An integrated method for finding key suppliers in SCM |
| |
| Authors: | Rong-Ho Lin Chun-Ling Chuang James JH Liou Guo-Dong Wu |
| |
| Affiliation: | 1. National Taipei University of Technology, Department of Business Management, No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC;2. Kainan University, Department of Information Management, No. 1, Kainan Road, Luzhu, Taoyuan 338, Taiwan, ROC;3. Kainan University, Department of Air Transportation, No. 1, Kainan Road, Luzhu, Taoyuan 338, Taiwan, ROC;1. College of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, People''s Republic of China;2. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, People''s Republic of China;1. College of Engineering & Mathematical Sciences, The University of Vermont, Votey Building, 33 Colchester Avenue, Burlington, VT 05405, United States;2. Mechanical Engineering Program, School of Engineering, The University of Vermont, Votey Building, 33 Colchester Avenue, Burlington, VT 05405, United States;3. Department of Mathematics & Statistics, Vermont Complex Systems Center, Vermont Advanced Computing Core, The University of Vermont, Farrell Hall, 210 Colchester Avenue, Burlington, VT 05405, United States;1. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China;2. Institute of Complexity Science, Qingdao University, Qingdao, 266071, China;1. University of Bucharest, Faculty of Physics, Bucharest-Magurele, P.O. Box MG 11, 077125, Romania;2. Institute of Space Science, Laboratory of High Energy, Astrophysics and Advanced Technologies, Bucharest-Magurele, P.O. Box MG 23, 077125, Romania;1. Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, PR China;2. Big data Application on Improving Government Governnance Capabilities, National Engineering Laboratory, Guiyang, PR China;3. Physics and Photoelectricity School, South China University of Technology, Guangzhou, 510641, PR China |
| |
| 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
