A decision support model for evaluating risks in a collaborative supply chain of the medical equipment manufacturing industry. Issue 3 (3rd July 2022)

• Record Type: Journal Article
• Authors: Bhalaji, R.K.A.; Sankaranarayanan, Bathrinath; Alam, Shahriar Tanvir; Ibne Hossain, Niamat Ullah; Ali, Syed Mithun; Karuppiah, Koppiahraj
• Is Part Of: Supply chain forum. Volume 23:Issue 3(2022)
• Page Start: 227
• Page End: 251
• Publication Date: 2022-07-03
• Subjects: Collaborative supply chain -- risk factors -- grey-dematel -- medical equipment manufacturing industry -- delphi method; Business logistics -- Periodicals; 658.7
• DOI: 10.1080/16258312.2021.1989268
• Languages: English
• ISSNs: 1625-8312

Abstract

ABSTRACT: As modern healthcare technologies are becoming more and more common, even in rural areas, the usage of various medical equipment is also increasing. The manufacturers of this equipment play a large role in providing timely healthcare by meeting healthcare providers' requirements by fulfilling their demands. As a manufacturer, establishing a strong collaborative supply chain (CSC) is crucial to ensure both the timely receipt of raw materials and the delivery of finished goods. The risks associated with CSCs and their interactions need to be investigated for decision-making in the CSCs. This paper presents a framework, integrated decision-making methodology based on the Delphi method and the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. The Delphi method was applied for identifying the risk based on opinions of the industrial managers in the medical equipment manufacturing industry of India, and then the DEMATEL method was applied to evaluate the interaction among the recognised key risks in CSCs. The findings of the study reveal that disruption risk was the most influential one in CSC. This study will provide more valuable guidelines to policymakers and relevant practitioners about the risks prevailing in the rising nations towards the implementation of CSC practices, which, in turn, can lead to taking necessary steps for mitigating those risks. The proposed framework can address CSC risk and optimise, plan, monitor, and control supply chains in real-time. A sensitivity analysis was performed to analyse the robustness of the obtained results.