Leveraging Advanced Machine Learning Algorithms for Optimized Supplier Selection: A Multi-Criteria Decision-Making Approach

Abstract

This study investigates the application of K-means clustering, a machine learning technique, to enhance supplier selection and management within large organizations. By segmenting suppliers based on key performance metrics, the research aims to establish more strategic supplier relationships. The methodology leverages purchase order data transformed for clustering analysis. K-means clustering is chosen for its effectiveness in grouping suppliers with similar characteristics. The analysis identifies four distinct supplier segments: high-ranked, steady growth, high spending, and rapid growth. These segments become the foundation for developing targeted supplier management strategies. The study explores practical implications like retention programs for high-ranked suppliers, growth support for steady performers, cost management for high spenders, and investment opportunities for rapidly growing suppliers. Additionally, it discusses supplier rationalization and the advantages of data-driven decision-making in supplier relationship management. The concluding remarks emphasize the effectiveness of K-means clustering for supplier segmentation. The research offers a framework for optimizing supplier selection and management, with the potential to support improved efficiency, stronger supplier relationships, and better business outcomes through more targeted and data-driven supplier management.