Artificial Intelligence Data-driven Internet of Things Systems, Robotic Wireless Sensor Networks, and Sustainable Organizational Performance in Cyber-Physical Smart Manufacturing.

• Author: Galbraith, Amanda

1. Introduction

   Industrial plants have to implement cyber-physical process monitoring systems to be competitive. (Ghouri et al., 2021) Industry 4.0-based manufacturing systems decrease operational costs by deploying predictive maintenance and cognitive automation. (Awan et al., 2021) Sustainable manufacturing Internet of Things, by harnessing massive digitization (Kliestik et al., 2021; Lazaroiu et al., 2021; Poliak et al., 2021; Stefko et al., 2020), can gather and process huge quantities of data, aiming knowledge extraction. (Sansana et al., 2021)

2. Conceptual Framework and Literature Review

   Sustainable Industry 4.0 wireless networks have reconfigured manufacturing processes (Andrei et al., 2016; Konhausner et al., 2021a, b, c; Lazaroiu and Harrison, 2021; Popescu et al., 2017; Vagner, 2021) across interconnected cyber-physical system-based smart factories (Nakagawa et al., 2021) that advanced due to the growing availability of cyber-physical production networks and cognitive automation that integrate machine intelligence-driven monitoring systems. (Turner et al., 2021) Internet of Things-based real-time production logistics is pivotal in networking smart devices and tools (Ginevicius et al., 2020; Lazaroiu et al., 2017; Novak et al., 2021; Shchekotin et al., 2021) throughout sustainable cyber-physical production systems. (Zikria et al., 2020) Based on robotic wireless sensor networks and industrial big data analytics, artificial intelligence-based decision-making algorithms can identify irregularities during machine operations (Andronie et al., 2021a, b; Kral et al., 2019; Lu et al., 2020; Popescu et al., 2018; Wallace and Lazaroiu, 2021), enabling predictive equipment maintenance. (Wang et al., 2021) The shift to big data-driven flexible production requires increased self-governance within cyber-physical process monitoring systems. (Harrison et al., 2021)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from EY, Kronos, IW Custom Research, McKinsey, and PwC, we performed analyses and made estimates regarding how sustainable Industry 4.0 wireless networks have reconfigured manufacturing processes as deep learning-assisted smart process planning can automate decision-making operations, while sustainable cyber-physical production systems can automate smart networked devices and artificial intelligence-based decision-making algorithms can identify irregularities during machine operations, with Internet of Things-based real-time production logistics being pivotal in networking smart devices and tools. 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: EY, Kronos, IW Custom Research, McKinsey, and PwC. Study participants: 6,300 individuals provided an informed e-consent.

   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. 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. 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. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

   Flow diagram of study procedures

5. Statistical Analysis

   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. 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.

   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. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. 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. 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. Groundbreaking computing systems and databases enable data gathering and processing, extracting meaning through robust deployment. 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

   Sustainable cyber-physical production systems can automate smart networked devices for data sensing, capturing, and storing across real-time events in Industry 4.0-based manufacturing systems by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, Internet of Things-based decision support systems, deep learning-assisted smart process...