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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年7月23日

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  • Sangoh Kim

摘要

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.