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IDENTIFYING CRITICAL NODES IN SUPPLY CHAIN NETWORK WITH THE MODIFICATION OF GLOBAL STRUCTURE MODEL.

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Title: IDENTIFYING CRITICAL NODES IN SUPPLY CHAIN NETWORK WITH THE MODIFICATION OF GLOBAL STRUCTURE MODEL.
Authors: Mukhtar, Mohd Fariduddin1 fariduddin@utem.edu.my, Abas, Zuraida Abal2 zuraidaa@utem.edu.my, Khashi'ie, Najiyah Safwa1, Hairol Anuar, Siti Haryanti3 sitiharyanti@utem.edu.my, Sulaiman, Sahimel Azwal4 sahimel@ump.edu.my
Source: Advances & Applications in Discrete Mathematics. Feb2025, Vol. 42 Issue 2, p163-175. 13p.
Subjects: Structural frame models, Supply chains, Resource allocation
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Advances & Applications in Discrete Mathematics is the property of Pushpa Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: IDENTIFYING CRITICAL NODES IN SUPPLY CHAIN NETWORK WITH THE MODIFICATION OF GLOBAL STRUCTURE MODEL.
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  Data: <searchLink fieldCode="JN" term="%22Advances+%26+Applications+in+Discrete+Mathematics%22">Advances & Applications in Discrete Mathematics</searchLink>. Feb2025, Vol. 42 Issue 2, p163-175. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Structural+frame+models%22">Structural frame models</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chains%22">Supply chains</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances & Applications in Discrete Mathematics is the property of Pushpa Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Feb2025
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