A method used to monitor the accuracy of a forecasting model involves comparing actual results to predicted values. This comparison generates a statistic that indicates whether the forecast is consistently over- or under-predicting. The computation typically involves dividing the cumulative sum of forecast errors by the mean absolute deviation (MAD). For example, if the sum of forecast errors is 100 and the MAD is 20, the resulting value would be 5, indicating a potential bias in the forecasting model.
This metric is important because it provides a straightforward way to assess forecast validity. A value close to zero suggests an unbiased forecast, while a value significantly different from zero may indicate systematic error. Monitoring this value over time can help organizations improve their forecasting processes, leading to better resource allocation, inventory management, and decision-making. Historically, its use has been prevalent in manufacturing and supply chain management, but its application extends to various fields where accurate forecasting is critical.
