  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.
