Using a data-based MCDM model for improving green supplier management

• Main Authors: Jun-Yu, Tong, 童俊毓
• Other Authors: 劉建浩
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/k5jhv8

碩士 === 國立臺北科技大學 === 工業工程與管理系 === 106 === Under the influence of economic globalization, the most important challenge for the enterprise is supplier management, regarded as the intangible assets. Due to the developing of the economic and rising awareness of environmental protection, it is essential for modern enterprises to pursue a pollution-free supply chain. Therefore, the green environment performance has become a necessary indicator for supplier management. Supplier management should have appropriate evaluation methods and regularly evaluate supplier performance. This study focuses on the green quality indicators and the improvement of suppliers performance. This study applied Dominance-based Rough Set Approach (DRSA), a data mining method, to extract the essential indicators from green quality indicators. Different from the traditional experts survey, we applied Random Forest (RF) to construct the matrix of the initial influence between indicators, and used of the Decision Making Trial and Evaluation Laboratory (DEMATEL) to understand structure of cause-effect relationship between the indicators. With the obtained influence relationship between indicators, this study further utilized DRSA-based "IF ..., THEN ..." rules to provide improvement strategies for green suppliers and identify the types of criteria for critical projects to save cost and time. With this model, decision makers can improve the supplier performance. In the end, based on the real data from an electronic company, a case study proves effectiveness of the model.