Internet of Things Smart Devices, Sustainable Industrial Big Data, and Artificial Intelligence-based Decision-Making Algorithms in Cyber-Physical System-based Manufacturing.

AuthorShaw, Susan
  1. Introduction

    In the manufacturing industry, there are massive quantities of data as a result of sharing smart technologies, networked devices, and groundbreaking applications. (Babu et al, 2021) The real-time monitoring of product quality is pivotal in the upgrading of machining processes. (Liu et al, 2021) A manufacturing system gathers heterogeneous information (Clark, 2020) for product quality modeling and big data-driven decision-making. (Li et al, 2021)

  2. Conceptual Framework and Literature Review

    The performance and digital resources across shop floors are decisive in articulating cutting-edge proposals. (Babu et al., 2021) Advancements throughout the digital economy assist the manufacturing sector in configuring groundbreaking business models (Andronie et al, 2021; Dusmanescu et al, 2016; Kovacova and Kliestik, 2017; Pelau et al, 2021; Walker et al, 2020) to attain operational performance. (Fang and Chen, 2021) Collaborative robots, by networking with the operators throughout a shared industrial unit, constitute relevant elements of cyber-physical system-based manufacturing. (Cohen et al., 2021) Smart devices are networked and connected to the centralized system (Balica, 2019; Ionescu, 2020; Lazaroiu et al., 2017; Petrescu and Mihalache, 2020; Zheng, 2020) to facilitate an instantaneous data transfer in a manufacturing facility. (Kim et al, 2021)

  3. Methodology and Empirical Analysis

    We inspected, used, and replicated survey data from CGI, Deloitte, Ericsson, Globant, MHI, PAC, PwC, and Software AG, performing analyses and making estimates regarding big data stream processing technologies. 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 -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

    Shop floors having sound analytical performance can harness big data for optimizing the current operations (Kliestik et al., 2018; Lazaroiu et al., 2020a, b; Popescu et al, 2018) and for inspecting groundbreaking manufactured items and business models. (Babu et al, 2021) The mass customization pattern requires production companies to adjust to market changes swiftly so as to satisfy customer requirements and establishes increased demands for designing smart manufacturing lines. (Yan et al, 2021) (Tables 1-7)

    Shop floors having robust data governance culture (Dabija et al., 2018; Kliestik et al., 2020; Malkawi and Khayrullina, 2021; Valaskova et al., 2018) facilitate information sharing within and throughout...

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