Title: A process to build new product development cycle time predictive models combining fuzzy set theory and probability theory

Authors: Deandra T. Cassone

Addresses: Systems Engineering, Missouri University of Science and Technology, 223 Engineering Management, 600 W. 14th St., Rolla, MO 65409-0370, USA

Abstract: New product development (NPD) is a dynamic environment and the cycle times of the projects undertaken in this environment vary significantly. To develop and judge the performance of a theoretical model to adequately fit this environment requires the combination analytical methods. The data and model should include quantitative and qualitative characteristics. Certain statistical performance characteristics of a model are easily identifiable. Empirical evidence and expert opinion form a foundation for the model to ensure that the model performance represents the real world operating environment. This paper describes a modelling approach for predicting NPD project cycle time based on both statistical and fuzzy data. Statistical performance characteristics are used to determine the fit of a model. Fuzzy set theory is used to define the membership of the statistical performance in a well performing model and to aggregate the statistical and |soft| performance characteristics to determine good overall model performance.

Keywords: model building; fuzzy set models; project prediction; probability; fuzzy set theory; new product development; NPD; cycle times; predictive modelling; fuzzy logic.

DOI: 10.1504/IJADS.2010.034838

International Journal of Applied Decision Sciences, 2010 Vol.3 No.2, pp.168 - 183

Published online: 24 Aug 2010 *

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