AI-Based Forecasting for Inventory Assessment: Improving Efficiency and Lowering Operational Expenses

Abstract

Effective inventory management is a critical component of supply chain efficiency and operational success across industries. However, traditional forecasting methods often fall short in addressing the complexities of modern business environments, leading to inefficiencies, stockouts, or overstocking. This study explores the application of AI-driven forecasting tools to assess and optimize inventory stock levels. Using a mixed-method approach, we analyze case studies from high-turnover industries and evaluate key performance metrics such as forecasting accuracy, inventory turnover ratios, and cost savings. The findings demonstrate that AI tools significantly enhance demand forecasting, enabling real-time decision-making and reducing operational costs. Insights from industry professionals further highlight the usability and adaptability of these tools. This research provides actionable recommendations for integrating AI-driven forecasting into inventory management systems, offering a roadmap for businesses to achieve operational excellence. The study concludes with a discussion of limitations and future research opportunities, including AI integration with IoT and blockchain for advanced inventory tracking.