Food Demand Forecasting System for Waste Reduction

Abstract

The food service industry continually grapples with the dual challenges of minimizing operational inefficiency and meeting increasing sustainability demands. Commercial kitchens, cafeterias, and institutional caterers often face food surplus or shortages due to inaccurate demand predictions, resulting in significant financial loss and environmental impact. This paper introduces a comprehensive food demand forecasting system designed to address these inefficiencies. Utilizing machine learning techniques, the system integrates historical consumption data and evolving contextual factors such as special events and daily attendance, along with real-time user feedback. The framework features a modular architecture designed for ease of integration and scalability. Through rigorous validation on realistic datasets and analysis of related domain literature, the proposed system demonstrates significant reduction in food waste, cost savings, labor efficiency improvements, and environmental benefits. This work establishes a robust foundation for data-driven kitchen management with positive implications for economic and environmental sustainability.

Introduction

Food waste is a major global issue with economic, environmental, and ethical consequences. A significant portion of food produced—about one-third—never gets consumed, and commercial kitchens are substantial contributors due to inaccurate food demand forecasting. Traditional forecasting methods in restaurants and institutional kitchens rely on static historical averages or staff experience, which fail to account for dynamic factors such as special events, attendance fluctuations, and day-of-week trends. This leads to costly overproduction or customer-impacting underproduction.

To address these challenges, the research proposes a comprehensive, data-driven food demand forecasting system designed to reduce waste, lower costs, and support sustainability goals. The system uses machine learning and pattern-recognition techniques to analyze contextual variables—such as event types, attendance patterns, and temporal trends—and to provide accurate daily predictions for different food categories. It emphasizes scalability, ease of use, and integration with existing kitchen workflows.

The literature review highlights the evolution from traditional statistical models to modern machine learning approaches, including neural networks, LSTMs, ensemble methods, and hybrid ARIMA-LSTM systems. While these advanced models improve accuracy, many fail to transition into practical operational tools due to complexity or lack of user-friendly interfaces. A gap remains for accessible, integrated decision-support systems that combine predictive accuracy with real-world usability.

The proposed system architecture is modular, consisting of a data layer, a Flask-based backend, a machine-learning pattern engine, and a dashboard frontend. It supports Docker-based deployment and allows integration with POS or inventory systems. Dataflows begin with user input (e.g., expected footfall, event type) and progress through validation, prediction, and feedback logging. The system is designed to evolve continually through user feedback and updated pattern tables.

The methodology includes data acquisition, cleaning, and feature engineering, followed by a multi-step prediction algorithm using historical averages, context multipliers, and food-category consumption ratios. The system also incorporates waste analytics, calculating expected reductions in waste, cost savings, and environmental benefits such as CO? and water savings. A worked example demonstrates significant waste reduction (68%), cost savings, and environmental impact improvements.

Finally, model performance is evaluated through backtesting using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE), ensuring the forecasting system remains accurate and reliable.

References

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Copyright

Copyright © 2025 Aradhy Umesh, Aman Raj, Aditya Gupta, Dr. Megha Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.