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Internet of Things-based Real-Time Production Logistics, Sustainable Industrial Value Creation, and Artificial Intelligence-driven Big Data Analytics in Cyber-Physical Smart Manufacturing Systems

Internet of Things-based Real-Time Production Logistics, Sustainable Industrial Value Creation,... This paper analyzes the outcomes of an exploratory review of the current research on Internet of Things-based real-time production logistics, sustainable industrial value creation, and artificial intelligence-driven big data analytics in cyber-physical smart manufacturing systems. The data used for this study was obtained and replicated from previous research conducted by Capgemini, Deloitte, IW Custom Research, Kronos, MHI, PwC, SME, and Software AG. We performed analyses and made estimates regarding deep learning-assisted smart process planning in cyber-physical manufacturing systems. Data collected from 4,700 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: E24; J21; J54; J64 Keywords: Internet of Things; artificial intelligence; smart manufacturing system http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Internet of Things-based Real-Time Production Logistics, Sustainable Industrial Value Creation, and Artificial Intelligence-driven Big Data Analytics in Cyber-Physical Smart Manufacturing Systems

Elvira Nica
Economics, Management, and Financial Markets , Volume 16 (1): 11 – Jan 1, 2021
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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
Publisher site
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Abstract

This paper analyzes the outcomes of an exploratory review of the current research on Internet of Things-based real-time production logistics, sustainable industrial value creation, and artificial intelligence-driven big data analytics in cyber-physical smart manufacturing systems. The data used for this study was obtained and replicated from previous research conducted by Capgemini, Deloitte, IW Custom Research, Kronos, MHI, PwC, SME, and Software AG. We performed analyses and made estimates regarding deep learning-assisted smart process planning in cyber-physical manufacturing systems. Data collected from 4,700 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: E24; J21; J54; J64 Keywords: Internet of Things; artificial intelligence; smart manufacturing system

Journal

Economics, Management, and Financial Markets – Addleton Academic Publishers

Published: Jan 1, 2021

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