Bayesian networks offer a mechanism for diagnosing the key changes necessary for system improvement and for predicting the impacts of potential change actions. A review of the fundamentals of Bayesian networks is presented, with a discussion of strengths and weaknesses. A model is constructed to assess the impact of potential changes in the decision-making process of a large global manufacturing organization. The procedure for building a network structure, estimating conditional probabilities, assessing internal consistency, and conducting probabilistic inference are provided by the application.
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Anderson et al. (2001) studied this question.
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