Reconstructing production networks using machine learning

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

Journal of Economic Dynamics and Control, 2023, vol. 148, issue C

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.

Keywords: Supply chains; Network reconstruction; Link prediction; Machine learning (search for similar items in EconPapers)
JEL-codes: C53 C67 C81 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:148:y:2023:i:c:s0165188923000131

DOI: 10.1016/j.jedc.2023.104607

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