Analysis and forecasting of demand during promotions for perishable items

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• 作者：K.H. van Donselaar ; ^{k.h.v.donselaar@tue.nl" class="auth_mail" title="E-mail the corresponding author} ; J. Peters ^{petersjordi@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; A. de Jong ^{a.de-jong@aston.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; R.A.C.M. Broekmeulen ^{r.a.c.m.broekmeulen@tue.nl" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Perishable ; Promotion ; Price discount ; Retail ; Demand forecasting ; Threshold ; Saturation
• 刊名：International Journal of Production Economics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：172
• 期：Complete
• 页码：65-75
• 全文大小：630 K

文摘

This study examines promotions for perishable products in a retail environment. We analyze the impact of relative price discounts on product sales during a promotion and shed light on how to build models to forecast promotional demand for perishable products. Preliminary analyses, based on regression models and a large dataset from a retailer, do not reveal conclusive evidence for the presence of threshold and/or saturation levels for price discounts for perishable products. A potential explanation comes from the observation that, although products like desserts on average allow 1,5 weeks time-to-consume, their sales during promotions on average are equal to 14 weeks of regular sales. This suggests that the success of a promotion is not so much determined by the restriction to stockpile (due to the short time-to-consume) but by the emergence of substitution effects (consumers switching between different products of the same category). We develop and test different models to forecast the demand during a promotion, including a moving average forecast and several regression models. Within the class of regression models we find that modeling threshold and saturation effects leads to worse forecasting performance than modeling price reductions linearly or quadratically. The largest improvements in forecast accuracy are gained by distinguishing between routine and non-routine product categories. Routine categories with routine demand processes and a large number of observations perform best when applying a regression based on direct observations of the product category, whilst non-routine categories benefit from a regression which also uses observations from other product categories.