Internet of Things-based Real-Time Production Logistics, Sustainable Industrial Value Creation, and Artificial Intelligence-driven Big Data Analytics in Cyber-Physical Smart Manufacturing Systems.

• Author: Nica, Elvira

1. Introduction

   The production pattern shifts to mass personalization, swift advancement of smart algorithms, Industry 4.0 wireless networks, and computation technologies. (Lu et al., 2020a) Big data-driven decision-making processes are developed on insights configured by analytics. (Kristoffersen et al., 2020)

2. Conceptual Framework and Literature Review

   The features of smart manufacturing encompass (1) digitalization and serviceorientation, (2) Internet of Things smart devices, and (3) connected production networks, to further financially rewarding, adjustable, and resilient mass personalization. (Lu et al., 2020a) The smart harnessing of resources (Collins, 2020; Kovacova et al., 2019; Noack, 2019; Popescu et al., 2017a, b) can be reinforced by the designing, deriving, processing, and sharing of data from digital technologies. (Kristoffersen et al., 2020) The actuation technologies facilitate networking between Industrial Internet of Things devices (Kliestik et al., 2018; Lazaroiu et al., 2017) and their ambient settings. (Khan et al., 2020)

3. Methodology and Empirical Analysis

   The data used for this study was obtained and replicated from previous research conducted by Capgemini, Deloitte, IW Custom Research, Kronos, MHI, PwC, SME, and Software AG. We performed analyses and made estimates regarding deep learning-assisted smart process planning in cyberphysical manufacturing systems. Data collected from 4,700 respondents are tested against the 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

   By use of personalized-product-based manufacturing process automation, fabrication operations are assimilated and computerized from design to analysis for each individual item (Pop et al., 2021; Valaskova et al., 2018), while networked self-governing manufacturing systems emerge as integrated infrastructure of autonomous production things. (Lu et al., 2020a) Adequately deploying digital transformation is determining for entities in shifting to the circular economy at scale. (Kristoffersen et al., 2020) (Tables 1-11)

   Data-driven distributed intelligence furthers the swift design of a network of production things to cost-effectively manufacture...