ArticleScientific reports2022
Monitoring supply networks from mobile phone data for estimating the systemic risk of an economy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Inferring firm-level supply chain networks with realistic systemic risk from industry sector-level data.Scientific reports · 2026Article
- Supply chain network rewiring dynamics at the firm level.PNAS nexus · 2026Article
- Tipping points toward sustainability: The role of industrial ecology.Journal of industrial ecology · 2025Article
- Early warning signals for predicting cryptomarket vendor success using dark net forum networks.Scientific reports · 2024Article
- Tracing Opioids Across the US: A High-Resolution Pharmaceutical Distribution Dataset.Scientific data · 2024Article
- Estimating the loss of economic predictability from aggregating firm-level production networks.PNAS nexus · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Remarkably little is known about the structure, formation, and dynamics of supply- and production networks that form one foundation of society. Neither the resilience of these networks is known, nor do we have ways to systematically monitor their ongoing change. Systemic risk contributions of individual companies were hitherto not quantifiable since data on supply networks on the firm-level do not exist with the exception of a very few countries. Here we use telecommunication meta data to reconstruct nationwide firm-level supply networks in almost real-time. We find the probability of observing a supply-link, given the existence of a strong communication-link between two companies, to be about 90%. The so reconstructed supply networks allow us to reliably quantify the systemic risk of individual companies and thus obtain an estimate for a country's economic resilience. We identify about 65 companies, from a broad range of company sizes and from 22 different industry sectors, that could potentially cause massive damages. The method can be used for objectively monitoring change in production processes which might become essential during the green transition.
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Registered trials
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