Using random forests and multi-criteria decision method for improving green supplier performance

• Main Authors: Wei-Fang, NI, 倪薇芳
• Other Authors: 劉建浩
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/pvbtkj

碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 105 === Under the influence of globalization, the most important challenge for the enterprise is supplier management, regarded as the best intangible assets. In addition, green environmental performance has become a necessary indicator for supplier management owing to the rising awareness of environment protection. Supplier management should have appropriate evaluation methods and regularly evaluate supplier performance. This study focuses on the quality system performance indicators and the improvement of supplier performance. This study applied the random forest, a data mining method, to extract the essential indicator from the quality auditing data set. Then, the Decision Making Trial and Evaluation Laboratory (DEMATEL) was used to understand the structure of cause–effect relationship between the indicators. By the obtained influence relationship between indicators, this study further utilized the DEMATEL-based Analytic Network Process (DANP) to obtain the weights of the indicators. Furthermore, in order to improve the existing supplier performance, the VIKOR method was used to discuss the performance gaps of each supplier and aspired level. The findings suggest that “final product control”, “quality management and planning”, “ship inspection” are the three most important indicators. This study can help firms proposing to improve supplier performance and providing the direction of improvement for the qualified suppliers. Decision makers can use this model to prioritize the suppliers. Based on the real data from experts, the case study proves that the process is effective.