Business Problem
A global FMCG organization relied on traditionalforecasting methods for commodity futures, resulting in limited visibilitybeyond short-term horizons. Forecast inaccuracies across soft commodities,energy, and metals led to suboptimal purchasing timing, inefficient inventorypositioning and increased exposure to market volatility.
Actions Taken
· Developed and operationalized custommultivariate AI forecasting models, including LSTM neural networks.
· Incorporated demand–supply signals,macroeconomic indicators, financial market data, and commodity-specificdrivers.
· Applied ensemble modelling and advancedfeature engineering to capture non-linear interactions.
· Validated models through back-testing andcontinuous performance monitoring.
Outcomes Achieved
· Achieved best-in-class forecasting accuracyover a 1-to-12-month horizon.
· Enabled more disciplined and timelypurchasing strategies.
· Optimized inventory levels and orderfrequency across commodities.
· Generated Millionplus Dollars in monetized benefits through improved price visibility anddecision-making.
