Cyber-Physical System-based Real-Time Monitoring, Industrial Big Data Analytics, and Smart Factory Performance in Sustainable Manufacturing Internet of Things.

AuthorStehel, Vojtech
  1. Introduction

    The pervasive manufacturing intelligence in big data-driven shop floors and production systems can network in conformity with instantaneous processing status and demands (Lazaroiu et al., 2017; Mircica, 2020), articulating the necessitated agility for fabricating mass-customized items. (Lu et al., 2020)

  2. Conceptual Framework and Literature Review

    By getting matured and adopting digital technologies, organizations can harness self-sensing functions and operational data. (Kristoffersen et al., 2020) Smart manufacturing deploys huge in-context data from production systems for artificially intelligent decision making. (Zheng and Sivabalan, 2020) Machine-to-machine interconnection technologies (Lazanyi et al., 2020) are pivotal in collaborative smart manufacturing automation across the Industrial Internet of Things-empowered big data-driven networks. (Lu and Asghar, 2020) Smart sensors are decisive in attaining the product-service system operation mode and putting into operation preventive maintenance. (Wang et al., 2020)

  3. Methodology and Empirical Analysis

    Building our argument by drawing on data collected from CGI, CompTIA, Deloitte, Globant, IW Custom Research, Kronos, McKinsey, MHI, PAC, and PwC, we performed analyses and made estimates regarding sustainable industrial value creation in Internet of Things-based real-time production logistics. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

  4. Survey Methods and Materials

    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. Any survey which did not reach greater than 50% completion was removed from subsequent analysis to ensure quality. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. The cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%. Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can also introduce error or bias into the findings of opinion polls. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. The design effect for the survey was 1.3. For tabulation purposes, percentage points are rounded to the nearest whole number. The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling. Multivariate analyses, and not univariate associations with outcomes, are more likely to factor out confounding covariates and more precisely determine the relative significance of individual variables. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. An Internet-based survey software program was utilized for the delivery and collection of responses. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. An informed econsent was obtained from individual participants. Study participants were informed clearly about their freedom to opt out of the study at any point of time without providing justification for doing so.

  5. Results and Discussion

    Manufacturing is emerging as data-driven at all smart production levels (the physical device, plant management, and fabrication networks), gaining abilities to assimilate, articulate, and perform with cognitive intelligence. (Lu et al., 2020) Cyber-physical systems are instrumental in digitizing production systems (Dusmanescu et al., 2016; Mihaila et al., 2016; Popescu et al., 2017...

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