ABSTRACT. Despite the relevance of big data-driven smart manufacturing, only limited research has been conducted on this topic. Using and replicating data from Accenture, Deloitte, Management Events, and PwC, I performed analyses and made estimates regarding current state of customer experience management (%), Industry 4.0 investments broken down by steps of the value chain (%, priorities), degree of digitization of the value chain by industry sector (%), top challenges in changing strategy and in preparing the workforce for Industry 4.0 (%), and worker time potentially augmented/automated by intelligent technologies (%). The results of a study based on data collected from 4,400 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire.
JEL codes: E24; J21; J54; J64

Keywords: big data; smart manufacturing; cyber-physical system; automation

How to cite: Hyers, Douglas (2019). “Sensor Networks and Intelligent Automation Systems for Big Data-driven Smart Manufacturing in Cyber-Physical Connected Environments,” Journal of Self-Governance and Management Economics 7(4): 14–20. doi:10.22381/JSME7420192

Received 9 August 2019 • Received in revised form 10 December 2019
Accepted 11 December 2019 • Available online 15 December 2019

Douglas Hyers
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The Center for Big Data-driven
Algorithmic Decision-Making at AAER, Perth, Australia

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