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

Dmitry Kucharavy (1) , Eric Schenk (1) , Roland de Guio (1)

1 LGeco - Laboratoire de Génie de la Conception

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.

Mots clés: component logistic model; innovation process; knowledge acquisition; OTSM-TRIZ

Domaines: Gestion et management

Dmitry Kucharavy, Eric Schenk, Roland de Guio. Long-Run Forecasting of Emerging Technologies with Logistic Models and Growth of Knowledge. 19th CIRP Design Conference, Mar 2009, Cranfield, United Kingdom. pp.277. ⟨halshs-00440438⟩