Robotic Wireless Sensor Networks, Big Data-driven Decision-Making Processes, and Cyber-Physical System-based Real-Time Monitoring in Sustainable Product Lifecycle Management.

• Author: Dawson, Andrew

1. Introduction

   Smart manufacturing integrates heterogeneous technologies that reinforce decision-making instantaneously (Lazaroiu et al, 2020a, b; Nelson, 2020; Popescu, 2014) throughout unstable circumstances in production operations, pushing ahead competitiveness and sustainability, but as a shop floor comes to be significantly computerized, physical asset management constitutes a decisive component of an operational life-cycle. (Lee et al, 2021)

2. Conceptual Framework and Literature Review

   Industry 4.0 is instrumental in attaining diminished product life-cycles, significantly customized products and increased large-scale competition. (Jimeno-Morenilla et al., 2021) Implementing routines associated with circular economy, cleaner production, and cyber-physical systems by shop floors (Adams et al, 2020; Lazaroiu and Adams, 2020; Nica, 2018; Popescu et al, 2017; Stevens, 2020) can optimize sustainability performance. (Gupta et al, 2021) The expansion of data availability in addition to improved computation performance articulates production planning and supervision strategies with real-time inputs. (Lugaresi et al., 2021) Reliability and safety assessments are pivotal in cutting down risk of failure events and maximizing operationality of smart manufacturing. (Soltanali et al, 2021) Plants have to design their production process sequences with the purpose that the manufactured item demands are satisfied adequately. (Beckers et al, 2021) The harnessing of big data-driven computational performance and storage into the industrial realm furthers the articulation of groundbreaking paradigms to convert the established manufacturing processes into smart systems. (Barbieri et al, 2021) Big data and cutting-edge analytic tools can optimize business, financial, and sustainability capabilities. (Raut et al, 2021)

3. Methodology and Empirical Analysis

   Using and replicating data from Capgemini, Deloitte, EY, Kronos, IW Custom Research, Management Events, McKinsey, MHI, and PwC, I performed analyses and made estimates regarding business process management systems. The results of a study based on data collected from 5,900 respondents provide support for my 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 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.

5. Results and Discussion

   The advancement of big data-driven technologies has brought about the articulation of smart product-service systems: a networked manufactured item operates coherently across the link between physical components and expert services for value co-development. (Li et al., 2021) Instantaneous supervision constitutes the integral part in Industry 4.0 with the swift deployment of artificial intelligence in smart manufacturing. Machine learning algorithms and...