在全球製造網絡中，由於高科技產業資本密集且高度競爭，產業鏈中的公司在競爭中合作開發技術創新和商業模式，以滿足日新月異的客戶需求並保持整個商業市場的增長。為了維持在產業中的競爭優勢，製造企業需要有效地進行供應鏈管理以最佳化公司資源投入和產出。目前的供應鏈不確定性高,上下游的資訊透明度低、產品生命週期短、持續的技術遷移、生產週期長，智慧化新形態供應鏈的建構與創新面臨巨大挑戰。如何在現存不透明、不確定的環境中，將大數據、人工智慧、物聯網等先進技術與產業人員經驗相結合應用於不同的決策情境中，成爲供應鏈管理中重要議題。
本研究提出基於工業3.5製造策略的智慧供應鏈架構，透過PDCA管理循環將數位技術與決策者經驗結合，以提升供應鏈管理的效能，加強產業競爭力。根據本研究提出之架構，企業可以逐步由經驗式向數位化供應鏈管理模式邁進，最終升級成為工業4.0之智慧供應鏈。本研究並以半導體供應鏈之需求預測與產能管理為實證，以檢驗所提出智慧供應鏈架構的效度。實證研究結果顯示所提出的方法可以有效減少需求預測誤差，提升產能規劃效率，可以幫助管理者瞭解各個決策單位的執行狀況，並作適當地資源配置，設定供應鏈管理改善的方向與幅度。

High-tech industry is capital intensive and increasingly competitive. Global manufacturing companies develop continuous technology transplantation and novel business models through competition and cooperation to meet customer demands and maintain the entire business market development. In order to maintain competitive advantages, manufacturing companies need to effectively manage supply chain activities to optimize resource investments and outputs. Currently supply chain uncertainty is high due to low information transparency in the upstream and downstream, long lead time for supply chain planning, short product life cycles, lengthy production cycle time, and continuous technology migration. The construction and innovation of the new paragram of supply chain faces huge challenges. Thus, it is a critical issue in supply chain management to integrate advanced technologies such as big data, artificial intelligence, and Internet of Things with human domain knowledge in an opaque and uncertain environment to support decision-making.
This study aims to propose a smart supply chain framework of decision-making under uncertainty schema based on Industry 3.5 manufacturing strategy, combining digital technology with the domain experience through PDCA management cycle to enhance supply chain management effectiveness and strengthen industrial competitiveness. Following the proposed framework in this dissertation, enterprises can gradually move from experience based decision-making to digital supply chain management model, and finally upgrade to the smart supply chain of Industry 4.0. Empirical studies for demand forecast and capacity management were conducted in in semiconductor supply chain to validate the proposed Industry 3.5 smart supply chain framework. The empirical results show that the proposed approach can effectively improve demand forecast accuracy and capacity management efficiency. It can help decision makers to understand the implementation status of each decision-making unit, make appropriate resource allocation, and set the innovation direction and magnitude of supply chain management.

Table of Contents
Table of Contents i
List of Figures iii
List of Tables iv
Nomenclature v
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Research aims 3
1.3 Dissertation structure 3
Chapter 2 Literature review 5
2.1 Smart supply chain strategies 5
2.2 Industry 3.5 strategy 11
2.3 Demand forecast 15
2.4 Capacity planning 22
2.5 Uncertainty programming 27
Chapter 3 Industry 3.5 smart supply chain framework 30
3.1 Industry 3.5 smart supply chain framework 30
3.2 Plan phase: Supply chain plan decision-making under uncertainty 38
3.3 Do phase: Plan execution & flexible mechanisms 43
3.4 Check phase: Data-driven supply chain diagnosis & internal and external benchmarking 45
3.5 Act phase: Plan improvement & stated objectives enhancement 49
Chapter 4 Semiconductor distribution demand forecast 51
4.1 Problem definition 51
4.2 Demand forecast decision-making framework 53
4.3 Demand forecast model 54
4.4 Experimental design 59
4.5 Validation 62
4.6 Discussion 67
Chapter 5 Semiconductor manufacturing capacity management under uncertainty 69
5.1 Problem definition 69
5.2 Capacity management decision-making framework 71
5.3 Uncertain capacity management model 72
5.4 Experimental design 79
5.5 Validation 80
5.6 Discussion 86
Chapter 6 Conclusion and Future research 88
6.1 Conclusion 88
6.2 Future research 89
References 91
Author Biography 101

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