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A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs

Martin, Dominik ORCID iD icon; Spitzer, Philipp ORCID iD icon; Kühl, Niklas ORCID iD icon

Abstract:

Forecasts of product demand are essential for short- and long-term optimization of logistics and production. Thus, the most accurate prediction possible is desirable. In order to optimally train predictive models, the deviation of the forecast compared to the actual demand needs to be assessed by a proper metric. However, if a metric does not represent the actual prediction error, predictive models are insufficiently optimized and, consequently, will yield inaccurate predictions. The most common metrics such as MAPE or RMSE, however, are not suitable for the evaluation of forecasting errors, especially for lumpy and intermittent demand patterns, as they do not sufficiently account for, e.g., temporal shifts (prediction before or after actual demand) or cost-related aspects.
Therefore, we propose a novel metric that, in addition to statistical considerations, also addresses business aspects. Additionally, we evaluate the metric based on simulated and real demand time series from the automotive aftermarket.


Preprint §
DOI: 10.5445/IR/1000098446
Veröffentlicht am 23.09.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000098446
Erschienen in 53rd Annual Hawaii International Conference on System Sciences (HICSS-53), Grand Wailea, Maui, HI, January 7-10, 2020
Veranstaltung 53rd Hawaii International Conference on System Sciences (HICSS 2020), Grand Wailea, Maui, Hawaii, 07.01.2020 – 10.01.2020
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