Demand Forecast in Retail Assortment Optimization—Based on an Empirical Analysis of Beverage Sales

Jun Chen, Xinyijing Zhang, Chenyang Zhao


This paper focus on establishing the demand forecasting model to optimize product assortments from a set of SKUs in the same category. The aim of the model is to achieve revenue maximization. Based on the attribute level, the demand model considers the consumers’ preference and the possibility of substitution between different attributes. Then it divides the product’s specific attributes and multiplies these attributes effects. Furthermore, one beverage case was applied to the demand model to do empirical analysis. Top beverage categories were selected and e-commerce sales data were collected to represent the pre-sale of whole categories. Moreover, a store named S with some beverage SKUs is assumed and applied to the model, which predicted sales volume of each existing SKU and the total revenue.

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Copyright (c) 2020 Jun Chen, Xinyijing Zhang, Chenyang Zhao

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