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