Impact of resource reconfiguration on the dairy supply chain resilience

Investment in food supply chain resilience, as a critical infrastructure, has become necessary for all governments. Disruption of food supply chains can lead to significant economic challenges. Building a resilient supply chain requires resources; however, it is difficult for firms to allocate resources to various resilience strategies. This study allocates the budget to resilient capacity, that is, absorptive, adaptive, and restorative capacity, to minimize supply chain costs and maximize service levels. We developed a novel multi-objective mixed-integer nonlinear programming method for problem formulation. The developed model was converted into an equivalent linear model. We used the Monte Carlo approach to generate the scenarios and the average sample approximation to determine the required scenarios. Finally, the Lexicographic max-min approach solves the model using actual data from a dairy supply chain. The analysis revealed that allocating 50% of the budget to restorative capacity and the remaining to adaptive and absorptive capacity optimizes supply chain performance. This study provides insights for managers to make better decisions with a knowledge-based background, allocate resources to various resilient strategies, and build a more resilient and efficient supply chain.