Title: Forecasting multiple generations of technology evolution: challenges and possible solutions

Authors: Barrett Caldwell, Enlie Wang, Sudip Ghosh, Chulwoo Kim, Raghuvir Rayalu

Addresses: School of Industrial Engineering, Purdue University, 315 N Grant St., West Lafayette, IN 47907-2023, USA. ' School of Industrial Engineering, Purdue University, 315 N Grant St., West Lafayette, IN 47907-2023, USA. ' School of Industrial Engineering, Purdue University, 315 N Grant St., West Lafayette, IN 47907-2023, USA. ' School of Industrial Engineering, Purdue University, 315 N Grant St., West Lafayette, IN 47907-2023, USA. ' School of Industrial Engineering, Purdue University, 315 N Grant St., West Lafayette, IN 47907-2023, USA

Abstract: This paper examines challenges in the development of technology forecasts in environments with decreasing technology development cycles. In these environments, the same time period of forecasting spans more generations of product evolution, with more interactions with the technological, social, economic, and political context of innovation and diffusion. Both data-centred extrapolations of past technology patterns and expert-centred predictions of future patterns can be subject to assumptions that ignore the non-rational and random effects of both human social systems and expert decision-makers. Technology forecasts should optimise stability and robustness of choices over a range of possible scenarios, rather than manage risk for sequential (and increasingly unlikely) specific predictions. Relevance of multiple domains of expertise and needs for improved information range and freshness (both of extrapolated data and expertise) are emphasised. Multi-generation technology forecasts must also incorporate greater breadth of awareness of socio-technical system dynamics that operate over multiple time scales.

Keywords: distributed expertise; knowledge development; non-rational decision making; scenario forecasting; sociotechnical systems; technology evolution; technology forecasting; product evolution; multi-generation forecasting.

DOI: 10.1504/IJTIP.2005.006511

International Journal of Technology Intelligence and Planning, 2005 Vol.1 No.2, pp.131 - 149

Published online: 18 Mar 2005 *

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