Integrated Storage Location Assignment for E-Commerce Product Categories Using Product Affinity Analysis

Abstract

This study focuses on improving warehouse management efficiency for an e-commerce business. The primary issue identified in the case study of ABC Co., Ltd. was inefficient product storage, which resulted in excessive picking time and travel distance for warehouse operators. To address this problem, the study proposes an improved storage strategy based on two months of historical order data, integrating Class-based storage and Product affinity analysis. Products were classified into three main groups using ABC analysis to support inventory segmentation, with particular emphasis placed on Group A items due to their highest picking frequency. The baseline analysis revealed that the total picking distance prior to warehouse reorganization was 923,868.5 meters. By applying mathematical optimization models and solving them using Excel Solver, the Class-Based Storage approach reduced the total picking distance to 153,207 meters, while the Product Affinity Analysis model achieved a further reduction to 150,471 meters. Experimental results indicate that the Product Affinity Analysis approach, which considers frequently co-purchased items, enables more effective storage location assignment and achieves the highest improvement in picking efficiency, reducing total travel distance by up to 83.7%.