Productivity Enhancement in the Indian Auto Component Manufacturing Supply Chain Through IoT, Digital Twins with Generative AI, and Stacked Encoder-Enhanced Neural Networks

Tushar D. Bhoite (), Rajesh B. Buktar (), Parikshit N. Mahalle (), Mohan P. Khond (), Ganesh S. Pise () and Yogeshrao Y. More ()
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Tushar D. Bhoite: MES’s Wadia College of Engineering, Wadia College Campus
Rajesh B. Buktar: BVB’s Sardar Patel College of Engineering, Andheri (W)
Parikshit N. Mahalle: Vishwakarma Institute of Technology
Mohan P. Khond: COEP Technological University, A Unitary Public University of Government of Maharashtra (Formerly College of Engineering Pune)
Ganesh S. Pise: Vishwakarma Institute of Technology
Yogeshrao Y. More: PES’s Modern College of Engineering

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-27

Abstract: Abstract The Indian auto component manufacturing sector has long struggled with inefficient decision-making and limited real-time data use. This research investigates how Industry 4.0 technologies, specifically the Internet of Things (IoT), digital twins, generative artificial intelligence, and advanced neural networks can revolutionize this sector. IoT-enabled smart sensors support real-time monitoring and predictive maintenance. Digital twins replicate physical assets virtually, aiding scenario simulation and process improvement. Generative AI facilitates defect detection, process optimization, and intelligent decision-making. A novel Bayesian Network-Stacked Encoder-Puma Optimizer (BN-SE-PO) model further improves anomaly detection, pattern recognition, and automation. Empirical results show that IoT-based systems achieve 85% efficiency, 30% downtime, 40% cost savings, and 90% quality significantly outperforming conventional approaches. This study provides a robust framework for implementing AI-driven technologies to transform productivity, reliability, and supply chain efficiency in the Indian auto component industry.

Keywords: Productivity enhancement; Indian auto component manufacturing; Supply chain; IoT (Internet of Things); Neural networks; Bayesian networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00522-0

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