Causal machine learning for supply chain risk prediction and intervention planning

Mateusz Wyrembek, George Baryannis and Alexandra Brintrup

International Journal of Production Research, 2025, vol. 63, issue 15, 5629-5648

Abstract: The ultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine learning for developing supply chain risk intervention models, and demonstrate its use with a case study in supply chain risk management in the maritime engineering sector. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions, allowing ‘what-if’ scenario planning. We therefore propose different machine learning developmental pathways for predicting risk and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning and harness its capabilities.

Date: 2025
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DOI: 10.1080/00207543.2025.2458121

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