AI-powered Master Data Management at global scale

Rather than approaching the project as a traditional data-cleansing exercise, Etteplan applied engineering know-how to the data itself. Mechanical, electrical, and automation engineers reviewed and interpreted technical items such as bearings, drives, and sensors, verified attributes against supplier datasheets, and aligned records with industry standards. This domain-level understanding ensured that the resulting data accurately reflected real components and usable specifications, not just text fields in a system. 

AI and automation were introduced to handle repetitive and high-volume work, such as attribute checks, list preparation, and language translations. These tools improved speed and consistency while engineers focused on complex items requiring judgment and validation. The result was an efficient workflow where automation accelerated delivery, and engineering expertise safeguarded technical accuracy. 

Etteplan also identified obsolete or unprocurable items that no longer existed in supply chains, helping the customer avoid unnecessary enrichment and direct effort toward data that created measurable business value.

After successful results, the project expanded rapidly—from a four-person pilot team to a global organization of more than 70 experts. At its peak, the team processed thousands of items each week, covering data from nearly 40 plants across four business areas. 

A multi-region delivery model leveraging Etteplan’s offshoring capabilities in Finland, Central Europe, and China balanced speed, cost efficiency, and technical quality. Workstreams were organized so that tasks were distributed to the most suitable regions, while batching similar items further increased throughput and consistency. The ability to scale quickly and maintain transparency across continents reflected the close cooperation between Etteplan and the customer’s teams—an approach that truly embodied being smarter together (one of Etteplan's company values). 

From the start, AI supported the project as a practical tool integrated into the engineers’ workflow. Early automation solutions helped with Excel-based processing and attribute validation; later, more dedicated AI methods assisted with classification, coding, and translations. Across every phase, AI multiplied the team’s capacity while engineers ensured quality control, compliance with standards, and technical reliability. 

The project showed how technology and expertise complement each other: AI provided scale and repeatability, while Etteplan’s engineers applied judgment to guarantee accurate and usable results. 

Throughout the project, both sides relied on clear metrics and dashboards to track progress and quality. Indicators such as items processed per week, the share of unclear items requiring plant input, and QA effort per item offered full visibility. This openness enabled early identification of bottlenecks, strengthened collaboration, and built mutual trust. Continuous improvement became part of the daily routine. 

Etteplan implemented a complete Master Data Management framework covering all key stages—cleaning, harmonization, enrichment, validation, classification, commodity coding, translations, de-duplication, and governance. The result was a unified, standardized, and technically verified dataset ready for global ERP deployment. 

The benefits extended across the customer’s business areas: 

The project created a sustainable master data foundation supporting both current operations and future digital initiatives. It demonstrated how a forward-looking approach, combined with close collaboration and a willingness to apply new tools, can turn years of scattered information into a strategic business asset.