Robotic Wireless Sensor Networks, Internet of Thingsenabled Sustainability, and Real-Time Advanced Analytics in Cyber-Physical System-based Smart Factories.

AuthorCampbell, Ella
  1. Introduction

    In big data-driven services, an industrial Internet platform assists companies by networked monitoring of design, advancement, supply, and production processes (the enhanced configuration of personnel, equipment, and materials, and swift reaction to demand shift are all attainable) and expeditiously shares cross-enterprise resources. (Peng et al, 2021) The intricate character of manufacturing operations necessitates integrated prediction models (Lazaroiu et al, 2020; Nica et al, 2019; Pelau et al, 2021) articulated for distinct parts and ensuing control and model adjustment. (Bachinger et al., 2021) Sustainable cyber-physical production systems can further sustainable remedies by making exemplary reactive and predictive decisions. (Khan et al, 2021)

  2. Conceptual Framework and Literature Review

    Smart manufacturing, artificial intelligence data-driven Internet of Things systems, and robotic wireless sensor networks can thoroughly optimize production capacity and time management (Andrei et al, 2016a, b; Ionescu, 2021; Lyons and Lazaroiu, 2020; Popescu et al, 2017; Valaskova et al, 2021), supplying robust assistance for administration and decision-making. (Qi et al, 2021) Computerized assembly systems developed across autoprogramming settings in smart manufacturing plants decrease setup time and expenses for reconfiguring and rescheduling big data-driven equipment (Andronie et al, 2021a, b; Popescu et al, 2018; Vatamanescu et al, 2021) participating in constant task reassignment. (Ji et al, 2021) Smart production processes encompass networked machine learning models (Filová and Hrdá, 2021; Kovacova et al, 2019; Shaw et al, 2021) and elaborate interconnections of heterogeneous input and environment parameters. (Bachinger et al, 2021) From unprocessed materials to obsolescence, Industry 4.0-based manufacturing systems have modified the organization of supply chain management (Du?manescu et al., 2016; Kral et al., 2019; Sfetcu and Popa, 2020; Zheng, 2020), altering how shop floors articulate sustainable prolonged remedies. (Khan et al, 2021)

  3. Methodology and Empirical Analysis

    Using and replicating data from Capgemini, EY, IW Custom Research, Kronos, McKinsey, PwC, Siemens, and Vodafone, we performed analyses and made estimates regarding how cyber-physical systems can support sustainable operations through smart production processes, Industrial Internetenabled resilient manufacturing, and sensing and computing technologies. The results of a study based on data collected from 5,800 respondents provide support for our research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

  4. Study Design, Survey Methods, and Materials

    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. 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. 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. Results are estimates and commonly are dissimilar within a narrow range around the actual value. If a participant began a survey without completing it, that was withdrawal of consent and the data was not used. To prevent missing data, all fields in the survey were required. 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 reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. 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. Throughout the research process, the total survey quality approach, designed to minimize error at each stage as thus the validity of survey research would be diminished, was followed. 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 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.

  5. Statistical Analysis

    At each step in the survey research process, best practices and quality controls were followed to minimize the impact of additional sources of error as regards specification, frame, non-response, measurement, and processing. 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). An informed e-consent 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. All data were interrogated by employing graphical and numeric exploratory data analysis methods. 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. Multivariate...

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