Master Data Optimization at FMCG Scale

Challenge

Global manufacturers carry hundreds of thousands of materials in their ERPs, with the same physical material recorded under different codes, names, and descriptions across plants, regions, and acquired subsidiaries. The result: missed volume discounts, overstocking, maverick spending, and an inability to consolidate procurement spend. Master data clean-up is universally known to be needed and universally deprioritized — until the cost of inaction adds up.

Approach

We built an AI-assisted similarity engine that detects duplicate and near-duplicate materials across plants and ERPs. The engine uses material descriptions, attributes, and purchasing patterns to cluster look-alike materials, then quantifies the consolidation savings per cluster — based on real purchasing volumes and vendor discount curves.

A steward workflow lets data managers review each cluster and decide: keep, block, or obsolete. Each clean-up action carries a business case behind it.

Outcomes

• 228 materials analyzed in the demo set, with 98.2% identified as sitting in duplicate clusters
• 48 candidates flagged for blocking or clean-up
• €1,382,973 estimated annual saving from 84 consolidation clusters (illustrative)
• Faster clean-up with a business case behind every change
• Lower overstocking, fewer missed discounts, less maverick spend
• One trusted material view supporting better sourcing and operational decisions

Technology

Numbers shown are illustrative — derived from a standalone demo version of the engagement built on synthetic data. Actual outcomes at the client site are NDA-protected.