Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090, Vienna, Austria.
Complexity Science Hub Vienna, 1080, Vienna, Austria.
Sci Rep. 2022 Aug 3;12(1):13347. doi: 10.1038/s41598-022-13104-5.
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.
关于构成社会基础之一的供应和生产网络的结构、形成和动态，人们知之甚少。这些网络的弹性尚不清楚，我们也没有系统监测其持续变化的方法。由于除了极少数国家外，企业层面的供应网络数据并不存在，因此个别公司的系统性风险贡献此前是无法量化的。在这里，我们使用电信元数据近乎实时地重建全国性的企业层面供应网络。我们发现，给定两家公司之间存在强通信链路，观察到供应链路的概率约为 90%。通过重建这些供应网络，我们可以可靠地量化个别公司的系统性风险，从而对一个国家的经济弹性做出估计。我们确定了大约 65 家公司，它们来自广泛的公司规模和 22 个不同的行业部门，这些公司可能会造成巨大的损失。该方法可用于客观监测生产过程的变化，这在绿色转型期间可能变得至关重要。
