The development of new and improved Information Technology (IT) methods for Supply Chain Management is important. Existing methods suffer from several shortcomings, especially the ability to deal with a mixture of quantitative and qualitative data. This study aims to apply decision support techniques to the area of Supply Chain Management in order to address some of the shortcomings. The methodology follows structuring and modeling. A three-step decision structuring framework is used to develop a model, based on Bayesian networks, to support Supply Chain Management scenarios. The result is a Bayesian network that incorporates the knowledge of experts into a decision support model. It is shown that the model is essential as it contains all the vital elements of the problem from a managerial viewpoint. The described model can be used to perform what-if analysis in various ways, thereby supporting the management of risk in different scenarios. The contribution of this research is not limited to the model, but the study also provides insights into how decision support, and especially Bayesian networks, can enhance IT methods.