Industrial Artificial Intelligence, Sustainable Product Lifecycle Management, and Internet of Things Sensing Networks in Cyber-Physical Smart Manufacturing Systems.

AuthorGray-Hawkins, Malcolm
  1. Introduction

    The Industry 4.0 production environment provides huge potential for embracing sustainability in cyber-physical smart manufacturing systems. (Felsberger et al., 2020) As a result of the swift advancement of big data technologies, supply chain network has advanced, entailing a significant level of networking between companies. (Li et al., 2020) Industry 4.0 implementation has a positive link with sustainable production that is pivotal in configuring circular economy capabilities. (Bag et al., 2020)

  2. Conceptual Framework and Literature Review

    Standard production capability assessment develops on integrated techniques with insufficient dissemination of performance and evaluation information (Dusmanescu et al., 2016; Kovacova et al., 2019; Lazaroiu et al., 2020; Peters et al., 2020), while its approaches are chiefly rooted in particular manual operation by harnessing insubstantial data. (Li et al., 2020) Sustainable manufacturing systems decrease diverse kinds of wastage and enhance production efficiency (Kliestik et al., 2018; Lazaroiu et al., 2017; Mihaila et al., 2016; Popescu et al., 2017), while diminishing the consequences on environment by generating less pollution. (Jena et al., 2020) The networking of Industry 4.0 and circular economy constitutes a real-time decision paradigm (Kliestik et al., 2020a, b, c; Lazaroiu, 2018; Nica et al., 2014) for the sustainable reverse logistics system, by organizing sustainable operations management. (Dev et al., 2020)

  3. Methodology and Empirical Analysis

    Using and replicating data from BCG, Capgemini, Deloitte, IW Custom Research, Kronos, McKinsey, MHI, PwC, and SME, we performed analyses and made estimates regarding the link between industrial artificial intelligence, sustainable product lifecycle management, and Internet of Things sensing networks. Data were analyzed using structural equation modeling.

  4. Results and Discussion

    Industry 4.0 harnesses massive amount of data to advance from organized, control-based mechanisms and systems to smart processes that can predict the behavior of machines across the industry value chain and monitor them by adapting their operations at various levels. (Ruiz-Sarmiento et al., 2020) Blockchain technology facilitates open and decentralized information maintenance and distribution, and supplies unbiased and computerized data transfer, improving production efficiency, decreasing risk, and articulating a sustainable production management across the supply chain network. (Li et al., 2020) The value creation range of sustainable manufacturing is associated with production networks. (Goncalves Machado et al., 2020) (Tables 1-11)

    Digitalization is altering the production ecosystem as companies deploy Internet of Things to network manufacturing assets, big data analytics to supervise assembly lines, and industrial artificial intelligence to reinforce decision-making operations. (Felsberger et al., 2020) Blockchain technology, Internet of Things, and machine learning are instrumental in carrying out instantaneous data gathering and computerized enterprise manufacturing capability assessment processes across supply chain networks. (Li et al., 2020) Industry 4.0-based manufacturing systems are enabling sustainable value-creation across life-cycle phases, by use of sustainable design, resource-coherent production operations, and circular and synergetic manufacturing systems. (Goncalves Machado et al., 2020) Swift developments in Internet of Things sensing networks further the refashioning of industrial production into cyber-physical smart manufacturing systems, shaping process automation by use of sustainable product lifecycle management and real-time process monitoring. (Waschull et al., 2020)

    Industry 4.0 and its advancement of big data analytics and self-governing manufacturing systems, together with their behavior as regards digital interactions, will shape production performance. (Felsberger et al., 2020) Smart technologies will improve sustainable consumption by adequately decreasing power use and its environmental consequences. (Schappert and von Hauff, 2020) Cyber-physical systems comprising heterogeneous sensors, actuators, and controllers necessitate value-based simulation, modeling, and resource management systems to network physical and computing components for real-time processing, resulting in robust computing services. (Kim et al., 2020) Industrial Internet of Things, big data analytics, and cyber-physical production systems merge to attain a significant level of operational coherence, output, and computerization in sustainable smart manufacturing. (Schafer Tesch da Silva et al., 2020)

  5. Conclusions and Implications

    Cloud computing and cyber-physical smart manufacturing systems enable the configuration and supervision of the real world in a virtual setting. (Kim et al., 2020) Cyber-physical systems network physical operations, electronic computing...

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