Graduate School of Simulation Studies, The University of Hyogo, Kobe, Japan.
Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Kyoto, Japan.
PLoS One. 2020 Oct 1;15(10):e0239669. doi: 10.1371/journal.pone.0239669. eCollection 2020.
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 γin = 2.42 and γout = 2.11. The clustering coefficient decays [Formula: see text] with an exponent βk = 0.46. The nodal degree-degree correlations 〈knn(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.
在本文中，我们研究了全球供应链网络的拓扑性质，包括其度分布、聚类系数、度-度相关性、蝴蝶结结构和社区结构，以检验 E.J.S.赫恩肖等人提出的有效供应链命题。我们通过从标准普尔资本智商平台的网站上收集各种公司数据来构建 2017 年的全球供应链数据。入度和出度分布的形式为γin = 2.42 和 γout = 2.11 的幂律。聚类系数随指数βk = 0.46 衰减。节点度-度相关性〈knn(k)〉表明不存在正关联。巨大弱连通组件 (GWCC) 的蝴蝶结结构表明 OUT 组件是最大的，占所有公司的 41.1%。巨大强连通组件 (GSCC) 由所有公司的 16.4%组成。我们观察到上游或下游公司距离 GSCC 只有几步之遥。此外，我们还揭示了网络的社区结构，并根据它们的位置和行业分类对其进行了特征描述。我们观察到最大的社区由主要位于美国（US）的消费者可选部门组成。这些公司属于全球供应链网络蝴蝶结结构中的 OUT 组件。最后，我们确认了赫恩肖等人的有效供应链命题的有效性，即命题 S1（短路径长度）、命题 S2（幂律度分布）、命题 S3（高聚类系数）、命题 S4（“适合变得更富有”的增长机制）、命题 S5（幂律度分布的截断）和命题 S7（具有重叠边界的社区结构），这些命题涉及全球供应链网络。虽然前面提到的原始命题 S1 只提到了短路径长度，但我们通过分析蝴蝶结结构发现了从 GSCC 到 IN 和 OUT 的短路径。因此，蝴蝶结结构中的短路径长度是赫恩肖原始命题的一个概念性补充。
