Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge

Dmitry Kucharavy (), Eric Schenk () and Roland de Guio ()
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Dmitry Kucharavy: LGeco - Laboratoire de Génie de la Conception - INSA Strasbourg - Institut National des Sciences Appliquées - Strasbourg - INSA - Institut National des Sciences Appliquées
Eric Schenk: LGeco - Laboratoire de Génie de la Conception - INSA Strasbourg - Institut National des Sciences Appliquées - Strasbourg - INSA - Institut National des Sciences Appliquées
Roland de Guio: LGeco - Laboratoire de Génie de la Conception - INSA Strasbourg - Institut National des Sciences Appliquées - Strasbourg - INSA - Institut National des Sciences Appliquées

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Abstract: In this paper applications of logistic S-curve and component logistics are considered in a framework of long-term 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.

Keywords: component logistic model; innovation process; knowledge acquisition; OTSM-TRIZ (search for similar items in EconPapers)
Date: 2009-03-30
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00440438
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Citations: View citations in EconPapers (1)

Published in 19th CIRP Design Conference, Mar 2009, Cranfield, United Kingdom. pp.277

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