An inventory management system for healthcare supply chains that incorporates epidemic outbreaks into consideration an investigation into COVID-19

Abstract

Epidemics are unique among calamities in two ways: the length of time they affect society and the speed with which they spread. In the absence of catastrophe prevention measures, supply chain (SC), and local communities would experience significant disruptions, leading to incalculable losses. The 2019 coronavirus illness (COVID-19) is one such calamity that has wreaked havoc on supply chains all across the globe, most notably the healthcare supply chain. As a result, this paper develops a practical decision support system based on doctors' knowledge and fuzzy inference system (FIS) for the first time to aid in insist management in the healthcare supply chain, lessen community pressure, disrupt the COVID-19 circulation chain, furthermore, in general, to diminish epidemic outbreaks for disruptions in the healthcare supply chain. This strategy first separates members of the community into four categories based on the sensitivity of their protected systems (normal, somewhat sensitive, extremely sensitive, and sensitive), as well as by two indications of age and pre-existing disorders and finally by two indicators of gender. These people are categorized after which they are obligated to follow the rules associated with their group. Finally, the success of the suggested strategy was evaluated in the real-world using data from four users, and the results demonstrated the efficiency and accuracy of the suggested strategy.