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

Sarah Zeiml, Ulrich Seiler, Klaus Altendorfer, Thomas Felberbauer

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 2020 Winter Simulation Conference, WSC 2020
EditorsK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781728194998
Publication statusPublished - 14 Dec 2020
Event2020 Winter Simulation Conference, WSC 2020 - Orlando, United States
Duration: 14 Dec 202018 Dec 2020

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2020 Winter Simulation Conference, WSC 2020
Country/TerritoryUnited States


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