臺灣博碩士論文加值系統

許多研究已經指出，科技整合，特別是人工智慧，對於供應鏈過程帶來顯著的好處。這促使許多研究人員探索人工智慧在供應鏈管理中的最佳實施方式，以增強效率和精簡操作。儘管人工智慧在供應鏈管理中實施所帶來的多種好處，但僅有少數企業採用這項技術。此外，COVID-19大流行對供應鏈過程產生了負面影響，導致許多企業尋求改善其供應鏈運營，以幫助企業恢復。在疫情期間，將人工智慧引入供應鏈被視為應對挑戰的潛在解決方案。本研究旨在確定影響台灣供應鏈管理人工智慧實施的關鍵成功因素。本研究的數據是通過問卷調查收集的，受訪者是人工智慧和供應鏈專家。問卷根據技術-組織-環境框架（Technological-Organizational-Environmental framework）收集因素並進行分類。在台灣向20位人工智慧和供應鏈專家發放問卷，12份問卷有效，應用分析網路程序進行數據分析。數據分析顯示，在維度方面，技術因素對於成功實施人工智慧最為關鍵，其次是組織因素和環境因素。在十項標準中，數據權重最高，其次是頂層管理支持、技術基礎設施、組織準備就緒和相對優勢，這些因素是實現人工智慧成功實施目標最關鍵的因素。

關鍵詞: 供應鏈管理、人工智慧、技術-組織-環境框架、關鍵成功因素、分析網路程序

Numerous studies have suggested that the integration of technology, particularly Artificial Intelligence, can bring significant benefits to the supply chain process. Despite the numerous benefits associated with the implementation of Artificial Intelligence in supply chain management, only a small number of enterprises have adopted this technology. Furthermore, COVID-19 pandemic has had an adverse impact on the supply chain process, leading many enterprises to seek ways to improve their supply chain operations to aid in business recovery. The implementation of Artificial Intelligence in the supply chain has been identified as a potential solution to address the challenges faced during the pandemic. This study aims to identify key successful factors affecting Artificial Intelligence implementation of Supply Chain Management in Taiwan. Data for this study was collected from Artificial Intelligence and Supply Chain experts using questionnaires. The questionnaire was designed by collecting the factors from literature and classified based on Technological-Organizational-Environmental framework. 20 questionnaires were distributed to Artificial Intelligence and Supply Chain experts in Taiwan, 12 questionnaires were valid for the application of Analytic Network Process. The data analysis reveals that among the dimensions, technological factors are most essential to successful Artificial Intelligence implementation, followed by organizational factors and environmental factors respectively. Among the ten criteria, data has the highest weight, followed by top management support, technology infrastructure, organizational readiness, and relative advantage making these factors the most crucial to the goal of successful Artificial Intelligence implementation.
Keywords: Supply Chain Management, Artificial Intelligence, Technological-Organizational-Environmental Framework, Key Successful Factors, Analytic Network Process

Table of Contents
摘要 i
ABSTRACT ii
ACKNOWLEDGMENTS iii
Table of Contents iv
List of Tables vii
List of Figures viii
1.INTRODUCTION 1
1.1 Background 1
1.2 Purpose of Research 4
1.3 Reserch Study Structure 4
2. LITERATURE REVIEW 6
2.1 Supply Chain Management Introduction 6
2.1.1 Definition of Supply Chain Management 6
2.1.2 Research related to Technology utilization in Supply Chain Management 7
2.2 Artificial Intelligent 11
2.2.1 AI Definition 11
2.2.2 AI History 13
2.2.3 AI Benefit for Enterprise 17
2.3 AI Technology Adoption 28
2.4 TOE Framework 29
2.5 Factors Affecting AI Implementation of Supply Chain Management. 30
2.5.1 Technological Factors 31
2.5.2 Organizational Factors 32
2.5.3 Environmental Factors 35
3. RESEARCH METHODOLOGY 37
3.1 Research Objects 37
3.2 Construct Operational Definition 37
3.2.1 Supply Chain Management 37
3.2.2 Artificial Intelligence 37
3.2.3 TOE 38
3.3 Multiple Criteria Decision Making 39
3.4 Research Framework 39
3.5 AHP and ANP 40
3.5.1 Construct Cluster and Node 42
3.5.2 Comparison Matrix and Compute The Eigenvector 42
3.5.3 Evaluate the Relative Weighting Of Each Criterion. 43
3.5.4 Consistency Test 43
3.5.5 Sample and Data Collection 44
3.6 Questionnaire Design 44
4. RESEARCH RESULT AND DISCUSSION 45
4.1 Description of Basic Information 45
4.2 Data Analysis 45
4.2.1 Consistency of ANP questionnaire 46
4.2.2 First Layer Analysis 46
4.2.3 Second layer analysis 47
4.3. Unweighted supermatrix 49
4.4. Weighted Supermatrix 50
4.5. Limit Supermatrix 51
4.6 Overall Ranking 52
5. CONCLUSION 53
5.1 Research conclusion 53
5.2 suggestions for practitioners 55
5.3 Suggestion for future research 56
REFERENCES 57
APPENDIX 71

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