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Reconstructing production networks using machine learning

Author

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

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.

Suggested Citation

  • Lafond, François & Farmer, J. Doyne & Mungo, Luca & Astudillo-Estévez, Pablo, 2022. "Reconstructing production networks using machine learning," INET Oxford Working Papers 2022-02, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, revised Jan 2023.
  • Handle: RePEc:amz:wpaper:2022-02
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    Keywords

    Supply chains; Network reconstruction; Link prediction; Machine learning;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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