The failure tolerance of mechatronic software systems to random and targeted attacks

This paper describes a complex networks approach to study the failure tolerance of mechatronic software systems under various types of hardware and/or software failures. We produce synthetic system architectures based on evidence of modular and hierarchical modular product architectures and known motifs for the interconnection of physical components to software. The system architectures are then subject to various forms of attack. The attacks simulate failure of critical hardware or software. Four types of attack are investigated: degree centrality, betweenness centrality, closeness centrality and random attack. Failure tolerance of the system is measured by a 'robustness coefficient', a topological 'size' metric of the connectedness of the attacked network. We find that the betweenness centrality attack results in the most significant reduction in the robustness coefficient, confirming betweenness centrality, rather than the number of connections (i.e. degree), as the most conservative metric of component importance. A counter-intuitive finding is that "designed" system architectures, including a bus, ring, and star architecture, are not significantly more failure-tolerant than interconnections with no prescribed architecture, that is, a random architecture. Our research provides a data-driven approach to engineer the architecture of mechatronic software systems for failure tolerance.Comment: Proceedings of the 2013 ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2013 August 4-7, 2013, Portland, Oregon, USA (In Print

A network science approach to analysing manufacturing sector supply chain networks: Insights on topology

Due to the increasingly complex nature of the modern supply chain networks (SCNs), a recent research trend has focussed on modelling SCNs as complex adaptive systems. Despite the substantial number of studies devoted to such hypothetical modelling efforts, studies analysing the topological properties of real world SCNs have been relatively rare, mainly due to the scarcity of data. This paper aims to analyse the topological properties of twenty-six SCNs from the manufacturing sector. Moreover, this study aims to establish a general set of topological characteristics that can be observed in real world SCNs from the manufacturing sector, so that future theoretical work modelling the growth of SCNs in this sector can mimic these observations. It is found that the manufacturing sector SCNs tend to be scale free with degree exponents below two, tending towards hub and spoke configuration, as opposed to most other scale-free networks which have degree exponents above two. This observation becomes significant, since the importance of the degree exponent threshold of two in shaping the growth process of networks is well understood in network science. Other observed topological characteristics of the SCNs include disassortative mixing (in terms of node degree as well as node characteristics) and high modularity. In some networks, we find that node centrality is strongly correlated with the value added by each node to the supply chain. Since the growth mechanism that is most widely used to model the evolution of SCNs, the Barabasi - Albert model, does not generate scale-free topologies with degree exponent below two, it is concluded that a novel mechanism to model the growth of SCNs is required to be developed

Topological Structure of Manufacturing Industry Supply Chain Networks

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