Product Decision-Making Information Systems, Real-Time Sensor Networks, and Artificial Intelligence-driven Big Data Analytics in Sustainable Industry 4.0.

AuthorNovak, Andrej
  1. Introduction

    For advancing and manufacturing new items, Industry 4.0 necessitates self-governing production process flow design (Andrei et al., 2016; Kliestik et al., 2018) whose planning is decisive for smart mass customization. (James and Mondal, 2021) In Industrial Internet of Things, heterogeneous services associated with operational technologies, production, utilities, machine supervision are harnessed to connected devices. (Qureshi et al., 2020)

  2. Conceptual Framework and Literature Review

    With the growing industrialization in Internet of Things, a massive volume of sensing data is transferred from diverse sensor devices. (Singh et al., 2020) Industry 4.0 has brought about paradigm reshapings for planning and advancing production processes. (Jwo et al., 2021) Machine learning and deep learning are leveraged to assess the generated data (Andronie et al., 2021a, b; Krizanova et al., 2019) and being about relevant information as regards manufacturing operations, while configuring industrial artificial intelligence. (Kotsiopoulos et al., 2021) Sharing data and assimilating heterogeneous systems or manufactured items into an automated, digital, and networked production environment are hard to achieve in Industry 4.0. (Uysal and Mergen, 2021) The growingly recurring remodeling and reorganization of production lines, required by the heterogeneity of customer demands, are pivotal in smart manufacturing. (Guo and Martinez-Garcia, 2021)

  3. Methodology and Empirical Analysis

    Using and replicating data from BCG, BDO, Capgemini, Management Events, PAC, and PwC, we performed analyses and made estimates regarding production network performance. 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

    Smart manufacturing is advancing from uncomplicated digitization and automation of distinct machines, to networking them by harnessing Internet of Things technologies and deploying the data gathered from the connected systems to settle on a plan of action instantaneously, consequently enhancing operational performance. (Shao et al., 2021) Smart manufacturing systems can manage huge quantities of unstructured data and enable decentralization by employing distributed systems. (Barari et al., 2021) (Tables 1-9)

    Smart manufacturing carries to completion a set-level of constitutive and agile efficiency that reinforces swift feedback to the requirements of all mechanisms across the supply chain network, addressing quality, output, and sustainability, while harnessing a broad variety of digitization approaches of Industry 4.0. (Barari et al., 2021) To further safe and adequate human-robot teamwork in Industry 4.0, continuous assimilation of sensing...

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