Industrial Big Data, Automated Production Systems, and Internet of Things Sensing Networks in Cyber-Physical System-based Manufacturing.

AuthorPopescu, Gheorghe H.
  1. Introduction

    For process manufacturing industries to carry out the upsides of Internet of Things sensing networks, adequate in-line and on-line sensors to supervise critical parameters are required. (Lewis Bowler et al., 2020) To commence expedient monitoring operations so as to enhance integral system performance, a key grasp of actual production characteristics is pivotal. (Chen et al., 2020)

  2. Conceptual Framework and Literature Review

    The modularity, adjustability, and agility of smart plants and the product customization standard of Industry 4.0 manufacturing digitization (Andrei et al., 2016; Dusmanescu et al., 2016; Krizanova et al., 2019; Lazaroiu et al., 2019; Popescu, 2018; Popescu et al., 2019; Popescu Ljungholm, 2018) further product cycles and precipitate the swift discontinuance of goods and services. (Ghobakhloo, 2020) Consumer-produced data indicate the (dis)advantages of products (Andrei et al., 2020; Huxley and Sidaoui, 2018; Lazaroiu, 2017; Lazaroiu et al., 2020; Popescu, 2014; Popescu et al., 2018; Zhulega et al., 2019), being instrumental in enhancing product advancement and manufacturing operations. (Sun et al., 2020) Digital twin facilitates broad-ranging performance assessment for operational reorganizing by deploying multiple-dimension patterns, configuring geometric characteristics, physics parameters, and functioning of the equipment. (Zhang et al., 2020)

  3. Methodology and Empirical Analysis

    Using and replicating data from Capgemini, Deloitte, Globant, McKinsey, MHI, PwC, and SME, we performed analyses and made estimates regarding the relationship between industrial big data, automated production systems, and Internet of Things sensing networks. Data were analyzed using structural equation modeling.

  4. Results and Discussion

    Industry 4.0 may enhance fluidity and coherence in manufacturing. (Guo et al., 2020) Numerous companies insist on carrying out productiveness by use of digitalization instead of undertaking a growth strategy, due to the challenges associated with determining cost-effective frameworks of expertise, assets, and data produced by digital technologies, organizing and harnessing them in an agile company. (Bjorkdahl, 2020) (Tables 1-9)

  5. Conclusions and Implications

    The digitalization operations in manufacturing plants and the assimilation of smart industrial unit devices and software control networks (Balica, 2019; Ionescu, 2018; Lazaroiu et al., 2017; Nica et al., 2014; Nica, 2015; Popescu et al., 2017) has brought about an increase in the statistics employable in production execution systems. (Morariu et al., 2020) The design of groundbreaking workstations should satisfy personnel requirements by coherently merging Industry 4.0 smart solutions and shared technologies. (Calzavara et al., 2020)

    Survey method

    The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. Sampling errors and test of statistical significance take into account the effect of weighting. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. For tabulation purposes, percentage...

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