Researchers create computer risk model to tackle supply chain disruption
4 min read
Science & Technology
Using mobile phone data, a team at the Complexity Science Hub Vienna mapped an entire nation’s supply chain network. The computer model, which predicts what’s at stake if supply chains break down, can be easily implemented by other countries



[Vienna, August 8 2022] Researchers at the Complexity Science Hub Vienna (CSH) used simple telecommunications data to map an entire country’s production network, including all relevant firms and supply relationships with their clients. The computer model, based on mobile phone data, was able to predict the systemic risk of each company within the country and estimate their resilience.

“Our model is cheap, scalable, and easy to implement by any country that has telecommunications data available. It allows leaders to prepare their economies for future shocks that have the capacity to inflict substantial disruption in their supply chains,” explains CSH scientist Tobias Reisch, one of the co-authors of the study published in the journal Scientific Reports available online June 3rd.

Additionally, the new method provides an image of the country’s entire economic behavior on a timescale of a single day, highlights Stefan Thurner, CSH president and co-author of the study.

“This precision enables monitoring of short-term changes in the economy, such as the effects of Chinese Covid-19 lockdowns on European companies from day to day. A standard economic data set can’t provide this kind of information due to delays of several months or even years,” says Thurner. “We might be able to use this new method to strategically increase the resilience of the economy under shocks and crises. And it comes at practically zero cost.”

Anonymized data

The team reconstructed the supply chain network of a medium-sized European country based on mobile phone data provided by a telecommunications service provider. The dataset – which includes various attributes of the call, such as time, duration, completion status, source number, and destination number – corresponds to tens of thousands of companies. The data, which was (…)