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IDENTIFYING CRITICAL NODES IN SUPPLY CHAIN NETWORK WITH THE MODIFICATION OF GLOBAL STRUCTURE MODEL
Keywords:
hybrid global structure model, network analysis, supply chain networkDOI:
https://doi.org/10.17654/0974165825011Abstract
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.
Received: August 29, 2024
Accepted: November 16, 2024
References
Y. Li Gao, S. Qu and M. Xu, Supply chain-wide sectoral water use characteristics based on multi-perspective measurements, J. Clean Prod. 268 (2020), 122345.
F. Wu, Z. Sun, F. Wang and Q. Zhang, Identification of the critical transmission sectors and typology of industrial water use for supply-chain water pressure mitigation, Resour. Conserv. Recycl. 131 (2017), 305-312.
B. Chae, Developing key performance indicators for supply chain: An industry perspective, Supply Chain Management 14 (2009), 422-428.
E. M. Ferragi, Integrating supply chain and production chain: a genesis in the ethanol industry, Journal of Operations and Supply Chain Management 9 (2019), 129.
C. Braziotis, M. Bourlakis, H. Rogers and J. Tannock, Supply chains and supply networks: Distinctions and overlaps, Supply Chain Management 18 (2013), 644-652.
M. N. Faisal, D. K. Banwet and R. Shankar, Information risks management in supply chains: An assessment and mitigation framework, Journal of Enterprise Information Management 20 (2007), 677-679.
R. Edgeman and Z. Wu, Supply chain criticality in sustainable and resilient enterprises, Journal of Modelling in Management 11(4) (2016), 869-888.
S. Wu, Y. Cao and L. He, Temporal changes of carbon emission transmissions in China’s supply chain, 1997-2017, J. Clean. Prod. 269 (2020), 122367.
T. Hanaka, S. Kagawa, H. Ono and K. Kanemoto, Finding environmentally critical transmission sectors, transactions, and paths in global supply chain networks, Energy Econ. 68 (2017), 44-52.
A. Ullah, B. Wang, J. Sheng, J. Long, N. Khan and Z. Sun, Identification of nodes influence based on global structure model in complex networks, Scientific Reports (2021), 6173.
Mohd. Fariduddin Mukhtar, Zuraida Abal Abas, Amir Hamzah Abdul Rasib, Siti Haryanti Hairol Anuar, Nurul Hafizah Mohd. Zaki, Zaheera Zainal Abidin, Siti Azirah Asmai and Ahmad Fadzli Nizam Abdul Rahman, Global structure model modification to improve influential node detection, ARPN JEAS 18 (2023), 220 225.
M. F. Mukhtar, Z. A. Abas, A. S. Baharuddin, M. N. Norizan, W. F. W. W. Fakhruddin, W. Minato, A. H. A. Rasib, Z. Z. Abidin, A. F. N. A. Rahman and S. H. H. Anuar, Integrating local and global information to identify influential nodes in complex networks, Scientific Reports 13(1) (2023), 11411.
S. Gao, J. Ma, Z. Chen, G. Wang and C. Xing, Ranking the spreading ability of nodes in complex networks based on local structure, Physica A: Statistical Mechanics and its Applications 403 (2014), 130-147.
A. Namtirtha, A. Dutta and B. Dutta, Weighted k-shell degree neighborhood: A new method for identifying the influential spreaders from a variety of complex network connectivity structures, Expert Syst. Appl. 139 (2020), 107-117.
A. Simsek, Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model, Journal of King Saud University - Computer and Information Sciences 34 (2022), 4810-4820.
M. G. Kendall, A new measure of rank correlation, Biometrika 30 (1938), 81-93.
D. A. Walker, JMASM9: Converting Kendall’s Tau for Correlational or Meta- Analytic Analyses, 2003.
S. H. H. Anuar, Z. A. Abas, N. M. Yunos, N. H. M. Zaki, N. A. Hashim and M. F. Mokhtar, Comparison between Louvain and Leiden algorithm for network structure: a review, Journal of Physics: Conference Series, 2129 (2023), 012028.
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