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

Martin, Dominik; Spitzer, Philipp; Kühl, Niklas

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.



Zugehörige Institution(en) am KIT Karlsruhe Service Research Institute (KSRI)
Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Jahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000098446
Erschienen in Proceedings of the 53rd Annual Hawaii International Conference on System Sciences (HICSS-53), Grand Wailea, Maui, HI, January 7-10, 2020
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