What if? Causal Machine Learning in Supply Chain Risk Management
This work addresses the need for better intervention planning in supply chain risk management, offering a novel approach but is incremental in applying existing causal methods to a specific domain.
The paper tackles the problem of using machine learning for optimal interventions in supply chain management by proposing causal machine learning to move beyond correlations, demonstrating its application in a maritime engineering case study to enhance decision-making through 'what-if' scenario planning.
The penultimate 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 for predicting risk, and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning.