AI-Powered Autonomous Procurement Systems for Supply Chain Optimization

Authors

Ravi Kumar Perumallapalli

Abstract

In the rapidly evolving landscape of supply chain management, traditional procurement processes often struggle with inefficiencies, lack of real-time insights, and delayed decision-making, leading to increased operational costs and reduced competitiveness. This paper addresses these challenges by proposing an AI-powered autonomous procurement system that leverages advanced machine learning algorithms and automation techniques to optimize procurement operations. The significance of this research lies in its potential to enhance decision-making speed and accuracy while minimizing human intervention, thereby streamlining supply chain processes. Our original contributions include a comprehensive framework for integrating AI technologies in procurement, a detailed analysis of case studies showcasing successful implementations, and recommendations for overcoming common barriers to adoption. This study aims to provide actionable insights for businesses seeking to harness the power of AI to transform their procurement strategies and achieve sustainable supply chain optimization.

Keywords

Supply Chain Optimization, Operational Efficiency, Machine Learning, Data-Driven Decision Making

AI-Powered Autonomous Procurement Systems for Supply Chain Optimization. Ravi Kumar Perumallapalli. 2021. IJIRCT, Volume 7, Issue 1. Pages 1-8. https://www.ijirct.org/viewPaper.php?paperId=2411017