What if? Causal Machine Learning in Supply Chain Risk Management

TL;DR

This paper explores the application of causal machine learning to improve decision-making and intervention planning in supply chain risk management, demonstrated through a maritime engineering case study.

Contribution

It introduces causal machine learning as a novel approach for supply chain risk intervention modeling and provides a framework for its development and application.

Findings

Causal ML improves scenario planning for supply chain interventions.

It helps identify achievable changes under different risk mitigation strategies.

The approach enhances decision-making accuracy in supply chain management.

Abstract

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.