Investigation of Product Process Dependency Models through Probabilistic Modeling

The motivation behind the idea of product focused process improvement is to make a process improvement program address certain product quality features in an explicit manner. The PROFES methodology [PROFES (2001)] describes such an improvement program through the notion of a PPD (Product-Process Dependency) repository. A PPD model tells which improvement action will result in the improvement of which product quality. Because of the associated cost, only a small number of improvement actions can be implemented. Therefore it becomes imperative that we must be sure of the impact of the improvement actions. In this paper, we discuss how Bayesian Networks [Jensen (1996)] can be used to predict the outcome of PPD models and hence the impact of the associated improvement actions.