How to fully leverage digitalization in automotive today – Transcript
In this episode of The Industry Forward Podcast, Royston Jones and Ryan Martin discuss how automotive companies can leverage digitalization to get more out of their data and connect their product lifecycles today.
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 are looking at the automotive industry, the shift towards software-defined vehicles, and the way digital transformation is accelerating at all. We’re joined again by Ryan Martin, Senior Research Director at ABI Research, and Royston Jones, Global Head of Automotive and Transportation here at Siemens. Welcome back, guys.
Kate Eby: We’ve talked before about how the comprehensive digital twin, AI, and software-defined vehicles are changing the way automakers use data across the entire life cycle, from simulation and engineering to over-the-air updates and insights coming back from vehicles already on the road. That can mean everything from making better decisions earlier in development to identifying real-world issues, like potholes, and using that information to improve performance, safety, and the driver experience. Ryan, when you look across that spectrum, how much of that data can OEMs realistically access today?
Ryan Martin: I think it’s pretty early on that spectrum in certain really specific high-end luxury vehicles. It does tend to be available, but it comes also, or historically has come at such a price that it can be prohibitive at scale. And that’s one of the fundamentals that is going to change over the next few years is that these capabilities that were really limited to a small portion of users become much more broadly accessible. The potholes example is a great one, actually.
Ryan Martin: I believe it was either Rolls-Royce or Bentley has had some kind of pothole prediction technology to dampen the suspension when the vehicle approaches a pothole. So those kinds of capabilities are going to become much more widely available because the barriers to entry are lowering due to these new digital solutions that integrate PLM, CAD, ALM, manufacturing, production, and so forth.
Kate Eby: So one aspect is, of course, being able to roll out these features over the air updates, right? But another aspect is also, or I’m sorry, to roll them out over air updates or even implement them as solutions in new vehicles. But another piece of that is for the OEMs to actually be able to collect that data in the first place to see where those pain points are. Is that something that’s being done in what role, you know, to what level and Where’s there room for improvement and what role does the digital level of the OEM play into their ability to collect and analyze that data?
Ryan Martin: It’s a really interesting point when it comes to data collection and also actionability because a lot of consumers tend to think of vehicles as the item that they receive or the experience of driving, but really a vehicle comprises a ton of components from a really broad array of suppliers. And maybe an OEM is able to collect data on how the vehicle is used, but that data actually pertains to the supplier that provides the suspension or tires or sensors or even the infotainment system.
Ryan Martin: And for that reason, it’s becoming even much more imperative today than ever has been for suppliers to be integrated with the process, the innovation process that OEMs are driving. And that extends beyond tier one to include tier two and tier three. So having everyone on the same page from a data perspective is really the prerogative right now. And I would say it’s still fairly early days. in that. And for that reason, the companies that are able to have this really strong relationship and alignment largely through digital twin really are seeing a pretty strong competitive advantage.
Royston Jones: I think that for me sort of highlights the importance of the comprehensive digital twin because you can get the field data back and you can analyze the field data and you can draw insight from the field data. But I think what the comprehensive digital twin would allow you to do is essentially do a deeper root cause analysis of, you know, what the issue was, a warranty issue or predictive maintenance. I think it gives you that insight. So again, I think it just, to emphasize the importance of creating that comprehensive digital twin, you’d start to reduce your warranty claims significantly if you can start developing that sort of insight. So that I think is quite exciting for any company that could basically really drive and connect the complete PLM lifecycle together with a twin. with a unique intelligent twin, an identical intelligent twin.
Ryan Martin: We do regular surveys of manufacturing decision makers and we find quality to be among the top three major concerns regularly year over year. And any solutions that can allay those concerns really do go a long way. So thinking about warranty claims or especially root cause analysis is a huge one. Forty percent of companies don’t have the ability to collect and analyze data in near real time and 70 percent can’t apply data for prescriptive analytics, which is a significant disconnect when we think about being able to know what has happened and then being able to do something proactive based on what has happened.
Kate Eby: We’ve covered a lot about how the comprehensive digital twin and AI are helping drive the continued evolution of software-defined vehicles. If a company is trying to put that kind of digitalization in place, whether it’s a more established automaker working to transform or a new player building from the ground up, what advice or best practices would you point to?
Ryan Martin: When embracing best practices around digitalization, there is a tremendous advantage for shifting left design and accelerating the process of bringing new product to market faster. So if the average OEM brings new products to market in three to four years, there’s a lot of legacy process that needs to be, in some cases, it does need to be respected. So maybe there’s a certain cadence or cycle for the operating pulse of the factory, which is just easier to maintain as it is.
Ryan Martin: At the same time, with the knowledge that competitors, and especially those that are starting fresh and are all in, are going to sort of be faster and probably better in a lot of ways. It means just doing the status quo is not enough to be competitive. So perhaps it’s supporting some of the legacy processes, but also introducing new modalities in parallel or when possible, even though maybe it doesn’t seem realistic, finding ways to kind of start over when it does make sense.
Royston Jones: I think for me, one closing comment that I just want to maybe emphasize is essentially the acceleration of the deployment of sort of digital. And obviously that can vary from company to company, how quickly they embrace it. Now and again, that’s a cultural thing. But I think there’s an opportunity out there to redefine the complete PLM process. And I think if you’re going to stick with processes that are nearly, you know, two decades old or even more, then that’s not what the technology is allowing you to do. I think the whole of the PLM process needs to be to be redefined. It’s a great opportunity with all of these really intelligent digital technologies out there from optimization to AI of basically reimagining what that process should be.
Ryan Martin: One other aspect I would add is there’s a lot of focus when we consider the concept of a digital twin to think of it in the context of a product. Where a lot of the prowess really comes through is thinking about the product and the context of its environment. And what that means is a digital twin, not just of the product, but especially of the process and maybe even the factory environment where those processes take place. So having a really integrated picture of maybe it’s product-centric, but at that product at different stages of its life cycle.
Ryan Martin: So before it’s designed, while it’s being made, and then importantly too, after it’s made for after-sales service and support, and then closing those loops in a coherent manner. And that is really what it means to have a truly comprehensive digital twin.
Royston Jones: Yeah, I think that’s a good distinction. I mean, if you look at particularly when it goes to the manufacturing side, then a lot of emphasis on creating the twin is around the process that gets to make the product and less of the imperfections, et cetera, that it imparts to the product. A lot of focus is on the design of the lines and so As Ryan said, it’s more about the environment that is around creating the twin in manufacturing. But in general, when you look at the twin, then you have the twin, but obviously its environment and the load cases around it are equally as important. The thing that will drive that product design is the environment that it lives in.
Ryan Martin: And what we’re really talking about here too is the role of a twin to be more than a static point in time. It’s not just what something is right now and then, but what it was and what it will be. And a lot of the focus is in that sense, potentially even misplaced for us as industry, which is what has gone wrong and how do we fix it. A twin can also be used to scale best practice. So maybe something is working really well. and you want to double down on it and replicate that in the future for future scenarios. And you can do that with simulation.
Kate Eby: Thank you, Ryan and Royston, for a great conversation on how software-defined vehicles are reshaping the automotive industry and how technologies like the comprehensive digital twin and AI are helping drive that transformation forward. I’d also like to extend a big thank you to everyone tuning in. We hope you found the discussion interesting and took away a few new insights. We also hope you’ll join us again as we continue to explore the future of today’s industries. I’m Kate Eby and we’ll see you next time on The Industry Forward Podcast.
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.
