Intelligent Demand Forecasting and Inventory Optimization Using Artificial Intelligence

DOI

10.2991/978-94-6239-693-7_97How to use a DOI?

Keywords

Artificial Intelligence; Smart Inventory Forecasting; Demand Prediction; Machine Learning; Supply Chain Optimization; Predictive Analytics

Abstract

Proper prediction of inventory demand is extremely significant in the case of a healthy, operating supply chain. In case companies make incorrect forecasts they can either have more inventory (which must be stored at a cost), or run out of inventory which leads to dissatisfied customers. The given study presents AI, an energy-efficient smart inventory forecasting, which can be used to make demand forecasting more precise and hence promote data, driven inventory decision-making. The proposed model will rely on both past sales data and seasonal factors in addition to a few external factors that can affect sales to create machine learning models that can detect non, as well as linear demand trends. It is highly important beforehand to achieve the best results some proper data treatment i.e. - filling in missing data, eliminating errors and expressing the variability of demand. This work also includes fixing the model parameters to allow the models to be useful in different product categories. On the one hand, the AI, powered forecasting model could outperform the traditional statistical methods on the accuracy of the prediction, on the other hand, it also exhibited higher adaptability to the changes in the demand as per the experiments. The results of the research indicate that maintaining the inventory at a moderate level is no longer a significant challenge to the companies that involves less overstocking and no stockouts.

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  - Shourya Agarwal
AU  - Rishu Mittal
AU  - Jay Tiwari
AU  - Aditya Sharma
AU  - Sharandeep Kaur
PY  - 2026
DA  - 2026/06/16
TI  - Intelligent Demand Forecasting and Inventory Optimization Using Artificial Intelligence
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 1001
EP  - 1014
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_97
DO  - 10.2991/978-94-6239-693-7_97
ID  - Agarwal2026
ER  -