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 decision-making 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

How to cite: Galbraith, A., and Podhorska, I. (2021). “Artificial Intelligence Data-driven Internet of Things Systems, Robotic Wireless Sensor Networks, and Sustainable Organizational Performance in Cyber-Physical Smart Manufacturing,” Economics, Management, and Financial Markets 16(4): 56–69. doi: 10.22381/emfm16420214.

Received 14 March 2021 • Received in revised form 7 December 2021
Accepted 15 December 2021 • Available online 20 December 2021