Cyber-Physical Process Monitoring Systems, Artificial Intelligence-based Decision-Making Algorithms, and Sustainable Industrial Big Data in Smart Networked Factories.

AuthorHiggins, Michael
  1. Introduction

    Industry 4.0-related digitalization processes have led to disruptive breakthroughs across cyber-physical system-based manufacturing. (Tortorella et al., 2021) Big data-driven algorithms and tools can enable product realization by use of available industrial plant resources while reducing manufacturing, planning, and logistics expenses. (Saniuk et al., 2021) Industry 4.0 furthers digital manufacturing options for automated production, integrating the deployment of networked technologies to share data to processing stations and of cloud-based computational resources in input mining. (Turner et al., 2021)

  2. Conceptual Framework and Literature Review

    Industry 4.0-based manufacturing systems enable the production of inexpensive customized items swiftly through a gradual productivity and efficiency optimization. (Saniuk et al., 2021) Big data analytics and circular economy performance can shape sustainable manufacturing. (Awan et al., 2021) Industry 4.0 automation systems require technological and organizational reconfiguration, digitalization, and interconnection. (Cirillo et al., 2021) The networking of ubiquitous edge computing and blockchain technologies is pivotal in Industrial Internet of Things applications. (Yu et al., 2021) Smart manufacturing systems can organize, develop, and adapt an elaborate production process coherently (Adams et al., 2021; Dusmanescu et al., 2016; Lazaroiu et al., 2019; Vatamanescu et al., 2020), enhancing the synergistic design of manufacturing resources on industrial plants. (Wang et al., 2021) Precise and real-time data gathering and analysis are crucial in the sustainable performance of manufacturing processes (Barbu et al., 2021; Kovacova et al., 2018; Novak et al., 2021; Stefko et al., 2019) throughout smart and collaborative production and logistics environments by use of predictive maintenance.

  3. Methodology and Empirical Analysis

    The data used for this study was obtained and replicated from previous research conducted by Algorithmia, Capgemini, Forrester, Management Events, and PwC. We performed analyses and made estimates regarding how big data-driven algorithms and tools can enable product realization by use of networks of smart connected devices and sensors, pattern-detecting decision-making equipment, and machine learning-based tools, leading to precise and real-time data gathering and analysis, while big data analytics applications across industrial plants are decisive in configuring digital manufacturing options for automated production. Data collected from 5,600 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

  4. Study Design, 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.

    Data sources: Algorithmia, Capgemini, Forrester, Management Events, and PwC.

    Study participants: 5,600 individuals provided an informed e-consent.

    All data were interrogated by employing graphical and numeric exploratory data analysis methods. 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. An Internet-based survey software program was utilized for the delivery and collection of responses. 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 (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can 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 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%.

    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. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

    Flow diagram of study procedures

  5. Statistical Analysis

    This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. Descriptive analyses (mean and standard deviations for continuous variables and counts and percentages for categorical variables) were used. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling.

    Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. AMOS-SEM analyzed the full measurement model and structural model.

    Panel research represents a swift method for gathering data recurrently, drawing a sample from a pre-recruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Groundbreaking computing systems and databases enable data gathering and processing, extracting meaning through robust deployment. Non-response bias and common method bias, composite...

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