Business Problem
A global FMCG organization relied on traditional forecasting methods for commodity futures, resulting in limited visibility beyond short-term horizons. Forecast inaccuracies across soft commodities, energy, and metals led to suboptimal purchasing timing, inefficient inventory positioning and increased exposure to market volatility.
Actions Taken
· Developed and operationalized custom multivariate AI forecasting models, including LSTM neural networks.
· Incorporated demand–supply signals, macroeconomic indicators, financial market data, and commodity-specific drivers.
· Applied ensemble modelling and advanced feature engineering to capture non-linear interactions.
· Validated models through back-testing and continuous performance monitoring.
Outcomes Achieved
· Achieved best-in-class forecasting accuracy over a 1-to-12-month horizon.
· Enabled more disciplined and timely purchasing strategies.
· Optimized inventory levels and order frequency across commodities.
· Generated 1M+ Dollars in monetized benefits through improved price visibility and decision-making.
