Optimal supply chain recovery strategies with consideration of component interdependencies


Current resilience assessment methods for supply chains do not sufficiently capture the risks of capacity losses due to cascading infrastructure failures and delayed recovery due to mismanaged repair resources. This paper presents a novel multi-stage stochastic programming model that optimizes pre-disruption capacity investments, as well as post-disruption dynamic allocation of repair resources and adaptation of supply chain operations. Disruption scenarios are generated using an input-output model that takes into account component interdependencies. With the proposed model, planners can better analyse trade-offs between capacity redundancies and improved recovery capabilities, as demonstrated with a numerical example of a real-world supply chain.

Nils Goldbeck
Visiting Research Associate
Panagiotis Angeloudis
Associate Professor