An essential component of large-scale economic agent-based models is a good representation of the population of firms and households. However, firm- and household-level data are often scarce and difficult to access, highlighting the need to construct synthetic populations of economic agents. 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 approach employs gradient-based optimization in an innovative but straightforward manner, leveraging only publicly available data. It is fully reproducible, open source, and can be easily extended.