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Comparison of statistical and machine learning methods for daily SKU demand forecasting

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs12351-020-00605-2;h=repec:spr:operea:v:22:y:2022:i:3:d:10.1007_s12351-020-00605-2
Time Added
6/26/2022, 6:28:16 PM
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Neural Networks
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0
Authors
Evangelos Spiliotis Spyros Makridakis Artemios-Anargyros Semenoglou and Vassilios Assimakopoulos Evangelos Spiliotis: National Technical University of Athens Spyros Makridakis: University of Nicosia Artemios-Anargyros Semenoglou: National Technical University of Athens Vassilios Assimakopoulos: National Technical University of Athens
Abstract
Abstract Daily SKU demand forecasting is a challenging task as it usually involves predicting irregular series that are characterized by intermittency and erraticness. This is particularly true when forecasting at low cross-sectional levels such as at a store or warehouse level or dealing with slow-moving items. Yet accurate forecasts are necessary for supporting inventory holding and replenishment decisions. This task is typically addressed by utilizing well-established statistical methods such as the Croston’s method and its variants. More recently Machine Learning (ML) methods have been proposed as an alternative to statistical ones but their superiority remains under question. This paper sheds some light in that direction by comparing the forecasting performance of various ML methods trained both in a series-by-series and a cross-learning fashion to that of statistical methods using a large set of real daily SKU demand data. Our results indicate that some ML methods do provide better forecasts both in terms of accuracy and bias. Cross-learning across multiple SKUs has also proven to be beneficial for some of the ML methods.
Keywords
Forecasting accuracy ; SKU demand ; Neural networks ; Regression trees ; Cross-learning (search for similar items in EconPapers)
Year Published
2022
Series
Operational Research 2022 vol. 22 issue 3 No 48 3037-3061
Rank
0.73
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