Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

George Lãzãroiu, Armenia Androniceanu, Iulia Grecu, Gheorghe Grecu and Octav Neguri?ã
Additional contact information
George Lãzãroiu: Spiru Haret University, Romania
Armenia Androniceanu: The Bucharest University of Economic Studies, Romania
Iulia Grecu: Spiru Haret University, Romania
Gheorghe Grecu: Spiru Haret University, Romania
Octav Neguri?ã: Spiru Haret University, Romania

Oeconomia Copernicana, 2022, vol. 13, issue 4, 1047-1080

Abstract: Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including “cognitive Industrial Internet of Things”, “cognitive automation”, “cognitive manufacturing systems”, “cognitively-enhanced machine”, “cognitive technology-driven automation”, “cognitive computing technologies,” and “cognitive technologies.” The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

Keywords: cognitive manufacturing; Artificial Intelligence of Things; cyber-physical system; big data-driven deep learning; real-time scheduling algorithm; smart device; sustainable product lifecycle management (search for similar items in EconPapers)
JEL-codes: E42 J33 O14 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://dx.doi.org/10.24136/oc.2022.030 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pes:ieroec:v:13:y:2022:i:4:p:1047-1080

Access Statistics for this article

Oeconomia Copernicana is currently edited by Adam P. Balcerzak and Michal Moszynski

More articles in Oeconomia Copernicana from Institute of Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by Adam P. Balcerzak ( this e-mail address is bad, please contact ).