Internet of Things Sensing Networks, Smart Manufacturing Big Data, and Digitized Mass Production in Sustainable Industry 4.0.

• Author: Hopkins, Emily

1. Introduction

   Industry 4.0 constitutes a technology-driven breakthrough to attain increased efficiency and productivity. (Xu et al., 2021) Circular systems may not be the most sustainable alternative or accountable for decreased emissions. (Dantas et al., 2021) Integrating Internet of Things-based decision support systems along production processes (Poliak et al., 2021) facilitates automatic data gathering and inspection: the smart factory management can make coherent decisions and configure enhanced manufacturing operations. (Wu, 2021)

2. Conceptual Framework and Literature Review

   Industry 4.0 articulates manufacturing processes in terms of output, openness to change, and mobility. (Saniuk et al., 2021) The flexibility associated with cutting-edge production systems considerably alters the behavior and the performance of the monitoring systems. (Derigent et al., 2021) By use of sustainable industrial big data and artificial intelligence-based decision-making algorithms, manufacturing operations are optimized (Andronie et al., 2021a, b; Konhausner et al., 2021a, b; Lazaroiu and Harrison, 2021; Popescu et al., 2017) as data concerning production, material resource consumption, product distribution, acquisition decisions, and first-rate items are swiftly available to consumers, resulting in informed decisions to shop floor and in advancing package recycling organization and reducing waste. (Awan et al., 2021) Assimilating machinery fault diagnosis data, cutting down equipment maintenance expenses, and enhancing maintenance performance (Woodward and Kliestik, 2021) through the coordination and recycling of progressive product malfunction data improve product failure prediction. (Wang et al., 2021) Amplifying the deployment of sustainable Industry 4.0 consolidates customer loyalty and technological breakthroughs. (Benitez et al., 2021)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from Forrester, LNS Research, Management Events, McKinsey, Plex Systems, and PwC, we performed analyses and made estimates regarding how integrating Internet of Things-based decision support systems along production processes facilitates automatic data gathering and inspection, through sustainable industrial big data and artificial intelligence-based decision-making algorithms, manufacturing operations are optimized, while by use of autonomous vehicle driving algorithms and perception sensor data, industrial big data analytics, and Internet of Things-based decision support systems, industrial plants network in real time. Data collected from 6,700 respondents are tested against the research model. 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.

   Data sources: Forrester, LNS Research, Management Events, McKinsey, Plex Systems, and PwC.

   Study participants: 6,700 individuals provided an informed e-consent.

   All data were interrogated by employing graphical and numeric exploratory data analysis methods. Results are estimates and commonly are dissimilar within a narrow range around the actual value. 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. 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 (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. 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. 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%.

   The precision of the online polls was measured using a Bayesian credibility interval. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments.

   Flow diagram of study procedures

5. Statistical Analysis

   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 purpose of variable reduction in regression modeling. 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. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed.

   Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. AMOS-SEM analyzed the full measurement model and structural model.

   An Internet-based survey software program was utilized for the delivery and collection of responses. Groundbreaking computing systems and databases enable data gathering and processing, extracting meaning through robust deployment. Panel research represents a swift method for gathering data recurrently, drawing a sample from a pre-recruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Non-response bias and common method bias, composite reliability, and construct validity were assessed.

   Flow diagram of statistical parameters and reproducibility

6. Results and Discussion

   Business...