Research on Key Barriers and Drivers for Bicycle Industry to Implement Reverse Logistics

被引:0
作者
Chang A.-Y. [1 ]
Lai P.-Y. [2 ]
机构
[1] Department of Industrial Management, National Formosa University
[2] Ph.D. Program in Smart Industry Technology Research and Design, National Formosa University
来源
Journal of Quality | 2023年 / 30卷 / 06期
关键词
analytic hierarchy process; barriers and drivers; fuzzy Delphi method; reverse logistics; technique for order preference by similarity to ideal solution;
D O I
10.6220/joq.202312_30(6).0003
中图分类号
学科分类号
摘要
Various countries are formulating stricter laws and regulations to restrict the environmental pollution caused by various pollution. At the same time, enterprises also need to find new ways to reduce production costs. In recent years, reverse logistics (RL) have attracted the attention of enterprises and professionals, and some scholars have pointed out that if enterprises use RL technology properly, in addition to reducing operating costs, it can also gain huge benefits. Many literatures have pointed out that the implementation of RL can fully and effectively utilize resources and achieve the purpose of environmental protection. In order to implement RL smoothly, it is necessary to understand what resistance is faced and what assistance is needed. Although the implementation of RL can bring many benefits, and there are many related foreign literature studies, RL has not been popularized in the bicycle industry. Also, the discussion and research on the key barriers and key drivers are still very limited in domestic literature research. This research aims to use the multiple attribute decision making method to find out the resistance and assistance faced by Taiwan’s bicycle industry in the implementation of RL, so as to fill this knowledge gap. Through the distribution of questionnaires, experts are invited to provide data with reference to their own experience. After data integration, the fuzzy Delphi method (FDM) is used to screen out important barriers and drivers, and the results are designed and distributed in a second questionnaire. Later on, the research combines with analytic hierarchy process (AHP) to get the key barriers. Finally, technique for order preference by similarity to ideal solution (TOPSIS) is used to obtain the key drivers, and the key factors are sorted in sequence. © 2023, Chinese Society for Quality. All rights reserved.
引用
收藏
页码:407 / 442
页数:35
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