Forecast error is the difference between the actual demand and the forecasted demand for a given period.
Forecast error can be caused by various factors, such as changes in customer preferences, market conditions, competitor actions, or random variation. Forecast error can be measured using different methods, such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), or tracking signal. Forecast error typically triggers forecast revision when it is continually increasing, which indicates that the forecast model is not capturing the underlying demand pattern or trend. A continually increasing forecast error can lead to poor customer service, excess or obsolete inventory, or lost sales opportunities. Therefore, it is important to monitor the forecast error and revise the forecast when necessary to improve the forecast accuracy and reliability. Forecast error does not trigger forecast revision when it is used in computing the tracking signal, associated with the introduction stage of the product life cycle, or caused by random variation. These are not valid reasons for revising the forecast, as they do not indicate a systematic or persistent deviation from the actual demand. References:
* CPIM Part 1 Study Guide, Chapter 4: Demand Management, Section 4.2: Forecasting Techniques and Performance Measurement
* CPIM Part 2 Study Guide, Chapter 3: Demand Management, Section 3.1: Demand Planning
* A Critical Look at Measuring and Calculating Forecast Bias, Section: What Is Forecast Bias?
* How Can Forecast Error be Calculated?, Section: Introduction