A two-stage stochastic optimization framework for retail supply chain modeling with contemporaneous resilient strategies

This study delves into challenges in designing resilient retail supply chains, examining recent research on strategies amidst uncertainty. It employs stochastic programming to integrate uncertainties into a two-stage model, incorporating nine resilient strategies for both pre and post-disruption scenarios. This model’s practicality is enhanced by considering proactive and reactive approaches. The inclusion of a centralized management system, a novel strategy in the global retail supply chain context, further refines the model. Tested under COVID-19 and geopolitical disruptions, the model offers valuable managerial insights while assessing total retail supply chain costs. Sensitivity assessment explores the effectiveness of resilient strategies by intentionally stressing certain parameters under different disruptive scenarios. Stress testing reveals a combined use of nine resilient strategies effectively minimizing total retail supply chain costs post-disruption, demonstrating the significance of a comprehensive approach to enhance resilience in unpredictable events.