The 5 risks of poorly planned PLM and CAD data migration

Avant d’entrer dans les 5 risques spécifiques à la migration PLM/CAO, il faut regarder en face la réalité statistique des migrations de données dans l’industrie IT.

These statistics relate to IT migrations in general. In PLM and CAD environments, however, specific factors make the risks even greater: technical data is structurally more complex than business data, involving multi-level hierarchical relationships, native links between CAD files and metadata, and revision histories that must remain intact to ensure regulatory traceability. A data corruption issue in an ERP system may result in an accounting error. In a PLM system, the same issue can lead to production downtime or non-compliance during a customer audit.

Behind every recurring non-conformity lies a systemic cause. In an industrial environment where the ERP system serves as the central source of truth and interacts with a PLM, MES, quality management tools, and suppliers, these causes almost always trace back to the quality of master data.

Native CAD files (CATIA, Creo, NX, SolidWorks) are not ordinary office files. They contain structured metadata, including revision, status, author, and links to parent assemblies, all of which must be preserved during a PLM migration. When this metadata is not correctly mapped to the target data model, the file is migrated but its relationships are lost. The drawing exists in the system, but it can no longer be retrieved through the BOM or Part Number.

In Windchill or Teamcenter environments, relationships between objects (Part – Document – CAD Document) are managed through vault structures whose logic varies depending on versions and configurations. Migrating without first mapping these structures is essentially a blind migration.

Real-world example: Aerospace supplier

Following a migration from Windchill to ENOVIA/3DEXPERIENCE, 12% of the CAD drawings belonging to a critical product family became inaccessible through the product structure. The files were physically present in the vault, but version metadata and relationships with Part Numbers had been truncated during mapping.

Manual reconstruction took 6 weeks and required 3 manufacturing engineers to be reassigned. Production in the affected area was suspended. The estimated cost exceeded the initial migration project budget for this scope alone.

A BOM (Bill of Materials) in an industrial PLM system is not a flat list. It is a multi-level hierarchical structure involving typed relationships (component-assembly, variant-configuration), version-controlled revisions, workflow statuses (In Work, Released, Obsolete), and links to the ECOs/ECNs that justified each change. This structural complexity is precisely what generic migration tools often fail to handle correctly.

During migration, a revision management mapping error can result in BOMs being migrated with the correct revision number displayed as text, but with links still pointing to components from the previous revision. The result: a BOM that looks correct on screen but references obsolete components in production.

In a mature industrial organization, PLM and ERP systems share a common repository of parts and bills of materials. PLM is the source of creation and validation for design data, while ERP uses this data for procurement, planning, and production. This integration relies on interfaces (APIs, connectors, middleware) configured around specific data identifiers: Part Numbers, revisions, and statuses.

When a PLM migration changes identifiers or data structures without updating ERP interfaces accordingly, the digital thread is broken. Purchase orders may continue to reference identifiers that no longer exist in the new PLM system, or production BOMs in ERP may no longer reflect the structure approved in PLM.

Silent integration failures are among the hardest risks to detect. Everything appears to work until the first real need for cross-system traceability.

> Real-world example: Multi-site industrial company

Following a migration from Windchill to ENOVIA, the PLM-SAP integration connector continued using legacy Windchill identifiers as join keys. These identifiers had been transformed during migration.

For four weeks, BOM updates approved in ENOVIA were no longer propagated to SAP. Production planners continued working with outdated, frozen BOMs. The discrepancies were only detected when the first manufacturing order was launched for a product modified after migration.

In regulated industries such as aerospace (DO-178, AS9100), automotive (IATF 16949), defense, and medical devices (ISO 13485), design decision traceability is not optional. It is a contractual and regulatory requirement.

Every component change must be traceable to an approved ECO/ECN, including its justification, validations, and corresponding signatures.

During a PLM migration, ECO workflow histories (intermediate statuses, comments, approvers, dates) are often among the first data sets to be sacrificed. This happens either because they do not match the target data model or because they represent large volumes that make migration more complex.

The seemingly practical decision to “migrate active data only” can therefore create a significant compliance risk during the next customer or regulatory audit.

The fifth risk is the most systemic and the hardest to anticipate during the initial project scoping phase.

When the four previous risks materialize simultaneously at go-live, the project team is forced into an operational crisis: engineers are reassigned to manually reconstruct data, production cannot launch manufacturing orders due to unreliable BOMs, ERP interfaces are silently broken, and management expects immediate explanations. This scenario is not an extreme exception. It reflects a recurring pattern.

Only 16% of data migrations are completed within their original timeline and budget (Bloor Research). Projects that exceed their budgets experience an average cost overrun of 30% and a schedule overrun of 41%.

For a six-month PLM migration project with a €500,000 budget, this translates into an additional €150,000 in unplanned costs and a 2.5-month delay to go-live.

> What McKinsey reveals about the causes of project overruns

McKinsey identifies source data quality as one of the most common causes of overruns in large-scale IT projects. Specifically for migration projects, 44% of teams cite underestimating data volumes and complexity as the main cause of budget overruns (Experian).

Data migration is never “just a transfer from A to B”. It is a transformation, and the depth of that transformation is directly proportional to the quality of the source data.

Certain operational warning signs indicate that recurring quality issues originate in master data quality rather than process or equipment failures. Here is what to look for.

ISO 9001:2015 requires organizations to implement a structured non-conformity management process. In practice, however, this process often stops at immediate corrective action without investigating the underlying systemic documentation issues.

Here is how to structure a process that goes all the way back to the source.

Pressure on PLM migrations has never been greater than in 2026, driven by three converging trends that are shortening decision-making windows for CIOs and Manufacturing Directors.

1. The end of legacy Windchill licenses and the shift to Windchill+

PTC introduced its new ePLM licensing model on July 1, 2025. Existing Windchill license packages moved to a “renewals only” model. As of September 30, 2026, legacy licenses can no longer be renewed. Companies still running Windchill 12 (whose standard support ended in June 2024) or Windchill 13 (with support scheduled to end in June 2027) face two simultaneous pressures: end of support and end of licensing. This constraint forces migration decisions that some manufacturers would otherwise have preferred to postpone.

2. The SAP S/4HANA migration is also driving PLM changes

The migration to SAP S/4HANA (with the 2027 deadline for standard ECC support) is not an isolated ERP project. It requires companies to review their existing PLM-ERP interfaces, which, in most industrial architectures, means migrating or significantly redesigning PLM connectors. These two projects often run in parallel, creating additional pressure on IT and engineering teams.

3. Industry 4.0 ambitions require a clean digital thread

Digital twin, predictive maintenance, and industrial AI projects all rely on a common prerequisite: a clean, traceable, and consistent digital thread connecting PLM, ERP, and MES systems.

Gartner predicts that by 2030, more than 30% of industrial manufacturers will rely on a PLM-based digital thread to contextualize data used to train their AI models (Manufacturing Predicts 2026, Gartner, décembre 2025). But these systems cannot deliver reliable results with degraded data. A poorly prepared migration is one of the fastest ways to permanently undermine the quality of technical master data.

The main problem with poorly prepared PLM migrations is not technology. It is the sequence of operations. Companies choose the target system, sign the licenses, launch the migration project, and treat source data quality as a final project task. By that stage, it is already too late to prevent problems. The project is in reactive mode.

KLERIA’s Zero-Backlog Engine™ methodology reverses this sequence. Source data auditing comes first, before target system configuration, before mapping, and before testing. We take care of this analysis and preparation work so your team can execute the migration using data that has already been assessed and qualified, rather than discovering problems at go-live.