AI-Powered PLM Migration with Axium: 85% Faster,
from 10 to 1.5 Years

Our client decided to adopt Model-Based Systems Engineering (MBSE), as the 25-year-old legacy landscape no longer supported modern workflows and digitalization goals. However, SAP’s ecosystem couldn’t fully address the evolving requirements. A new system was needed that meets the demands while preserving PLM knowledge, spanning hundreds of complex machines, without losing critical logic.

However, the migration process was highly complex and resource-intensive. Each machine represented decades of Subject Matter Experts’ (SMEs) experience and logic, stored as constraint rules, defined characteristics, and configuration procedures, deeply embedded within SAP using a proprietary language. Recreating all configuration logic manually would have consumed enormous time, budget, and engineering resources while introducing significant opportunity costs.

A manual pilot migration of a single machine’s PLM data to a modern system took the client six months. At that pace, migrating all machines would have tied up engineering resources for 10 years, leaving no capacity for innovation.

The core bottleneck was how knowledge had been stored in SAP through years of custom configurations per machine. As SMEs left or retired, much of this critical knowledge was at risk of disappearing, turning delay into a strategic threat.

A manual translation of this logic was not an option, the client needed a scalable, automated migration approach to accelerate the process while retaining existing knowledge.

While modern data architecture principles had been introduced, they were not yet consistently applied in the sustainability domain. Reusable data products, defined service levels for data quality, and value-driven governance cycles were still missing, limiting the ability to scale sustainability use cases efficiently.