Cyber-Physical Production Networks, Internet of Things-enabled Sustainability, and Smart Factory Performance in Industry 4.0-based Manufacturing Systems.

• Author: Hawkins, Mark

1. Introduction

   The implementation of soft sensors is pivotal in configuring smart manufacturing. (Jelsch et al., 2021) Smart manufacturing is developed on digitization to configure automated manufacturing and management during production processes. (Yeh et al., 2021) Inexpensive and suitable sensors for gathering data are essential in configuring smart factories. (Jung et al., 2021) The smart assembly line enhances operational quality by upgrading manufacturing parameters. (Aderiani et al., 2021)

2. Conceptual Framework and Literature Review

   To digitize production operations, Internet of Things technologies have to be implemented to gather and inspect process data. (Yeh et al, 2021) Manufacturing companies have to implement heterogeneous cutting-edge technologies (Costea, 2020) to coherently ensure their competitiveness. (Jung et al, 2021) A massive volume of Internet of Things devices (e.g., sensors and actuators) are leveraged to gather data so as to reinforce safety throughout essential smart cyber-physical system infrastructures. (Alemayehu et al, 2021) The swift advancement of the Industrial Internet of Things has articulated manufacturing as a cyber-physical system. (Liu et al., 2021) Groundbreaking industrial manufacturing systems have to network and harness growing volumes of data (Andrei et al, 2016; Krizanova et al, 2019; Mircica, 2020; Pop et al, 2021; Sfetcu and Popa, 2020) from smart items, software systems, and Industrial Internet of Things devices. (Kuhn and Franke, 2021) Industry 4.0 technologies configure digital resources for production automation. (Bag et al, 2021) Industry 4.0 reconfigures manufacturing with swift technological advancements addressing enhancements of the production process performance. (Reiman et al., 2021) Cyber-physical systems can decrease production time in manufacturing. (Dafflon et al, 2021)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from Algorithmia, BDV, Capgemini, Eclipse Foundation, Management Events, and PwC, I performed analyses and made estimates regarding intelligent autonomous manufacturing systems. The data for this research were gathered via an online survey questionnaire. 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

   By digitizing manufacturing operations and networking all phases (from manufacturing item design to service), the objective of enhancing...