Reconstructing production networks using machine learning

Luca Mungo, François Lafond, Pablo Astudillo-Estévez, J. Doyne Farmer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The vulnerability of supply chains and their role in the propagation of shocks has been highlighted multiple times in recent years, including by the recent pandemic. However, while the importance of micro data is increasingly recognised, data at the firm-to-firm level remains scarcely available. In this study, we formulate supply chain networks’ reconstruction as a link prediction problem and tackle it using machine learning, specifically Gradient Boosting. We test our approach on three different supply chain datasets and show that it works very well and outperforms three benchmarks. An analysis of features’ importance suggests that the key data underlying our predictions are firms’ industry, location, and size. To evaluate the feasibility of reconstructing a network when no production network data is available, we attempt to predict a dataset using a model trained on another dataset, showing that the model's performance, while still better than a random predictor, deteriorates substantially.

Original languageEnglish
Article number104607
JournalJournal of Economic Dynamics and Control
Volume148
DOIs
StatePublished - Mar 2023

Keywords

  • Link prediction
  • Machine learning
  • Network reconstruction
  • Supply chains

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