really good example where, It sounds like you're, you're using, um, some sort of genetic technology to propose or predict different types of designs, right?

That then can augment the engineers to at least jumpstart their kind of thinking or invention or analysis, right? Where, uh, is that right? You kind of see these AI technologies as a way to kind of supercharge or augment capabilities to allow you to get kind of faster results or kind of be more efficient, or maybe engineers or inventors kind of come up with new ideas that like the right kind of mental model.

Chris: I think it definitely is, you know, we're, you've got a lot of data. It's helping you create the insights, but you still need those final engineers to make the final decisions. Right. And so I think it's really in that insights area, getting the insights in a whole different way faster. Maybe even to different insights, as you said, that's really where I see those use cases that I described.

Evan: Are there any kind of upcoming applications, right? Maybe not even the short term, but like when you think about future opportunities for, you know, AI to impact your business, whether it's the kind of running the business or actually the product and service you, you delivered to your customers, what are things that you think you feel bullish about, right? That maybe, you know, again, people unfamiliar with the industry might kind of underestimate the opportunity for AI to impact, you know, a good year. 

Chris: So, you know, I'm going to go back to my, you know, the, the R and D example that I gave and trying to make engineers, you know, more efficient. Historically, we're completely a build and test.

And so we have, you know, really sophisticated testing capabilities, huge facilities in like St. Angelo, Texas, Miroval, France. If you can get an assignment, that's the place to go, it's in the South of France. But the bottom line is where we test these tires under all those conditions and we capture data.

Then people like myself came and we started doing simulation. And to be honest, we were told, you know why we're doing this simulation? We want to get rid of all this testing. We failed miserably. We used just as much testing as we ever do, we just use it differently. Because we need information that's uniquely can be provided by testing versus the simulation data.

And now we have a goal, one of our, what we call our bold goals, where we would like to make each one of our tires smart that we sell as of, like, 2028, the end of 2027. And so now think about it. Every tire out there could be streaming data back to us specifically about itself and we could combine that with the birth to death or, you know, now thinking about birth to death for that very product, because we also have traceability of all the raw materials, all that manufacturing.

Remember, I gave you the example of quality. So you have to be able to trace for that quality Exactly what came out of the mixer, what came out of the extruder, what made its way into each and every tire. And now I've got these three huge robust streams of data. It's impossible that we're going to think engineers are going to be able to consume that data.

And so, you're going to need these types of technologies in order to discern those insights. Right. And so I just think the job is getting harder. And on top of the fact, your optimization is getting harder for an engineer to do. So today, let's say they do a lot around, well, I balance my rolling resistance, my wet traction and my tread wear.

That's great. But performance is only one thing you have to do. And that's only one. Those are only three of at least a dozen performances that are often very important. But, oh, by the way, guys, I want you to have a mind on your materials cost. I want you to have a mind on the manufacturability. Oh, by the way, that's going to be put into a plant that's already running 300 SKUs.

Tell me about the complexity impact. Tell me about the sustainable content. And, oh, are those sustainable materials on a lifecycle analysis actually more sustainable than the other materials? And I want you to make a decision on that. And now I want you to combine it also on the data that you learned on the last product that was in the field.

It's just overwhelming. I just don't think you can do it. Even with some of the really immersive visualization techniques to deal with. Now we're probably up to 20 or 30 variable optimization. Can't do it right. So these technologies, because we're asking more and more of people to consider cost, performance, sustainability, you name it, you have to have them if you're going to optimize truly across all those parameters. 

And so I just think, you know, that's where I'm thinking it's going. Those who can use all of this data from all sources better than anybody else, I have an advantage. 

Saam: So maybe I'll ask the flip of Evan's question, which is there's obviously a lot of excitement around AI and potentially talked about some of the potential applications. There's also a lot of noise. And I think there are areas where, you know, people may be getting ahead of where the reality is today, maybe in some way, similar to what happened with autonomy over the last 10 years, as you referenced, what are some of the areas that you think, you know, your peers and other enterprises who might be listening should be more cautious around AI, and not presume AI will be a magic fix? 

Chris: I think it's one of these things. I don't know that I would, I would have some specific areas I'd call out, but I, I, I'm a big believer in, you know, just start, start working with it.

And I'll give you an example of something I personally did. And I actually drive my staff crazy with this sometimes is I do have ChatGPT up on the side as we're working on things sometimes. And just for just for the heck of it, I typed into ChatGPT very early on, I said, what are the top 10 technologies the chief technology officer of a major tire manufacturer should be most aware of?

The darn thing gave me a really good, robust answer. It didn't just list the technologies, but it really talked about what would be the use cases, even. And it included our Internet of Things and things. Now, it was being, Drawing upon a lot of what we've all been posting. So, you know, it's almost going to do some echo back.

So I think maybe thinking that you're going to go draw some huge insights from that public information, maybe an area where it's interesting and cool. And it was a great story for me to use. But then when I reflected on it, I'm like, yeah, that's. Telling me what I knew. Now we're taking that same thing and training it on like our tens of thousands of internal documents and creating our own internal chat GPT.

I think that is going to be really interesting, right? And our first couple test cases on that has really been Insightful. Where we said, Hey, tell me about the three time. You know, tell me about the times where this particular material was pursued in the past and what were some of the key results, and then keep asking better questions.

That's much better than having engineers dig through tens of thousands of documents, right? And so I think there's a great use case there. So maybe the, the one thing I would say is there's probably going to be a limit to what that same thing does, though, in just the public domain, right? For a company like us, because we get pretty specific, pretty fast, in terms of what we need to understand and know. And there's not a lot of people posting that kind of information. 

Evan: Chris. I know we're coming up on time. So, um, uh, we're going to try at the end of the episode, we'd like to do a kind of a quick lightning round, just get a couple kind of quick hit one tweet responses. We'll try to squeeze it in before we sign off or make sure none of you guys are late to your next meeting. So, um, Saam, do you want to kick it off for us? 

Saam: Yeah, absolutely. Chris, to start, how do you think companies should measure the success of a CTO? 

Chris: You know, it's got to be number one, uh, end products, are your products winning or not? And you get a scorecard on that cause you got competition. And then secondly, of course, it's a productivity measurement because you are an engineer and that's output over input. So if you've measured already, am I doing better than the others in terms of performance? What's your, what's your percent spend sales?

Right? I mean, it's, those are, those are two really high level metrics that'll allow you to get a quick gauge on is your CTO or R& D leader kind of carrying the weight. 

Evan: Chris, what's one piece of advice you wish someone told you when you first became CTO? 

Chris: People matter. 

Evan: That's a good, very good advice. 

Chris: When I started, yeah, I would have said just give me great tech and I could solve any problem. I merged mid career to a lean Disciple and Six Sigma and I could fix anything with a good process. And now I'm a more tenured associate, we'll just say it that way. That's a nice way to say old. I would say, yeah, those things are great. Just make a decision of what you want people to do, put in place a system to track your progress and get great people and let them do their work. I mean, it's really almost that simple. And I wish I had that learning earlier. 

Saam: And maybe speaking of people, um, how do you think CTOs should position themselves to best collaborate with the rest of the c suite. 

Chris: You've got to show that you can speak their language. I benefited greatly by running a business for three and a half years. They have to know you're not off the reservation just enamored with tech. You're trying to solve real business problems And when you're talking about a business problem with them, you got to quantify it in the language of business, which is often dollars. 

Evan: Chris, maybe switching gears to our personal side, um, is there a book you've read that's had a big impact on you and love to hear why?

Chris: Well, I mentioned The Playing to Win. The, the other one is every year I read The Seven Habits. It's about this time of year, uh, usually over the Christmas holidays. I reread it and I usually come away saying, man, I suck. So, you know, I think it's just one of those books that you're never going to be as good as the books as you, you know, lay, it puts the bar out there.

And it's a, I just think it's tremendous to reread every year. It reinforces, you know, what your relationship needs to be with those you report to, how you show up as a learner. You know, it just they're just timeless. And there's a lot of books have been written off those themes, but it's just such a cornerstone.

The only other one that I referred to a lot is the Effective Executive by Peter Drucker. Bit of the challenges. The examples, et cetera, represent a workplace that's very different than our workplace, so let's just, you know, leave it at that. And so some people will struggle to get past that aspect of it, but the ideas in there on how to manage knowledge workers are actually pretty darn good. And he, you know, in particular, I use the deliver results build capability framework as being two things that every leader needs to be able to do.

You know, you're not just about delivering today's results. You have to prepare your organization to deliver even better next year, right? And you got to be working on both at the same time. That's out of effect of the executive. 

Evan: It's a classic. 

Saam: Yeah, it's a great book. Maybe sticking on the personal side, what's an upcoming new technology, and it doesn't need to be AI related, uh, Chris, that you're personally most excited about. 

Chris: So there's a lot of really interesting new materials technology and, we deal a lot with carbon in our space, and so there's some very interesting technologies out there with like nanotubes, those kind of specifically engineered materials, and I'll just say in general, those new engineered materials, that I think we're, we're very much on the very early Stage of starting to think about how to use them in our product.

It, you know, our products require just such huge scale when we make materials. You know, even our examples of our sustainable materials, and this year we're putting up for sale is 70 percent in a couple of sizes. You know, we can, we could conjure up enough material to make 5 million and we make 150 million and we're, we're like 20 percent of the market. So you just got to think about the scale that needs to be put in and these new classes of material to really have an impact just on our industry. Uh, but this, the, the area of material science, I think is really, whether that's the synthetic biology, whatever it is, those, those are going to be really new game changers, uh, going forward.

Evan: Well, we're, we're headed into an excited future. Um, uh, so Chris, really appreciate you taking time to join us today. Um, looking forward to chatting again soon. 

Saam: Thanks, Chris. 

Chris: Thank you. I really enjoyed it. Appreciate the time.

Evan: That was Chris Helsel, SVP of Global Operations and CTO of The Goodyear Tire & Rubber Company.

‍Saam: Thanks for listening to the Enterprise Software Innovators podcast. I’m Saam Motamedi, a general partner at Greylock Partners.

‍Evan: And I’m Evan Reiser, the CEO and founder of Abnormal Security. Please be sure to subscribe, so you never miss an episode. You can find more great lessons from technology leaders and other enterprise software experts at enterprisesoftware.blog.

‍Saam: This show is produced by Luke Reiser and Josh Meer.

See you next time!