During a pandemic, various resources, including personal protective equipment (PPE), are required to protect people and healthcare workers from getting infected. Due to the high demand and limited supply chain, countries experience a shortage in PPE products. This global crisis imposes a decline in the international trade of PPE supplies. In fact, most governments implement a localization strategy motivating domestic manufacturers to pivot their operations to respond to PPE demands. An oligopolistic market cannot reach the socially optimal coverage without government subsidies. On the other hand, the government subsidy pays the proportion of production costs to reach the socially optimal coverage, while the government's budget is limited. Therefore, the government collaborates with manufacturers via procurement contracts to increase the supply of PPE products. We propose the first supply chain model of PPE products that investigates manufacturer costs and government expenditure. We consider how different behavioral aspects of manufacturers and government can self-organize towards a system optimum. Additionally, we integrate the consumer surplus, producer surplus, and societal surplus into the game model to maximize social benefit. A cost-sharing contract under the system optimum between government and manufacturers is designed to increase the production of PPEs and hence, helps in reducing the number of infected individuals. We conducted our computational study on real data generated from the mask usage during the Covid-19 pandemic in Los Angeles (LA) County to respond to the reported PPE shortage. Under the socially optimal strategy, the PPE coverage increases by up to 33%, and the number of infected individuals reduces by up to 30% compared to other strategies.
Keywords: Cost-sharing contract; Epidemiology; Government subsidy; Resilient supply chain; Social welfare; System optimum.
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