Using complex network theory to model supply chain network resilience: a review of current literature

Traditionally, supply networks are modelled as multi-agent systems, in order to represent explicit communications between various entities involved. However, due to the increasingly complex and interconnected nature of the global supply networks, a recent trend of research work has focussed on modelling supply networks as complex adaptive systems. This approach has enabled researchers to investigate various topological properties which give rise to resilience characteristics in a given supply network. This paper presents a critical review of the published research work on this field. Key insights provided by this paper include; (1) the importance of defining the concepts of ‘resilience’ and ‘disruptions’ as measurable variables; (2) the limitations of existing network models to realistically represent supply networks; (3) potential improvements to the currently used growth mechanisms, which rely on node ‘degree’ to derive attachment probability instead of the more realistic and relevant node ‘fitness’; (4) importance of incorporating operational aspects, such as flows, costs, and capacities of connections between the nodes as well as topological aspects; and (5) derivation of a new set of resilience metrics capturing operational as well as topological aspects. Finally, a conceptual approach incorporating the above improvements to the existing supply network modelling approach is presented

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

Topological robustness of the global automotive industry

The manufacturing industry is characterized by large-scale interdependent networks as companies buy goods from one another, but do not control or design the overall flow of materials. The result is a complex emergent structure with which companies connect to each other. The topology of this structure impacts the industry’s robustness to disruptions in companies, countries, and regions. In this work, we propose an analysis framework for examining robustness in the manufacturing industry and validate it using an empirical dataset. Focusing on two key angles, suppliers and products, we highlight macroscopic and microscopic characteristics of the network and shed light on vulnerabilities of the system. It is shown that large-scale data on structural interdependencies can be examined with measures based on network science

Network robustness improvement via long-range links

Abstract Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies