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

Abstract

Due to the increasingly complex and interconnected nature of global supply chain networks (SCNs), a recent strand of research has applied network science methods to model SCN growth and subsequently analyse various topological features, such as robustness. This paper provides: (1) a comprehensive review of the methodologies adopted in literature for modelling the topology and robustness of SCNs; (2) a summary of topological features of the real world SCNs, as reported in various data driven studies; and (3) a discussion on the limitations of existing network growth models to realistically represent the observed topological characteristics of SCNs. Finally, a novel perspective is proposed to mimic the SCN topologies reported in empirical studies, through fitness based generative network models.

Keywords: Fitness based attachment; Network science; Supply chain network modelling; Supply network topology and robustness.

Fig. 1

Comparison of random, small-world and scale free networks. Topological structure of benchmark network models. Random and Small-world network topologies do not include hub nodes. In contrast, scale-free topologies are characterised by the presence of small number of highly connected hub nodes and a high number of feebly connected nodes. Presence of distinct hubs in scale-free networks make them more vulnerable to targeted attacks, compared to random and small-world networks

Fig. 2

Modelling Perspectives obtained from Generative and Evolving Network Models

Fig. 3

General methodological framework of research on topology and robustness of SCNs

Fig. 4

Transitions from random to winner-take-all graphs observed as σ parameter is increased