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Testing “efficient supply chain propositions” using topological characterization of the global supply chain network
by
Chakraborty, Abhijit
, Ikeda, Yuichi
in
Alliances
/ Analysis
/ Bankruptcy
/ Biology and Life Sciences
/ Business logistics
/ Clustering
/ Coefficients
/ Community structure
/ Computer and Information Sciences
/ Correlation
/ Ecology and Environmental Sciences
/ Engineering and Technology
/ Global economy
/ Growth models
/ International trade
/ People and places
/ Physical Sciences
/ Power law
/ Research and Analysis Methods
/ Suppliers
/ Supply chains
/ Systems analysis
/ Topology
/ Websites
2020
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Testing “efficient supply chain propositions” using topological characterization of the global supply chain network
by
Chakraborty, Abhijit
, Ikeda, Yuichi
in
Alliances
/ Analysis
/ Bankruptcy
/ Biology and Life Sciences
/ Business logistics
/ Clustering
/ Coefficients
/ Community structure
/ Computer and Information Sciences
/ Correlation
/ Ecology and Environmental Sciences
/ Engineering and Technology
/ Global economy
/ Growth models
/ International trade
/ People and places
/ Physical Sciences
/ Power law
/ Research and Analysis Methods
/ Suppliers
/ Supply chains
/ Systems analysis
/ Topology
/ Websites
2020
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Testing “efficient supply chain propositions” using topological characterization of the global supply chain network
by
Chakraborty, Abhijit
, Ikeda, Yuichi
in
Alliances
/ Analysis
/ Bankruptcy
/ Biology and Life Sciences
/ Business logistics
/ Clustering
/ Coefficients
/ Community structure
/ Computer and Information Sciences
/ Correlation
/ Ecology and Environmental Sciences
/ Engineering and Technology
/ Global economy
/ Growth models
/ International trade
/ People and places
/ Physical Sciences
/ Power law
/ Research and Analysis Methods
/ Suppliers
/ Supply chains
/ Systems analysis
/ Topology
/ Websites
2020
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Testing “efficient supply chain propositions” using topological characterization of the global supply chain network
Journal Article
Testing “efficient supply chain propositions” using topological characterization of the global supply chain network
2020
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Overview
In this paper, we study the topological properties of the global supply chain network in terms of its degree distribution, clustering coefficient, degree-degree correlation, bow-tie structure, and community structure to test the efficient supply chain propositions proposed by E. J.S. Hearnshaw et al. The global supply chain data in the year 2017 are constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform. The in- and out-degree distributions are characterized by a power law of the form of [gamma].sub.in = 2.42 and [gamma].sub.out = 2.11. The clustering coefficient decays ( C ( k) ) ~ k - [beta] k with an exponent [beta].sub.k = 0.46. The nodal degree-degree correlations (k.sub.nn (k)) indicates the absence of assortativity. The bow-tie structure of giant weakly connected component (GWCC) reveals that the OUT component is the largest and consists 41.1% of all firms. The giant strong connected component (GSCC) is comprised of 16.4% of all firms. We observe that upstream or downstream firms are located a few steps away from the GSCC. Furthermore, we uncover the community structures of the network and characterize them according to their location and industry classification. We observe that the largest community consists of the consumer discretionary sector based mainly in the United States (US). These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity of Hearnshaw et al.'s efficient supply chain propositions, namely Proposition S1 (short path length), Proposition S2 (power-law degree distribution), Proposition S3 (high clustering coefficient), Proposition S4 (\"fit-gets-richer\" growth mechanism), Proposition S5 (truncation of power-law degree distribution), and Proposition S7 (community structure with overlapping boundaries) regarding the global supply chain network. While the original propositions S1 just mentioned a short path length, we found the short path from the GSCC to IN and OUT by analyzing the bow-tie structure. Therefore, the short path length in the bow-tie structure is a conceptual addition to the original propositions of Hearnshaw.
Publisher
Public Library of Science,Public Library of Science (PLoS)
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