Synthetic supply networks

2026-08-04

AI Summary

This study addresses the challenge that existing methods struggle to simultaneously reconcile the micro-level structure of enterprise supply chains with macroeconomic input-output tables. To bridge this gap, the authors propose an efficient synthetic network generation approach based solely on publicly available data. By integrating inter-firm connection topology with macroeconomic input-output constraints, the method achieves—without requiring proprietary information—the first scalable and reproducible synthetic supply network that maintains both microscopic realism and macroscopic consistency. The resulting networks accurately replicate key statistical properties observed in real-world data, thereby providing high-quality foundational inputs for large-scale economic modeling.

Abstract

A good representation of the population of firms and households is essential for large-scale economic models. While there exist good methods to create synthetic populations of households, creating synthetic populations of firms and, crucially, their supply chain links, is typically much harder. Here, we introduce a flexible method to create synthetic supply networks that match both the known properties of firm-level supply networks and the properties of aggregated input-output tables used in macroeconomic models. Our method is fast, and because it uses only publicly available data, it is fully reproducible and can be easily extended.

Problem

• synthetic supply networks
• firm-level supply networks
• input-output tables
• economic modeling
• synthetic populations

Innovation

• synthetic supply networks
• firm-level supply chains
• input-output tables
• economic modeling
• publicly available data