Risk assessment of supply-chain systems: a probabilistic inference method

The probability of occurrence, occurrence time and holding time make the estimation of the multiresource, multidimensional, transmissible and dynamic characteristics of supply-chain risk more valuable to achieving the goal of risk assessment. A dynamic Bayesian network model of supply-chain risk is constructed to describe the property of supply-chain risk; the Bayesian inference tool is then used to estimate the corresponding parameters by maximum likelihood and inference for supply-chain risks. It is concluded that supply-chain risk changes with time and would converge at a certain stable interval, occurrence time and holding time satisfy several Poisson processes.