Date of Award
Doctor of Philosophy
Industrial and Manufacturing Engineering
Dr. Steven Butt
Dr. Azim Houshyar
Dr. David Meade
Dr. Sime Curkovic
The increasing complexity of modern supply chains is widely accepted as one of the many drivers of risk that can leave organizations vulnerable to the effects of a disruption. This acceptance has contributed to an emerging interest in Supply Chain Resilience in the literature. Resilient supply chains are able to both resist the effects of a disruption and recover efficiently if the disruption is severe. This new body of literature is still in a formative phase and has thus far focused on defining Supply Chain Resilience, proposing frameworks for its implementation, exploring factors that contribute to resilience, and investigating methods to design resilient supply chains. Among the most accepted factors of resilience is redundancy, which refers to the auxiliary resources and capacity of a supply chain that can be called upon during a disruption. Furthermore, the ability of managers to efficiently navigate the recovery process is believed to be central to overall resilience. Unfortunately, relatively few studies have sought to align the tools and methods of recovery with the findings from the broader Supply Chain Resilience literature. This research aims to narrow this gap by developing and validating a mathematical supply chain network model that integrates the known resilience construct of redundancy into its formulation in a flexible manner to support managerial decision-making at the beginning of a disruption.
The value of the model stems from its efficiency in deploying redundant capacity throughout a supply chain to minimize the impact of a disruption and from its ability to provide sensitivity information relating to that deployment. The deterministic formulation incorporates common supply chain structures from contemporary literature. Numerical studies were performed to examine the behavior of simulated networks under disruption and to assess the ability of the model to achieve the desired objectives. The findings of this research support the proposition that the model is able to efficiently distribute capacity throughout a supply chain to support a recovery effort. The provision of sensitivity analysis relating to incremental capacity deployment decisions is able to provide insights for managers who are responsible for balancing the trade-offs between demand and costs during a disruption.
Burns, James D., "Development of a Capacity Deployment Model for Disrupted Supply Chain Networks" (2015). Dissertations. 1167.