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AI and Machine Learning in Predictive Analytics for Supply Chain Optimization

Chief Editors:  Mr. Irshadullah Asim Mohammed, Dr. Yogesh Mohan Gosavi, and Prof. (Dr.) Vineeta Kaur Saluja

Associate Editor: Mrs. Sruthi S

Co-Editors: Dr. S. Rajeswari, Dr. Nikhil Saini, and Ms. Atreyee Banerjee

ISBN:  978-81-985805-1-1

Chapter: 17

DOI: https://doi.org/10.59646/mrnc17/321

Author: Dr. Nidal Al Said

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in predictive analytics has revolutionized supply chain optimization by enhancing decision-making, improving operational efficiency, and minimizing uncertainties. AI-driven models analyze vast datasets, identify patterns, and generate accurate demand forecasts, leading to reduced inventory costs and better resource allocation. Machine learning techniques, such as deep learning and reinforcement learning, facilitate dynamic supply chain management by enabling real-time adaptations to disruptions. Predictive analytics powered by AI optimizes logistics, enhances risk management, and improves supplier relationship management through data-driven insights. The adoption of AI and ML in supply chain optimization fosters automation, agility, and resilience, allowing businesses to remain competitive in an increasingly volatile market. This paper explores the role of AI and ML in predictive analytics, discussing their impact, challenges, and future prospects in supply chain management.

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