SCAIL · Supply Chain AI Lab Data Hub

Complex Supply Networks

Yunbo Long 439 words 3 minutes Network Science Resilience Risk Management

Complex supply networks represent a growing area of research that applies network science to understand the structural properties and emergent behaviours of modern supply chains. Unlike traditional linear models of supply chains, network-based perspectives capture the intricate web of relationships between suppliers, manufacturers, distributors, and customers—revealing patterns that are invisible at the firm level.

Key research questions in this field include: How do network structures influence the propagation of disruptions? What makes certain supply networks more resilient than others? How can firms identify critical nodes and bottlenecks? By applying tools from graph theory, statistical physics, and computational network analysis, researchers have uncovered important insights about clustering, centrality, and the scale-free properties of real supply networks.

Pioneering work from researchers at institutions including the Supply Chain AI Lab at the University of Cambridge has demonstrated that supply networks exhibit complex topological features—such as community structure and small-world properties—that have direct implications for risk management and operational strategy.

We invite you to explore the curated collection of key publications below, tracing the development of complex network approaches in supply chain research.

List of Publications

  1. Brintrup, A., Wang, Y. and Tiwari, A., 2017. Supply networks as complex systems: A network-science-based characterization. IEEE Systems Journal, 11(4), pp.2170-2181. [PDF]
  2. Ledwoch, A., Brintrup, A., Mehnen, J. and Tiwari, A., 2018. Systemic risk assessment in complex supply networks. IEEE Systems Journal, 12(2), pp.1826-1837. [PDF]
  3. Brintrup, A., Wichmann, P., Woodall, P., McFarlane, D., Nicks, E. and Krechel, W., 2018. Predicting hidden links in supply networks. Complexity, 2018, 9104387. [PDF]
  4. Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P. and McFarlane, D., 2020. Supply chain data analytics for predicting supplier disruptions: A case study in complex asset manufacturing. International Journal of Production Research, 58(11), pp.3330-3341. [PDF]
  5. Kosasih, E.E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N. and Brintrup, A., 2024. Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research, 62(15), pp.5596-5612. [PDF]
  6. Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G. and MacCarthy, B.L., 2024. Digital supply chain surveillance using artificial intelligence: Definitions, opportunities and risks. International Journal of Production Research, 62(13), pp.4674-4695. [PDF]