A hybrid risk management framework for resilient medical supply chain design in the post-pandemic era

Abstract

The COVID-19 pandemic has exposed critical vulnerabilities in medical supply chains (MSCs), leading to severe and long-lasting disruptions characterized by the ripple effect. Traditional risk mitigation strategies have proven inadequate for ensuring the resilience and long-term viability of MSCs in such volatile environments. This paper aims to design a resilient MSC by developing a hybrid risk management framework that enhances supply chain adaptability and survivability in the post-pandemic era. To address this issue, a risk-averse two-stage stochastic programming model that integrates Conditional Value at Risk (CVaR) and chance constraints (ChanceCon) is proposed to effectively manage the risk of unsatisfied demand. The hybrid CVaR-ChanceCon approach allows for a more comprehensive risk assessment by combining the benefits of both methods. To efficiently solve the complex optimization problem, a novel math-heuristic algorithm is developed that generates high-quality solutions within reasonable computational times. Compared to traditional risk measures and solution methods, the proposed hybrid framework demonstrates significant advantages in balancing cost efficiency and service level requirements. Key findings from extensive computational experiments reveal that the proposed method effectively reduces expected shortages, stabilizes supplier and warehouse utilization decisions, and enhances overall MSC resilience under various disruption scenarios. Policy implications suggest that adopting this hybrid risk management approach can substantially improve the preparedness and responsiveness of MSCs to future disruptions. It is recommended that policymakers and supply chain managers incorporate advanced risk aversion strategies like the CVaR-ChanceCon method to ensure the continuous supply of medical products, thereby safeguarding public health during crises.