AI-Driven Predictive Analytics for Streamlined Inventory and Supply Chain Management

DOI

10.2991/978-94-6463-787-8_47How to use a DOI?

Keywords

Supply Chain Management; Demand Forecasting; Inventory Optimization; Supply Chain Resilience; Machine Learning in Supply Chains; Logistics and Transportation Optimization

Abstract

This study investigates the application of AI-driven predictive analytics to optimize inventory management and streamline supply chain operations in the digital era. It explores how machine learning, time series analysis, optimization algorithms, and natural language processing enhance demand forecasting, inventory control, supplier relationship management, and logistics optimization. The research highlights the benefits of improved decision-making, enhanced operational efficiency, reduced operational costs, and proactive risk mitigation achieved through AI implementation. Practical applications across industries such as retail, manufacturing, and logistics are discussed, demonstrating the tangible value of AI integration. Furthermore, the study addresses critical challenges, including data quality issues, model complexity, ethical considerations such as bias and fairness, and the growing need for skilled AI personnel. By synthesizing recent advancements and analyzing case studies, this paper provides actionable insights into leveraging AI for building more resilient, adaptive, and efficient supply chains. Finally, it proposes future research directions focused on integrating emerging technologies like blockchain and IoT, developing real-time analytics frameworks, designing ethical AI systems, and exploring sector-specific applications to further advance the field.

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.

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TY  - CONF
AU  - Srikanth Kamatala
AU  - Chiranjeevi Bura
PY  - 2025
DA  - 2025/07/17
TI  - AI-Driven Predictive Analytics for Streamlined Inventory and Supply Chain Management
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 607
EP  - 622
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_47
DO  - 10.2991/978-94-6463-787-8_47
ID  - Kamatala2025
ER  -