This online talk will be presented by CSH researcher William Schueller and will take place on February 11 at 3 pm via Zoom.
Please send us an email if you would like to attend the presentation at firstname.lastname@example.org
This is a two-part presentation, in which William will first tell us about his latest paper and second on how to deal with data security, data pipelines and software design
- “Where are my schnitzels? Population-level risk from disruptions in food supply networks, with the example of the pork meat in Austria”
- “Behind the curtain: Dealing with data security, data pipelines and software design”
- 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 ~23.000 business premises, ~44,000 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.
- Did you ever: swear at your screen for hours because of an unknown bug in your code? Wait an infinite time in front of the computer just to get an incomplete result? Spot inconsistency in your source data really late in your project? Forget until the day of the deadline about a random assumption that was made at the beginning? Argue with a collaborator about which version of the code was used for generating data_final_2.csv? Using concepts from software engineering good practices, you can forget all these lower significantly their frequency. I will go through some of the software design choices that we made.