Optimizing Supply Chain Logistics Using AI and Machine Learning Algorithms

Abstract

The optimization of supply chain logistics has become increasingly critical in today's globalized economy, where efficiency and responsiveness are paramount for maintaining competitive advantage. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in enhancing supply chain logistics operations. By leveraging advanced data analytics, predictive modeling, and automation, organizations can streamline processes, reduce costs, and improve decision-making capabilities. This study presents a comprehensive review of current AI and ML techniques applied in various aspects of supply chain management, including demand forecasting, inventory optimization, transportation planning, and supplier selection. Empirical case studies are discussed, showcasing successful implementations of these technologies and their quantifiable impacts on logistics performance metrics. The results indicate that the adoption of AI-driven approaches leads to significant improvements in operational efficiency, accuracy, and responsiveness to market changes. Finally, the paper identifies potential challenges and future research directions in the field of AI and supply chain logistics optimization, emphasizing the need for ongoing innovation and collaboration across industries