An integrated method for finding key suppliers in SCM

Authors:
Rong-Ho Lin;Chun-Ling Chuang;James J. H. Liou;Guo-Dong Wu
Affiliations:
National Taipei University of Technology, Department of Business Management, No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC;Kainan University, Department of Information Management, No. 1, Kainan Road, Luzhu, Taoyuan 338, Taiwan, ROC;Kainan University, Department of Air Transportation, No. 1, Kainan Road, Luzhu, Taoyuan 338, Taiwan, ROC;National Taipei University of Technology, Department of Business Management, No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 5
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Abstract

Association rule is a widely used data mining technique that searches through an entire data set for rules revealing the nature and frequency of relationships or associations between data entities. Supplier selection is a significant work in supply chain management. Often, there will be thousands of potential suppliers and identifying a subset of these suppliers can be a complex process of determining a satisfactory subset based on a number of factors. In this paper, the supplier selection can be viewed as the problem of mining a large database of shipment. The proposed method incorporates the extended association rule algorithm of data mining with that of set theory to find key suppliers. This research has employed a numerical example for the integrated method to develop suitable supplier clusters. The results show that the method is effective and applicable.