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In this paper, we propose new forecasting methods based on advance demand information, and perform a case study to compare them to existing ones based on advance demand information and also to methods based on expert judgments. Numerical results are obtained using data from a large mail order/Internet retailer based in the Netherlands. This company currently bases its forecasts on advance demand information. Based on a data set of around seven hundred SKUs and for two successive summer seasons, we compare the accuracy of the various methods based on advance demand information. For a smaller subset of around one hundred SKUs, we also obtained forecasts from a number of company experts. For this subset, we compare methods based on these expert judgments to methods based on advance demand information.
The remainder of the paper is organized as follows:We will ?rst give an overview of the relevant literature in Section and outline our contributions. In Section, we describe the different forecasting methods. Section introduces the case company and available data in more detail. Finally, we present our conclusions, discuss limitations, and provide directions for further research.
The total sales are not necessarily equal to total demand, since some demand may have been lost. For the case that lost demand is not recorded at all, Fisher et al. (2000) propose
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