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Enhancing Efficiency in Advanced Manufacturing through IoT Integration
Open accessAbstract
Integrating the Internet of Things (IoT) in advanced manufacturing, known as Industry 4.0, has revolutionized traditional manufacturing processes by introducing a network of interconnected devices, sensors, and systems. This paper examines the various uses of IoT in advanced manufacturing, emphasizing its role in raising productivity, sustainability, and efficiency throughout the industrial spectrum. Predictive maintenance, where real-time data from machinery sensors permits the anticipation of equipment breakdowns, supports proactive maintenance plans, and limits costly downtime, is a critical use of IoT in manufacturing. An in-depth discussion of the ability of IoT-driven automation to optimize production processes is provided in this study. Adaptive and responsive manufacturing systems that can lower waste, increase energy efficiency, and dynamically adjust to variations in demand are made possible. Optimization of the supply chain is yet another crucial area where IoT technology provides revolutionary advantages. Manufacturers can obtain real-time visibility into the movement and status of completed goods and raw materials by deploying IoT-enabled devices. This can result in shorter lead times, enhanced inventory management, and overall supply chain efficiency. The study examines IoT's function in quality control and monitoring, highlighting how IoT-enabled sensors and cameras support waste reduction, real-time quality evaluation, and defect reduction. The paper also intr…
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Research works printed but not linked: 14
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Sudarmo, S., Vandika, A. Y., & Fahrijal, R. (2024) · West Science Information System and Technology · doi:10.58812/wsist.v2i02.1209 · Cited by 4
“The integration of the Internet of Things (IoT) has transformed traditional manufacturing by enabling real-time data collection and predictive maintenance, which reduces downtime and optimizes production processes [1].”
Background
Metadata
- Authors
- Zaidi, S. M. R., Alam, A., & Khan, M. Y.
- Affiliations
- Aix-Marseille Université (FR)
- Aligarh Muslim University (IN)
- Corresponding author
- Zaidi, S. Meesum Raza
- ORCID
- Khan, Mohd Yunus
- Venue
- Engineering Headway
- Publisher
- Trans Tech Publications Ltd
- Published
- 2024-07-22
- Volume
- 11
- Pages
- 55-61
- ISSN
- 2813-8333 (ISSN-L)
- OpenAlex
- W4400891489
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 1
- FWCI
- 7.83
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Engineering headway (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Control and Systems Engineering › Industrial Automation and Control Systems
- Keywords
- Internet of Things, advanced manufacturing, Industry 4.0, predictive maintenance, supply chain optimization, inventory management, quality control, defect detection, collaborative robots, digital twin, energy efficiency, waste reduction, real-time monitoring, manufacturing efficiency
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