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ABSTRACT. Empirical evidence on deep learning-enabled smart process planning has been scarcely documented in the literature. Using and replicating data from Deloitte, KSM, PwC, SME, Statista, and Tractica, we performed analyses and made estimates regarding top challenges to implementing smart manufacturing solutions (%) and business organizations’ reasons for adopting artificial intelligence (%). Data were analyzed using structural equation modeling.
JEL codes: E24; J21; J54; J64

Keywords: smart process planning; cyber-physical system-based manufacturing

How to cite: Valaskova, Katarina, Odile Throne, Pavol Kral, and Lucia Michalkova (2020). “Deep Learning-enabled Smart Process Planning in Cyber-Physical System-based Manufacturing,” Journal of Self-Governance and Management Economics 8(1): 121–127. doi:10.22381/JSME8120205

Received 9 January 2020 • Received in revised form 16 March 2020
Accepted 17 March 2020 • Available online 28 March 2020

Katarina Valaskova
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic
Odile Throne
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The Cognitive Labor Institute,
New York City, NY, USA
(corresponding author)
Pavol Kral
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic
Lucia Michalkova
This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic

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