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

Sarah Zeiml, Ulrich Seiler, Klaus Altendorfer, Thomas Felberbauer

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

4 Zitate (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.

TitelProceedings of the 2020 Winter Simulation Conference, WSC 2020
Redakteure/-innenK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9781728194998
PublikationsstatusVeröffentlicht - 14 Dez. 2020
Veranstaltung2020 Winter Simulation Conference, WSC 2020 - Orlando, USA/Vereinigte Staaten
Dauer: 14 Dez. 202018 Dez. 2020


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


Konferenz2020 Winter Simulation Conference, WSC 2020
Land/GebietUSA/Vereinigte Staaten


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