Context. Inventory controllers needed a clearer way to understand why production orders created over-range inventory and which cases were worth acting on first.
Approach. Designed a simulation-first Python pipeline that combines synthetic detection data with simulated SAP evidence, performs deterministic root-cause diagnosis, and produces both a controller worklist and audit-oriented records. Added an AI investigation layer for grounded case exploration, alongside a planner workbench for portfolio prioritization, evidence review, recommendations, human decisions, and follow-up.
Impact. Created an auditable foundation for reducing over-range inventory while keeping planners in control. The pilot separates one-time order corrections from recurring master-data issues and makes unsupported production justifications visible for review.