AI-Powered Demand Forecasting Models for Retail Industries
TL;DR
Results indicate that hybrid models combining statistical and AI approaches improve forecasting accuracy, and AI-based models significantly enhance prediction accuracy, helping retailers optimize inventory, reduce costs, and improve decision-making.
Abstract
Demand forecasting plays a critical role in retail by influencing inventory management, supply chain efficiency, and customer satisfaction. Traditional statistical methods, while effective in stable environments, often fail to capture the complex and nonlinear patterns of modern retail data influenced by seasonality, promotions, and changing consumer behavior. With the advancement of Artificial Intelligence (AI), particularly machine learning and deep learning, demand forecasting has shifted toward more adaptive and accurate models. This paper presents a comprehensive study of AI-based demand forecasting models tailored for the retail sector. It examines various techniques, including regression models, decision trees, ensemble methods, neural networks, and deep learning approaches such as Long Short-Term Memory (LSTM) networks. The study highlights the ability of these models to process large, high-dimensional datasets, including both structured and unstructured data such as historical sales, pricing, weather, and consumer sentiment. The proposed framework integrates feature engineering, model training, and performance evaluation using metrics like Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Results indicate that hybrid models combining statistical and AI approaches improve forecasting accuracy. Overall, AI-based models significantly enhance prediction accuracy, helping retailers optimize inventory, reduce costs, and improve decision-making.