Simulation Based Forecast Data Generation and Evaluation of Forecast Error Measures

Sarah Zeiml, Klaus Altendorfer, Thomas Felberbauer, Jamilya Nurgazina

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

8 Citations (Scopus)


Production planning is usually performed based on customer orders or demand forecasts. The demand forecasts in production systems can either be generated by manufacturing companies themselves, i.e. forecast prediction, or they can be provided by customers. For both alternatives, forecast prediction, as well as the customer-provided forecasts, the quality of those forecasts is critical for success. In this paper, a simulation model to generate forecast data that mimic different forecast behaviors is presented. In detail, an independent forecast distribution and a forecast evolution model are investigated to discuss the value of customer-provided forecasts in comparison to the simple moving average forecast prediction method. Main findings of the paper are that Root-Mean-Square-Error and Mean-Absolute-Percentage-Error describe the forecast error well if no systematic effects are present and Mean-Percentage-Error provides a good measure for systematic effects. Furthermore, systematic effects like overbooking are significantly reducing the value of customer-provided forecast information.

Original languageEnglish
Title of host publication2019 Winter Simulation Conference, WSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781728132839
Publication statusPublished - Dec 2019
Event2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States
Duration: 8 Dec 201911 Dec 2019

Publication series

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


Conference2019 Winter Simulation Conference, WSC 2019
Country/TerritoryUnited States
CityNational Harbor


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