Assessing new product development project risk by Bayesian network with a systematic probability generation methodology

Authors:
Kwai-Sang Chin;Da-Wei Tang;Jian-Bo Yang;Shui Yee Wong;Hongwei Wang
Affiliations:
Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong and Manchester Business School, University of Manchester, UK;Manchester Business School, University of Manchester, UK;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong;Institute of Systems Engineering, Huazhong University of Science and Technology, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

New product development (NPD) is a crucial process to keep a company being competitive. However, because of its inherent features, NPD is a process with high risk as well as high uncertainty. To ensure a smooth operation of NPD, the risk involved in the process need to be assessed and the uncertainty should also be addressed properly. Facing these two tasks, in this paper, the critical risk factors in NPD are first analyzed. Since Bayesian network is specialized in dealing with uncertainties, those risk factors are then modeled into a Bayesian network to facilitate the assessing of the risk involved in an NPD process. To generate the probabilities of different kinds of nodes in a Bayesian network, a systematic probability generation approach is proposed with emphasis on generating the conditional probabilities of the nodes with multi-parents. A case study is also given in the paper to test and validate the critical risk factors as well as the probability generation approach.