Simulation Evaluation of Automated Forecast Error Correction Based on Mean Percentage Error

Abstract

A supplier-customer relationship is studied in this paper, where the customer provides demand forecasts that are updated on a rolling horizon basis. The forecasts show systematic and unsystematic errors related to periods before delivery. The paper presents a decision model to decide whether a recently presented forecast correction model should be applied or not. The introduced dynamic correction model is evaluated for different market scenarios, i.e., seasonal demand with periods with significantly higher or lower demand, and changing planning behaviors, where the systematic bias changes over time. The study shows that the application of the developed dynamic forecast correction model leads to significant forecast quality improvement. However, if no systematic forecast bias occurs, the correction reduces forecast accuracy.