Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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IoT and Machine Learning Approaches for Supply Chain Optimization in Industry 4.0 Logistics

Authors
Nitiraj V. Kulkarni1, Abdul Hannan R. Dalal1, Yuvraj Lahoti2, *, Kailas Patil3, *
1Department of Artificial Intelligence, Vishwakarma University, Pune, Maharashtra, India
2Department of Commerce & Management, Vishwakarma University, Pune, Maharashtra, India
3Department of Computer Engineering, Vishwakarma University, Pune, Maharashtra, India
*Corresponding author. Email: yuvraj.lahoti@vupune.ac.in
*Corresponding author. Email: kailas.patil@vupune.ac.in
Corresponding Authors
Yuvraj Lahoti, Kailas Patil
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_68How to use a DOI?
Keywords
Internet of Things (IoT); Industry 4.0; Smart Manufacturing; Logistics 4.0; Supply Chain Management; Digital Twin; Predictive Maintenance
Abstract

The incorporation of the Internet of Things (IoT) into the industry 4.0 is now one of the most important change agents in the manufacturing and logistic sectors. The newest research marks the presence of IoT as a trigger of automation, digital connectivity, and real-time decision-making and shows substantial growth in efficiency and productivity, coupled with resilience of supply chains. RFID-enabled tracking technologies, digital twins, predictive maintenance, and blockchain-based interoperability technologies are transforming industrial operations by streamlining processes and improving visibility and the ability to support data-driven initiatives. Furthermore, the combination of the IoT with artificial intelligence, edge computing, and 5G systems contribute to the adaptability and responsiveness of modern industrial systems. Although the issues of cybersecurity, inter-operability, and high implementation costs remain, modern studies prove that IoT-based smart-factories and Logistics 4.0 solutions are speeding up the shift towards more sustainable and intelligent processes. In this paper, the author summarizes recent literature on IoT applications in Industry 4.0 and its role in transforming manufacturing and logistics, as well as presenting the prospects and obstacles to implementing it in the future.

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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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_68How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Nitiraj V. Kulkarni
AU  - Abdul Hannan R. Dalal
AU  - Yuvraj Lahoti
AU  - Kailas Patil
PY  - 2026
DA  - 2026/01/06
TI  - IoT and Machine Learning Approaches for Supply Chain Optimization in Industry 4.0 Logistics
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 990
EP  - 1004
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_68
DO  - 10.2991/978-94-6463-948-3_68
ID  - Kulkarni2026
ER  -