Risk analysis and management of New Jersey food supply chain
New Jersey is known as the Garden State for its dynamic, thriving food production industry that runs the gamut from vegetable growing to sophisticated manufacturing operations. Today New Jersey has a thriving $126 billion food industry and agriculture sector that grows every day. With such a vast and complex system, the food supply chain in New Jersey is vulnerable because a single disruption to one element could spread out and bring huge impact to the entire system. Such a ripple effect may
... e a tremendous impact on not only the state's economy and job market, but also the state's security, vulnerability, and resiliency. Food supply chain risks may occur naturally, intentionally, or accidentally. No matter how a risk originates, it may propagate along the connected members and then impact the entire network. Hence, it is critical to identify the risks in the New Jersey food supply chain and analyze their impacts. Understanding how risks propagate through the network will provide us with important insights into vulnerability assessment for the critical assets in the New Jersey food supply chain. Risks can then be better controlled, mitigated, and prepared for.This thesis first introduces the current status of the New Jersey food supply chain and then reviews the existing studies on supply chain risk modeling and propagation. To identify the critical assets in the New Jersey food supply chain and their relationship, the important nodes, links, risks, and failure probabilities are analyzed. The New Jersey food supply chain is then configured with 293 nodes. A new model for risk propagation is developed based on the traditional virus propagation models. The proposed model is then implemented in simulation for the New Jersey food supply chain network. Simulation results demonstrate how risks propagate through the network and which assets are the most critical ones in the New Jersey food supply chain. Future efforts will be devoted to more simulation analysis and improving the risk propagation model.