Evaluation of forecasting methods in aggregate production planning: A Cumulative Absolute Forecast Error (CAFE)

Abstract

Abstract The purpose of forecasting error measures is to estimate forecasting methods and choose the best one. Most typical forecasting error measures are designed based on the gap between forecasts and actual demands and, consequently, a forecasting method yielding forecasts in accordance with real demands is considered as good. However, in some applications such as aggregate production planning, these measures are not suitable because they are not capable for considering any effects caused by forecasting error such as increasing cost or decreasing profit. To tackle this issue, we propose a new measure, CAFE (Cumulative Absolute Forecast Error), to evaluate forecasting methods in terms of total cost. Basically, the CAFE is designed to consider not only forecasting errors but also costs occured by errors in aggregate production planning which is set up based on forecasts. The CAFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We have demonstrated the effectiveness of the proposed measure by conducting intensive experiments with demand data sets from M3-competition.