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Chukwuka, OJ, Ren, J, Wang, J and Paraskevadakis, D (2024) Managing risk in emergency supply chains–An empirical study. International Journal of Logistics Research and Applications. pp. 1-37. ISSN 1367-5567
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Abstract
Relief organisations face significant logistical challenges in the aftermath of natural disasters, characterised by volatile environments. Effective risk management in these contexts hinges on the identification, evaluation, and mitigation of potential risk events. This study addresses this critical need by aiming to identify and prioritise the most critical strategies to improve resilience. To achieve this, a novel framework, the Fuzzy Analytical Hierarchy Process - A fuzzy Technique for Order Performance by Similarity to Ideal Solution (FAHP-FTOPSIS), is proposed. An empirical application demonstrates that this framework effectively and systematically prioritises strategies for mitigating risks. This study examined existing mitigation strategies and identified eight that are particularly significant. Among these, collaboration and coordination, flexible transportation capabilities, and flexible supply bases emerged as the three most critical mitigation strategies for emergency supply chains. This finding emphasises the importance of prioritising these critical strategies in the development of strategic emergency supply chain plans. The practical implementation of these strategies, substantiated by empirical data from credible sources, would significantly enhance the preparedness of stakeholders and relief actors. This translates to a proactive ability to anticipate and respond to potential risk factors, ultimately leading to a more effective response to natural disasters.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 01 Mathematical Sciences; 15 Commerce, Management, Tourism and Services |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Engineering Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20) |
| Publisher: | Taylor and Francis Group |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 20 Jun 2024 14:58 |
| Last Modified: | 20 Jun 2024 15:00 |
| DOI or ID number: | 10.1080/13675567.2024.2359645 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23616 |
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