Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge
Date published
2009-03-31
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Cranfield University Press
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Conference paper
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Citation
D. Kucharavy, E. Schenk, R. De Guio, Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge, Proceedings of the 19th CIRP Design Conference – Competitive Design, Cranfield University, 30-31 March 2009, pp277
Abstract
In this paper applications of logistic S-curve and component logistics are considered in a framework of longterm forecasting of emerging technologies. Several questions and issues are discussed in connection with the presented ways of studying the transition from invention to innovation and further evolution of technologies. First, the features of a simple logistic model are presented and diverse types of competition are discussed. Second, a component logistic model is presented. Third, a hypothesis about the usability of a knowledge growth description and simulation for reliable long-term forecasting is proposed. Some interim empirical results for applying networks of contradictions are given.
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Organised by: Cranfield University
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Github
Keywords
Component logistic model, Innovation process, Knowledge acquisition, OTSM-TRIZ
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Copyright: Cranfield University 2009
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Mori Seiki – The Machine Tool Company