Optimizing E-Commerce Supply Chain Management with Artificial Intelligence: Enhancing Demand Forecasting and Inventory Optimization

Abstract

The rapid expansion of e-commerce has necessitated sophisticated supply chain management strategies to meet dynamic consumer demands and maintain operational efficiency. Artificial Intelligence (AI) has emerged as a transformative technology, offering capabilities that enhance demand forecasting and inventory optimization. This study explores the integration of AI into e-commerce supply chain management, focusing on its impact on predictive accuracy, inventory levels, and customer satisfaction. Machine learning algorithms, natural language processing, and data analytics are central to modern AI applications, enabling businesses to predict trends, identify inefficiencies, and automate decision-making processes. By leveraging AI-driven insights, companies can reduce costs, mitigate stockouts, and adapt to real-time changes in consumer behavior. This paper examines state-of-the-art AI methodologies applied to demand forecasting and inventory management, highlighting their benefits and challenges. We also discuss the implementation of hybrid models that combine statistical techniques with AI to enhance forecasting accuracy. Additionally, ethical considerations, including data privacy and algorithmic transparency, are addressed. The findings underscore the potential of AI to revolutionize supply chain practices, providing a competitive edge in a highly volatile e-commerce environment.