Optimized Deep Learning Predictive Model for Food Sales and Demand Forecasting

DOI

10.2991/978-94-6239-616-6_86How to use a DOI?

Keywords

Supply chain management; Demand forecasting; CatBoost; BiLSTM; Coati optimization

Abstract

In modern complex business milieu, managing various aspects of the supply chain has become increasingly challenging. It is vital to improve viability, sales, and customer satisfaction by predicting key relational factors. However, traditional forecasting methods often yield inaccurate results and are time-consuming. To solve these issues, CatBoost, a Machine Learning (ML) algorithm, offers advanced features for demand forecasting. Yet, it was computationally intensive when utilizing huge or high-dimensional data with numerous unique categorical values. Also, parameter tuning can be complex. Thus, this paper proposes an optimized Deep Learning (DL) model to predict food sales, profit, and delivery times using e-commerce dataset. First, the raw data from food supply chain-related business logs is collected and pre-processed to handle missing values and outliers. Then, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed for demand prediction. Besides, the Binary Coati Optimization Algorithm (BCOA) is applied to optimize the BiLSTM hyperparameters. Finally, experimental results show that BCOA-BiLSTM outperforms traditional ML algorithms in Supply Chain Management (SCM) with a minimum forecasting error.

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  - N. Valliammal
AU  - R. Thanusree
PY  - 2026
DA  - 2026/03/31
TI  - Optimized Deep Learning Predictive Model for Food Sales and Demand Forecasting
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1177
EP  - 1188
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_86
DO  - 10.2991/978-94-6239-616-6_86
ID  - Valliammal2026
ER  -