Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets14 Sep 2020
International supply chain networks play a prominent role in shaping the economic outlook of the world. It has been a recent trend to analyse the topology of supply chain networks in order to gain a wholistic understanding about the interdependencies of firms in this regard. In this work, we undertake an extensive structural and topological analysis of the supply chain networks constructed from the Factset Revere dataset. The dataset is provided by FactSet Research Systems Inc. that captures global supply chain relationships between companies. The dataset consists of 154, 862 companies from 216 countries, with 1,571, 949 supply relationships among them. In addition to considering the global network, we also analysed country-specific networks of ten countries, which are the most significant nations represented in the dataset. The analysis revealed that all supply chain networks studied were relatively sparse scale-free networks, with scale-free exponents ranging from 1.0 to 2.0. In terms of centrality analysis, quite predictably, large multi-national corporates dominated. Comparing the centrality values of firms in terms of the global vs the country-specific networks, two classes of firms were found where the difference in centrality was significant. The first group was small firms with locally-centered business operations, such as Volunteers of America, New York State Teachers Retirement System, CarePlus Health Plan etc, where the country-based centrality scores and the rankings based on them were significantly more prominent than the global equivalent. The second group was firms with specific countries of origin which register themselves in other countries, such as China Shengda Packaging Group Inc (registered in US), Chinacast Education Corps (registered in the US), and China Biologic Products Inc (registered in the US). These firms all had significantly higher global centrality scores compared to country-based centrality scores. Overall, however, it was found that there was strong correlation between global centrality-based ranking and country-specific centrality ranking of firms. This indicated that in general, firms which are important to the global supply chain network are also important to the supply chain networks of individual countries. Studying the community structure of the supply chain networks, we identified twelve dominant communities, many of which had significant correlations with particular industries or countries. Some of these communities were made of firms primarily from a pair of countries, or had other interesting features. Therefore, the topological analysis of the supply chain networks created from this large dataset gives interesting insights about how the international supply chain networks are structured, and how they operate.