Enhancing Vendor Selection In Supply Chains Using Machine Learning: A Comparative Study Of Optimization Algorithm

Abstract

Vendor selection is a critical aspect of business operations, impacting the efficiency, competitiveness, and ultimately, the success of organizations across various industries. Traditionally, this process has been labor-intensive and subjective, relying on manual assessments and predefined criteria. However, with the advent of machine learning (ML) techniques, there has been a paradigm shift in how vendors are selected. This survey paper explores the application of ML in vendor selection, examining existing literature, methodologies, case studies, and future directions, aiming to provide insights into how ML is revolutionizing vendor selection processes.

Through an in-depth exploration of ML techniques in vendor selection, this survey paper aims to provide valuable insights for researchers, practitioners, and decision-makers, ultimately contributing to the optimization and improvement of vendor selection processes in various industries.