Identifizierung archetypischer Netzwerkstrukturen komplexer Lieferkettennetzwerke

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).
Research, furthermore, shows, that the impact of disruptions is especially high in case the disruptions propagate through the supply chain network, affecting supply chain entities that were initially not affected (Ivanov, Sokolov and Dolgui, 2014). This effect is also called the ripple effect, which is driven by the interdependencies between supply chain entities (Sokolov et al., 2015).
To be able to analyze the impact these dependencies have on the severity of supply chain disruptions, researchers tend to conceptualize supply chains as complex networks (Hearnshaw and Wilson, 2013). This allows investigating how network characteristics (such as node degree and path length) influence the spread and impact of disruptions.
Research shows, that networks with specific network characteristics are very resilient as compared to networks that do not possess these characteristics (Kim, Chen and Linderman, 2015). However, these networks are highly theoretical and do not represent real life supply chains.

The goal of this bachelor thesis is, therefore, to investigate given real life supply chain networks in terms of their network characteristics and to analyze how these properties change the impact of possible disruptions. Metrics relating to resilient supply chain design and the propagation of risks are to be considered. Furthermore, the potential of identifying archetypical supply chain structures to deal with various types of disruptions along the networks might be investigated. An affinity to or interest in network modelling or graph theory will be useful.


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

Hearnshaw, E. and Wilson, M. (2013) ‘A complex network approach to supply chain network theory’, International Journal of Operations & Production Management, 33. doi:10.1108/01443571311307343.

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.

Ivanov, D., Sokolov, B. and Dolgui, A. (2014) ‘The Ripple effect in supply chains: trade-off “efficiency-flexibility-resilience” in disruption management’, International Journal of Production Research, 52(7), pp. 2154–2172. doi:10.1080/00207543.2013.858836.

Kim, Y., Chen, Y.-S. and Linderman, K. (2015) ‘Supply network disruption and resilience: A network structural perspective’, Journal of Operations Management, 33–34(1), pp. 43–59. doi:10.1016/j.jom.2014.10.006.

Sokolov, B. et al. (2015) ‘Structural quantification of the ripple effect in the supply chain’, International Journal of Production Research, 54. doi:10.1080/00207543.2015.1055347.