In today’s increasing globalization in a competitive environment, supply chains (SCs) are exposed to vulnerability and risk events occurrence. In the complexity of the involved network supply chain network design (SCND) needs to be managed in an efficient way. This paper presents a mixed integer linear programming (MILP) model for the SCND problem that handles economic, environmental, and risk objectives. Economic aims are considered by minimizing the overall costs of the whole SC. Environmental consideration is achieved by incorporating gas emissions into the SCND as a sustainability dimension. Risk modeling has been considered in the SCND model as the network is sensitive to risks and disruptions. The paper aims to develop a model attempting to determine a trade-off between the total costs associated with the SCN, encompassing the disruption risk costs that occur in one or more nodes of the SCN. To evaluate the disruption risks, a Belief Bayesian Network (BBN) model has been developed to predict and assess the risk factors and sub-factors marginal probabilities. A belief propagation analysis is used and applied in an automotive case study to draw meaningful managerial insights and discover the most sensitive factors and sub-factors that impact the SC. The findings demonstrate the utility of BBN model in identifying the factors and their sub-factors that have the greatest impact on the SC disruption risks. The proposed model will serve managers to predict the disruption risks to develop mitigation strategies and strategic plans to manage disruption risks and damage related to SCND.