Bayesian network approach to change propagation analysis

The prediction of change propagation is one of the important issues in engineering change management. The aim of this article is to explore the capability of Bayesian network (BN), which is an emerging tool for a wide range of risk management, in modeling and analysis of change propagation. To this end, we compare the BN with change prediction method (CPM), which is the most established probabilistic methods for predicting change propagation. This paper shows that a CPM-based model can be converted into an equivalent BN, and the probabilistic inference technique on the latter results in the same change prediction result obtained from the former. Then, this paper shows that several improvements can be obtained at various levels using the BN. At the modeling level, complex relationship between components such as combined effect of simultaneous changes or multistate relationship can be naturally represented with the BN. At the analysis level, various change propagation scenarios can be analyzed using probabilistic inference on the BN. Finally, BN provides a robust framework for learning change propagation probabilities from empirical data. The case study is conducted to show the feasibility of the model.