Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63860

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

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Title:A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs
Authors:Martin, Dominik
Spitzer, Philipp
Kühl, Niklas
Keywords:Big Data and Analytics: Pathways to Maturity
forecast
intermittent
lumpy
metric
show 1 moretime series
show less
Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63860
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.121
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data and Analytics: Pathways to Maturity


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