Big Data-driven Innovation, Deep Learning-assisted Smart Process Planning, and Product Decision-Making Information Systems in Sustainable Industry 4.0.

• Author: Grant, Edwin

1. Introduction

   Industry 4.0 disestablishes the data barriers between various components of an assembly line (Dusmanescu et al., 2016; Kovacova et al., 2019; Nica, 2015; Popescu et al., 2018), as smart networked products, furthered by cutting-edge information and communication technologies, can interconnect to gather, process and produce evidence. (Huo et al., 2020) The functioning period of manufacturing data encompasses collecting and inspecting information from machines, human operators, and smart networked products. (Xu et al., 2020)

2. Conceptual Framework and Literature Review

   Manufacturing data gathering and inspection constitute the essential enablers to carry out data-driven production. (Xu et al., 2020) Smart supervision of an assembly line is feasible by use of huge volumes of Industry 4.0-related realtime production data. (Huo et al., 2020) Growing competition across the worldwide market has constrained firms to branch out their product ranges (Andrei et al., 2016; Kliestik et al., 2018; Lazaroiu et al., 2017; Peters et al., 2020; Valaskova et al., 2018) so as to satisfy consumers' fluctuating demands and implement product development approaches for mass customization or personalized production, necessitating designing modular manufactured items. (Varl et al., 2020) Cyber-physical systems assist companies in maintaining relevant traceability and monitoring in production for superior quality and enhanced output. (Wang et al., 2020) Internet of Things designates the ability to network physical objects online, enabling them to operate unsupervised in a context-adequate fashion. (Strohmeier, 2020) Perceived upsides and management support serve as stages in the adoption of data and digital technologies in smart manufacturing (Ghobakhloo, 2020), that is developed on elaborate data-based decision-making instantaneously derived from networked equipment and sensors. (Epureanu et al., 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from Capgemini, Deloitte, Management Events, McKinsey, MHI, Microsoft, and PwC, I performed analyses and made estimates regarding artificial intelligence-based decision-making algorithms in big data-driven innovation in sustainable Industry 4.0. 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

   Due to the advancement of sensoring and data inspection...