Cyber-Physical Process Monitoring Systems, Real-Time Big Data Analytics, and Industrial Artificial Intelligence in Sustainable Smart Manufacturing.

AuthorCohen, Sarah
  1. Introduction

    Attaining a high level of resilience is pivotal in smart manufacturing through data acquisition and management. (Peng et al, 2021) A smart factory is a big data-driven industrial integrated networked assembly, aiming mass customization, supplying customers with sustainable items and services, and facilitating instantaneous adjustment (Costea, 2020; Konhäusner et al, 2021; Nica et al, 2018; Popescu et al., 2018) for flexible alterations of user demand, shop floor environments, and value networks. (Cohen et al., 2019) Sustainable manufacturing Internet of Things has a part in circular economic purposes by attaining social, economic, and environmental upsides. (Khan et al, 2021)

  2. Conceptual Framework and Literature Review

    Typified by self-control and agile adjustment to swift dynamics in intricate production environments (Bailey, 2021; Kliestik et al., 2021; Lyons and Lazaroiu, 2020; Popescu et al, 2017a, b, c; Vatamanescu et al, 2020), sustainable manufacturing Internet of Things optimizes the reliability of production output. (Zhang and Gao, 2021) Industry 4.0-based manufacturing systems configure networked embedded smart assembly stations, cognitive reconfigurable equipment, and data-driven assemblies and parts that integrate the physical operations with virtual data to neutralize mismanagements, and optimize the production process. (Cohen et al, 2019) Predictive models are articulated within cyber-physical production systems for the monitoring of industrial plants (Croitoru and Co?ciug, 2021; Kral et al, 2019; Mihaila et al, 2016; Rowland et al, 2021), resulting in the demand for extensive supervision of model performance and pattern adjustment (Andrei et al., 2016; Dawson, 2021; Lazaroiu et al., 2017; Pelau et al, 2021; Svabova et al., 2020) if surrounding conditions are altered and the aimed prediction precision is not satisfied. (Bachinger et al, 2021) Cyber-physical systems and Internet of Things facilitate enhanced output and time management, carrying out heterogeneous digital manufacturing processes entailing sensors and networked technologies by use of deep learning-assisted smart process planning, automated production systems, and industrial big data. (Khan et al, 2021)

  3. Methodology and Empirical Analysis

    Using and replicating data from Capgemini, Forrester, McKinsey, PwC, and World Economic Forum, we performed analyses and made estimates regarding how networked integrated production equipment and sensors and machine learning tools configure the predictive monitoring of manufacturing plants. 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. 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. An Internet-based survey software program was utilized for the delivery and collection of responses. Panel research represents a swift method for gathering data recurrently, drawing a sample from a prerecruited set of respondents. 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. Results are estimates and commonly are dissimilar within a narrow range around the actual value. If a participant began a survey without completing it, that was withdrawal of consent and the data was not used. To prevent missing data, all fields in the survey were required. 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 highquality 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). 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%. At each step in the survey research process, best practices and quality controls were followed to minimize the impact of additional sources of error as regards specification, frame, non-response, measurement, and processing. 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. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

  5. Statistical Analysis

    Sampling errors and test of statistical significance take into account the effect of weighting. Throughout the research process, the total survey quality approach, designed to minimize error at each stage as thus the validity of survey research would be diminished, was followed. 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. All data were interrogated by employing graphical and numeric exploratory data analysis methods. 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...

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