MACHINE LEARNING FOR PREDICTIVE ANALYTICS: TRANSFORMING INVENTORY MANAGEMENT IN E-COMMERCE

Abstract

In the rapidly evolving landscape of e-commerce, efficient inventory management remains a critical challenge for businesses seeking to balance customer satisfaction with cost optimization. Traditional inventory management approaches often fall short in predicting demand fluctuations, leading to issues such as overstocking or stockouts. Machine learning (ML) for predictive analytics has emerged as a transformative solution, enabling e-commerce platforms to make data-driven decisions that enhance operational efficiency and profitability. This paper explores the application of machine learning techniques in predictive inventory management within the e-commerce sector. It examines various algorithms, including regression models, time series analysis, neural networks, and reinforcement learning, highlighting their effectiveness in demand forecasting and inventory optimization. The study also discusses the integration of diverse data sources such as sales records, customer behavior analytics, and market trends to build accurate predictive models. Furthermore, the paper presents real-world case studies of successful implementations by leading e-commerce giants, showcasing the practical benefits and challenges of adopting machine learning-based inventory systems. Key benefits include improved demand accuracy, reduced holding costs, and enhanced customer satisfaction. However, challenges such as data quality, model interpretability, and integration with existing systems are also addressed. By leveraging machine learning for predictive analytics, e-commerce businesses can significantly transform their inventory management processes, fostering smarter decision-making and long-term competitiveness. This research offers insights into emerging trends and future directions, including the use of deep learning models and real-time analytics powered by IoT and blockchain technologies.