Real-Time Big Data Analytics, Smart Industrial Value Creation, and Robotic Wireless Sensor Networks in Internet of Things-based Decision Support Systems.

• Author: Watkins, Darren

1. Introduction

   By integrating fully configured, collaborative systems, smart manufacturing can upgrade production with improved adjustability. (Vancza et al., 2020) Smart manufacturing necessitates uninterrupted assimilation of intra- and inter-business production operations and systems (Ionescu, 2020; Kovacova et al., 2019) in the direction of mass customization and responsive plant automation, while developing on standards-conforming and consonant networks between various production stages and procedures. (Lu et al., 2020)

2. Conceptual Framework and Literature Review

   By networking production operations, equipment, factories, and personnel (Andrei et al., 2016; Andronie et al., 2021; Kliestik et al., 2018; Rowland et al., 2021), smart manufacturing can enhance overall performance and adjustability of the system (Vancza et al., 2020), configuring mass-producing customized items by use of responsive self-governing processes at a competitive cost. (Lu et al., 2020) The flexibility provided by cutting-edge production systems (Kliestik et al., 2020a, b; Lazaroiu et al., 2019) results in a thorough alteration of the operations and performance of the monitoring systems. (Derigent et al., 2020) The capability to robotically, precisely, and predictably validate process signatures and communicate the upgrade of production parameters leads to optimization in quality, time management, and consistent transparency throughout the entire value chain. (Lenz et al., 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from BCG, Capgemini, CompTIA, Deloitte, EY, PAC, PwC, SME, and World Economic Forum, I performed analyses and made estimates regarding product decision-making information systems in data-driven sustainable smart manufacturing. Data collected from 5,400 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

   To gather and harness the data in smart manufacturing, massive volumes of sensors are typically integrated within interconnected communication systems...