Muqbil Burhan - Academia.edu (original) (raw)
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Universitat Autònoma de Barcelona
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Papers by Muqbil Burhan
New product development (NPD) is a crucial process to keep a company being competitive. However, ... more 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 project, the critical risk factors in NPD are first analysed. Interpretive Structural Modelling is used to set these factors into different categories based on their driving power. Since Bayesian network is specialized in dealing with uncertainties, those risk factors are then modelled 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 presented to test and validate the critical risk factors as well as the probability generation approach.
New product development (NPD) is a crucial process to keep a company being competitive. However, ... more 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 project, the critical risk factors in NPD are first analysed. Interpretive Structural Modelling is used to set these factors into different categories based on their driving power. Since Bayesian network is specialized in dealing with uncertainties, those risk factors are then modelled 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 presented to test and validate the critical risk factors as well as the probability generation approach.