Demand Forecast Explanation

To explain demand forecast drivers, this Skill decomposes statistical forecasts into baseline, trend, seasonality, promotions, and price elasticity, generating a clear business narrative. It translates complex model outputs into actionable explanations for demand planners.

The best way to attribute demand forecasting errors is to calculate metrics like MAPE, bias, and tracking signal, then map deviations to specific triggers. This Skill identifies root causes such as promotion spikes, distribution issues, or external shocks.

To decompose a statistical demand forecast, this Skill breaks down the predicted volume into baseline, trend, seasonality, promotions, price elasticity, and external factors. This reveals the individual impact of each driver on overall demand.

For S&OP forecast accuracy analysis, MAPE and tracking signal are calculated by comparing predicted values against actual demand to measure percentage error and sustained bias. This Skill automatically computes these metrics to evaluate forecast reliability.

Yes, you can analyze forecast bias and deviations for a specific SKU and time period by providing the product identifier, location, and date range. The Skill then evaluates the forecast accuracy and explains the variance drivers for that exact scope.

Automated forecast decomposition relies on the quality of input data and historical patterns, meaning it may struggle to attribute sudden external shocks accurately without contextual inputs. It explains deviations but relies on planners to validate the root causes.