臺灣博碩士論文加值系統

成長曲線模型常常被使用來預測科技產品之走向與趨勢，本研究利用22組科技產品資料來比較The simple logistic model, the Gompertz model, and the time-varying extended logistic model，此三種科技預測模型之預測準確度，再歸納出此三種模型之優缺點及建議使用時機。結果發現，The time-varying extended logistic model對在70%的科技產品上，都有比Simple logistic model 與Gompertz model兩個模型較好的預測準確度；但由於The time-varying extended logistic model在模型設定時需要有較多的參數來估計成長上限，在資料點太少的情況下，有約20%的機率無法得到收斂的結果，因此本研究建議若欲使用Extended logistic model，最好有15點以上之連續資料，且產品成長曲線有S曲線的軌跡，將會有較準確的預測結果。本研究亦提出一個選擇預測模型的決策流程，建議若在Extended logistic model無法收斂的情況下，但該產品成長曲線之反曲點已出現，則Simple logistic model 與Gompertz model可被使用來預測產品未來的發展空間。本研究亦利用大陸RFID專利申請案數量為一應用該決策流程之個案，並進一步預測未來RFID產業的發展趨勢。最後，本研究也提出對產品生命週期各階段之策略建議。

Many successful technology forecasting models have been developed but few researchers have explored a model that can best predict short product lifecycles. This research studies the forecast accuracy of long and short product lifecycle datasets using simple logistic, Gompertz, and the extended logistic models. Time series datasets for 22 electronic products were used to evaluate and compare the performance of the three models. The findings show that the time-varying extended logistic model fits short product lifecycle datasets 70% better than the simple logistic and the Gompertz models. A decision diagram is proposed to select a suitable forecasting model among the three models. The results suggest that there should be at less fifteen data points for the extended logistic model to reach better predictions. However, if the extended logistic model cannot be applied and the inflection point of the growth curve is revealed, the simple logistic and the Gompertz models can be the alternatives for forecasting the future trend of the product. A case study of China RFID patent forecast is also presented to demonstrate the selection procedure proposed in this research. Finally, the suggestions for product lifecycle management strategies in different lifecycle stages are also discussed.

中文摘要 I
ABSTRACT II
致謝辭 III
Table of Contents IV
List of Figures VI
List of Tables VII
1. Introduction 1
1.1 Motivation 1
1.2 Research Purpose 3
1.3 Research Process 4
2. Literature Review 6
2.1 Technology Forecasting Methods 7
2.1.1 Qualitative forecasting methods 7
2.1.2 Quantitative forecasting methods 9
2.1.3 The selection of technological forecasting models 11
2.2 Forecasting Short Product Lifecycles 12
2.3 Growth Curve Models 15
2.3.1 Simple logistic curve model 15
2.3.2 Gompertz model 16
2.3.3 Time-varying extended logistic model 18
3. Methodology 21
3.1 Data Collection 21
3.2 Data Settings 22
3.3 Model Comparison Analytical Process 27
3.4 The Proposed Model Selection Procedure 28
4. Comparison Results 31
4.1 Comparison of Performances 31
4.2 Test of the Comparison Results 38
4.3 Suggestions for Applying Forecasting Models 40
5. Case Study of China Radio Frequency Identification Patent Analysis 44
5.1 RFID Industry Introduction 44
5.2 Patent Technology Forecasting 47
5.2.1 Technology and patent document clustering 47
5.2.2 Patent technology forecasting 52
5.2.3 Technology life cycle analysis 53
5.3 China RFID Patent Analysis 55
5.4 The Development of China’s RFID Industry 60
6. Conclusion and Discussion 65
6.1 Conclusion 65
6.2 Implication and Limitation 68
Reference 72
Appendix 1 Simple logistic model derivations 77
Appendix 2 Gomperz model derivations 78
Appendix 3 RFID technology ontology tree with simplified and traditional Chinese characters 79
Autobiography 81

ABI Research. (2008), RFID Annual Market Overview: Vertical Market and Application Market Overviews for Tags, Readers, Software, and Services.
Andersen, B. (1999), "The hunt for S-shaped growth paths in technological innovation: a patent study." Journal of Evolutionary Economics, Vol. 9, pp. 487-526.
Bass, F.M. (1969), “A new product growth model for consumer durables.” Management Science, Vol. 15, pp. 215-27.
BEA Systems Inc. (2006), RFID for Retail: Blueprints for Bottom-Line Benefits. White paper. 23 pp.
Bengisu, M. and Nekhili, R. (2006), “Forecasting emerging technologies with the aid of science and technology databases.” Technological Forecasting and Social Change, Vol.73, pp.835-844.
Berkhin, P. (2002), “Survey of clustering and data mining techniques.” Technical Report, Accrue Software, Inc.
Berry, M. J. A. and Linoff, G. (1997), Data Mining Techniques: For Marketing, Sale, and Customer Support, John Wiley & Sons Inc, New York, NY, pp. 55.
Boretos, G. P. (2007), “The future of the mobile phone business.” Technological Forecasting and Social Change, Vol. 74, pp. 331-340.
Campbell, R. S. (1983), “Patent trends as a technological forecasting tool.” World Patent Information, Vol. 5, pp. 137-143.
Chen, B., Tai, P.C., Harrison, R., and Yi, P. (2005), “Novel hybrid hierarchical-K-means clustering method (H-K-means) for microarray analysis,” Computational Systems Bioinformatics Conference, Aug. 8-11, Standford CA, USA.
Chen, C.-P. (2005), The Test of Technological Forecasting Models. Master thesis, National Chiao Tung University.
Chen, J.-L., Liu, S.-J., and Tseng, C.-H. (2000), “Technological Innovation and Strategy Adaptation in the Product Life Cycle.” Technology Management-Strategies & Applications, Vol. 5, pp.183-202.
Clampitt, H. (2005), “RFID and China.” RFID Journal News, Aug. 8, 2005. <http://www.rfidjournal.com/article/articleview/1391/1/82/>. (last visited January, 18, 2009)
Cohen, J.E. (1995), “Population growth and the earth’s human carrying capacity.” Science, Vol. 269, pp. 241-246.
Directorate General of Telecommunications and Chunghwa Telecom Co, Taiwan. (2007), http://www.dgt.gov.tw/english/flash/index.shtml. Taiwan. (last visited February, 10, 2007)
Directorate General of Budget, Accounting and Statistics, Executive Yuan, Taiwan. (2007), http://eng.dgbas.gov.tw/mp.asp?mp=2. Taiwan. (last visited February, 13, 2007)
Ernst, H. (1997), “The use of patent data for technological forecasting: The diffusion of CNC-technology in the machine tool industry.” Small Business Economics, Vol.9, pp.361-81.
Frank, L.D. (2004), “An analysis of the effect of the economic situation on modeling and forecasting the diffusion of wireless communications in Finland.” Technological Forecasting and Social Change, Vol. 71, pp. 391-403.
Havstad, J.W., and Ehlers, C.L. (1989), “Attractor dimension of nonstationary dynamical systems from small data sets.” Physical Review A, Vol. 39, pp.845-53.
Hsu, F. C. (2006), Intelligent Patent Document Analysis Based on Clustering and Categorization Methods, Ph.D. Thesis, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan.
Hsu, F.C., A.J.C. Trappey, C.V. Trappey, J.L. Hou and S.J. Liu (2006), “Technology and knowledge document cluster analysis for enterprise R&D strategic planning.” International Journal of Technology Management, Vol. 36, pp. 336–353.
Hussain, O. and Fernandez, D. (2005), “Strategy intellectual property and emerging standards for entering the Chinese market.” http://www.ipfrontline.com/downloads/fernandez_china.pdf, Intellectual Property & Technology Magazine, pp. 1-6. (last visited February, 22, 2009)
IDTeckEx RFID Forecasts, Players & Opportunities 2008-2018. By Raghu Das and Dr Peter Harrop.
Islam, T., and Meade, N. (1996), “Forecasting the Development of the Market for Business Telephones in the UK.” The Journal of the Operational Research Society, Vol. 47, pp. 906-918.
Kotler, P. (2003), Marketing Management, 11th Ed. New Jersey: Prentice Hall.
Kotler, P., Keller, K.L., Ang, S.H., Leong, S.M., and Tan, C.T. (2006), Marketing Management: An Asian Perspective, 4th Ed. Singapore: Pearson Education South Asia Pte Ltd, Prentice Hall.
Kurawarwala, A.A., and Matsuo, H. (1996), “Forecasting and inventory management of short life-cycle products.” Operations Research, Vol. 44, pp. 131-50.
Kurawarwala, A.A., and Matsuo, H. (1998), “Product growth models for medium-term forecasting of short life cycle products.” Technological Forecasting and Social Change, Vol. 57, pp. 169-96.
Lackman, C. L. (1993), “Logit forecasting of high tech products.” Industrial Management, Vol. 35, pp. 20-21.
Levary, R.R, and Han, D. (1995), “Choosing a technological forecasting method.” Industrial Management, Vol. 37, pp. 14.
Liu, S. (2000), “Industry competition and patent strategy.” Sci-Tech Policy Review, Vol. 8908. pp. 1085-1098.
Ma, J. (2009), “National economy: Steady and fast growth in 2008.” National Bureau of Statistics of China, January 22, 2009, available at http://www.stats.gov.cn/was40/gjtjj_en_detail.jsp?searchword=GDP&channelid=9528&record=2. (last visited February, 20, 2009).
Mahaian, V., Muller, E., and Bass, F.M. (1990), “New product diffusion models in marketing: a review and directions for research.” Journal of Marketing, Vol. 54, pp. 1-26.
Market Intelligence Center (MIC). (2007), Advisory & Intelligence Service Programs (AISP). http://mic.iii.org.tw/intelligence/. Taiwan. (last visited February, 20, 2007)
Martino, J.P. (1993), Technological Forecasting for Decision Making. 3rd ed. New York: McGraw-Hill.
Martino, J.P. (2003), “A review of selected recent advances in technological forecasting,” Technology Forecasting & Social Change, Vol. 70, pp.719-733.
Meade, N., and Islam, T. (1995), “Forecasting with growth curves: An empirical comparison.” International Journal of Forecasting, Vol. 11, pp.199-215.
Meade, N., and Islam, T. (1998), “Technological forecasting--model selection, model stability, and combining models.” Management Science, Vol. 44, pp.1115-1130.
Meyer, P.S., and Ausubel, J.H. (1999), “Carrying Capacity A Model with Logistically Varying Limits.” Technological Forecasting and Social Change, Vol. 61, pp.209-214.
Meyer, P.S., Yung, J.W., and Ausubel, J.H. (1999), “A Primer on Logistic Growth and Substitution.” Technological Forecasting and Social Change, Vol. 61, pp. 247-271.
Morrison, J.S. (1995), “Life-cycle approach to new product forecasting.” The Journal of Business Forecasting Methods and Systems, Vol. 14, pp. 3-5.
Morrison, J.S. (1996), “How to use diffusion models in new product forecasting.” The Journal of Business Forecasting Methods & Systems, Vol. 15, pp. 6-9.
Nieto, M., Lopez, F., and Cruz, F. (1998), “Performance analysis of technology using the S curve model: the case of digital signal processing (DSP) technologies.” Technovation, Vol. 18, pp. 439-457.
Pitzek, S. (2005), “An Ontology for the RFID Domain,” available at: http://move.ec3.at/Ontology/RFIDOntology0903/RFIDOntology-Report.pdf
Porter, A.L., Roper, A.T., Mason, T.W., Rossini, F.A., and Banks, J. (1991), Forecasting and Management of Technology, New York: John Wiley and Sons, Inc.
Qualls, W., Olshavsky, R.W., and Michaels, R.E. (1981), “Shortening of the PLC - an empirical test.” Journal of Marketing, vol. 45, pp. 76-80.
Radjou, N. (2005), “How India, China Redefine the Tech World Order.” Forrester Research, 1 Nov.
Rai, L.P., and Kumar, N. (2003), “Development and application of mathematical models for technology substitution.” PRANJANA, Vol. 6, pp. 49-60.
Sprent, P., and Smeeton, N.C. (2007), Applied Nonparametric Statistical Methods. 4th ed. Boca Raton, FL: Chapman & Hall.
State Intellectual Property Office of the People’s Republic of China (SIPO), http://www.sipo.gov.cn/sipo2008/zljs/, 2008. (last visited December, 31, 2008)
Taghaboni-Dutta, F. and Velthouse, B. (2005), “RFID Enabled Supply Chain: Harvesting the Opportunities.” International Journal of Electronic Business Management, Vol. 3, pp. 165-173.
Tukey, J.W. (1997), Exploratory Data Analysis, Reading Massachusetts: Addison-Wesley.
Walton, S. R. (2005), “Wal-Mart, supplier-Partners, and the buyer power issue.” Antitrust Law Journal, Vol. 72, pp. 509-27.
Want, R. (2006), “An introduction to RFID technology.” IEEE Pervasive Computing, Vol. 5, pp. 25-33.
Ward, M. and Kranenburg , R. V. (2006), “RFID: Frequency, standards, adoption and innovation.” JISC Technology and Standards Watch, available at: http://www.rfidconsultation.eu/docs/ficheiros/TSW0602.pdf (last visited February, 21, 2009)
Wu, N. C., Chang, Y. S. and Yu, H. C. (2007), “The RFID industry development strategies of Asian countries.” RFID Eurasia, 2007 1st Annual.
Young, P. (1993), “Technological growth curves: A competition of forecasting models.” Technological Forecasting and Social Change, Vol.44, pp.375-389.
Zhu, K., and Thonemann, U.W. (2004), “An adaptive forecasting algorithm and inventory policy for products with short life cycles.” Naval Research Logistics, Vol. 51, pp. 633-653.