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Internet of Things Sensing Networks, Deep Learning-enabled Smart Process Planning, and Big Data-driven Innovation in Cyber-Physical System-based Manufacturing

Internet of Things Sensing Networks, Deep Learning-enabled Smart Process Planning, and Big Data-driven Innovation in Cyber-Physical System-based Manufacturing

ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore cyber-physical system-based manufacturing. Using and replicating data from Capgemini, CompTIA, EY, Microsoft, PAC, and PwC, we performed analyses and made estimates regarding the relationship between Internet of Things sensing networks, deep learning-enabled smart process planning, and big data-driven innovation. Data were analyzed using structural equation modeling.
JEL Codes: E24; J21; J54; J64

Keywords: Internet of Things; big data; cyber-physical system-based manufacturing

How to cite: Connolly-Barker, M., Gregova, E., Dengov, V. V., and Podhorska, I. (2020). “Internet of Things Sensing Networks, Deep Learning-enabled Smart Process Planning, and Big Data-driven Innovation in Cyber-Physical System-based Manufacturing,” Economics, Management, and Financial Markets 15(2): 23–29. doi:10.22381/EMFM15220203

Received 6 March 2020 • Received in revised form 15 June 2020
Accepted 17 June 2020 • Available online 23 June 2020

Melissa Connolly-Barker
m.connolly-barker@aa-er.org
The Center for Digital Labor Markets
at CLI, Sydney, Australia
(corresponding author)
Elena Gregova
elena.gregova@fpedas.uniza.sk
Department of Economics,
Faculty of Operation and Economics
of Transport and Communications,
University of Zilina, Zilina, Slovak Republic
Victor V. Dengov
vvdengov@mail.ru
Faculty of Economics,
Department of Economics and Economic Policy,
Saint Petersburg State University,
Saint Petersburg, Russia
Ivana Podhorska
ivana.podhorska@fpedas.uniza.sk
Department of Economics,
Faculty of Operation and Economics
of Transport and Communications,
University of Zilina, Zilina, Slovak Republic