Optimizing Smart Manufacturing Processes and Human Resource Management through Machine Learning Algorithms
The integration of smart manufacturing technologies presents both technological and human resource challenges for developing economies transitioning toward Industry 4.0. This study developed and implemented a comprehensive machine learning framework that optimizes manufacturing processes while effec...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Novi Sad, Faculty of Technical Sciences
2025-06-01
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| Series: | International Journal of Industrial Engineering and Management |
| Subjects: | |
| Online Access: | http://www.ijiemjournal.uns.ac.rs/images/journal/volume16/IJIEM_382.pdf |
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| Summary: | The integration of smart manufacturing technologies presents both technological and human
resource challenges for developing economies transitioning toward Industry 4.0. This study
developed and implemented a comprehensive machine learning framework that optimizes
manufacturing processes while effectively integrating human resource capabilities across 50
manufacturing facilities in Uzbekistan's automotive, textile, and food processing sectors. The
research implemented a three-tier machine learning framework combining random forest
algorithms, k-means clustering, and deep neural networks, while simultaneously developing
workforce capabilities through structured training programs. Data collection utilized 1,250
IIoT sensors per facility, complemented by workforce performance metrics. The implementation yielded significant improvements: Overall Equipment Effectiveness increased by
18.7%, unplanned downtime decreased by 32.4%, defect rates reduced from 3.8% to 1.0%,
and workforce digital competency improved by 45%. The framework demonstrated robust
performance across all sectors, with the automotive sector showing the highest improvements.
The study provides empirical evidence that machine learning frameworks, when integrated
with human resource development, can significantly enhance manufacturing performance in
developing industrial contexts. |
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| ISSN: | 2217-2661 2683-345X |