ABSTRACT. This article presents an empirical study carried out to evaluate and analyze Internet of Things sensing networks, smart manufacturing big data, and digitized mass production in sustainable Industry 4.0. Building our argument by drawing on data collected from Forrester, LNS Research, Management Events, McKinsey, Plex Systems, and PwC, we performed analyses and made estimates regarding how integrating Internet of Things-based decision support systems along production processes facilitates automatic data gathering and inspection, through sustainable industrial big data and artificial intelligence-based decision-making algorithms, manufacturing operations are optimized, while by use of autonomous vehicle driving algorithms and perception sensor data, industrial big data analytics, and Internet of Things-based decision support systems, industrial plants network in real time. Data collected from 6,700 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: Internet of Things; smart manufacturing; big data; sustainability; Industry 4.0

How to cite: Hopkins, E., and Siekelova, A. (2021). “Internet of Things Sensing Networks, Smart Manufacturing Big Data, and Digitized Mass Production in Sustainable Industry 4.0,” Economics, Management, and Financial Markets 16(4): 28–41. doi: 10.22381/emfm16420212.

Received 15 March 2021 • Received in revised form 13 December 2021
Accepted 16 December 2021 • Available online 20 December 2021

Emily Hopkins
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The Institute of Smart Big Data Analytics,
New York City, NY, USA
(corresponding author)
Anna Siekelova
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Department of Economics,
Faculty of Operation and Economics
of Transport and Communications,
University of Zilina, Zilina, Slovak Republic

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