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系統識別號 U0026-0608202017384200
論文名稱(中文) 以系統性文獻回顧法探索深度學習於供應鏈管理之應用
論文名稱(英文) Applications of Deep Learning in Supply Chain Management: A Systematic Literature Review Approach
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 108
學期 2
出版年 109
研究生(中文) 王柏淵
研究生(英文) Po-Yuan Wang
學號 R56071020
學位類別 碩士
語文別 英文
論文頁數 106頁
口試委員 指導教授-魏健宏
共同指導教授-沈宗緯
口試委員-林珮珺
口試委員-陳正杰
口試委員-王瑩瑋
中文關鍵字 人工智慧  機器學習  深度學習  供應鏈管理  系統性文獻回顧 
英文關鍵字 Artificial Intelligence (AI)  Machine Learning (ML)  Deep Learning (DL)  Supply Chain Management (SCM)  Systematic Literature Review (SLR) 
學科別分類
中文摘要 深度學習(DL)被廣泛應用於圖像識別,醫療診斷,自動駕駛系統及影音設計等多元領域,其源自商業行銷所包裝的深度(多層)神經網路,依據學術資料庫(Web of Science)的統計數據,深度學習在過去的兩年中盛行於供應鏈管理(SCM)之領域,應用深度學習之文獻量大幅增加。實際上,多數應用相似概念之文獻因未使用「深度學習」一詞而受到忽視,本項研究欲通過系統性文獻回顧法(SLR)有系統地辨識與篩選出具深度學習理念的文獻。有別以往供應鏈管理之回顧文獻,本研究將供應鏈的每一個階段視為一個獨立任務並歸類為微觀流程,再利用宏觀流程建構全景圖。藉由清楚地定義微觀和宏觀流程,本研究得以定位深度學習的核心與相應應用,提供企業一個適當應用深度學習來開發及改善其供應鏈之指導方針。最後,深度學習的發展趨勢顯示了眾多研究具有利用多層神經網絡來解決供應鏈管理議題的潛力。
英文摘要 Deep learning (DL) has been widely used in the fields of image recognition, medical diagnosis, self-driving systems, audio-visual design and so forth. The provenance of DL is derived from deep (multi-layer) neural network packaged by commercial marketing. Pursuant to the statistics from a prestigious database (Web of Science), it has prevailed over the past two years in the field of supply chain management (SCM). The amount of literature applying DL has increased significantly. In fact, some literature applying similar concepts has been neglected since they did not use the term "deep learning" as a topic or in content. This research aims to systematically identify and screen out documents possessing the concepts of DL by applying systematic literature review (SLR). Discrepant from those reviews in SCM, this research considers each stage of the supply chain as an independent task classified as micro processes, and utilised macro processes to complete panorama. Through clearly defining micro and macro processes, this research locates major and corresponding applications of DL for each task. The endeavour constructs guidance for enterprises to develop and improve their supply chain by appropriately applying DL. Eventually, the tendency of DL presents that a multitude of research has the potential of utilising multilayer neural network to cope with issues in SCM.
論文目次 CONTENTS

ABSTRACT i
摘要 ii
ACKNOWLEDGMENTS iii
CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES ix
CHAPTER 1 Introduction 1
1.1 Motivation and Research Background 1
1.2 Research Preparation 2
1.3 Research Structure 2
CHAPTER 2 Review of AI and Applications 6
2.1 Machine Learning (ML) 7
2.1.1 Definition of ML 8
2.1.2 ML in SCM 13
2.2 Artificial Neural Networks (ANNs) 15
2.2.1 Definition of ANNs 17
2.2.2 ANNs in SCM 19
2.3 Deep Learning (DL) 20
2.3.1 Definition of DL 20
2.3.2 DL in SCM 21
2.4 Evolution Timeline 22
2.5 Summary 23
CHAPTER 3 Research Methodology 24
3.1 Terminology 25
3.1.1 Literature Review 25
3.1.2 Topic 25
3.1.3 Neural Networks 25
3.2 SLR Structure 26
3.2.1 Evidence-Based Practice 28
3.2.2 About PICO 30
3.3 Examination and Adjustment 31
3.3.1 Question Checking 31
3.3.2 Bias Handling 31
3.4 Develop Review Protocol 33
CHAPTER 4 Conduct SLR for DL in SCM 36
4.1 Formulate Review Question 36
4.2 Document 36
4.3 Locate Resources 37
4.3.1 Supply Chain Macro Processes 43
4.3.2 Supply Chain Micro Processes 45
4.3.3 Distribution of SC Macro & Micro Processes 58
4.4 Exclude Improper Resources 58
4.4.1 Supply Chain Macro Processes 59
4.4.2 Supply Chain Micro Processes 61
4.4.3 Distribution of Inclusive Data 68
4.5 Make Appraisal for Adoptable Sources 68
4.6 Data Extraction 70
4.6.1 Supply Chain Macro Processes 70
4.6.2 Supply Chain Micro Processes 71
CHAPTER 5 Analysis and Output 76
5.1 Synthesis 76
5.1.1 Supply Chain Macro Processes 76
5.1.2 Supply Chain Micro Processes 77
5.2 Results and Tendency 82
CHAPTER 6 Conclusions and Suggestions 85
REFERENCES 87
APPENDIX Ⅰ - PROTOCOL 104
APPENDIX Ⅱ - PICO Worksheet and Search Strategy 105
APPENDIX Ⅲ- Author relationship 106
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