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Propagation of disruptions in supply networks of essential goods: A population-centered perspective of systemic risk

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Listed:
  • William Schueller
  • Christian Diem
  • Melanie Hinterplattner
  • Johannes Stangl
  • Beate Conrady
  • Markus Gerschberger
  • Stefan Thurner

Abstract

The Covid-19 pandemic drastically emphasized the fragility of national and international supply networks (SNs),leading to significant supply shortages of essential goods for people, such as food and medical equipment. Severe disruptions that propagate along complex SNs can expose the population of entire regions or even countries to these risks. A lack of both, data and quantitative methodology, has hitherto hindered us to empirically quantify the vulnerability of the population to disruptions. Here we develop a data-driven simulation methodology to locally quantify actual supply losses for the population that result from the cascading of supply disruptions. We demonstrate the method on a large food SN of a European country including 22,938 business premises, 44,355 supply links and 116 local administrative districts. We rank the business premises with respect to their criticality for the districts' population with the proposed systemic risk index, SRIcrit, to identify around 30 premises that -- in case of their failure -- are expected to cause critical supply shortages in sizable fractions of the population. The new methodology is immediately policy relevant as a fact-driven and generalizable crisis management tool. This work represents a starting point for quantitatively studying SN disruptions focused on the well-being of the population.

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  • William Schueller & Christian Diem & Melanie Hinterplattner & Johannes Stangl & Beate Conrady & Markus Gerschberger & Stefan Thurner, 2022. "Propagation of disruptions in supply networks of essential goods: A population-centered perspective of systemic risk," Papers 2201.13325, arXiv.org.
  • Handle: RePEc:arx:papers:2201.13325
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    1. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    2. Christian Diem & Andr'as Borsos & Tobias Reisch & J'anos Kert'esz & Stefan Thurner, 2023. "Estimating the loss of economic predictability from aggregating firm-level production networks," Papers 2302.11451, arXiv.org.

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