Supply chain networks are integral to modern business operations, and understanding the significance of individual nodes within these networks is vital for optimal resource allocation and network resilience. This study focuses on the problem of identifying critical nodes within supply chain data networks. Traditional methods have limitations in providing a comprehensive solution. In response, we propose a hybrid method named GDK of global structure model (GSM) with degree centrality (DC) and K-shell decomposition (KS). Our objective is to leverage the proposed method to pinpoint pivotal nodes in supply chain networks, with the aim of enhancing network efficiency and resilience. This research showcases the practical applicability of GDK in solving real-world supply chain challenges. By identifying these key nodes, organizations can proactively allocate resources and manage disruptions more effectively, ultimately improving supply chain performance. In conclusion, this study introduces GDK as a valuable tool for addressing the problem of critical node identification in supply chain data networks. The application of GDK offers a promising solution to enhance supply chain efficiency and resilience in an increasingly interconnected world.

hybrid global structure model, network analysis, supply chain network.

Received: August 29, 2024; Accepted: November 16, 2024; Published: December 12, 2024

How to cite this article: Mohd Fariduddin Mukhtar, Zuraida Abal Abas, Najiyah Safwa Khashi’ie, Siti Haryanti Hairol Anuar and Sahimel Azwal Sulaiman, Identifying critical nodes in supply chain network with the modification of global structure model, Advances and Applications in Discrete Mathematics 42(2) (2025), 163-175. https://doi.org/10.17654/0974165825011

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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