Abstract—Previous studies in product-to-shelf assignment area usually applied the space elasticity to optimize product assortment and space allocation models. However, a well product-to-shelf assignment strategy should not only consider product assortment and space elasticity. Thus, this study develops a data mining method for solving the product-to-shelf assignment problem with consideration of both product association rules and moving behavior of consumer. Specifically, the first task of this research is to develop a method to discover product association rules and consumers’ moving behaviors of which are collected through RFID systems in the store. The second task is to construct and solve a product-to-shelf assignment model, based on the information provided in the first task. In this research, products are classified as major item, minor item and the others. Only minor items are reassigned to ensure customers can follow their preferred moving behaviors. Experimental result shows our proposed method can reassign minor items to suitable shelves and increase cross-selling opportunity of major and minor items.
Index Terms—Data mining, product-to-shelf assignment, moving and purchase patterns, customer behavior.
Chieh-Yuan Tsai is with the Department of Industrial Engineering and Management; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chungli City, Taoyuan County, Taiwan, R.O.C. (e-mail: email@example.com).
Sheng-Hsiang Huang is with the Department of Industrial Engineering and Management, Yuan Ze University, Chungli City, Taoyuan County, Taiwan, R.O.C. (e-mail: firstname.lastname@example.org).
Cite: Chieh-Yuan Tsai and Sheng-Hsiang Huang, "Integrating Product Association Rules and Customer Moving Sequential Patterns for Product-to-Shelf Optimization," International Journal of Machine Learning and Computing vol.5, no. 5, pp. 344-352, 2015.