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研究生： 范美娘
Mei-Niang Fan
論文名稱： 應用資料包絡分析法進行永續供應鏈績效評估
Using DEA Model to Evaluate Supply Chain Sustainability
指導教授： 呂俊德
Jun-Der Leu
口試委員:
學位類別： 博士
Doctor
系所名稱： 管理學院 - 企業管理學系
Department of Business Administration
論文出版年： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 83
中文關鍵詞： 永續供應鏈 、資料包絡分析法 、非需求產出模式 、差額指標模式
外文關鍵詞： sustainable supply chain, data envelopment analysis (DEA), bad-output model, slacks- based measure (SBM)
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    • 永續主要是由經濟、環境及社會三個構面所組成的，有鑑於過往的企業的營運只以獲利來衡量經營管理績效，來自利益相關者的需求驅使下，企業不能只考量財務的表現來衡量永續管理績效，而對於全球化的製造工廠來說更是一大挑戰，要如何衡量公司以這三個構面的企業的永續績效? 資料包絡分析法(data envelopment analysis)很廣泛在學術界以決策單位之使用投入產出之效率來研究探討及實務界進行模型應用。此研究是使用資料包絡分析法的非需求產出模式(bad-output model)及差額指標模式(slacks-based measure)來進行永續供應鏈績效評量，應用這兩個模式分析比較永續供應鏈績效，其結果並無顯著差異，資料包絡分析法是提供企業強化競爭優勢的工具之一，並持續不斷提升永續供應鏈的經營續效並貢獻後代子孫。

    Sustainability consists of three paradigms: economic, environment and social in operations, driven by stakeholders to create sustainable business, in contrast to the traditional operating model based on profit. It is a challenge for manufacturing enterprises to measure these three paradigms to benchmark their sustainable supply chain performance at multiple locations globally. Data Envelopment Analysis (DEA) is widely studied and applied to evaluate the efficiency of decision making units. We apply bad-output model and slacks-based measure (SBM) for benchmarking the performance. The results show there is no difference between bad-output model and SBM for benchmarking the sustainable supply chain performance. Thus, DEA can increase the enterprise’s competitive advantage as a tool for driving continuous improvement toward supply chain excellence, thus contributing to the lives of future generations.

    摘要 i Abstract ii Acknowledgements iii List of Figures v List of Tables vi Chapter I Introduction 1 Chapter II Literature review 4 2-1 Sustainable supply chain 4 2-2 Resource-based views (RBV) 7 2-3 Undesirable outputs of sustainable supply chains 8 Chapter III Methodology 11 3-1 Slacks-based measure (SBM) 11 3-2 Bad-output model 14 Chapter IV Model application 16 4-1 Case introduction 16 4-2 Model Setup 16 4-3 Results of SBM 19 4-3-1 Performance of SBM 19 4-3-2 Sensitivity analysis of SBM 22 4-4 Results of bad-output model 24 4-4-1 Performance of bad-output model 24 4-4-2 Sensitivity analysis of bad-output model 28 Chapter V Discussions of SBM and bad-output models 31 5-1 The scoring comparison 32 5-2 The slacks comparison 34 5-3 Sensitivity analysis’ comparison 39 Chapter VI Conclusions 47 References 50 Appendix A. Window analysis of bad-output model 58 Appendix B. Window analysis SBM 64

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