文章基本信息

标题：Decision making with association rule mining and clustering in supply chains
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作者：Reshu Agarwal
期刊名称：International Journal of Data and Network Science
印刷版ISSN：2561-8148
电子版ISSN：2561-8156
出版年度：2017
卷号：1
期号：1
页码：11-18
DOI：10.5267/j.ijdns.2017.1.003
出版社：Growing Science
摘要：This paper deals with data mining applications for the supply chain inventory management. ABC characterization is typically utilized for stock items arrangement on the grounds that the quantity of stock items is large to the point that it is not computationally practical to set stock and admin-istration control rules for every individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could influence the sale of other items (cross selling effect). Consequently, within each cluster, the inventories should be classified. In this paper, a modified approach is proposed considering both cross-selling effect and clusters to rank stock items. A numerical case is utilized to clarify the new ap-proach. It is represented that by utilizing this modified approach; the ranking of items may get influenced bringing about higher profits.
关键词：Data mining; Association rule mining; Inventory management; Cross-selling; ABC classification; Clustering