Bayesian Process Networks: An approach to systemic process risk analysis by mapping process models onto Bayesian networks

This paper presents an approach to mapping a process model onto a Bayesian network resulting in a Bayesian Process Network, which will be applied to process risk analysis. Exemplified by the model of Event-driven Process Chains, it is demonstrated how a process model can be mapped onto an isomorphic Bayesian network, thus creating a Bayesian Process Network. Process events, functions, objects, and operators are mapped onto random variables, and the causal mechanisms between these are represented by appropriate conditional probabilities. Since process risks can be regarded as deviations of the process from its reference state, all process risks can be mapped onto risk states of the random variables. By example, we show how process risks can be specified, evaluated, and analysed by means of a Bayesian Process Network. The results reveal that the approach presented herein is a simple technique for enabling systemic process risk analysis because the Bayesian Process Network can be designed solely on the basis of an existing process model.