Supply chain risk analysis garnered increased attention, both in academia and in practice, since the early 2000s. Modern production methodologies such as just-in-time and lean manufacturing, globalized supply chains, shorter product life cycle, and the emphasis on efficiency have increased the risk faced by many supply chains. Managing such risks that is faced by a supply chain is vital to the success of any company. Currently employed methods lack consideration of market reaction and incorporation of decision maker preferences in managing supply chain risk. In this thesis, these two factors are taken into consideration to develop quantitative methods to analyze supply chain risk.

The first study is focused on supply chain risk from the market side in case of a major disruption. A probabilistic model based on different types of customer behaviors is developed to identify the impact on the firm’s revenue by forecasting the lost revenue in case of a production shut down from a disruption event. Results from a simulation of the developed model is analyzed to draw useful insights to manage the risk of such an event.

The second study is centered on supplier selection. It presents a 5-step framework based on KPIs derived from the performance metrics of the SCOR (Supply Chain Operations Reference) model. The framework can be used for supplier selection as well as for supplier performance monitoring as the firm continues to work with the selected supplier. Decision makers from a firm can incorporate their own preference within the presented framework to determine the most preferred supplier and assess the cost effectiveness to select a supplier in different scenarios to minimize supply side risk.