Supply Chain Risk

Supply chain risk management aims to identify and mitigate disruptions that threaten the flow of goods and services. Current research heavily emphasizes the use of advanced machine learning techniques, including transformer networks, graph neural networks, and ensemble methods like Random Forests and Gradient Boosting Machines, to improve predictive accuracy and enable "what-if" scenario planning. These models leverage diverse data sources and incorporate domain expertise to forecast disruptions, assess credit risk, and optimize resource allocation. The resulting improvements in predictive capabilities and risk mitigation strategies have significant implications for operational efficiency and resilience across various industries.