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ABSTRACT. This article presents an empirical study carried out to evaluate and analyze sustainable Industry 4.0. Building our argument by drawing on data collected from Capgemini, Deloitte, McKinsey, MHI, we.CONECT, and World Economic Forum, we performed analyses and made estimates regarding the relationship between product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning. Data collected from 4,600 respondents are tested against the research model by using structural equation modeling.
JEL codes: E24; J21; J54; J64

Keywords: big data analytics; Industry 4.0; smart process planning; deep learning

How to cite: Peters, E., Kliestik, T., Musa, H., and Durana, P. (2020). “Product Decision-Making Information Systems, Real-Time Big Data Analytics, and Deep Learning-enabled Smart Process Planning in Sustainable Industry 4.0,” Journal of Self-Governance and Management Economics 8(3): 16–22. doi:10.22381/JSME8320202

Received 7 July 2020 • Received in revised form 5 September 2020
Accepted 7 September 2020 • Available online 9 September 2020

Elisabeth Peters
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The Cognitive Artificial Intelligence
Systems Laboratory at ISBDA, Cambridge, England
(corresponding author)
Tomas Kliestik
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Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic
Hussam Musa
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Faculty of Economics, Matej Bel University,
Banska Bystrica, Slovak Republic
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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