FMCG Sales Forecasting
Hierarchical forecasting enhanced with external drivers, competitor effects, and machine-learning sub-models for stronger commercial planning.
Technology
AWSPython
Selected project note
The business context
In a fast-moving retail environment, accurate forecasting is critical for inventory planning, commercial decision-making, and operational performance. The forecasting landscape was complex, requiring models that could account for hierarchy, external drivers, and competitor effects.
What was delivered
Enhanced hierarchical sales forecasting models by incorporating exogenous variables, competitor analysis inputs, and machine learning sub-models. Supported weekly-granularity forecasting with a one-year horizon and contributed to broader production, deployment, and version control processes associated with the forecasting pipeline.
The practical impact
Delivered measurable forecast accuracy improvements that strengthened commercial planning, improved decision-making confidence, and contributed to more robust forecasting processes.