Most PLM projects apparently follow a familiar path. Leadership decides the organization needs PLM, a platform is selected, budget is approved, the program is launched – and then reality hits. Engineering works with multiple CAD systems, operations and engineering use different data structures, product data lives in several places, and the “simple” integrations that need to be built, turn into months of work. Two years later, the program is over budget, behind schedule, and nobody is sure whether it really delivers value.
After leading a manufacturer from proof of concept to a successful MVP (a “walking skeleton”) in under a year, one pattern stands out: PLM isn’t complex because the technology is impossible; it becomes complex when organizations try to deliver too many capabilities at the same time.
Problem 1: “PLM” Means Everything
When leaders say “we need PLM,” they often mean a long list of capabilities at once: CAD data management, document control, change management, BOM and configuration management, supplier collaboration, compliance, requirements, and more. The result is an unmanageable scope that tries to redesign the entire product lifecycle in one program.
The shift is to define three or four capabilities that address the most urgent business problems and treat the rest as future phases. In the manufacturing case, focusing on multi‑CAD management, engineering documents, change management, and BOMs reduced scope dramatically without weakening the impact – it made progress realistic.
Problem 2: Seeing PLM as “Just” an Engineering System
PLM sits between disciplines with different priorities. Engineering optimises for design intent and technical quality; operations cares about buildability, material, cost, and capacity. When these worlds don’t align, you end up with engineering‑friendly EBOMs that manufacturing can’t use, changes that never reach the shop floor, and manual reconciliation between systems.
The alternative is to map how engineering and operations need to collaborate before configuring the platform. By designing end‑to‑end flows such as new product introduction, engineering change, configuration, and supplier collaboration, PLM becomes a backbone for real processes instead of a feature checklist.
Problem 3: Underestimating Product Data
Most organizations overestimate the cleanliness of their product data. Once you look closely, you find duplicate and conflicting part numbers, incomplete BOMs, broken CAD references, and documents without usable metadata. Simply migrating everything into PLM doesn’t clean it up; it just creates structured chaos.
Reducing complexity means scoping data deliberately: which parts and BOM levels are in scope, which metadata is mandatory, and which quality criteria must be met before migration. A dedicated business data team, with clear ownership for the data model, rules, and governance, often makes the difference between a clean PLM and an expensive mess.
Problem 4: Ignoring the CAD–PLM–ERP Flow
CAD, PLM, and ERP are often treated as separate systems owned by different teams, yet they form one data flow: CAD for design intent, PLM for product definition, and ERP for execution. If that flow breaks at any point, the impact shows up immediately in operations.
A more effective approach is to model the complete data flow before building integrations. Clarifying which data moves from CAD to PLM and from PLM to ERP, under which triggers, with what transformations and error handling, allows you to validate the architecture with real data in a proof of concept before scaling.
Problem 5: Trying to Implement Everything Before Testing
Many PLM programs are scoped as one large, all‑inclusive implementation, with the idea that everything must be built before it can be used. This maximizes complexity and risk: too many moving parts, too many unknowns, and no early feedback.
A staged path reduces this risk: proof of concept to validate feasibility, a minimum viable product to deliver core capabilities in production and then scale‑out and continuous improvement. In the manufacturing example, this meant proving that the chosen platform could handle multi‑CAD and large assemblies first, then building a thin end‑to‑end flow and expanding functionality step by step.
Problem 6: Treating PLM as an IT Project
PLM touches engineering, manufacturing, supply chain, IT, and data governance, yet many programs are run as if they were primarily technology upgrades. When disciplines work in isolation, integration gaps, data issues, and adoption problems are almost guaranteed.
Successful implementations use small cross-functional expert teams with clear ownership: for CAD‑PLM integration, PLM‑ERP integration, product data and governance, and user experience and adoption. These teams bring the right expertise together around specific parts of the solution and solve issues quickly at the points where systems and processes meet.
The Path Forward: Using Twin Transformation to Design Better PLM
PLM implementations will always carry a level of complexity, because they connect CAD platforms, product data, engineering processes, manufacturing, and ERP into a single product backbone. With a twin transformation lens, you stop treating this as a purely technical challenge and turn PLM into a driver of digital performance, sustainability, and better work for people.
Practically, this means linking each PLM decision to three outcomes:
- Digital: cleaner data, fewer manual reconciliations, faster and more reliable engineering‑to‑manufacturing handovers.
- Sustainability: better traceability over the product lifecycle, less scrap and rework, more repair and reuse instead of replace.
- Human: clearer ownership, simpler processes, and cross‑functional teams (engineering, operations, IT, sustainability) that co‑create solutions instead of working in silos.
By narrowing scope to the most important capabilities, designing PLM for how engineering and operations truly work together, scoping and cleaning data, modelling the CAD–PLM–ERP flow, building in stages, and empowering these cross‑functional “twin teams,” you manage complexity in a structured way. That is how PLM becomes a concrete twin transformation step: a system that not only works technically, but also helps your organization deliver better products, more sustainable operations, and a smoother way of working.