Reconstructing production networks using machine learning

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

INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford

Abstract: The vulnerability of supply chains and their role in the propagation of shocks has been high- lighted 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 di↵erent 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)
Pages: 40 pages
Date: 2022-01, Revised 2023-01
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp and nep-net
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Citations: View citations in EconPapers (3)

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