PARTNER CONTENT
This article was produced by AMS in partnership with Siemens Digital Industries
At a glance
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PLM is evolving from a mechanical engineering repository into a connected lifecycle platform managing software, electronics and mechanical parts, ensuring compatibility and safe over-the-air updates for software-defined vehicles.
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Connecting PLM, ERP and MES data across engineering, manufacturing and service speeds change management and reduces costly rework
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Commercial vehicle makers rely on modular bills of materials and configuration rules so every customer-specified vehicle is producible, while a “vehicle DNA” digital twin tracks lifetime changes.
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AI grounded in trusted lifecycle data helps engineers find information faster, while integrating simulation outputs into PLM shows how designs perform across production, service and operation.
Product lifecycle management (PLM) has long been the backbone of engineering data and design workflows in the automotive industry. But the last decade has seen radical shifts in what PLM must manage: rising software content, electrification, modular platforms and new business models focused on uptime and serviceability. Speaking to AMS, Andreas Eek, Account Executive at Siemens Digital Industries Software, laid out how PLM is changing from a siloed engineering tool into a connected lifecycle platform that enables faster change management, richer simulations and a trusted digital record of what each vehicle actually is.
PLM’s broadened scope: software, electronics and context
Historically, PLM was dominated by mechanical data. As Eek observes, “If you go back more than 10 years ago, software was never discussed from a PLM perspective. It was purely mechanical discussions.” The transition to software-defined vehicles means that PLM must now manage not only mechanical and mechatronic components but also software modules and electronics – and their compatibility.
When a customer configures a vehicle, you need to make sure it’s actually producible… that needs to be built up on a foundation from a PLM system based on modularisation of the product together with highly integrated configuration rules
Andreas Eek – Siemens Digital Industries
Eek sums up the implication: “They need to have the software part of the PLM system and be able to manage the compatibility between mechanical parts, mechatronic parts, and software parts as a group of components to build a complete vehicle.” This is vital not only for correct initial assembly but also for safe, reliable over‑the‑air (OTA) updates: “Once an update goes out, it has to work.”
For commercial vehicle makers – a sector that traditionally emphasises modularity and long‑lived platforms – PLM is especially critical. Eek explains that commercial OEMs must ensure that every customer configuration is sure it’s actually producible… that needs to be built up on a foundation from a PLM system based on modularisation of the product together with highly integrated configuration rules.”
Moving to a connected, contextualised lifecycle data
Many established manufacturers evolved complex IT landscapes made up of many specialised systems. Eek describes the current trend away from that fragmentation. New approaches target a small set of core systems – typically PLM, ERP and MES – while placing emphasis on connecting and contextualising data across the lifecycle, rather than merely centralising it.
“You need to put much more data into one common system where you have both engineering, manufacturing [and] service people so that the manufacturing people can see the engineering data much earlier,” Eek says. The goal is faster, clearer change management and fewer costly re‑works. In his words: “When they make changes, they can get a much better understanding to perform those changes without having to re-loop them once you get feedback.”
We see a lot of concepts in the commercial vehicles requirements that we don’t see in automotive, for example, to really ensure that all the modules that they produce can be used in a vast amount of vehicle configurations
Configuration management and the commercial vehicle challenge
Commercial vehicle OEMs face unique configuration pressures. Unlike high‑volume passenger cars, many commercial OEMs develop platform families and then run continuous changes on those platforms for a decade or more. This forces PLM to support highly flexible modular architectures while ensuring manufacturability and functional compatibility across many variants.
Eek highlights the trade‑offs OEMs face: some choose maximal flexibility; others limit options to optimise production. “If you have a limited flexibility, you might be able to optimise the production aspects and maybe have a larger volume of specific vehicles,” he says. But for OEMs that must support diverse customer applications, sophisticated configuration rules and modular bill of materials (BOMs) are a must. Eek says: “We see a lot of concepts in the commercial vehicles requirements that we don’t see in automotive, for example, to really ensure that all the modules that they produce can be used in a vast amount of vehicle configurations.”
Smart energy and data solutions power shift to green auto manufacturing
As sustainability has become a priority for global automotive manufacturing, OEMs and suppliers are adopting digital energy management systems to track, reduce and optimise energy consumption and carbon emissions across plants and supply chains. Siemens is helping to lead this transformation, enabling measurable reductions in CO₂ and operational gains, especially in paintshops and bodyshops.
The comprehensive Digital Twin, vehicle DNA and serviceability
As vehicles accumulate lifetime changes – repairs, dealer retrofits, third‑party modifications – maintaining an accurate digital representation becomes vital. Eek warns that updates performed outside the OEM’s ecosystem risk creating a drift between physical reality and the digital record. “If you do some maintenance on a vehicle, by yourself or by a non‑authorized dealership, you will never update that DNA, and then the OEM will not be able to understand what’s fitted to that vehicle.”
That “vehicle DNA” – essentially part of a comprehensive Digital Twin that reflects what is engineered, manufactured, configured and maintained for each vehicle – will be increasingly important as trucks and vans become more software‑driven. Eek puts it bluntly: “I think a digital twin is going to become more important in the future when you are starting to deal with, what I might call, a software-defined truck.”
The service and business model angle is equally compelling. If OEMs move from selling vehicles to selling transportation services (or uptime guarantees), they will need PLM to embed serviceability, maintenance workflows and lifecycle cost visibility from day one. “If in the future OEMs start to sell transportation services, then they take on the responsibility of serviceability and uptime… the big money and profit for the OEM would be to not only ensure that you have excellent quality, but also a good serviceability,” Eek said.
AI and automation: tackling information overload
Eek sees a practical role for AI within PLM: helping humans find and maintain information in massive, interconnected data graphs. “One of the big uses for AI is finding information… the data inside a PLM system is vast and engineers today can spend a lot of time just looking for accurate information,” he observes. Applied thoughtfully, AI can surface the right requirements, test cases, linked parts and reduce time engineers spend on administrative maintenance. This is especially true when AI is grounded in trusted engineering, manufacturing and service contexts.
He also believes AI could reduce the degradation of model-based engineering artifacts: “What usually happens is that people are stuck maintaining it, and then it degrades and suddenly it’s not usable anymore. If you can start to apply AI to something like that, it could be a huge benefit.”
If you can incorporate all the simulation data to the actual product, and also make that data available, you can provide a lot of value for people to understand how that product is performing
Simulations and lifecycle context
Another trend is the tighter integration of simulation data into PLM. Rather than isolating simulations in specialist tools, Eek argues for making simulation outputs part of the product context. This way engineers and service teams can understand how a design performs across manufacturing, service and operational use cases. “If you can incorporate all the simulation data to the actual product, and also make that data available, you can provide a lot of value for people to understand how that product is performing,” he says, pointing to structural, CFD, manufacturing and service simulations as useful additions.
What this means for OEMs and suppliers
The practical implication is that PLM vendors and OEMs will need to balance two imperatives: supporting a wide range of specialist tools while maintaining connected, trusted, master data that can feed the Digital Twin across the product, production and service lifecycle. Eek stresses that this is not about consolidating every endpoint tool but rather maintaining authoritative product and lifecycle context twin inside PLM while supporting many end tools.
That approach supports modern objectives: faster change management, improved manufacturability and serviceability, clearer business‑case justification for system changes, and the foundations for new service‑oriented business models. As Eek puts it, companies that “integrate the solutions so that they can have a more streamlined process and especially speed up the change management process, get huge gains.”
PLM is maturing from the engineering CAD vault into a strategic, cross‑discipline, lifecycle platform. For automotive and commercial‑vehicle manufacturers, that means embedding software, electronics, configuration rules, the comprehensive Digital Twin and simulation context into a connected digital thread across the lifecycle. Combined with industrial AI grounded in trusted lifecycle data and smarter integration across PLM, ERP and MES, the next generation of PLM promises to reduce rework, accelerate change cycles and enable new business models focused on uptime and total cost of ownership – precisely the capabilities Andreas Eek says OEMs need as vehicles become ever more complex.
