近年來消費性電子產品競爭激烈，且消費者意識抬頭，因此，若企業無法開發出滿足顧客需求之創新產品技術，將面臨企業虧損與市占率下降之窘境。因此，本研究將發展以顧客需求為導向之產品技術開發規劃策略方法論。該方法論使用層級分析法與品質機能展開，分析每項顧客需求項目之相對重要性，以及顧客需求項目對應關鍵技術之關係程度，進而協助企業分析每項關鍵技術滿足顧客需求的程度。接著，透過專利資料與關鍵技術生命週期辨識模式，以了解關鍵技術之生命週期階段，並判定關鍵技術可取得方式。隨後，本研究建立多目標產品技術開發規劃策略評估模式，分析不同的產品技術開發規劃策略之績效。為協助企業擬定的產品技術開發規劃策略，因此，本研究運用多目標基因演算法，以找出在產品開發時間之限制條件下，最高顧客滿意度與最低的產品開發成本之最佳化產品技術開發規劃策略。最後，本研究使用綠色智慧型手機開發為案例說明與驗證本方法論之實務上的貢獻。

In recent years, due to the highly competitive marketplace for consumer electronics products and the increase in consumer consciousness, if a company cannot develop innovative product-technology to meet customer demands, enterprises will face the dilemma of decreasing market share and even making a loss. Therefore, this study develops a methodology for creating a customer demand-oriented product-technology development planning strategy. The proposed methodology uses AHP (Analytical Hierarchical Process) and QFD (Quality Function Deployment) to analyze the relative importance of each customer demand and the corresponding relationships between key technologies and customer demands, and then to further help a company analyze the satisfactory levels that each key technology meets the customer demand items. Next, through an identification model of key technology life cycles to understand the life cycle stage of each key technology and to determine the way of obtaining critical technologies. Subsequently, this research creates a multi-objective evaluation model of product-technology development planning strategies to analyze the performance of different product-technology development planning strategies. To help a company establish the product-technology development planning strategy, this study applies a multi-objective genetic algorithm to find the optimal product-technology development planning strategy with the maximum customer satisfaction level and the minimum product development cost. Finally, this study uses green smartphone development as a case study to explain and verify the practical contributions of the proposed methodology.

摘 要 i
ABSTRACT ii
誌 謝 iv
目 錄 v
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 1
1.3研究程序 3
第二章 文獻探討 4
2.1品質機能展開 4
2.2科技成熟度預測 5
2.3產品技術開發規劃 7
第三章 以顧客需求為導向之產品技術開發規劃策略方法論 9
3.1關鍵技術滿足顧客需求程度分析 11
3.2關鍵技術生命週期階段辨識模式 13
3.3多目標產品技術開發規劃策略評估模式 14
第四章 案例分析 19
第五章 結論與建議 28
參考文獻 29
附錄 34 

[1]A. Kengpol and C. O’Brien, “The development of a decision support tool for the selection of advanced technology to achieve rapid product development,” Journal of Production Economics, vol. 69, no. 2, 2001, pp. 177-191.
[2]A. Halog, F. Schultmann and O. Rentz, “Using quality function deployment for technique selection for optimum environmental performance improvement,” Journal of Cleaner Production, vol. 9, no. 5, 2001, pp. 387-394.
[3]B. Almannai, R. Greenough and J. Kay, “A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies,” Journal of Robotics and Computer-Integrated Manufacturing, vol. 24, no. 4, 2008, pp. 501-507.
[4]B. Nepal, O. P. Yadav and A. Murat “A fuzzy-AHP approach to prioritization of CS attributes in target planning for automotive product development,” Journal of Expert Systems with Applications, vol. 37, no. 10, 2010, pp. 6775-6786.
[5]C. P. Blocker and D. J. Flint, “Customer segments as moving targets: integrating customer value dynamism into segment instability logic,” Journal of Industrial Marketing Management, vol. 36, no. 6, 2007, pp. 810-822.
[6]C. P. Chen, The Test of Technological Forecasting Models: Comparison between Extended Logistic Model, Fisher-Pry Model, and Gompertz Model, Master Thesis, University of Chiao Tung, Hsinchu, T. W., 2005.
[7]C. V. Trappey and H. Y. Wu, “An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles,” Journal of Advanced Engineering Informatics, vol. 22, no. 4, 2008, pp. 421-430.
[8]C. H. Lo, K. C. Tseng and C. H. Chu, “One-Step QFD based 3D morphological charts for concept generation of product variant design,” Journal of Expert Systems with Applications, vol. 37, no. 11, 2010, pp. 7351-7363.
[9]D. Ford, “Develop your Technology Strategy,” Journal of Long Range Planning, vol. 21, no. 5, 1988, pp. 85-95.
[10]E. B. Roberts and W. K. Liu, “Ally or acquire? How technology leaders decide,” Journal of MIT Sloan Management Review, vol. 43, no. 1, 2001, pp. 26-34.
[11]H. Raharjo, A. C. Brombacher and M. Xie, “Dealing with subjectivity in early product design phase: A systematic approach to exploit Quality Function Deployment potentials,” Journal of Computers & Industrial Engineering, vol. 55, no. 1, 2008, pp. 253-278.
[12]H. Ernst, “The Use of Patent Data for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry,” Journal of Small Business Economics, vol. 9, no. 4, 1997, pp. 361-381.
[13]H. T. Liu, “The extension of fuzzy QFD: From product planning to part deployment,” Journal of Expert Systems with Applications, vol. 36, no. 8, 2009, pp. 11131-11144.
[14]J. Kim, M. Hwang, D. H. Jeong and H. Jung, “Technology trends analysis and forecasting application based on decision tree and statistical feature analysis,” Journal of Expert Systems with Applications, vol. 39, no. 16, 2012, pp. 12618-12625.
[15]J. Cho and J. Lee “Development of a new technology product evaluation model for assessing commercialization opportunities using Delphi method and fuzzy AHP approach,” Journal of Expert Systems with Applications, vol. 40, no. 13, 2013, pp. 5314-5330.
[16]J. H. Lee, R. Phaal and S. H. Lee, “An integrated service-device-technology roadmap for smartcity development,” Journal of Technological Forecasting and Social Change, vol. 80, no. 2, 2013, pp. 286-306.
[17]K. Deb, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” Journal of IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, 2002, pp. 182-197.
[18]L. H. Chen and W. C. Ko, “Fuzzy linear programming models for new product design using QFD with FMEA,” Journal of Applied Mathematical Modelling, vol. 33, no. 2, 2009, pp. 633-647.
[19]L. Y. Zhai, L. P. Khoo and Z. W. Zhong, “A rough set based QFD approach to the management of imprecise design information in product development,” Journal of Advanced Engineering Informatics, vol. 23, no. 2, 2009, pp. 222-228.
[20]L. H. Chen and W. C. Ko, “Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept,” Journal of Operational Research, vol. 201, no. 2, 2010, pp. 619-632.
[21]L. Gao, A. L. Porter, J. Wang, S. Fang, X. Zhang, T. Ma, W. Wang and L. Huang, “Technology life cycle analysis method based on patent documents,” Journal of Technological Forecasting & Social Change, vol. 80, no. 3, 2013, pp. 398-407.
[22]M. Bengisu and R. Nekhili, “Forecasting emerging technologies with the aid of science and technology databases,” Journal of Technological Forecasting and Social Change, vol. 73, no. 7, 2006, pp. 835-844.
[23]M. Y. Wang, D. S. Chang and C. H. Kao, “Identifying technology trends for R&D planning using TRIZ and text mining,” Journal of R&D Management, vol. 40, no. 5, 2010, pp. 491-509.
[24]M. Bevilacqua, F. E. Ciarapica and B. Marchetti, “Development and test of a new fuzzy-QFD approach for characterizing customers rating of extra virgin olive oil,” Journal of Food Quality and Preference, vol. 24, no. 1, 2012, pp. 75-84.
[25]N. Gerdsri and D. F. Kocaoglu, “Applying the Analytic Hierarchy Process (AHP) to build a strategic framework for technology roadmapping,” Journal of Mathematical and Computer Modelling, vol. 46, no. 7-8, 2007, pp. 1071-1080.
[26]P. T. Meade and L. Rabelo, “The technology adoption life cycle attractor: Understanding the dynamics of high-tech markets,” Journal of Technological Forecasting and Social Change, vol. 71, no. 7, 2004, pp. 667-684.
[27]P. Yu and J. H. Lee, “A hybrid approach using two-level SOM and combined AHP rating and AHP/DEA-AR method for selecting optimal promising emerging technology,” Journal of Expert Systems with Applications, vol. 40, no. 1, 2013, pp. 300-314.
[28]R. Haupt, M. Kloyer and M. Lange, “Patent indicators for the technology life cycle development,” Journal of Research Policy, vol. 36, no. 3, 2007, pp. 387-398.
[29]R. Pearl and J. Reed, Proceedings of the National Academy of Sciences, vol. 6, 1920, pp. 275-288.
[30]S. Yousefie, M. Mohammadi and J. H. Monfared, “Selection effective management tools on setting European Foundation for Quality Management (EFQM) model by a quality function deployment (QFD) approach,” Journal of Expert Systems with Applications, vol. 38, no. 8, 2011, pp. 9633-9647.
[31]S. K. Lee, G. Mogi, S. K. Lee, K. S. Hui and J. W. Kim, “Econometric analysis of the R&D performance in the national hydrogen energy technology development for measuring relative efficiency: The fuzzy AHP/DEA integrated model approach,” Journal of hydrogen energy, vol. 35, no. 6, 2010, pp. 2236-2246.
[32]S. K. Lee, G. Mogi, S. K. Lee and J. W. Kim, “Prioritizing the weights of hydrogen energy technologies in the sector of the hydrogen economy by using a fuzzy AHP approach,” Journal of hydrogen energy, vol. 36, no. 2, 2011, pp. 1897-1902.
[33]T. Dereli and A. Durmuşoğlu, “Classifying technology patents to identify trends: Applying a fuzzy-based clustering approach in the Turkish textile industry,” Journal of Technology in Society, vol. 31, no. 3, 2009, pp. 263-272.
[34]T. L. Saaty, The Analytic Hierarchy Process, New York: McGraw-Hill, 1980.