Demand Forecast Accuracy Review Agent

Each month the forecast is compared with what sold, but few teams act on the errors, so the same items miss again. This agent calculates forecast error and bias by item and family, then ranks items by the cost of their error, not just the percentage. Before blaming the forecast, it checks that the sales history is clean: it removes one-time orders and uses orders instead of shipments where stockouts hid demand. If the data is not clean, it fixes the history and recalculates. For the worst items it looks for causes such as promotions, lost customers or a model that misses seasonality. It drafts adjustment suggestions for the next cycle with the reason for each. The planner approves any change to the forecast.