[Paper Review] Network Science approach to Modelling Emergence and Topological Robustness of Supply Networks: A Review and Perspective

Supun Perera, Michael G.H. Bell+1 more|arXiv (Cornell University)|Mar 27, 2018

Complex Network Analysis Techniques79 references3 citations

TL;DR

This paper reviews network science approaches to modeling supply chain network (SCN) topology and robustness, identifying limitations in existing generative models. It proposes a fitness-based network model to better replicate empirical SCN structures, improving realism in simulating emergence and topological resilience in complex supply networks.

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.

Motivation & Objective

• To review and synthesize existing methodologies for modeling supply chain network (SCN) topology and robustness using network science.
• To summarize empirically observed topological features of real-world SCNs from data-driven studies.
• To identify shortcomings in current network growth models in replicating observed SCN characteristics.
• To propose a novel fitness-based generative network model that better mimics real-world SCN topologies.
• To enhance understanding of SCN emergence and topological robustness through improved modeling frameworks.

Proposed method

• Systematic review of literature on network science applications to supply chain networks (SCNs).
• Analysis of empirical data from real-world SCNs to extract key topological features such as degree distribution, clustering, and path length.
• Evaluation of existing network growth models (e.g., preferential attachment, small-world models) for their ability to reproduce observed SCN structures.
• Development of a fitness-based generative network model where node connectivity depends on intrinsic fitness values, enabling more realistic network evolution.
• Comparison of synthetic networks generated by the fitness model with real-world SCN data to validate topological fidelity.
• Use of network metrics such as degree distribution, betweenness centrality, and robustness under node/edge removal to assess model performance.

Experimental results

Research questions

• RQ1 What are the dominant network science methodologies used to model the topology and robustness of supply chain networks?
• RQ2 What key topological features characterize real-world supply chain networks as revealed by empirical data?
• RQ3 Why do existing network growth models fail to accurately replicate the structural properties observed in real SCNs?
• RQ4 How can fitness-based generative models improve the realism of synthetic supply chain network topologies?
• RQ5 To what extent does the proposed fitness-based model reproduce the topological robustness and emergence patterns seen in real SCNs?

Key findings

• Real-world supply chain networks exhibit scale-free and small-world characteristics, with high clustering and short path lengths.
• Existing network growth models such as preferential attachment often fail to reproduce the specific degree distributions and clustering patterns observed in empirical SCNs.
• The fitness-based generative model successfully replicates key topological features of real SCNs, including power-law degree distributions and high clustering.
• The proposed model demonstrates improved topological robustness under targeted and random node/edge removal compared to traditional models.
• Fitness-based models better capture the heterogeneous roles of suppliers and manufacturers in real supply chains, reflecting real-world asymmetries in connectivity.
• The study identifies a gap in current modeling approaches, highlighting the need for models that incorporate node-specific fitness to reflect strategic and operational differences in supply networks.