Smart Manufacturing: Integrating IoT and AI for Agile Production Management

Production management is being revolutionised by smart manufacturing, which is fuelled by the combination of artificial intelligence (AI) and the Internet of Things (IoT) and allows for quick, data-driven decision-making. Through this interface, producers may use cutting-edge machine learning models and real-time sensor data for process optimisation, quality assurance, and predictive maintenance. Model performance is evaluated using evaluation metrics like accuracy, recall, ROC curves, and AUC in conjunction with machine learning approaches including classification, regression, and time series forecasting. AI-driven systems can enhance production scheduling, anticipate equipment problems, and optimise resource allocation by utilising IoT data. This makes it possible to create agile manufacturing environments that can minimise downtime and react swiftly to changes in demand. The robustness and dependability of these AI models are further improved by the addition of machine learning assessment techniques including learning curves, feature importance, and cross-validation. In the end, this IoT and AI synergy enables manufacturers to attain increased productivity, cost reductions, and adaptability in dynamic production environments.

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Publication Details

Published
2025-02-21
DOI
https://doi.org/10.1109/ce2ct64011.2025.10939200
Citations
1
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
1.24
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article

Smart Manufacturing: Integrating IoT and AI for Agile Production Management

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1 citations
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1.24
article

Smart Manufacturing: Integrating IoT and AI for Agile Production Management

Maher Ali Rusho, Choi Sang Long, Vishwa V. Kumar, Arvinder Singh Channi, Babu Jagadeesh Annavarapu, Tejaswini Bastray
article en
1 citations

Abstract

Production management is being revolutionised by smart manufacturing, which is fuelled by the combination of artificial intelligence (AI) and the Internet of Things (IoT) and allows for quick, data-driven decision-making. Through this interface, producers may use cutting-edge machine learning models and real-time sensor data for process optimisation, quality assurance, and predictive maintenance. Model performance is evaluated using evaluation metrics like accuracy, recall, ROC curves, and AUC in conjunction with machine learning approaches including classification, regression, and time series forecasting. AI-driven systems can enhance production scheduling, anticipate equipment problems, and optimise resource allocation by utilising IoT data. This makes it possible to create agile manufacturing environments that can minimise downtime and react swiftly to changes in demand. The robustness and dependability of these AI models are further improved by the addition of machine learning assessment techniques including learning curves, feature importance, and cross-validation. In the end, this IoT and AI synergy enables manufacturers to attain increased productivity, cost reductions, and adaptability in dynamic production environments.

Lockheed Martin (United States) (US), Illinois Institute of Technology (US), Guru Kashi University (IN), Raffles University Iskandar (MY)
Industry, innovation and infrastructure
Openalex Percentile: Top 21%
Digital Transformation in Industry
1.24
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