Common PLM Selection and Implementation Mistakes

What are the most common PLM mistakes?

Publications from Aras, Arena and the Dassault partner TECHNIA, all vendor-side sources, name overlapping lists: unclear requirements, missing executive support, poor change management, weak integration planning, dirty data and thin training. None of them quotes a failure rate, and the register quotes none. The register groups the mistakes by project stage below, and the closing table pairs each with an early sign and a fix.

Mistakes during selection

Selection mistakes are cheapest to fix, because nothing is configured yet. They also shape every later phase, since requirements, shortlist and contract all rest on the same assumptions.

• Feature checklists without scenarios. A checklist rewards the longest list of ticks. Aras warns that functions are sometimes oversold and that vendor best practice may not fit the buyer's processes. Scripted scenarios test fit instead.
• No named data owner. Part numbering, BOM structure and release rules need one accountable person before any vendor configures them.
• Demos on vendor data. A polished demonstration shows the vendor's data model. Sample data from the buyer's own products exposes gaps early.
• Price-only comparison. TECHNIA notes that decisions driven by sales pitches, popularity or price can pick a system that does not match workflows. Compare cost over the full term, as in how much PLM software costs.
• Requirements written by a small group. Aras describes software imposed on users who were not involved, which invites resistance. Workshops across engineering, manufacturing, quality and purchasing widen the input.

Mistakes during implementation

Implementation mistakes tend to appear as schedule slips and rework. Most trace back to a decision made during selection that nobody revisited.

• Big-bang scope. Arena lists implementing too fast among its adoption mistakes and suggests phases that start with core BOM, change and document management.
• Migration left late. Profiling and cleansing take calendar time, and Arena notes that poor inputs give poor results.
• Over-customisation. Each extension adds to the cost of every later upgrade.
• Integration planning left to the end. Arena and TECHNIA both describe data silos when ERP, CAD and other links are planned late.
• No executive sponsor. Aras points to leaders who expect a quick fix and lose patience.
• Unclear goals. TECHNIA lists unrealistic expectations that the system will solve everything on its own. Specific, time-bound objectives tied to named pain points give the team something to test at each phase.

Mistakes after go-live

A release that works technically can still fail to change daily work. The signs show in how engineers, buyers and quality staff behave in the months after launch.

• Retained spreadsheets. Arena lists old habits as a mistake. Teams keep using spreadsheets and paper unless the old routes are closed.
• Training that stops at go-live. Role-based training for internal users and external partners continues after each release.
• No governance. Without a change board and data stewards, part records drift and the migration effort is wasted.
• No measurement. TECHNIA suggests tracking indicators such as development time and product quality. Track usage and data quality alongside them.

How can these mistakes be avoided?

Use the table as a review checklist at each stage gate, with the project sponsor, the data owner and the key users each signing off their own rows. A mistake caught at selection costs a rewritten requirement. The same mistake caught after go-live costs rework in configured processes and migrated data.

Frequently asked questions

Sources

• Aras: Why do PLM selection projects fail?
• Arena: Top 10 PLM adoption mistakes to avoid
• TECHNIA: Barriers to PLM implementation

Written by Nathalie Paniuta. Reviewed 4 November 2026 by the PLM Vendors editorial team. Regulatory dates and vendor details change; check the sources for the latest position. Corrections with a source: info@plmvendors.com.