臺灣博碩士論文加值系統

積體電路 （IC） 設計在晶圓製造中扮演著重要的角色，然而，對於中小型IC設計公司經常面臨供應鏈不確定性問題。由於需求大幅波動，縮短了IC設計產品的生命週期，加上供應鏈日趨複雜，高技術製造產能擴張的訂貨交付時間較長。因此，需求規劃已成為這些公司做產能規劃時的重要關鍵。
準確的需求預測可使中小型IC設計公司達到資本效益，從而提高公司的獲利能力，然而，這些公司仍大多數依靠人為經驗進行需求規劃。故本研究開發具有不同預測模型和決策演算法的智慧代理人，以支援現有系統中的需求預測。為了結合領域知識與數據分析預測結果，這項研究遵循UNISON框架作為識別利基的系統方法，建構不同類型產品的最佳需求預測模型，並增強工業 3.5 的智慧供應鏈。
本研究以一IC設計公司為實證案例驗證提出架構之效果。驗證結果顯示本研究提出的解決方案具有實用可行性，能夠解決IC設計需求規劃的挑戰，滿足實際需要。

Integrated Circuit (IC) Design plays an important role in wafer fabrication. Distinguished from large-scale IC design companies which dominate the market share, medium sized IC design companies often suffers from issues of supply chain uncertainties. Due to the large fluctuations in demand, the shortened product life cycle of the industry, the supply chain is becoming increasingly complex, and the lead time for high-tech manufacturing capacity expansion is long, so demand planning has become critical for these companies. Accurate demand forecasting enables these companies to achieve capital effectiveness and thus enhance the profitability for the company, yet these companies are used to rely on domain insights and human experience to conduct demand planning. To adopt systematic agility, this study develops intelligent agent featuring different forecasting models and decision algorithm to support demand forecasting in the existing system. Derived from domain knowledge and data integrity, this research follows the UNISON framework as a systematic method to identify niches, to perceive the best demand forecast models for different types of products, and empower the intelligent supply chain for Industry 3.5. An empirical study was conducted at an IC design company. The results show that the developed solution has practical feasibility and can solve the challenge of IC design demand planning to meet actual needs.

Table of contents
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives 4
1.3 Thesis Organization 4
Chapter 2 Literature Review 5
2.1 Industry 3.5 5
2.2 Demand planning in semiconductor supply chain 7
2.3 Aggregation model 9
2.4 Demand forecast models 10
2.4.1 ARIMA 11
2.4.2 Cumulative moving average 11
2.4.3 Simple exponential smoothing 12
2.4.4 Simple moving average 12
2.4.5 Holt’s linear trend method 13
2.4.6 AdaBoost 13
2.4.7 Extreme gradient boosting 14
Chapter 3 Proposed Research Framework 15
3.1 Understanding and defining the decision problem 15
3.2 Identifying niches 16
3.3 Structuring the influence relationship among uncertainty in the problem 17
3.3.1 Data collection and pre-processing 17
3.3.2 Feature engineering 18
3.3.3 Model Architecture 19
3.4 Sensing and describing the outcomes 21
3.5 Judge and measure overall performance 21
3.6 Tradeoff and decision 22
Chapter 4 An Empirical Study 23
4.1 Problem Definition and Background 23
4.2 Identifying niches 26
4.3 Data collection and pre-processing 26
4.4 Model Architecture and Optimization 28
4.4.1 Model Selection 28
4.4.2 Final Selector 29
4.4.3 Empirical Framework 29
4.4.4 Model Optimization 30
4.5 Objectively narrate the results 31
4.6 Optimal decision-making and execution 39
Chapter 5 Conclusion and future direction 40
5.1 Conclusion 40
5.2 Future direction 43

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