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Artificial Intelligence Data-driven Internet of Things Systems, Robotic Wireless Sensor Networks, and Sustainable Organizational Performance in Cyber-Physical Smart Manufacturing

Artificial Intelligence Data-driven Internet of Things Systems, Robotic Wireless Sensor Networks,... The purpose of this study was to empirically examine artificial intelligence data-driven Internet of Things systems, robotic wireless sensor networks, and sustainable organizational performance in cyber-physical smart manufacturing. Building our argument by drawing on data collected from EY, Kronos, IW Custom Research, McKinsey, and PwC, we performed analyses and made estimates regarding how sustainable Industry 4.0 wireless networks have reconfigured manufacturing processes as deep learning-assisted smart process planning can automate decisionmaking operations, while sustainable cyber-physical production systems can automate smart networked devices and artificial intelligence-based decision-making algorithms can identify irregularities during machine operations, with Internet of Things-based real-time production logistics being pivotal in networking smart devices and tools. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: D53; E22; E32; E44; G01; G41 Keywords: Internet of Things; smart manufacturing; robotic wireless sensor network http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Artificial Intelligence Data-driven Internet of Things Systems, Robotic Wireless Sensor Networks, and Sustainable Organizational Performance in Cyber-Physical Smart Manufacturing

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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

The purpose of this study was to empirically examine artificial intelligence data-driven Internet of Things systems, robotic wireless sensor networks, and sustainable organizational performance in cyber-physical smart manufacturing. Building our argument by drawing on data collected from EY, Kronos, IW Custom Research, McKinsey, and PwC, we performed analyses and made estimates regarding how sustainable Industry 4.0 wireless networks have reconfigured manufacturing processes as deep learning-assisted smart process planning can automate decisionmaking operations, while sustainable cyber-physical production systems can automate smart networked devices and artificial intelligence-based decision-making algorithms can identify irregularities during machine operations, with Internet of Things-based real-time production logistics being pivotal in networking smart devices and tools. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. JEL codes: D53; E22; E32; E44; G01; G41 Keywords: Internet of Things; smart manufacturing; robotic wireless sensor network

Journal

Economics, Management, and Financial MarketsAddleton Academic Publishers

Published: Jan 1, 2021

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