Business Problem
SAP and legacy ERP systems provided unreliable raw material volume forecasts due to master data limitations and organizational maturity issues. This resulted in frequent spot buys, reduce switching costs, and reactive procurement decisions.
Actions Taken
· Implemented AI-driven forecasting models integrating actual consumption data.
· Embedded material-to-logistics mapping rules to reflect real operational constraints.
· Enabled rolling forecasts across 1 - 24 month horizons to support medium - and long - term planning.
· Forecasts directly consumable by procurement and planning teams
· Proactive decision making enablement
Outcomes Achieved
· Empowered planners to take proactiveprocurement actions rather than reactive responses.
· Minimized spot buys and reduced hedge switching costs.
· Improved procurement efficiency and planning confidence.
. Delivered Multi Million USD in operational efficiency savings.
