The purpose of this paper is to provide a methodology for modeling and assessing disaster risks in supply chain networks. The methodology uses Bayesian networks for the development of supply chain risk profiles. The networks are used to determine a supplier’s external, operational, and network risk probabilities and the potential revenue impact a supplier can have on an organization using a metric called value-at-risk (VAR). The methodology is offered as an evaluative tool to assist managers in the assessment of disaster risk levels corresponding to their supply base. An examination of the literature in the areas of SCM and supply chain risks is presented in the next section of the paper to provide a theoretical foundation for the proposed methodology. Provided afterwards is an overview of the research methodology used in the study, which includes a discussion on Bayesian network procedures along with data collection procedures. Results and conclusions are offered in the final section of the paper, including implications with respect to study limitations and directions for future research.