現今全球市場環境競爭激烈，產品不斷的推陳出新，各家企業為提升自身的競爭力必須因應環境快速的調整銷售及生產計畫，使得產品生命週期不斷縮短。企業必須在有限的時間與資訊內做出最佳的規劃，此時能夠以有限的資料進行準確的銷售預測便成為一項十分重要的工作。傳統的統計預測方法常需要大量數據，數據數量不足時，會有無法進行預測或預測誤差大的情況，若欲對消費電子等生命週期短的產品進行銷售預測，也難以用統計預測方法得到準確的預測結果。而在競爭激烈的消費性電子產品市場中，市場不斷快速成長的智慧型手機為一項相當重要的產品，為各家廠商必爭的項目。然而智慧型手機的的發展時間較其他消費性電子產品較短，且機型的汰換十分快速，在銷售的預測上更加的不易。
本研究利用灰色預測適用於小樣本的特性，建立智慧型手機之銷售預測模式，並且分析該預測樣本的特性，找出適用的灰色預測改良模型以得到較佳的預測績效。本研究提出以灰色預測改良模型p value rolling GM(1,1)模型加入三項經濟指標因子，全球GDP成長率、消費者價格成長率及人均所得成長率，以改善智慧型手機銷售量受經濟環境因素影響產生波動而較難以預測的情況。本研究中以2006年至2012年全球智慧型手機銷售量進行實證，預測結果發現該灰色預測模型對於全球智慧型手機銷售預測有準確的預測結果，為一適用於智慧型手機的銷售模式。

The global market is in an intense competition nowadays, therefore the product renew frequently. Enterprises need to elevate their own competitiveness by modifying the plan of marketing and manufacturing. As a result, the product life circle became shorter. Anyway, forecasting the sales precisely has became a significant event, especially in a limited period of time and data. The traditional methods of statistical forecast require a lot of data; therefore, the sales forecasting is in a difficult situation by lacking of data. Also, it is not easy to forecast consumer electronics product which with short life cycle. In consumer electronics products market, smartphone has became important because of the blooming market of itself. The development period of smartphone is shorter than other electronics product as well; therefore it is a challenge to forecast the sales of smartphone.

In this study, we constructed a p value rolling grey forecasting model to predict global smartphone sales, and hypothesized that making the p value a variable of time could generate more accurate forecast. It was expected the sales of smartphone is influenced by global economic environment. Consequently, this study proposed p value rolling GM(1,1) model and added three economic indicators. We let the p value be determined by annual percentage growth rate of gross domestic product（GDP）, gross national income（GNI）per capita and consumer price index（CPI）. The result of this study is that the modified grey model could improve the accuracy of smartphone sales forecasting, and the average residual error is 7.12% from 2009 to 2012.

中文部分：
溫坤禮、張簡士琨、葉鎮愷、王建文、林慧姍（2006）；MATLAB在灰色系統理論的應用，全華科技圖書股份有限公司。
鄧聚龍（2002）；灰預測與灰決策，華中科技大學出版社。
鄧聚龍（2005）；灰色系統基本方法，華中科技大學出版社。
賴怡誠（1998），「房地產景氣預測之研究」，國立中央大學土木工程學系，碩士論文。
外文部分：
Akay, D., & Atak, M. （2007）, “Grey Prediction with rolling mechanism for electricity demand forecasting of Turkey.” Energy, 32(9), pp.1670-1675.
Bernstein, G. L.（1984）,”The art of using Forecasts Effective.” Directors & Boards, 8(5), pp.23~26.
Baskaran, V., Nachiappan, S. & Rahman, S.（2012）, ”Indian textile suppliers’ sustainability evaluation using the grey approach.” International Journal of Production Economics, 135(2), pp.647~658.
Cetron, M.J. ＆ Monahan, T.I., （1968）, “An evaluation and appraisal of various approaches to technological forecasting,” Technological Forecasting for Industry and Government, pp.144~179.
Chang, S. C.（2005）,”The TFT–LCD industry in Taiwan: competitive advantages and future developments.” Technology in Society, 27(4), pp.199~215.
Chang, S.C., Wu, J., Lee, C.T., （1999）, “A study on the characteristics of ∝(k) of Grey prediction.” Proc. of the 4th Conference on Grey Theory and Applications, pp.291~296.
Chang, T. S., Ku, C. Y. & Fu, H. P.（2013）,”Grey theory analysis of online population and online game industry revenue in Taiwan.” Technological Forecasting and Social Change, 80(1), pp.175~185.
Chang, S. C., Lai, H. C. & Yu, H. C. （2005）, “A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production.” Technological Forecasting and Social Change, 72(5), pp. 623~640.
Chen, H. S., Yan, T. M. & Kung, C. Y. （2008）, “Application of Grey Prediction Theory to Forecast 3G Mobile Phone in Taiwanese Market.” Journal of Grey System, 11(1), pp.43~48.
Chen, Y. F., Hsieh, C. L., Tsai, C. W., Lin, W. H. & Chen, W. Y.（2012）, “Forecasting the Imminent Market Shares Pertinent to HTC Corporation Domestically: The Vanguard Regarding Taiwan’s Smartphone Industry.” International Journal of Technology Diffusion, 3, pp.1~7.
Deng, J. L. (1982), “Control Problems of Grey Systems.” Systems and Control Letters, 1(5), pp.288~294.
Deng, J. L. (1989),“Grey Information space. ” The Journal of Grey System, 1(2), pp.103~118.”
Dong, F. Y. , Tian, J. （2007）. “Optimization integrated background value with original condition for GM(1,1).” System Engineering and Electronics, 29,pp.464~466.
Hanke, J. E., Reitsch, A. G. & Wichern, D. W.（1989）, Business Forecasting, Prentice Hall.
Hsu, C. C., Liou, J. H. & Chuang Y. C.（2013）,” Integrating DANP and modified grey relation theory for the selection of an outsourcing provider.” Expert Systems with Applications, 40(6), pp.2297~2304.
Hsu, L. C. （2003）, “Applying the Grey prediction model to the global integrated circuit industry.” Technological Forecasting and Social Change, 70(6), pp.563~574.
Hsu, L. C. （2011）, “Using improved grey forecasting models to forecast the output of opto-electronics industry.” Expert Systems with Applications, 38(11), pp.13879~13885.
Lebell, D. and Krasner O. J.（1977）,”Selecting Environment Forecasting Techniques from Business Planning Requirements.” The Academy of Management Review, 2(3),pp.373~383.
Lewis, C. D.（1982）, Industrial and Business Forecasting Method, Butterworths.
Liou, J. J. H., Hsu, C. C., Yeh, W. C. & Lin, R. H.（2011）, ”Using a modified grey relation method for improving airline service quality.” Tourism Management, 32(6), pp.1381~1388.
Luo, E. X., Qian, X. S., Li, R.（2006）, “Construction and empirical research of the variable parameter value rolling grey forecasting model.” Journal of University of Shanghai for Science and Technology, 28(5), pp. 465~468.
Ma, Z. L., Shao, C.F. & Hao, H.R.（2009）, “Forecasting grey model of traffic accidents based on variable parameter value rolling.” Journal of Chang-An University (Natural Science Edition), 29, pp.87~96.
Tao, Y. Z. & Xi, T. （2010）, “Using GM(1,1) Model Forecasting Chinese Car Production.” 2010 International Conference on Computer Application and System Modeling, pp.567~579.
Tseng, M. L. & Chiu A. S. F.（2013）,”Evaluating firm's green supply chain management in linguistic preferences.” Journal of Cleaner Production, 40, pp.22~31.
Wang C.H.（2004）,”Predicting tourism demand using fuzzy time series and hybrid grey theory”, Tourism Management, 25(3), pp.367-374.
Wen, J.C., Huang, K.H. & Wen, K.L.（2000）, “The study of α in GM(1,1) model.” Journal of the Chinese Institute of Engineers,23(5), pp.583~589.
Wang, C. P., Li, F. G., Lu, H. Y., Yuan, Z. W. & Chen, B.（2013）,” Optimization of structural parameters for elliptical cross-section spiral equal-channel extrusion dies based on grey theory.” Chinese Journal of Aeronautics, 26(1), pp.209~216.
Wang, F. J., Li, T. Q., Yu, C. Z.（2006）,”Grey Verhulst Predictive Model of Road Traffic Accidents.” Journal of Traffic and Transportation Engineering, 6(1), pp. 122~126.
Wilson, J. H. & Keating, B（1990）, Business forecasting, The McGraw-Hill/Irwin.
Zhao, Z., Wang, J. Z., Zhao, J. & Su, Z. Y. （2012）,”Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China.” Omega, 40(5), pp.525~532.