Audit, build, stabilize, validate and go to production. A chain designed as an execution system, not as a sequence of steps to tick off.
Face the reality of the data
The audit phase aims to understand the data as it actually exists in the source systems.
It relies on extracting and analyzing the real data, measuring the effective volumes, identifying inconsistencies, duplicates and edge cases, and understanding the functional dependencies.
The goal is to get a clear picture of what is genuinely migratable and the associated risks.
Build the migration system
Migration is not treated as a set of one-off scripts, but as an industrial system.
This phase consists of building a configurable, traceable and repeatable migration chain, able to evolve run after run.
The system is designed to support iterations, corrections and scaling up, while keeping full visibility over the processing performed.
Stabilize through execution
The system is run on real functional scopes (parts, assemblies, structures, etc.).
Each run brings discrepancies or anomalies to light, which are then analyzed and corrected.
These Run / Fix loops chain together gradually, scope after scope, until business rules, data and processing are stabilized. Each iteration strengthens the overall reliability of the migration.
Validate the whole chain
Once the scopes are stabilized, a dry run is carried out across the entire chain.
It is a complete execution, at real volume, covering all the data and processing.
This is not a demonstration, but a dress rehearsal meant to confirm that the system holds up at real scale.
Go to production without surprises
Going to production happens once the migration is already under control.
The data, the business rules and the processing have been proven through repeated execution.
Going live becomes a controlled step, with no late discoveries and no surprise effect.
From a few months to over a year, depending on the real volumes, the quality of the source data and the number of systems involved. That is exactly what the audit phase is for: measuring the actual scope to give a reliable estimate, instead of a convenient figure announced before anyone has looked at the data.
Because the true state of the source data is discovered too late. Outdated documentation, implicit business rules, underestimated volumes: these discrepancies only surface at execution time, on real data. Our answer is to execute early and often, scope by scope, so the chain is stabilized well before cutover.
A dry run is a dress rehearsal of the migration: the complete chain is executed at real volume, from extraction to loading. Classic acceptance testing validates selected test cases; the dry run proves the system holds at real scale, across all the data.
SAP S/4HANA, Windchill, 3DEXPERIENCE, IFS and Teamcenter, among others. The methodology is tool-independent: it applies to any migration where data must be qualified against a target model, record by record.
Between €200,000 and €1 million depending on the scope. Set against the cost of a failed migration, which runs into tens of millions of euros: the expertise represents less than 1% of the cost of a fiasco.
ERP or PLM, legacy with a long history, large volumes: let's talk about the real constraints of your migration.
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