ABSTRACT. This paper analyzes the outcomes of an exploratory review of the current research on cyber-physical process monitoring systems, artificial intelligence-based decision-making algorithms, and sustainable industrial big data in smart networked factories. The data used for this study was obtained and replicated from previous research conducted by Algorithmia, Capgemini, Forrester, Management Events, and PwC. We performed analyses and made estimates regarding how big data-driven algorithms and tools can enable product realization by use of networks of smart connected devices and sensors, pattern-detecting decision-making equipment, and machine learning-based tools, leading to precise and real-time data gathering and analysis, while big data analytics applications across industrial plants are decisive in configuring digital manufacturing options for automated production. Data collected from 5,600 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: sustainability; industrial big data; smart factory; artificial intelligence

How to cite: Higgins, M., and Horak, J. (2021). “Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making Algorithms, and Sustainable Industrial Big Data in Smart Networked Factories,” Economics, Management, and Financial Markets 16(4): 42–55. doi: 10.22381/emfm16420213.

Received 14 March 2021 • Received in revised form 13 December 2021
Accepted 18 December 2021 • Available online 20 December 2021

Michael Higgins
This email address is being protected from spambots. You need JavaScript enabled to view it.
Interconnected Sensor Networks Research Unit
at AAER, Bangor, Wales
(corresponding author)
Jakub Horak
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The School of Expertness and Valuation,
The Institute of Technology and Business
in Ceske Budejovice, Czech Republic

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