A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 138; p. 101967
• Main Authors: Govindan, Kannan, Mina, Hassan, Alavi, Behrouz
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.06.2020; The Authors. Published by Elsevier Ltd
• Subjects: COVID-19; Disaster management; Epidemic outbreaks; Fuzzy inference system; Healthcare supply chain disruption mitigation; COVID-19; Fuzzy inference system; Healthcare supply chain disruption mitigation; Epidemic outbreaks; Disaster management
• ISSN: 1366-5545; 1878-5794; 1878-5794
• DOI: 10.1016/j.tre.2020.101967

•Developed a practical decision support system for COVID-19 healthcare supply chain.•Grouped people and provided an independent classification method for each group.•Evaluated the efficiency of the proposed approach using real-world data. The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.