Examining transportation disruption risk in supply chains: A case study from Bangladeshi pharmaceutical industry

Transportation is one of the logistical drivers in supply chains. Transportation disruption is costly in supply chains. This paper aims to assess transportation disruption risks using a Bayesian Belief Network (BBN). First, the disruption risk factors and their sub-factors were identified from the relevant articles and experts' opinions. The BBN-based model is developed to calculate the marginal probabilities of the risk factors and their sub-factors to determine the most sensitive factors/sub-factors. The framework was demonstrated using an example case of the pharmaceutical industry in Bangladesh. The findings reveal the usefulness of BBN in examining transportation disruptions in supply chains. BBN captured the interdependencies between the disruption risk factors/sub-factors effectively. The proposed model will be useful to managers for predicting transportation disruptions and to build resilient strategies to tackle them.