ABSTRACT. Empirical evidence on artificial intelligence-driven big data analytics, predictive maintenance systems, and Internet of Things-based real-time production logistics in sustainable Industry 4.0 wireless networks has been scarcely documented in the literature. Using and replicating data from BDO, Capgemini, The Economist Intelligence Unit, EEF, McKinsey, PAC, PwC, and Vodafone, we performed analyses and made estimates regarding how sustainable manufacturing Internet of Things is instrumental in industrial plants as smart devices can be harnessed to monitor data flows, while disruptive technologies are networked across cyber-physical system-based smart factories by use of artificial intelligence-based decision-making algorithms and deep learning-assisted smart process planning. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.
JEL codes: E24; J21; J54; J64

Keywords: Internet of Things; artificial intelligence; Industry 4.0; wireless network

How to cite: Lawrence, J., and Durana, P. (2021). “Artificial Intelligence-driven Big Data Analytics, Predictive Maintenance Systems, and Internet of Things-based Real-Time Production Logistics in Sustainable Industry 4.0 Wireless Networks,” Journal of Self-Governance and Management Economics 9(4): 62–75. doi: 10.22381/jsme9420215.

Received 26 April 2021 • Received in revised form 9 December 2021
Accepted 17 December 2021 • Available online 21 December 2021

Joan Lawrence
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Institute of Smart Big Data Analytics,
New York City, NY, USA
(corresponding author)
Pavol Durana
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic

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