Development of an AI-based restaurant menu demand prediction model utilizing sales and meteorological data

Accurate demand forecasting in the restaurant industry is critical for optimizing inventory management, minimizing food waste, and enhancing operational efficiency. This study developed an AI-based system that predicts menu-specific daily sales using historical sales and meteorological data collected from 2021 to 2023. Approximately 384 menu items were individually modeled using deep neural networks configured for multi-class classification. The system achieved strong predictive performance with a mean Pearson correlation coefficient of 0.7945. Additionally, flexible visualization options were implemented to sort predictions by expected or actual sales volumes. The results demonstrate the feasibility of AI-driven demand prediction systems and their potential to transform food service operations toward greater sustainability and efficiency.