AI-Driven Machine Learning Techniques and Predictive Analytics for Optimizing Retail Inventory Management Systems

Abstract

This comprehensive research paper explores the application of machine learning techniques and predictive modelling in retail inventory management systems. The study investigates a wide range of ML algorithms, including time series analysis, regression models, classification techniques, and deep learning approaches, to optimize inventory forecasting and management. Through the analysis of large-scale retail datasets, we demonstrate the superior performance of ML-based methods compared to traditional inventory management systems. The research highlights the potential for significant improvements in inventory accuracy, reduced stockouts, and enhanced operational efficiency in the retail sector. Furthermore, we discuss the challenges, ethical considerations, and future directions for integrating advanced ML techniques with emerging technologies like IoT for real-time inventory optimization. Our findings provide valuable insights for retailers seeking to leverage AI-driven solutions to gain a competitive edge in an increasingly data-driven market landscape.