Machine Learning Techniques for Cafeteria Demand Forecasting: An Institutional Case

This research investigates the application of machine learning techniques for cafeteria demand forecasting within institutional settings, addressing critical operational challenges in food service management. Using a comprehensive methodological framework, the study analyzes turnstile entry data from an academic institution across November-December 2023 to develop and evaluate three complementary forecasting models: XGBoost with time-based features, Long Short-Term Memory (LSTM) networks, and Prophet models with domain-specific components. The comparative analysis reveals differentiated performance characteristics across various forecasting dimensions, with XGBoost demonstrating superior accuracy for daily forecasting (MAE=16.23, MAPE=8.32%), LSTM excelling at high-resolution 15-minute interval prediction (MAE=5.37, MAPE=11.64%), and Prophet exhibiting greater stability for extended forecast horizons. A weighted ensemble methodology integrating these complementary approaches yields consistent performance improvements across multiple evaluation metrics, achieving 4.7% reduction in daily MAE and 3.4% reduction in 15-minute interval MAE compared to the best individual models. Feature importance analysis reveals the significance of recent historical patterns, weekly cyclical components, and academic calendar effects, validating the theoretical multi-level temporal structure of institutional demand. The operational impact assessment demonstrates substantial potential benefits, including estimated food waste reduction of 6.2%, enhanced service level maintenance, and improved resource utilization. This research contributes methodological advancements through its multi-resolution forecasting framework, systematic feature engineering approach, and context-sensitive ensemble integration methodology, while providing practical implementation guidance for institutional food service operations seeking to enhance operational efficiency and sustainability through improved demand prediction.