Inventory Optimization and Demand Forecasting Using Machine Learning

DOI

10.2991/978-94-6463-866-0_62How to use a DOI?

Keywords

LSTM (Long-Short-Term Memory); ARIMA (Auto-regressive Integrated Moving Average); Random Forest; Supervised Learning; Optimization; Time Series

Abstract

Businesses must practice effective inventory management to satisfy client requests and keep prices down. Preventing instances where an oversupply or stock out largely depends on accurate demand forecasting. Inventory demand forecasting systems have advanced considerably by integrating machine learning techniques with large amounts of historical data. The objectives are to better match production schedules with purchasing trends, optimize inventory levels, make educated judgments through accurate demand estimates, and improve resource allocation for increased profitability and customer satisfaction. We use methods for preparing the data and addressing missing values as part of our data preparation. We then introduce ARIMA for time series forecasting and Random Forest Regression using supervised learning. According to our findings, both models successfully forecast inventory demand, which promotes optimal stock levels, lower costs, and increased operational effectiveness.

Copyright

© 2025 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  - V. Dhilip Kumar
AU  - S. Maheswari
AU  - Y. Sethu Raman
AU  - S. Iniyavan
AU  - S. Abdul Hashim
PY  - 2025
DA  - 2025/10/31
TI  - Inventory Optimization and Demand Forecasting Using Machine Learning
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 760
EP  - 773
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_62
DO  - 10.2991/978-94-6463-866-0_62
ID  - Kumar2025
ER  -