Machine Learning Framework for Food Demand Forecasting and Inventory Optimization

DOI

10.2991/978-94-6239-707-1_12How to use a DOI?

Keywords

Mean absolute error (MAE); Mean squared error (MSE); Root mean square error (RMSE); R2 Score; Machine Learning; Demand Forecasting; Inventory Optimization; Gradient Boosting; XGBoost; Ensemble Learning; Time-Series Analysis; Supply Chain Analytics; Feature Engineering

Abstract

In an evolving supply chain management landscape, accurate demand forecasting and inventory management are critical for cost reduction and meeting customer demands. This paper proposes a machine learning model to forecast food demand and manage inventory using real-world data such as fulfilment centre operations, meal data, and past order quantities. We use various machine learning algorithms such as Gradient Boosting to develop and predict demand with high accuracy. The developed solution combines demand forecasting and simple inventory logic to determine stock needs and prevent over- and understocking. The result is that the Gra- dient Boosting model had a lower prediction error than other algorithms. The proposed solution demonstrates how data-driven forecast models could significantly improve operational efficiency and decision-making processes in food logistics.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Diya Shah
AU  - Harsh Majithia
AU  - Parmi Kenia
AU  - Tirth Shah
AU  - Avinash Tandle
PY  - 2026
DA  - 2026/06/18
TI  - Machine Learning Framework for Food Demand Forecasting and Inventory Optimization
BT  - Proceedings of the International Conference on Recent Advances in Intelligent and Sustainable Technologies (RAIST 2026)
PB  - Atlantis Press
SP  - 132
EP  - 142
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-707-1_12
DO  - 10.2991/978-94-6239-707-1_12
ID  - Shah2026
ER  -