Design is an uncertain human activity involving decisions with uncertain outcomes. Sources of uncertainty in product design include uncertainty in modeling methods, market preferences, and performance levels of subsystem technologies, among many others. The performance of a technology evolves over time exhibiting improving performance as research and development efforts continue. As the performance of a technology in the future is uncertain, quantifying the evolution of these technologies poses a challenge in making design decisions. Designing systems involving evolving technologies is a poorly understood problem. The objective of this research is to create a computational method allowing designers to make decisions encompassing the evolution of technology. Techniques for modeling evolution of a technology that has multiple performance attributes are developed. An S-curve technology evolution model is used. The performance of a technology develops slowly at first, quickly during heavy R&D effort, and slowly again as the performance approaches its limits. Pareto frontiers represent the set of optimal solutions that the decision maker can select from. As the performance of a technology develops, the Pareto frontier shifts to a new location. The assumed S-curve form of technology development allows the designer to apply the uncertainty of technology development directly to the S-curve evolution model rather than applying the uncertainty to the performance, giving a more focused application of uncertainty in the problem. Monte Carlo simulations are used to the propagate uncertainty through the decision. The decision-making methods give designers greater insight when making long-term decisions regarding evolving technologies. The scenario of an automotive manufacturing firm entering the electric vehicle market deciding which battery technology to include in their new line of electric cars is used to demonstrate the decision-making method. Another scenario of a wind turbine energy company deciding which technology to invest in demonstrates a more sophisticated technology evolution modeling technique and the decision making under uncertainty method.

Made available in DSpace on 2016-06-02T19:51:39Z (GMT). No. of bitstreams: 1 2495.pdf: 689346 bytes, checksum: 9c79c3f45b345f035630b8770d19f0b3 (MD5) Previous issue date: 2009-05-27 / Financiadora de Estudos e Projetos / The technology forecast and management methods have been considered by academics and managers important tools to support technology development. Generally the organizations, which are worried about this issues, needs data and information to look ahead and clarify technologies course before it happens. Considering that development s time has become shorter, these kind of methodologies may turn more and more important. Despite of these matters, it s rare to find papers and related cases to this subject in Brazil. That s why this study has the objective of collect and discuss methodologies, and right after that, compare the Technology Roadmap (TRM) to the CNPDIA s method of technology forecast and management. This TRM method is frequently presented in the related magazines. The work realized has compared the dimension of the TRM, considering some extras observations. These extras looks offered a more complete view and understanding of the environment in which the technology activities took place. The expected intention was to collaborate by analyzing some of the CNPDIA s characterizes. One extra hope is to inspire some discussion in the unit observed, based on this work. / Os métodos de prospecção e gestão de tecnologia são importantes instrumentos para a inovação em organizações que visam o desenvolvimento de pesquisas e tecnologia. Em geral, estas organizações precisam de dados, informações e orientações que embasem as decisões acerca das atividades que serão conduzidas. Considerando que as atividades de inovação tecnológica aceleraram-se nestas últimas décadas, reduzindo o tempo de desenvolvimento, a importância deste tipo de metodologia tende a aumentar. Entretanto, no Brasil ainda é escassa a literatura sobre o tema, com poucos relatos de utilização de algumas destas metodologias. Por isso, esta dissertação possui o objetivo de aprofundar este tema. Visa-se coletar e selecionar, das diferentes fontes identificadas, conteúdos relevantes sobre prospecção e gestão de tecnologia para compor uma densa revisão desta literatura e, em seguida, realizar a comparação entre o modelo de prospecção e gestão de tecnologia presente no Centro Nacional de Pesquisas e Desenvolvimento de Instrumentação Agropecuária CNPDIA e o método do Technology Roadmapping, método freqüente em revistas de referência no tema. A comparação foi baseada nas dimensões presentes no Technology Roadmaping, entretanto os limites dimensionais da metodologia foram extrapolados para aprofundar o conhecimento acerca do objeto de estudo. A partir desta compreensão, objetivou-se contribuir ao analisar algumas características desta importante unidade da Embrapa, o CNPDIA. Espera-se, também, que algumas das recomendações realizadas nesta dissertação possam ser alvo de debates na unidade pesquisada.

全球行動電話用戶數於2002年達到12億，首度超過固定電話用戶數之11億；行動電話用戶數並於2008年達41億，為固定電話用戶數(13億)之3倍以上。行動電話相對於固定電話之主要優勢在於系統之建置成本低及佈建速度快；行動電話之快速普及已成為創新擴散研究之重要題材。 行動電話擴散之研究為選取一成長模型(例如Gompertz、Logistic或Bass模型)並類比該模型以求出擴散之參數(例如成長速率)，以進一步(1)了解相關驅動因子(例如技術創新、市場開放等)對擴散參數之影響，及(2)延伸擴散模型曲線以預測未來之成長。 惟成長模型之選取尚無原理原則可供遵循而具隨機性(ad hoc basis)。為找出模型選用之可能規律，以降低模型選用之隨機性並提高成長預測之準確度，本研究以十二個代表性國家(巴、中、法、德、印、日、韓、俄、星、台、英、美)至2007年之資料以比較三個最常用之成長模型之績效，即Gompertz、Logistic及Bass模型。模型績效逐年比較標準係採用rmse值，並輔以Friedman test檢測模型績效差異之顯著性，再對照模型之機制意涵，以進一步了解最適模型之選用原則。 此外，台灣行動電話普及率於2002年為108%居全球之冠，而中國自2001年起取代美國成為全球具最多行動電話用戶數之單一國家，台灣及中國屬行動電話擴散之重要個案，惟目前尚缺此二個案之實證研究。為補足此一缺口，本研究亦對台灣及中國行動電話擴散之驅動因子進行實證研究，以進一步了解擴散之關鍵驅動力。 研究發現由於目前統計軟體之進步，Gompertz、Logistic及Bass三模型均可獲致極佳之匹配度而難分軒輊，惟模型預測力(延伸曲線)則具差異性。12個模型選用樣本國家中之8個國家(巴、中、法、德、日、韓、英、美)係以Gompertz模型具較佳之預測力；依Gompertz模型機制意涵，代表行動電話擴散早期係受網路外部性(口耳相傳)影響，惟至擴散後期(例如過了擴散極大值之一半)則已不相關。此外，若因市場開放等重大變因造成行動電話之快速擴散，則Logistic模型具有較佳之績效，如台灣及俄羅斯屬之。依Logistic模型機制意涵，代表擴散係受網路外部性所影響。Bass模型應用於行動電擴散時，因該模型所算出之創新係數偏低，績效與Logistic模型相近，而Logistic模型為Bass模型之創新係數為0時之特例。 台灣及中國行動電話擴散之驅動因子研究發現(1)價格下降及(2)預付卡之推行對加速擴散具顯著性，兩者均對低階市場之採用具影響力。鑑於高階市場將先飽和，爰未來加速行動電話擴散之關鍵驅動因子應係與推動低階市場採用具密切相關性。以中國為例，未來市場開放競爭造成價格再度大幅下降，將進一步促低階市場採用，加速中國行動電話之普及。 / The number of mobile telephone subscriptions reached 1.2 billion globally in 2002, exceeding fixed-line telephony subscriptions (1.1 billion) for the first time. The number of mobile telephone subscriptions reached 4.1 billion globally in 2008, over three times the number of fixed-line telephone subscriptions (1.3 billion). The main advantages of mobile telephony over fixed-line are low cost and rapid facility deployment. The rapid diffusion of mobile telephony has become an important topic in innovation diffusion. The conventional approach to studying mobile telephony diffusion is to analogize a single growth model, such as the Gompertz, Logistic or Bass model, and calculate the model parameters, for example growth rate. The significance of certain selected driving forces, such as technology innovation or market competition, to the studied parameters, such as growth rate, is then estimated. The diffusion growth can also be forecast by extrapolating the diffusion curve. Utilizing the growth model analogy is the first step in analyzing mobile telephony diffusion. However, no principles or rules exit for selecting a growth model. To identify rules for model selection to reduce randomness and increase forecast accuracy, this work uses 12 sample countries, namely Brazil, China, France, Germany, India, Japan, Korea, Russia, Singapore, Taiwan, the UK and the USA, employing data prior to 2008 to compare the performance of three most commonly used models, namely the Gompertz, Logistic and Bass models. The root mean square error (rmse) is chosen as the criterion for measuring annual model performance. The work uses the Friedman test to examine the significance of differences in performance between models. The implications of model mechanisms are emphasized to investigate the selection rule for the most appropriate model. The penetration of mobile telephony in Taiwan was 108% in 2002, ranking first in the world. Furthermore, in 2001 the number of mobile telephony in China replaced the United States as number one in the world. Both Taiwan and China are important examples for mobile telephony diffusion. However, no empirical investigation has been performed in these two cases. To fill this gap, this work estimated the driving forces for mobile telephony diffusion in Taiwan and China to learn about the critical drivers of the mobile telephony diffusion. Empirical results indicate that due to improvements in statistical software, providing good fitness for all three models, namely the Gompertz, Logistic and Bass models, distinguishing which has the best fitness is difficult. However, the performance of the three models is distinguishable when forecasting based on extrapolating the diffusion curve. In eight of the 12 examples, namely Brazil, China, France, Germany, Japan, Korea, the UK and the USA, the Gompertz model is the most appropriate model for forecasting. The mechanism of the Gompertz model means that during the initial stage the diffusion is correlated with network externalities (namely word of mouth), however, this correlation reduces during the later stages (such as pass one half of the maximum potential). Moreover, the cases of Taiwan and Russia demonstrated that the Logistic model performs well provided some significant driver of the diffusion exists. The mechanism of the Logistic model means that the diffusion is correlated with network externalities throughout the whole diffusion. Furthermore, using Chinese data, when the Bass model is applied, because of its low innovation coefficient, it performs similarly to the Logistic model, which is a special case of the Bass model in which the innovation coefficient equals zero. Empirical results for the critical driving forces of mobile telephony diffusion in Taiwan and China indicate that (1) reducing prices and (2) the launch of pre-paid services are crucial to mobile telephony diffusion. Both factors are essential to mobile telephony adoption in low-end markets. The high-end market is the first to be saturated by mobile telephony adoption, future drivers of the mobile telephony diffusion should be highly correlated with low-end market demand. Taking China as an example, the opening of the market to further reduce tariffs will attract mobile telephony adoption in the low-end market, facilitating the mobile telephony diffusion.