A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)
Open accessAbstract
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.
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- Authors
- Govindan, K., Mina, H., & Alavi, B.
- Affiliations
- University of Southern Denmark (DK)
- University of Tehran (IR)
- Sharif University of Technology (IR)
- Corresponding author
- Govindan, Kannan
- Venue
- Transportation Research Part E: Logistics and Transportation Review
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 138
- Pages
- 101967
- ISSN
- 1366-5545 (ISSN-L)
- PMC
- PMC7203053
- OpenAlex
- W3023414002
- Cited by
- 641
- References
- 60
- Citations by year
- 2026: 26 · 2025: 56 · 2024: 81 · 2023: 126 · 2022: 160 · 2021: 154 · 2020: 38
- FWCI
- 127.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Transportation Research Part E Logistics and Transportation Review (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Supply Chain Resilience and Risk Management
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Facility Location and Emergency Management
- Keywords
- decision support system, healthcare supply chain, demand management, COVID-19 pandemic, infectious disease outbreaks, supply chain disruption, fuzzy inference system, physician knowledge, risk group classification, pre-existing disease, age, pandemic mitigation
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