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

Date

2009-03-31

Supervisor/s

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Journal ISSN

Volume Title

Publisher

Cranfield University Press

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Type

Conference paper

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Format

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.

Description

Organised by: Cranfield University

Software Description

Software Language

Github

Keywords

Component logistic model, Innovation process, Knowledge acquisition, OTSM-TRIZ

DOI

Rights

Copyright: Cranfield University 2009

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