Real-Time Big Data Analytics, Sustainable Industry 4.0 Wireless Networks, and Internet of Things-based Decision Support Systems in Cyber-Physical Smart Manufacturing.

AuthorHamilton, Steve
  1. Introduction

    A smart factory is efficient in manufacturing heterogeneous products in compliance with consumers' preferences at superior quality and output. (Lee et al., 2021) Production data obtained from the equipment harnessing machine learning algorithms can be deployed to advance a streamlined sustainable manufacturing system. (Jamwal et al., 2021) Industrial Internet of Things improves smart manufacturing operations (Tucker, 2021) that optimize productivity by use of groundbreaking approaches. (Singh et al., 2021)

  2. Conceptual Framework and Literature Review

    Internet of Things devices/equipment are instrumental in the performance and management of smart factories. (Lee et al, 2021) The advancement of a machine learning-based sustainable manufacturing framework is rewarding for most industries to attain operability. (Jamwal et al, 2021) Industry 4.0 developments in technology further sustainable supply chain proposals that can optimize economic gains, decrease environmental consequences, and catalyze social development. (Sharma et al., 2021) Industry 4.0 is a smart manufacturing ecosystem developed on cyber-physical systems, merging Internet of Things solutions across a robust horizontal and vertical system integration pattern. (Ciliberto et al., 2021) Big data analytics can convert massive volumes of input into valuable information in Industry 4.0, facilitating ingenious and swift decision-making approaches (Andronie et al., 2021a, b; Grayson, 2020; Lazaroiu et al, 2017; Popescu Ljungholm and Olah, 2020) when connected with competent domain knowledge. (Chang et al., 2021) Manufacturing quality prediction is decisive in enterprise production management, supplying data support for soundness evaluation and parameter upgrade, and consequently enhancing the smart management level of shop floors and assisting in achieving first-rate items at diminished expenses. (Bai et al, 2021)

  3. Methodology and Empirical Analysis

    Using and replicating data from Capgemini, Forrester, Microsoft, PwC, Siemens, and SME, I performed analyses and made estimates regarding big data-driven manufacturing. 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 e-consent 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

    As smart plants integrate data technologies, facilities and devices are networked across the central wireless communication, so as input can be smoothly linked between operations (Kliestik et al., 2020a, b, c) and configure a coherent, consolidated, and flawless production environment. (Lee et al., 2021) Smart manufacturing is reinforced through time series...

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