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Performance Improvement through Better Understanding of Supply Chain Resilience


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Businesses operate every day in a disruptive environment. Supply and demand uncertainty, natural disasters, global pandemics, and mishaps can all cause chaos to a supply chains flow. It is impossible to predict every disruption a supply chain may encounter. The best an organization can do to protect network performance is to build resilience in the supply chain and life-blood of its operations. Ensuring that a supply chain has the proper built-in mechanisms to resist and recover from disruptions is referred to as Supply Chain Resilience (SCR). While it is generally agreed that SCR can be improved through the implementation of SCR strategies, the links between these strategies, performance improvement and resilience is understudied. This dissertation leans on resource based view and theory of constraints to categorize these SCR strategies, examine the links between the strategies and performance, and develop a metric to measure network resilience over time. First, a meta-analytical study identifies generalizable relationships between SCR strategies and firm performance measures. Then, the SCR redundancy strategies are applied to a model simulation to illustrate the resilience curve response to different SCR strategic decisions. Resilience outcomes are compared using a developed Resilience Capability Metric (RCM) utilizing Area under the Curve (AUC) to measure the cumulative performance level of the system from disruption to predetermined endpoint, representing how much of the system demand can be served by different network resilience designs. Finally, SCR flexibility strategies are analyzed to see how constraints imposed on a supply chains response time could impact the resilience of the supply chain.



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