Topological Structure of Manufacturing Industry Supply Chain Networks

Abstract

Empirical analyses of supply chain networks (SCNs) in extant literature have been rare due to scarcity of data. As a result, theoretical research have relied on arbitrary growth models to generate network topologies supposedly representative of real-world SCNs. Our study is aimed at filling the above gap by systematically analysing a set of manufacturing sector SCNs to establish their topological characteristics. In particular, we compare the differences in topologies of undirected contractual relationships (UCR) and directed material flow (DMF) SCNs. The DMF SCNs are different from the typical UCR SCNs since they are characterised by a strictly tiered and an acyclic structure which does not permit clustering. Additionally, we investigate the SCNs for any self-organized topological features. We find that most SCNs indicate disassortative mixing and power law distribution in terms of interfirm connections. Furthermore, compared to randomised ensembles, self-organized topological features were evident in some SCNs in the form of either overrepresented regimes of moderate betweenness firms or underrepresented regimes of low betweenness firms. Finally, we introduce a simple and intuitive method for estimating the robustness of DMF SCNs, considering the loss of demand due to firm disruptions. Our work could be used as a benchmark for any future analyses of SCNs.

Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks

“…The following section outlines the methodology proposed to evaluate the impact of localised disruptions on SCNs, along with a worked example. Figure 3 illustrates a sample four-tier SCN which is structurally similar to that of a directed material flow SCNas discussed in [6]. The top tier (nodes 1 and 2) represent the suppliers, the second tier (nodes 3 and 4) represent the manufacturers, the third tier (nodes 5 and 6) represents the distributors and the fourth tier (node 7) represents a single retailer in this case.…”

“…Due to the difficulty in obtaining large scale datasets on SCNs (supplier-customer relationships), which are often proprietary and confidential, early studies have relied on computer simulations to generate network topologies (through various growth mechanisms) supposedly representative of real world SCNs [3]. Recently however, a number of data driven studies have appeared in literature, which used Bloomberg database to obtain SCN data for publicly listed firms [5,6,7]. These network-based models of complex supply chain systems have shown the existence of non-trivial and universal topological footprints, from which valuable system level insights can be obtained [6,7,8].…”

“…Recently however, a number of data driven studies have appeared in literature, which used Bloomberg database to obtain SCN data for publicly listed firms [5,6,7]. These network-based models of complex supply chain systems have shown the existence of non-trivial and universal topological footprints, from which valuable system level insights can be obtained [6,7,8]. For instance, studies have found that the SCNs tend to be scale free in nature (with distinct hub firms), where the distribution of the number of firm level connections in the SCN (i.e.…”

“…For instance, studies have found that the SCNs tend to be scale free in nature (with distinct hub firms), where the distribution of the number of firm level connections in the SCN (i.e. the degree distribution) follows power-law with the power law exponent between 2 and 3 [6].…”

“…1. Analytically determining the topological metrics of the networks, such as the degree distribution, average path These metrics reveal the structural features of the SCN which have direct or indirect robustness implications [6,7,8,10,11,12].…”