Research on Category Management and Inventory Control Strategies of Multi Regional Network Retail Enterprises under Alternative Demand

Kefan Guo

doi:10.54691/x0bn8964

Authors

Kefan Guo

DOI:

https://doi.org/10.54691/x0bn8964

Keywords:

Online Retail; Substitutability; Category Management; Inventory Control; Attribute Analysis.

Abstract

This article proposes a data mining based substitution rate estimation method to address the issue of product substitution demand in online retail. By introducing the Apriori algorithm to mine association rules between product attribute combinations and sales status, effective attribute combinations that meet support, confidence, and improvement thresholds are extracted, solving the problem of uniform nominal attribute dimensions and sequencing. On this basis, a substitution rate estimation model considering the effect of neighboring substitution and the strength of attribute combination association was constructed, avoiding the red blue bus problem in traditional models. This method provides a data-driven solution for category management and inventory control decision-making in online retail enterprises, and has important theoretical value and practical significance.

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