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Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making Algorithms, and Sustainable Industrial Big Data in Smart Networked Factories

Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making... This paper analyzes the outcomes of an exploratory review of the current research on cyber-physical process monitoring systems, artificial intelligencebased decision-making algorithms, and sustainable industrial big data in smart networked factories. The data used for this study was obtained and replicated from previous research conducted by Algorithmia, Capgemini, Forrester, Management Events, and PwC. We performed analyses and made estimates regarding how big data-driven algorithms and tools can enable product realization by use of networks of smart connected devices and sensors, pattern-detecting decision-making equipment, and machine learning-based tools, leading to precise and real-time data gathering and analysis, while big data analytics applications across industrial plants are decisive in configuring digital manufacturing options for automated production. Data collected from 5,600 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: D53; E22; E32; E44; G01; G41 Keywords: sustainability; industrial big data; smart factory; artificial intelligence http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making Algorithms, and Sustainable Industrial Big Data in Smart Networked Factories

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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 cyber-physical process monitoring systems, artificial intelligencebased decision-making algorithms, and sustainable industrial big data in smart networked factories. The data used for this study was obtained and replicated from previous research conducted by Algorithmia, Capgemini, Forrester, Management Events, and PwC. We performed analyses and made estimates regarding how big data-driven algorithms and tools can enable product realization by use of networks of smart connected devices and sensors, pattern-detecting decision-making equipment, and machine learning-based tools, leading to precise and real-time data gathering and analysis, while big data analytics applications across industrial plants are decisive in configuring digital manufacturing options for automated production. Data collected from 5,600 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: D53; E22; E32; E44; G01; G41 Keywords: sustainability; industrial big data; smart factory; artificial intelligence

Journal

Economics, Management, and Financial Markets – Addleton Academic Publishers

Published: Jan 1, 2021

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