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Table 3 Summary of empirical studies of SCN topologies

From: Network science approach to modelling the topology and robustness of supply chain networks: a review and perspective

Study Data source and SCNs considered Key findings
Parhi (2008) Customer-supplier linkage network in the Indian auto component industry has been considered (618 firms), using the data from the Auto Component Manufacturers Association of India. The Indian auto component industry SCN was found to be scale free in topology, with a power-law exponent, γ = 2.52a.
Keqiang et al. (2008) Guangzhou automotive industry supply chain network has been investigated. Data has been collected from 94 manufacturers, between November 2007 and January 2008. Guangzhou automotive industry SCN was found to be scale-free in topology. Based on the data presented by the authors, we have calculated the power-law exponent of the degree distribution, γ to be 2.02.
Kim et al. (2011) Three case studies of automotive supply networks (namely, Honda Accord, Acura CL/TL, and Daimler Chrysler Grand Cherokee) presented by Choi and Hong (2002). This study has developed SCN constructs based on a number of key network and node level analysis metrics. In particular, the roles played by central firms, as identified by various network centrality measures, have been outlined in the context of SCNs.
Büttner et al. (2013) Present network analysis results for a pork supply chain of a producer community in Northern Germany. Data has been obtained by the producer community for a period of 3 years. Reports that the degree distribution of the SCN follows power law (in and out degree distributions follow power-law with power-law exponents, γ = 1.50 and γ = 1.00, respectively). Disassortative mixingb has been observed in terms of node degree.
Kito et al. (2014) A SCN for Toyota has been constructed using the data available within an online database operated by Marklines Automotive Information Platform. The authors have identified the tier structure of Toyota to be barrel-shaped, in contrast to the previously hypothesized pyramidal structure. Another fundamental observation reported in this study is that Toyota SCN topology was found to be not scale free.
Brintrup et al. (2015) Airbus SCN data obtained from Bloomberg database. Reports that the Airbus SCN illustrates power-law degree distribution, i.e. scale free topology, with a power-law exponent, γ = 2.25a. Assortative mixing was observed based on node degree and community structures were found based on geographic locations of the firms.
Gang et al. (2015) Authors have investigated the urban SCN of agricultural products in mainland China. Data collection is based on author observations over 2 years. The SCN of agricultural products was found to be scale free in topology, with a power-law exponent, γ = 2.75. High levels of disassortative mixingb has been observed in terms of node degree.
Orenstein (2016) SCN data for food (General Mills, Kellogg’s and Mondelez) and retail (Nike, Lowes and Home Depot) industries have been obtained from Bloomberg database. The SCNs considered in this study were found to have scale free topologies with γ < 2. In particular, for the food industry SCNs for General Mills, Kellogg’s and Mondelez were found to have γ = 1.25, 1.47 and 1.56, respectively. For the retail industry, the SCNs for Nike, Lowes and Home Depot were found to have γ = 1.83, 1.73 and 1.67, respectively.
Perera et al. (2016b) Analysis has been undertaken for 26 SCNs (which include more than 100 firms) out of 38 multi echelon SCNs presented in Willems (2008) for various manufacturing sector industries. 22 out of the 26 SCNs analysed display 80% or higher correlation with a power-law fit, with power-law exponent γ = 2.4 (on average). Furthermore, these SCNs were found to be highly modularb and robust against random failures. Also, disassortative mixingb was observed on these SCNs.
Sun et al. (2017) A GIS based SCN structure has been simulated for the automobile industry using the data of top twelve car brands of Chinese market in recent five years as basic parameters. The Chinese automobile SCN simulated using real world data as basic parameters, indicates that the degree distribution conforms to the power-law, with a power-law exponent, γ = 3.32.
  1. aNote that in some research papers, the power-law exponent is presented for the cumulative degree distribution. In such cases, the power-law exponent of the degree distribution has been established by adding 1 to the power-law exponent of the cumulative degree distribution since the power-law exponent of the cumulative degree distribution is 1 less than the power-law exponent of the degree distribution (Newman, 2005). These instances have been identified with an asterisk in Table 2
  2. bRefer to Appendix 1 for detailed definitions (including mathematical formulations) of these metrics