Product Decision-Making Information Systems, Real-Time Big Data Analytics, and Deep Learning-enabled Smart Process Planning in Sustainable Industry 4.0.

AuthorPeters, Elisabeth
  1. Introduction

    Industry 4.0 generates significant alterations to the structure of manufacturing plants concerning their value proposition (Andrei et al., 2016a, b; Hoffman and Friedman, 2018; Nica, 2015) and the advancement of their production interconnected system, supplier base, and customer networks. (Culot et al., 2020)

  2. Conceptual Framework and Literature Review

    By networking machines, parts, and systems (Andrei et al., 2020; Krizanova et al., 2019; Lazaroiu et al., 2019; Mihaila et al., 2018; Popescu et al., 2018a, b), smart shared interconnected channels can be configured throughout the supply chain (Lazaroiu et al., 2017; Majerova et al., 2020; Nica et al., 2014; Popescu, 2014; Reicher, 2019), articulating smart manufacturing by self-governing supervision. (Zolotova et al., 2020) Large-scale product customization requires companies to swiftly react to customer demands, flexibly reorganize equipment and calibrate operational specifications for accidental system breakdowns and product quality issues (Dusmanescu et al., 2016), and modernize obsolete systems with cutting-edge technologies. (Kim et al., 2020)

  3. Methodology and Empirical Analysis

    Building our argument by drawing on data collected from Capgemini, Deloitte, McKinsey, MHI, we.CONECT, and World Economic Forum, we performed analyses and made estimates regarding the relationship between product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning. Data collected from 4,600 respondents are tested against the research model by using structural equation modeling.

  4. Results and Discussion

    The workplace monitoring system advances towards adjustable smart manufacturing (Lazaroiu, 2018; Lazaroiu et al., 2020a, b; Mihaila, 2017; Moghtader, 2018; Pilkington, 2018; Popescu et al., 2019) through heterogeneous customer requirements. (Li et al., 2020) To scale up the machine learning patterns for data inspection, huge volumes of data are needed to train them and facilitate incessant model updates (Lewis Bowler et al 2020) (Tables 1-8)

  5. Conclusions and Implications

    Machine learning procedures necessitates massive quantities of quality training datasets, while concerning supervised machine learning, manual input is routinely needed for labeling them. (Alexopoulos et al., 2020) The volume of data gathered and distributed has improved both predictive precision and enablement of prescriptive solutions. (Schniederjans et al., 2020)

    Survey method

    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. Sampling errors and test of statistical significance take into account the effect of weighting. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. 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. An Internet-based survey software program was utilized for the delivery and collection of responses.

    Data and materials availability

    All research mentioned has been published and data is available from respective outlets.

    Funding

    This paper was supported by the Slovak Research...

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