Basic Search / Detailed Display

Author: 蕭裕耀
Yu-Yao Hsiao
Thesis Title: 全球銅箔基層板市場預測分析
Forecasting Analysis for Global Copper Clad Laminate Market
Advisor: 王福琨
Fu-Kwun Wang
Committee: 歐陽超
Chao Ou-Yang
林則孟
James T. Lin
Degree: 碩士
Master
Department: 管理學院 - 管理研究所
Graduate Institute of Management
Thesis Publication Year: 2010
Graduation Academic Year: 98
Language: 英文
Pages: 45
Keywords (in Chinese): 銅箔基層板印刷電路板灰色模式滾動灰色模式貝氏擴散模式
Keywords (in other languages): Rolling GM, Print circuit board, GM(1,1), Bass diffusion model, Copper clad laminate
Reference times: Clicks: 284Downloads: 3
Share:
School Collection Retrieve National Library Collection Retrieve Error Report
  • 需求預測為高階主管在未來策略投資之重要參考之一. 銅箔基層板為印刷電路板之基礎材料,廣泛運用在民生家電,電腦,通訊,手機,汽車,醫療,軍事用途.而全球印刷電路板在2008年之產值為美金四佰捌拾貳億.在本研究中,我們運用灰色模式,滾動灰色模式,貝氏擴散模式,分析2001年至2008年全球銅箔基層板六大產品市場,分別為紙基板,複合板,FR-4板,高玻璃轉化温度之FR-4板,無鹵素之FR-4板與特殊板.而運用平均絕對值百分比誤差(Mean Absolute Percentage Error,簡稱MAPE)進行預測效益評估.本研究結果分析,貝氏擴散模式之平均絕對值百分比誤差低於灰色模式與滾動灰色模式,可運用在全球銅箔基層板之預測分析.


    Demand forecasting is one of critical reference by top managers to make the strategy decision for future investment. The copper clad laminate (CCL) is the key material for print circuit board (PCB) and it can apply for consumer, computer, LCD, communication, automotive, aero space, medicine and defense application. The total global sale for PCB in 2008 is US$ 48.2 billion. In this research, we use grey model GM(1,1), rolling grey model (RGM) and Bass diffusion model to analysis global CCL market by six market segments –paper, composite, FR-4, FR-4 High Tg, FR-4 halogen free, Specialty between 2001-2008. The forecasting accuracy of global CCL market by six market segment was evaluated along with mean absolute percentage error (MAPE). In this study, Bass diffusion model MAPE outperforms the others two models GM(1,1) and RGM for this global CCL market forecasting analysis and is recommend for global CCL market forecasting analysis.

    Chapter 1 Introduction………………………………………………………………………1 1.1. Motivation……………………………………………………………………………1 1.2. Research Objectives………………………………………………………………….4 1.3. Research Procedure…………………………………………………………………. 4 Chapter 2 Literature Review………………………………………………………………...6 2.1. Global CCL Industry ………………………………………………………………..6 2.2. Forecasting Models………………………………………………………………….15 Chapter 3 Methodology …………………………………………………………………….19 3.1. Forecasting Models…………………………………………………………………....19 3.2. Performance Measurement…………………………………………………………....22 Chapter 4 Forecasting Analysis for Global CCL …………………………………………25 4.1. Comparison by Different Market Segment……………………………………. 26 4.2. Hierarchical Forecasting For Global CCL Market………………………………….34 4.3. MAPE Comparison ……………………………………………………………… 34 Chapter 5 Conclusions and Future Reasrch ………………………………………………36 5.1. Conclusions ………………………………………………………………………. 36 5.2. Future Research…………………………………………………………………….37 References……………………………………………………………………………………38

    1.Bass, F.M., (2004). Comment on: A new product growth model for consumer
    durables. Management Science, 50, 1833-1840.
    2.Chang, P.C., Liu, C.H. and Lai, R. K., (2008). A fuzzy case-based reasoning model
    for sales forecasting in print circuit board industry. Expert Systems with Applications, 34, 2049-2058.
    3.Chang, S.C., Lai , H.C. and Yu, H.C., (2005). A variable p value rolling grey forecasting model for Taiwan semiconductor industry production. Technological Forecasting & Social Change, 72, 623-640.
    4.Deng, J.L., (1989). Introduction to grey system. Journal of Grey System, 1, 1-24.
    5.Evolver, (2000). Software. Newfield, NY: Palisade Corporation.
    6.Hsu, L.C., (2003). Applying the grey prediction model to the global integrated circuit industry. Technological Forecasting & Social Change, 70, 563-574.
    7.Hsu, L.C. and Wang, C.H., (2007). Forecasting the output of integrated circuit industry using a grey model improved by the bayesian analysis. Technological Forecasting & Social Change, 74, 843-853.
    8.Hsu, L.C. and Wang, C.H., (2009). Forecasting integrated circuit output using multivariate grey model and grey relational analysis. Expert Systems with Applications, 36, 1403-1409.
    9.http://www.batesquote.com/pcb-build-process.
    10.Lai, C.J., (2002). A modified rolling grey model for nonlinear time series forecasting. Journal of Grey System, 14, 133-140.
    11.Lewis, C.D., (1982). Industrial and business forecasting methods. London: Butterworth Scientific.
    12.Prismark, (2009). Annual report.

    13.Tsaur, R.C., (2008). Forecasting analysis by using fuzzy grey regression model for solving limited time series data. Soft Computer, 12, 1105-1113.
    14.Tseng, F.M and Hu, Y.C., (2009). Quadratic-Interval Bass model for new products sales diffusion. Expert Systems with Applications, 36, 8496-8502.
    15.Wilson, J.H. and Keating, B., (2009). Business forecasting with forecast X. Sixth Edition. Boston, MA: Mc-Graw Hill.

    QR CODE