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研究生： 林揚倫
Lin, Yiang-luen
論文名稱： 獨立成份分析法降低ARIMA及渾沌需求下供應鏈長鞭效應之研究
Reducing the bullwhip effect in supply chain with ARIMA and chaotic demand by independent component analysis
指導教授： 吳植森
Wu, Chih-sen
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 85
中文關鍵詞： 獨立成份分析法 、啤酒遊戲 、主成份分析法 、長鞭效應 、渾沌需求 、整合自我迴歸移動平均
外文關鍵詞： Bullwhip effect, Beer game, PCA, ICA, Chaotic demand, ARIMA
相關次數： 點閱：100  下載：2 
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    • 長鞭效應存在於供應鏈中，並使供應鏈上的成員蒙受重大損失。而造成長鞭效應的一個原因就是資訊預測需求更新(demand forecasting updating)，資料的準確性非常的重要。本研究利用獨立成份分析法(ICA)來減少供應鏈中需求資料中的雜訊以提高預測準確性。本文探討供應鏈之需求型態可由整合自我迴歸移動平均(ARIMA)以及渾沌來描述的資料型態下ICA能有效減少資料中的雜訊並提升預測準確度以降低長鞭效應。啤酒遊戲模擬結果顯示ICA確實較以主成份分析法(PCA)為前處理的方式有更好的結果。除了驗證ICA對於減少長鞭效應的效果外，本研究也透過迴歸分析法來分析影響長鞭效應的重要因素。我們發現預測的準確度對於訂單長鞭效應有顯著的影響，而預測的變異顯著影響存貨的長鞭效應。此結果之管理意涵顯示，控制需求的變異程度無法同時使訂貨及存貨的長鞭效應下降，因為需求變異對於存貨及訂貨長鞭效應是反向的關係。我們建議要使訂單長鞭應效應減少的話則要使需求的變異變小；而要使存貨長鞭應效應減少的話則要使需求的變異變大。

    In the supply chain, the bullwhip effect means that the ordering variation in the upstream would be higher than demand variation in the downstream. The accuracy of data is important because members in the supply will predict for decision making using historical data. In this thesis, we treat independent component analysis (ICA) as a novel technique in supply chain for reducing the noise in the demand data. Experiments simulate beer games for demonstrating that ICA has the ability to increase the predicting accuracy for reducing the bullwhip effect with autoregressive integrated moving average (ARIMA) and chaotic demand time series. In contrast to ICA, PCA is applied as a benchmark in this study. Results show that ICA outperforms PCA. Furthermore, multiple regression analysis is used to identify important factors affecting the bullwhip effect. We prove that predicting accuracy is crucial for bullwhip effect on order while predicting variation contributes to bullwhip on inventory. We also suggest that if the wholesaler could control the demand from retailers, pursuing low bullwhip on order should manipulate lower demand variation while pursuing low bullwhip on inventoy should resort to higher demand variation.

    ABSTRACT I 中文摘要 II 誌謝 III TABLE OF CONTENTS IV LIST OF TABLES VII LIST OF FIGURES VIII Chapter 1 Introduction 1 1.1 Research motivation 1 1.2 Research objectives 2 1.3 Research flow 3 1.4 Contributions 4 1.5 Limitations 5 Chapter 2 Literature Review 6 2.1 The bullwhip effect 6 2.1.1 The origin of the bullwhip effect 6 2.1.2 Causes of the bullwhip effect 7 2.1.3 Solutions for the bullwhip effect 8 2.1.4 Quantifying the bullwhip effect 9 2.2 The beer game 10 2.3 Independent component analysis (ICA) 10 2.3.1 ICA for blind source separation (BSS) 11 2.3.2 Independent component analysis 13 2.3.3 Assumptions of ICA 14 2.3.4 Preprocess for ICA 15 2.3.5 FastICA 16 2.3.6 Difference between ICA and PCA 18 2.4 Autoregressive Integrated Moving Average 18 2.4.1 Autoregressive model 19 2.4.2 Moving average model 20 2.4.3 Autoregressive moving average model 20 2.4.4 Autoregressive integrated moving average model 21 2.4.5 Stationarity and invertibility conditions 22 2.4.6 Autocorrelation and partial autocorrelation function 23 2.4.7 Building ARIMA models 25 2.4.8 An ARIMA supply chain model 28 2.5 Chaos theory 29 2.5.1 Chaos theory 29 2.5.2 Strange attractor 31 2.5.3 Lyapunov exponents 31 2.5.4 Chaotic demand 32 2.5.5 Artificial neural networks for predicting chaotic time series 32 Chapter 3 The research design and methodology 34 3.1 Research framework 34 3.2 The beer game 38 3.3 Building an ARIMA model for original data 39 3.4 Back-propagation network for chaotic time series 39 3.5 Data preprocessing for ICA 41 3.6 FastICA for finding de-noise time series 42 3.7 PCA for finding de-noise time series 45 3.8 Building an ARIMA or BPN model for de-noise data 46 3.9 Test for effectiveness 46 3.10 Regression analysis 47 Chapter 4 Experimental results 49 4.1 Simulation environment 49 4.1.1 The beer game 49 4.1.2 The demand time series 50 4.2 Building order and inventory time series 56 4.2.1 ARIMA model for ARIMA demand time series 56 4.2.2 BPN for chaotic demand time series 56 4.3 Independent component analysis 57 4.3.1 Data preprocessing for ICA 57 4.3.2 Finding dominant independent components 59 4.4 Principal component analysis 60 4.5 Results of hypothesis testing 61 4.5.1 Results of ARIMA demand time series 61 4.5.2 Results in chaotic demand time series 64 4.6 Regression analysis 66 4.6.1 Multiple regression analysis for ARIMA demand time series 67 4.6.2 Multiple regression analysis for chaotic demand time series 71 Chapter 5 Conclusions and future works 78 5.1 Conclusions 78 5.2 Future work 79 References 80

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