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Sustainable Organizational Performance, Cyber-Physical Production Networks, and Deep Learning-assisted Smart Process Planning in Industry 4.0-based Manufacturing Systems

Sustainable Organizational Performance, Cyber-Physical Production Networks, and Deep... Empirical evidence on sustainable organizational performance, cyberphysical production networks, and deep learning-assisted smart process planning in Industry 4.0-based manufacturing systems has been scarcely documented in the literature. Using and replicating data from Capgemini, the Economist Intelligence Unit, McKinsey, Management Events, and World Economic Forum, we performed analyses and made estimates regarding how data-driven supervision, predictive analytics, and optimization systems integrate product traceability, manufacturing maintenance, and process performance in smart manufacturing. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: E24; J21; J54; J64 Keywords: cyber-physical; production network; Industry 4.0; smart manufacturing http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Sustainable Organizational Performance, Cyber-Physical Production Networks, and Deep Learning-assisted Smart Process Planning in Industry 4.0-based Manufacturing Systems

Economics, Management, and Financial Markets , Volume 16 (3): 14 – Jan 1, 2021

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
Publisher site
See Article on Publisher Site

Abstract

Empirical evidence on sustainable organizational performance, cyberphysical production networks, and deep learning-assisted smart process planning in Industry 4.0-based manufacturing systems has been scarcely documented in the literature. Using and replicating data from Capgemini, the Economist Intelligence Unit, McKinsey, Management Events, and World Economic Forum, we performed analyses and made estimates regarding how data-driven supervision, predictive analytics, and optimization systems integrate product traceability, manufacturing maintenance, and process performance in smart manufacturing. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: E24; J21; J54; J64 Keywords: cyber-physical; production network; Industry 4.0; smart manufacturing

Journal

Economics, Management, and Financial MarketsAddleton Academic Publishers

Published: Jan 1, 2021

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