Evaluating the Effectiveness and Impact of Machine Learning Models for Forecasting Meal Demand in University Restaurants - Santos, Maria Fernanda; Gomide, Janaina (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2026)
Research Context: University restaurants face the challenge of balancing meal supply and demand, where efficient food management is essential to reduce waste and optimize costs, both crucial for service sustainability. Scientific and/or Practical Problem: Forecasting meal demand is complex due to consumption variability, the influence of holidays, and frequent menu changes. Proposed Solution and/or Analysis: This study applies machine learning models to forecast daily meal demand, using historical consumption data, holiday information, and menu attributes. The research also investigates the impact of these variables on forecast quality and accuracy. Related IS Theory: The study is based on Information Systems theory, particularly in the use of data and analytical intelligence to support decision-making. In this context, machine learning models act as information processors, capable of transforming real data into useful predictions for operational management. Research Method: The research followed the CRISP-DM framework: definition of objectives and context (Business Understanding); data collection and exploration (Data Understanding); cleaning and transformation (Data Preparation); training and calibration of models (Decision Tree, XGBoost, LSTM) (Modeling); performance evaluation with R² and comparison to the restaurant’s forecasts (Evaluation); and preparation for practical application (Deployment). Summary of Results: The XGBoost model achieved the best performance, reaching R²=0.92, outperforming the restaurant’s original forecast (R²=0.83). This represents a significant improvement in forecast accuracy, contributing to waste reduction and better inventory management. The inclusion of menu information was also shown to enhance prediction quality. Contributions and Impact to IS area: The study demonstrates how machine learning can be applied in institutional foodservice, providing operational, economic, and environmental benefits. It reinforces the role of Information Systems in enabling more accurate and sustainable decision-making.