<p>The production of Molybdenum-99 (99Mo) is critical to the field of nuclear medicine, where it is utilized in roughly 80% of all nuclear imaging procedures. In October of 2016, the National Research Universal (NRU) reactor in Canada, which historically had the highest 99Mo production capability worldwide, ceased routine production and will be permanently shut down in 2018. This loss of capacity has led to widespread concern over the ability of the 99Mo supply chain and to meet demand. There is significant disagreement among analyses from trade groups, governments, and other researchers, predicting everything from no significant impact to major worldwide shortages. Using Bayesian networks, this research focused on modeling the 99Mo supply chain to quantify how a disrupting event, such as the unscheduled downtime of a reactor, will impact the global supply. This not only includes quantifying the probability of a shortage occurring, but also identifying which nodes in the supply chain introduce the most risk to better inform decision makers on where future facilities or other risk mitigation techniques should be applied.