Model identification for simulating supply chain disruptions

Recent events like the Corona Pandemic and the blockage of the Suez Canal show the susceptibility of supply chains to disruptions. Those disruptions have detrimental impacts on supply chain performance (Hendricks and Singhal, 2005), which urges supply chain managers to increase supply chain resilience (Christopher and Peck, 2004).

A common focus of supply chain resilience research is to analyze how disruptions cascade through supply chain networks (the ripple effect), affecting supply chain performance (Ivanov, 2017). The severity of the disruption is mainly influenced by two aspects: the supply chain network structure (Falasca, Zobel and Cook, 2008) and the propagation of the disruption between supply chain entities (Li et al., 2020).

For modeling the impact of disruptions on supply chain networks, graph theory is used, which offers a simplified view on supply chain networks: Supply chain entities (suppliers, manufacturers, customers,) are illustrated as nodes, the transportation routes between two entities as links connecting two nodes. Disruptions are modeled by removing nodes and/or edges from the initial graph.

Approaches for modeling the propagation of disruptions are manifold, and include (but are not limited to) Markov Chains, Bayesian Networks, and percolation theory. These approaches all have their benefits, but often come with a flaw.

The goal of this thesis is to deploy requirements that are crucial for modeling supply chain disruptions and their propagation and, subsequently, investigate and compare suitable approaches.


Christopher, M. and Peck, H. (2004) ‘Building the Resilient Supply Chain’, The International Journal of Logistics Management [Preprint]. doi:10.1108/09574090410700275.

Hendricks, K.B. and Singhal, V.R. (2005) ‘Association Between Supply Chain Glitches and Operating Performance’, Management Science, 51(5), pp. 695–711. doi:10.1287/mnsc.1040.0353.

Falasca, M., Zobel, C. and Cook, D. (2008) ‘A decision support framework to assess supply chain resilience’, Proceedings of ISCRAM 2008 - 5th International Conference on Information Systems for Crisis Response and Management [Preprint].

Ivanov, D. (2017) ‘Simulation-based ripple effect modelling in the supply chain’, International Journal of Production Research, 55(7), pp. 2083–2101. doi:10.1080/00207543.2016.1275873.

Li, Y. et al. (2020) ‘Ripple Effect in the Supply Chain Network: Forward and Backward Disruption Propagation, Network Health and Firm Vulnerability’, European Journal of Operational Research [Preprint]. doi:10.1016/j.ejor.2020.09.053.