Demand Forecast Explanation

Explain demand forecast deviations by decomposing statistical outputs into baseline, trend, seasonality, and promotion drivers, then generating planner-friendly narratives that quantify impacts and attribute errors to root causes.

Attribute demand forecasting errors by analyzing historical actuals, promotion calendars, price history, and external signals to identify root causes like distribution gaps or external shocks, quantifying their specific impact on the deviation.

Track MAPE, bias, tracking signal, and Forecast Value Added (FVA) to measure demand forecasting accuracy for S&OP, enabling clear evaluation of model performance and value contribution across the supply chain planning cycle.

Use historical actuals and promotion calendars to improve forecast accuracy by decomposing past deviations, identifying bias patterns, and synthesizing actionable recommendations to adjust future baseline, trend, and seasonality expectations.

Translate statistical demand forecasting outputs into business narratives by breaking down forecasts into actionable drivers, calculating accuracy metrics, and synthesizing recommendations that bridge data science outputs with supply chain decision-making.