Abstract
Efficient supply chain management relies on accurate demand forecasting. Typically, forecasts are required at frequent intervals for many items. Forecasting methods suitable for this application are those that can be relied upon to produce robust and accurate predictions when implemented within an automated procedure. Exponential smoothing methods are a common choice. In this empirical case study paper, we evaluate a recently proposed seasonal exponential smoothing method that has previously been considered only for forecasting daily supermarket sales. We term this method ‘total and split’ exponential smoothing, and apply it to monthly sales data from a publishing company. The resulting forecasts are compared against a variety of methods, including several available in the software currently used by the company. Our results show total and split exponential smoothing outperforming the other methods considered. The results were also impressive for a method that trims outliers and then applies simple exponential smoothing.
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Acknowledgements
We would like to thank the collaborating publishing company for providing the data and background information for the study. We are also grateful for the very useful comments of two anonymous referees.
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Taylor, J. Multi-item sales forecasting with total and split exponential smoothing. J Oper Res Soc 62, 555–563 (2011). https://doi.org/10.1057/jors.2010.95
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DOI: https://doi.org/10.1057/jors.2010.95