Supply chain risk analysis is an important field in operations management and logistics. Identifying those risks, assessing the probability of those risks, and understanding how those risks change if mitigation strategies are implemented contribute to better supply chain risk management. Reliability analysis has a long tradition of assessing the probability of failure, and fault trees are typically used to understand how the failure of individual components can lead to system failure within an engineered system. More recently, fault trees have been proposed to assess the probability of a supply chain failure. Dynamic fault trees, which are relatively new in reliability analysis, model the dependency among possible component failure and how these probabilities change over time. This paper applies dynamic fault trees to model supply chain risk for different types of supply chains. The dynamic fault tree allows a firm to model complex interactions among suppliers and understand how those interactions impact its risk. The model incorporates an information system that relays information about the status of suppliers to the firm, and this information system could also fail. A Markov chain model and Monte Carlo simulation are used to numerically assess supply chain risk as modeled by these dynamic fault trees.