Mitigating disruption impact in Q-commerce through optimization of dark store resilient portfolio

In the rapidly expanding Q-commerce sector, which promises delivery to customers within 10––15 min, even the slightest disruption can impose substantial harm on the growth and reputation of businesses. This study examines the optimization of dark store resilient portfolio, i.e., dark store selection and protection against disruptions, alongside strategic allocation of risk mitigation inventory at the protected dark stores. We develop a mixed integer linear programming (MILP) model to minimize costs linked to protection and prepositioning of inventory at dark stores as well as costs related to pick-up and delivery of customers’ orders and stockouts. Additionally, we incorporate risk assessment measures such as value-at-risk and conditional value-at-risk in the MILP model to evaluate a Q-commerce company’s risk-averse decisions, and compare risk-neutral with risk-averse strategies for analysing impact of disruption risks in the operations of dark stores. The findings suggest that protecting key dark stores and allocating more customer orders to these protected dark stores would maintain continuity in customer order fulfilment during disruptive events. The results further highlight the quantity of risk mitigation inventory that needs to be prepositioned at the protected dark store for better inventory decisions under disruption risks. Moreover, we also observe that dark stores exhibiting higher disruption risk are usually not selected for order fulfilment unless protected, emphasizing the importance of operational resilience. Finally, this study uncovers various managerial insights for Q-commerce companies in effectively implementing a resilient strategy, thereby enabling decision-makers to improve the overall performance of dark stores.