Design and Implementation of an Intelligent Hybrid Assessment Framework for Supplier Risk

Against the backdrop of an ever-changing global economic environment and increasingly complex supply chains, supplier risk assessment has become a key component of enterprise supply chain management. This paper aims to address the issue of incomplete data utilization in traditional supplier assessment methods, especially in handling unstructured data and responding to market fluctuations. For this purpose, the paper proposes a multi-model hybrid scoring framework that combines machine learning and deep learning. This framework integrates these technologies to comprehensively assess supplier risks. Experimental results show that the framework designed in this paper is more accurate and effective in identifying suppliers of different risk levels compared to traditional methods, helping enterprises better navigate market changes and supply chain complexities, thereby maintaining a competitive edge.