Machine Learning Based Supply Chain Risk Prediction and Management

Traditional supply chain risk management methods show limitations when facing unexpected events and complex supply chain networks, however, machine learning techniques that can handle unstructured and multi-dimensional data and extract valuable risk information from complex data sets through efficient algorithms have provided new ideas for supply chain management in recent years. This paper proposes a continuous multi-perspective conceptual framework that aims to systematically explore how machine learning can be effectively applied in supply chain risk prediction and management. Meanwhile, this paper will also investigate the potential contribution of utilizing innovative technologies such as blockchain in enhancing supply chain security and competitiveness, with a view to providing more comprehensive and profound insights into modern supply chain management.