Digitized Mass Production, Real-Time Process Monitoring, and Big Data Analytics Systems in Sustainable Smart Manufacturing.

AuthorWhite, Thomas
  1. Introduction

    By harnessing Internet of Things-related technologies, regular physical objects come to be smart ones that can be particularly detected and connected, while sensing the environment (Krech, 2019), interacting among them (Krizanova et al., 2019), and making adequate decisions. (Guo et al., 2020)

  2. Conceptual Framework and Literature Review

    Manufacturing plants are preoccupied with the adoption of the Industry 4.0 technologies (Ionescu, 2018; Krizanova et al., 2019; Lazaroiu et al., 2019; Mihaila, 2019; Mircica, 2018; Popescu et al., 2017) in consequence of the significant investments, the demand for first-rate personnel, absence of software standards, and groundbreaking hardware. (Mahlmann Kipper et al., 2020) State- or data-driven modeling represents a coherent strategy to determine the instantaneous performance of the system (Andrei et al., 2016a, b; Hanappi, 2018; Kwasny et al., 2019; Lazaroiu et al., 2020; Nica, 2015; Popescu Ljungholm, 2018) and enable actual production control. (Chen et al., 2020) Digital twin technology furthers the assimilation of data to assist planners in streamlining their product designs swiftly. (Sun et al., 2020) The configuration of a cutting-edge, age-friendly, and convenient workstation by deploying Industry 4.0 paradigms and tools (Dusmanescu et al., 2016; Krech, 2019; Lazaroiu et al., 2017; Majerova et al., 2020; Popescu, 2014) is progressively projected for manufacturing plants while requiring certain devices, instructions, and design standards. (Calzavara et al., 2020)

  3. Methodology and Empirical Analysis

    Using and replicating data from Capgemini, Deloitte, Forrester, PwC, Software AG, we.CONECT, and World Economic Forum, we performed analyses and made estimates regarding the relationship between digitized mass production, real-time process monitoring, and big data analytics systems. Data were analyzed using structural equation modeling.

  4. Results and Discussion

    The deployment of digital manufacturing technologies is altering the architecture of the production environment while improving competitiveness among companies. (Gillani et al., 2020) The carrying out of smart manufacturing is instrumental in furthering the financial and innovation performance of networked production enterprises. (Y ang et al., 2020) Precise determination of the model specifications of the manufacturing operations developed on online process data constitutes a pivotal requirement for its model-based monitoring and diagnostics. (Lee, 2020) Connecting the current discontinuity between the design and manufacturing phases of a product by adequate data flow, digital twins, cyber-conceptualizations of physical entities, are instrumental in smart industries' exigency of having incessant enhancement of their production operations. (Roy et al., 2020) (Tables 1-8)

  5. Conclusions and Implications

    The impulse in the direction of digitalization of manufacturing in relation to Industry 4.0 has transformed data processing throughout the design and performance of production systems. (Mourtzis, 2020) The advancement of data and process technologies furthers the swift updates in groundbreaking products and smart manufacturing through harnessing significant volumes of sensors connected to give evidence of equipment condition from start to finish of the production operations. (Hsu and Liu, 2020)

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