A dynamic supply chain resilience model for medical equipment's industry. (18th September 2019)

• Record Type: Journal Article
• Authors: Jafarnejad, Ahmad; Momeni, Mansoor; Razavi Hajiagha, Seyed Hossein; Faridi Khorshidi, Maryam
• Is Part Of: Journal of modelling in management. Volume 14:Number 3(2019)
• Page Start: 816
• Page End: 840
• Publication Date: 2019-09-18
• Subjects: Dynamics -- Supply chain management -- Fuzzy -- Supply chain resilience model -- Resilience's enablers -- System dynamics -- Hesitant fuzzy Delphi method; Industrial management -- Mathematical models -- Periodicals; Industrial management -- Computer simulation -- Periodicals; Business -- Mathematical models -- Periodicals; Business -- Computer simulation -- Periodicals; 658.4033
• DOI: 10.1108/JM2-11-2018-0195
• Languages: English
• ISSNs: 1746-5664

Abstract

Abstract : Purpose: Medical equipment's supply chains play a vital role in performance of national 1healthcare systems. This supply chain is confronted with different internal and external risks. The purpose of this study is to investigate and find the key factors affecting the resilience of the supply chain of medical equipment and to examine the dynamic relations among these factors. Design/methodology/approach: A hybrid methodology is used for meeting the purpose of this study. First, the Delphi method is extended by using hesitant fuzzy linguistic term sets to identify the key factors of supply chain resilience. At the second phase, using the system dynamic methodology, the dynamic relations among identified resilience factors are analyzed. Findings: Using the Delphi method, agility, collaboration among actors, sharing of information, trust among actors, explicitness of supply chain, risk management culture, adaptability, structure, funding and environment conditions are identified as ten major factors affecting medical equipment's supply chain resilience. Also, four scenarios are simulated along with their impacts on the system. Originality/value: The main contribution of this study is extending a hesitant fuzzy linguistic term sets-based Delphi and applying it along a system dynamic analysis to identify the key factors affecting resilience of medical equipment's supply chain for the first time.