Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 14
DOI: https://doi.org/10.59646/819/01
Author: Ms. M. Nasreen
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology in supply chain optimization and logistics management, enabling organizations to improve efficiency, reduce operational costs, and enhance decision-making capabilities. Modern supply chains generate vast amounts of data from procurement, inventory management, transportation, warehousing, and customer interactions. AI technologies such as machine learning, deep learning, predictive analytics, computer vision, and intelligent automation help organizations analyze this data in real time and derive actionable insights. Through demand forecasting, route optimization, inventory planning, warehouse automation, and risk management, AI significantly improves supply chain visibility and responsiveness. AI-powered predictive models enable businesses to anticipate demand fluctuations, reduce stockouts, and optimize inventory levels. Logistics operations benefit from intelligent routing algorithms that minimize transportation costs, fuel consumption, and delivery times. In warehouse environments, AI-driven robots and automated systems streamline material handling, order fulfillment, and inventory tracking. Furthermore, AI supports proactive risk identification by detecting disruptions caused by supplier issues, market volatility, weather conditions, or geopolitical events. These capabilities contribute to greater supply chain resilience and sustainability. The integration of AI with Internet of Things (IoT) devices, cloud computing, and big data analytics further enhances real-time monitoring and decision-making across supply chain networks. This study explores the practical applications, benefits, challenges, and future prospects of AI in supply chain optimization and logistics management, highlighting its role in creating intelligent, agile, and customer-centric supply chain ecosystems.
Keywords: Artificial Intelligence, Supply Chain Management, Logistics Optimization, Machine Learning, Predictive Analytics, Inventory Management, Route Optimization, Warehouse Automation, Demand Forecasting, Intelligent Transportation Systems, Supply Chain Resilience, IoT Integration, Big Data Analytics, Smart Logistics, Digital Transformation.