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研究生:林俐妘
研究生(外文):Liyun Lin
論文名稱:中小尺寸面板之產值預測
論文名稱(外文):Small and Meduium Size LCD Production Forecast
指導教授:袁建中袁建中引用關係
指導教授(外文):Benjamin J.C., Yuan
學位類別:碩士
校院名稱:國立交通大學
系所名稱:理學院碩士在職專班應用科技學程
學門:環境保護學門
學類:環境防災學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:130
中文關鍵詞:技術預測液晶顯示器產值
外文關鍵詞:Technological ForecastingLCDShipment value
相關次數:
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隨著「LCD panel:Anytime、Anywhere」產業願景的提出,TFT LCD 顯示器(Thin Film Transistor Liquid Crystal Display)產值的強勁成長是帶動FPD(Flat Panel Display)產業快速發展的主要動力。中小尺寸面板產業的市場需求在多媒體商品需求刺激下,同步呈現大幅度的成長。但因中小尺寸面板規格繁多,客製化程度高與多種不同顯示器技術的競合關係複雜,使中小尺寸面板企業主對於策略的評估和方向產生很多難以估計的變因,建立一中小尺寸面板產值和市場需求預測模式,對市場行銷策略的決定具有高度重要性。
過去Display Search、MIC、Tsr..等市場調查單位皆以使用專家意見法或問卷訪查等方法進行中小尺寸面板產值預測,本研究使用成長曲線法中的珀爾曲線(Pearl curve model)作為預測分析模型,分別由TFT LCD供給面與應用產品需求面觀察市場競合的關係,並針對各家主要中小面板企業在2007 ~ 2011年的產量變化趨勢進行分析。
以殘差值結果分析證實,珀爾曲線法(Pearl curve model)、甘培茲曲線法(Gompertz curve model)及多階成長曲線法(multi-regression)此三種模型具有較高的預測可信度;趨勢外差法、灰預測及線性迴歸模型等預測模式的可信度較低。本研究使用珀爾曲線模式進行中小尺寸面板產值預測可反應實際中小尺寸面板產業現況,可提供企業主投資評估參考依據。
結果發現:(1)中小尺寸面板波動性漸趨於季節性調節,2008年後價格競爭將不再如2004~2006年激烈,產業價值鏈的重新組合創造更多樣化的合作模式;(2)上、下游供應鏈的整合將會決定企業競爭力的核心;(3)未來的五年內手機應用市場仍然會是占有中小面板最大量;(4)2009手機應用產品將達到中小尺寸面板78%的產值,手機面板尺寸需求將會集中在2.8吋和1.8吋。
The inevitable trend of the new IT-industry requires services at anytime and anywhere, thus, Thin Film Transistor Liquid Crystal Display (TFT-LCD) gain its momentum and receives immense interests and investments to advance its product volume. The Flat Panel Display (FPD) Industry is therefore experiencing explosive growth rate in these years.

Recently, the incredulous growth and demand of portable multimedia devices has stimulated the production and investment of Small-to-Medium size LCD industry. However, since LCD panels need to cut to various sizes and each product process requires a unique customization and management process, optimal production management and accurate cost-analysis become impossible. Hence, an analytical model, which can provide reliable predictions of the supply and demand curve and figures, will assist decision makers of LCD industry to adjust their strategy and direction when necessary.

Traditional analytical models used in leading Market Intelligent Centre are either Expert Opinion. e.g. Display Search, MIC and TSR…etc. Firstly, this dissertation predicts the demand and supply chain of TFT-LCD by using Pearl curve model in the Growth curve; Secondly, analyzing and visualize the strategic relationship and competitiveness of TFT-LCD leaders and observing the development of the demand and supply in the multimedia consumable market; Finally, the emphasis of this dissertation is to predict the production volume and its forecast on major TFT-LCD manufactures between 2007~2011 based on our findings and models shown above.

The residue test had shown and confirmed that Pearl curve model, Gompertz curve model and multi-regression methods reveals more accurate prediction than these other models; Trend, Linest, Grey prediction, ARIMA, Multi-regression etc. This study demonstrated and confirmed that Pearl curve model depicted more accurate prediction results and reflect more realistic demand and supply curve of the Small-to-Medium Size TFT-LCD industry than others and will assist decision makers of LCD industry to adjust their strategy and direction.

Our study had shown the demand of the Small-to-Medium size TFT-LCD tends to adjust and vary through different season, and the price-war in 2008 is not as severe as the era between 2004~2006. Therefore, it is time to restructure and forming a new strategic relationship of value chain in the market place and benefit from seamless partnership and coherent logistic channel model. From this study, we found that the core competitiveness of a leading company in the future depends on its leveraging and integration ability between supply and demand value chains; Cellphone manufactures and its appliances will still capture largest portion of the demand of the Small-to-Medium size TFT-LCD market in the next 5 years; 81% of the Small-to-Medium size TFT-LCD will be utilized by cellphone and its appliances in 2009, and size will mainly be 2.8 and 1.8 inch.
摘 要 2
圖目錄 9
表目錄 11
第1章 緒論 14
1.1 研究背景與動機 15
1.2 研究目的 15
1.3 研究對象與範圍 17
1.3.1 研究對象 17
1.3.2 研究範圍 17
1.4 研究步驟 19
1.4.1 研究方法與流程 19
1.4.2 研究架構 21
第2章 文獻回顧 22
2.1 預測概論 22
2.1.1 預測方法的介紹 24
2.1.2 技術預測方法之比較 26
2.1.3 文獻整理 30
第3章 中小尺寸液晶顯示器產業的發展概況 32
3.1 全球中小尺寸面板產業概況 33
3.1.1 中小尺寸面板產業發展歷程 34
3.1.2 中小尺寸面板的技術種類與說明 41
3.1.3 TFT LCD顯示器製程說明 43
3.1.4 生產線世代與生產規模 44
3.1.5 中小尺寸面板技術Roadmap 45
3.2 台灣中小尺寸面板企業介紹 49
3.2.1 台灣中小尺寸面板產業地圖 52
3.3 全球中小尺寸產值預測與市場趨勢分析 56
第4章 理論 59
4.1 成長曲線法介紹 59
4.2 珀爾曲線法理論 61
4.3 甘培茲曲線法理論 62
4.4 珀爾曲線和甘培玆曲線比較 62
4.5 成長曲線的選擇與極限之估計 64
4.6 中小型面板產值影響因子 65
4.6.1 液晶循環(Crystal Cycle) 65
4.6.2 產能結構 69
第5章 實證結果分析 71
5.1 全球中小型面板產值預測 74
5.2 面板應用市場趨勢預測 78
5.2.1 手機應用 (Mobile Phone) 81
5.2.2 個人數位助理應用 (PDA) 88
5.2.3 車用電子顯示器 (Automotive Monitor) 90
5.2.4 數位相機 (Digital Still Camera) 92
5.3 個案產值預測分析 95
5.3.1 三洋 愛普生 (Sanyo Epson) 95
5.3.2 三星 (Samsung Electronics) 97
5.3.3 友達光電 (AUO) 99
5.3.4 夏普 (Sharp) 101
5.4 台灣其他中小尺寸面板廠個案產值預測 104
5.4.1 群創光電 (Innolux) 104
5.4.2 中華映管 (CPT) 106
5.4.3 勝華科技 (Wintek) 108
5.5 台灣中小尺寸面板企業競合觀察 110
5.5.1 中小型面板技術發展趨勢 112
5.5.2 台、日、韓的產業中小尺寸面板產業競爭分析 114
5.6 台灣中小尺寸面板產業發展契機 116
第6章 結論與建議 117
6.1 後續研究建議 121
APPENDIX A 各種不同應用面產品之趨勢預測 122
APPENDIX B TFT LCD原物料價格趨勢預測 123
參考文獻 127
一、 英文部分
1. Levary ,P. R. and Han D. (1995) .Choosing a Technology Forecasting Method,Forecasting January, February, pp.14-18.
2. Martino, J. P. (1993). Technological Forecasting for Decision Making”, 3rd ed.,New York: McGraw-Hill, Inc., p.1
3. Mann DL 1 Technological Forecasting and Social Change, 2003 - ingentaconnect.comter technology forecasting using systematic innovation methods. October 2003, vol.70, no. 8, pp. 779-795(17). Publisher: Elsevier Science.
4. . Ascher, W. (1978), Forecasting. An Appraisal for Policy-Makers and Planners, The Johns Hopkins University Press, Baltimore.
5. Bhargava, S. C. (1995), “A Generalized From of the Fisher-Pry Model of Technological Substitution”, Technological Forecasting and Social Change, 49(1), pp.27-34.
6. Chen, T. C. and Chang, C. C. and Tzeng, G. H. (2001), “Applying Fuzzy Measures to Establish Priority Setting Procedures for the Pavement Management System”, Pan Pacific Management Review, 4(1), pp.23-33
7. Huan, J. S. and James, T. L. and Trefor, P. W. (1996), “Using Neural Networks to Predict Component Inspection Requirements for Aging Aircraft”, Computer Ind. Engng, 30(2).
8. Kaszubowski, M. J. (1995), “An Analysis of Payload Growth for Major U.S. and European Launch Vehicles”, Technological Forecasting and Social Change, 48(3), pp.269-284.
9. Levary, R. R. And Han, D. (1995), “Choosing a technological Forecasting Method”, Industrial Management, 37(1), pp.14-18.
10. Malthus, T.R.(1798), An essay on the princuple of population. Johnson, London, republished by Cambridge Univeristy Press, New York, 1992.
11. Martino, J. P. (1993), Technological Forecasting for Decision Making, 3 rd Edition., McGraw-Hill, New York.
12. Meade, N. and Islam, T. (1995), “Forecasting with growth curves: an Empirical Comparison”, International Journal of Forecasting, 11(2), pp.199-215.
13. Porter, A.L. (1991), Forecasting and Management of Technology, John Wiley & Sons, Inc., New York.
14. Zaika, L. L. and Scullen, J. (1996), “Growth of Shigella Flexneri in Foods: Comparison of Observed and Predicted Growth kinetics Parameters”, International Journal of Food Microbiology, 32(1/2), pp.91-102. 6.
15. CT Lin, SY Yang,2003。 Forecast of the output value of Taiwan's opto-electronics industry using the Grey forecasting model,Technological Forecasting and Social Change, Volume 70, Number 2, February 2003, pp. 177-186(10),Elsevier.
16. Chin-Tsai Lin and Shih-Yu Yang,Forecast of the output value of Taiwan's IC industry using the Grey forecasting model,International Journal of Computer Applications in Technology,Volume 19, Number 1 / 2004 pp. 23-27 (4).
17. Ascher, W. (1978), Forecasting. An Appraisal for Policy-Makers and Planners, The Johns Hopkins University Press, Baltimore.
18. Bhargava, S. C. (1995), “A Generalized Form of the Fisher-Pry Model of Technological Substitution”, Technological Forecasting and Sicial Change, 49(1), pp. 27-34.
19. Chen, T. C. and Chang, C. C. and Tzeng, G. H. (2001), “Applying Fuzzy Measures to Establish Priority Setting Procedures for the Pavement Management System”, Pan Pacific Management Review, 4(1), pp. 23-33.
20. Huan, J. S. and James, T. L. and Trefor, P. W. (1996), “Using Neural Networks to Predict Component Inspection Requirements for Aging Aircraft”, Computer Ind. Engng, 30(2).
21. Kaszubowski, M. J. (1995), “An Analysis of Payload Growth for Major U.S. and European Launch Vehicles”, Technological Forecasting and Social Change, 48(3), pp. 269-284
22. Levary, R. R. and Han, D. (1995), “Choosing a technological Forecasting Method”, Industrial Management, 37(1), pp. 14-18.
23. Malthus, T. R. (1798), An essay on the principle of population. Johnson, London, republished by Cambridge University Press, New York, 1992.
24. Martino, J. P. (1993). Technological Forecasting for Decision Making”, 3rd ed.,New York: McGraw-Hill, Inc., p.1.Meade, N. and Islam, T. (1995) “Forecasting with growth curves : an Empirical Comparison”, International Journal of Forecasting, 11(2), pp. 199-215.
25. Porter, A. L. (1991), Forecasting and Management of Technology, Hohn Wiley & Sons, Inc., New York.
26. Zaika, L. L. and Scullen, J (1996), “Growth of Shigella Flexneri in Food : Comparison of Observed and Predicted Growth kinetics Parameters”, International Journal of Food Microbiology, 32(1/2), pp. 91-102.

二、 中文部分
1. 黃欣怡,工業技術研究院產業經濟與趨勢研究中心,2006光電工業年鑑(2006 Opto-Electronics Industry Year book),初版,新竹,經濟部技術處, 95年5月。
2. 鍾俊元,張文珊,工業技術研究院產業經濟與趨勢研究中心, 2006平面顯示器年鑑(2006 Flat Panel Display Industry Year book),初版,新竹,經濟部技術處,95年5月。
3. 工業技術研究院 影像顯示產業推動辦公室,平面顯示器產業2006年第四季各國產業動態調查報告,初版,台北,經濟部工業局,96年1月。
4. 經濟部技術處,台日韓電子材料產業競爭分析,初版,何巧玲,葉仰哲,林天行作,經濟部產業技術資訊服務中心,新竹,民國九十五年五月
5. 王信陽,「台灣背光模組產業現況及技術發展Roadmap」,光連雙月刊,43:40~44頁,95年。
6. 黃志鴻,2003,以技術預測方法探討家庭自動化系統需求與發展之趨勢,國立交通大學,科技管理研究所碩士論文
7. 張士其,2004,交通大學,產值預測與企業競爭力
8. 袁建中(民87),技術預測模式之建立與應用結案報告,交通大學科技管理研究所,9-11頁。
9. 吳漢雄、鄧聚龍、温坤禮(民89年),灰色分析入門,台北:高立圖書有限公司。
10. 吳孟圜(民92),「台灣通訊用三五族產業國產設備發展需求預測」,國立交通大學碩士論文。
11. 韓季霖(民89),「台灣地區醫師人力供需之研究—灰色預測模式之應用」,銘傳大學管理科學研究所碩士論文。
12. 邱鎮湘(1998.1),數位相機現在與未來,資訊與電腦月刊,116-1~116-5頁。
13. 王啟秀,2006,交通大學,台灣資訊產業產值預測模型之研究
14. 林飛雄,2005,交通大學,DRAM產業分析與產值預測
15. 光電工業年鑑(2006),工業技術研究院產業經濟與資訊服務中心,3-22~3-33頁。
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