Predictive Analytics in Retail: Leveraging AI for Demand Forecasting and Inventory Optimization

Abstract

The integration of artificial intelligence (AI) in retail has revolutionized traditional supply chain operations, particularly in the areas of demand forecasting and inventory optimization. This study investigates the application of predictive analytics powered by AI algorithms to enhance retail decision-making processes. The research aims to evaluate how machine learning models—such as time series forecasting, regression analysis, and deep learning—can accurately predict consumer demand and optimize inventory levels to reduce stockouts and overstock situations. Using a case study approach involving three large retail chains in Indonesia, this study analyzes historical sales data, seasonal trends, and customer behavior patterns over a 24-month period. The results demonstrate that AI-based predictive models significantly outperform traditional forecasting methods in terms of accuracy, speed, and adaptability to sudden market changes. Furthermore, the implementation of predictive analytics leads to measurable improvements in inventory turnover rates, cost efficiency, and customer satisfaction. The study highlights key enablers of successful AI adoption, including data quality, cross-functional collaboration, and continuous model training. This research contributes to the field of retail analytics by providing a framework for integrating AI-driven forecasting tools into supply chain management strategies, ultimately supporting data-informed retail operations in an increasingly dynamic marketplace.