Investigating the role of knowledge-based supply chains for supply chain resilience by graph theory matrix approach

Deniz Sezer, Muruvvet, Ozbiltekin-Pala, Melisa, Kazancoglu, Yigit, Garza-Reyes, Jose Arturo, Kumar, Anil and Kumar, Vikas (2023) Investigating the role of knowledge-based supply chains for supply chain resilience by graph theory matrix approach. Operations Management Research. ISSN 1936-9735 (In Press)

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Abstract / Description

Nowadays, providing information flow at every phase of a knowledge-based supply chain with technologies has become a vital issue due to rapid population growth, globalisation, and increases in demand in the supply chain. Knowledge-based supply chains have a critical role in increasing resilience in supply chain processes with emerging technologies. Thus, it is necessary to determine the critical factors that increase SC resilience. Therefore, this study aims to determine SC resilience improvement factors in knowledge-based supply chains and investigate the importance level of determining factors using the Graph Theory Matrix Approach. The results suggest that the most important supply chain resilience improvement factor is Adaptive Capacity (F3), followed by Product Prioritization (F9) and Flexibility (F1), respectively. This study is expected to benefit managers and policymakers as it provides a better understanding of critical SC resilience improvement factors that play a role in knowledge-based supply chains. In order to increase resilience in the supply chain, system thinking and solutions should be encouraged by businesses to increase collaboration with stakeholders. Businesses and governments should provide collaborative long-term solutions for the uncertain environment to ensure a sustainable and resilient environment.

Item Type:	Article
Uncontrolled Keywords:	Knowledge-Based, Resilience, Supply Chain, Graph Theory Matrix Approach
Subjects:	600 Technology > 650 Management & auxiliary services
Department:	Guildhall School of Business and Law
Depositing User:	Anil Kumar
Date Deposited:	05 Jun 2023 08:33
Last Modified:	05 Jun 2023 08:33
URI:	https://repository.londonmet.ac.uk/id/eprint/8567

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