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Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

1
Department of Economic Sciences, Spiru Haret University, 030045 Bucharest, Romania
2
Institute of Smart Big Data Analytics, New York, NY 11377, USA
3
Department of Juridical Sciences and Economic Sciences, Spiru Haret University, 500152 Brașov, Romania
4
Department of Law, The National University of Political Studies and Public Administration, 012244 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Academic Editors: Matteo Golfarelli and Enrico Gallinucci
Electronics 2021, 10(20), 2497; https://doi.org/10.3390/electronics10202497
Received: 13 September 2021 / Revised: 8 October 2021 / Accepted: 11 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)
With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks. View Full-Text
Keywords: cyber-physical; production; system; artificial intelligence; Internet of Things; algorithm cyber-physical; production; system; artificial intelligence; Internet of Things; algorithm
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MDPI and ACS Style

Andronie, M.; Lăzăroiu, G.; Iatagan, M.; Uță, C.; Ștefănescu, R.; Cocoșatu, M. Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics 2021, 10, 2497. https://doi.org/10.3390/electronics10202497

AMA Style

Andronie M, Lăzăroiu G, Iatagan M, Uță C, Ștefănescu R, Cocoșatu M. Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics. 2021; 10(20):2497. https://doi.org/10.3390/electronics10202497

Chicago/Turabian Style

Andronie, Mihai, George Lăzăroiu, Mariana Iatagan, Cristian Uță, Roxana Ștefănescu, and Mădălina Cocoșatu. 2021. "Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems" Electronics 10, no. 20: 2497. https://doi.org/10.3390/electronics10202497

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