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Appl. Sci. 2019, 9(8), 1581; https://doi.org/10.3390/app9081581

Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems

1
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
2
Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan
3
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
*
Author to whom correspondence should be addressed.
Received: 21 March 2019 / Revised: 9 April 2019 / Accepted: 15 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Industrial Engineering and Management: Current Issues and Trends)
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Abstract

A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful. View Full-Text
Keywords: shelf allocation; frequent pattern mining; product association; network analysis shelf allocation; frequent pattern mining; product association; network analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Huang, H.; Yao, L.; Chang, J.-S.; Tsai, C.-Y.; Kuo, R. Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems. Appl. Sci. 2019, 9, 1581.

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