Artificial Intelligence Data-driven Internet of Things Systems, Real-Time Advanced Analytics, and Cyber-Physical Production Networks in Sustainable Smart Manufacturing.

• Author: Durana, Pavol

1. Introduction

   Industrial Internet of Things and digital twin technologies can attain real-time data sharing with perceptibility and traceability across the entire production process. (Guo et al., 2020) Big data technology infrastructures in smart factory gather heterogeneous digital statistics from manufacturing resources (Peters, 2020) to further operations management. (Park et al., 2020)

2. Conceptual Framework and Literature Review

   Cloud services assimilate instantaneous task distribution, and execution systems are advanced to carry out manufacturing planning, scheduling, performance, and monitoring with decreased elaborateness and uncertainty. (Guo et al., 2020) Data analytics is decisive in smart manufacturing decision making. (Zhang et al., 2020) Industrial Internet of Things sensors assist in developing production process modeling and supervision. (Shah et al., 2020) Industrial Internet of Things automates big data-driven objects for sensing, gathering, processing, and transmitting real-time events (Krizanova et al., 2019; Miller, 2020; Svabova et al., 2020) across manufacturing systems. (Khan et al., 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from BCG, Capgemini, CompTIA, Deloitte, Eclipse Foundation, The Economist Intelligence Unit, MHI, PAC, Siemens, SME, Software AG, and we.CONECT, we performed analyses and made estimates regarding big data-driven decision-making processes in cyber-physical system-based smart factories. The results of a study based on data collected from 4,800 respondents provide support for our research model. 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

   Industry 4.0 technologies assist smart manufacturing in automatically gathering and distributing instantaneous field data. (Guo et al., 2020) Industrial Internet of Things necessitates superior levels of safety, security, and trustworthy networking without the interruption of real-time large-scale processes (Dabija et al., 2017; Ionescu, 2020; Mihaila et al., 2016; Sion, 2019) as a result of mission-critical settings. (Khan et al., 2020) (Tables 1-12)

   Cutting-edge Industry 4.0 technologies have been reconfiguring the manufacturing operations management considerably...