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ABSTRACT. We develop a conceptual framework based on a systematic and comprehensive literature review on robotic wireless sensor networks, industrial artificial intelligence, and deep learning-assisted smart process planning in sustainable cyber-physical manufacturing systems. Building our argument by drawing on data collected from McKinsey, Ovum, PwC, and World Economic Forum, we performed analyses and made estimates regarding industrial big data analytics. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.
JEL codes: E24; J21; J54; J64

Keywords: industrial artificial intelligence; cyber-physical manufacturing system

How to cite: Blake, R., Michalkova, L., and Bilan, Y. (2021). “Robotic Wireless Sensor Networks, Industrial Artificial Intelligence, and Deep Learning-assisted Smart Process Planning in Sustainable Cyber-Physical Manufacturing Systems,” Journal of Self-Governance and Management Economics 9(4): 48–61. doi: 10.22381/jsme9420214.

Received 22 April 2021 • Received in revised form 13 December 2021
Accepted 16 December 2021 • Available online 21 December 2021

Richard Blake
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The Internet of Things-based Real-Time
Production Logistics Laboratory
at AAER, Brisbane, Australia
(corresponding author)
Lucia Michalkova
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic
Yuriy Bilan
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Sumy State University, Sumy, Ukraine;
Al-Farabi Kazakh National University,
Almaty, the Republic of Kazakhstan

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