Cognitive Decision-Making Algorithms, Internet of Things Smart Devices, and Sustainable Organizational Performance in Industry 4.0-based Manufacturing Systems.

AuthorCoatney, Karen
  1. Introduction

    Cyber-physical systems constitute the outcome of technological advancements to a particular stage, shaping the trends in Industry 4.0-based manufacturing systems (Yi et al., 2020) that harness automated production assets supervised by networked sensors (Davis et al., 2020; Kliestik et al., 2018) and monitored by cognitive decision-making algorithms. (Li et al., 2020)

  2. Conceptual Framework and Literature Review

    The assistance of the production disruption by smart manufacturing technologies (Andrei et al., 2016a, b; Hughes et al., 2020; Kovacova et al., 2019; Lyons and Lazaroiu, 2020; Popescu et al., 2018) enables the link of the product and service flows throughout the network (Englund, 2019; Kliestik et al., 2020a, b; Lazaroiu et al., 2020a, b; Popescu et al., 2017), moderating the consequences of industrial chain disarray. (Li et al., 2020) Smart cyber-physical systems are constituted by a physical element that is supervised by a computer-based algorithm. (Lezoche and Panetto, 2020) Industry 4.0 is developed on the components of Industrial Internet of Things, instantaneous data gathering, and predictive analytics (Lazaroiu and Adams, 2020; Peters et al., 2020; Popescu et al., 2020) by harnessing big data, machine learning, and cloud manufacturing. (Tiwari and Khan, 2020)

  3. Methodology and Empirical Analysis

    The data used for this study was obtained and replicated from previous research conducted by BCG, Capgemini, CompTIA, Deloitte, Globant, MHI, Omdia, and PwC. We performed analyses and made estimates regarding the link between cognitive decision-making algorithms, Internet of Things smart devices, and sustainable organizational performance. Data collected from 5,200 respondents are tested against the research model by using structural equation modeling.

  4. Results and Discussion

    Industrial networks have to be resilient to manufacturing disruptions and market environment alterations. (Li et al., 2020) The Industry 4.0 production pattern is typified by significant intercommunicating features of its production components across the manufacturing mechanisms: the systems have to be structurally improved to attain the satisfactory degree of redundancy to be adequately robust. (Lezoche and Panetto, 2020) (Tables 1-12)

    Big data analytics approaches are designed to investigate and consistently derive information from massive and heterogeneous datasets having structured, semi-structured, or unstructured configurations. (Li et al., 2020) Cyber-physical systems integrate sensing, data gathering and sharing, and mechanical actuation. (Lezoche and Panetto, 2020) Industrial Internet of Things, cyber-physical production systems, smart sensors, and big data adoption constitute determinants for sustainable development. (Nara et al., 2021) The networking of smart manufacturing, cyber-physical systems, big data analytics, and Industrial Internet of Things shape enhancements in business process administration. (Queiroz et al., 2020)

    First-rate computing ability enables cyber-physical systems's instantaneous and precision applications, while the development of distributed technology configures the adoption likelihood of cutting-edge smart manufacturing. (Yi et al., 2020) Industrial Internet of Things approaches and smart devices are pivotal in remote equipment condition supervision and maintenance organization. (Li et al., 2020) Industry 4.0 and the digitization of supply chains have resulted in the implementation of Internet of Things solutions as instrumental in the articulation of sustainable management. (Mastos et al., 2020) Smart manufacturing systems are embraced by production companies as a procedure to enhance their operational performance. (Kamble et al., 2020) Industry 4.0 assimilates cyber-physical systems across the Internet of Things to fully leverage the value-added chain. (Wortmann et al., 2020)

  5. Conclusions and Implications

    Physical and software elements deployed throughout the cyber-physical systems are thoroughly entangled, performing on various spatial and temporal scales and networking in context-related manners. (Lezoche and Panetto, 2020) The advanced Industry 4.0 solution constitutes a determining enabler for sustainable supply chain management performance. (Mastos et al., 2020) Smart manufacturing system integrates artificial intelligence, automation, Internet of Things, data transfers, cyber-physical systems, and semi-autonomous industrial systems. (Kamble et al., 2020) Deep intricacy, capability for self-governance, upgrading, and integrative emergent features typify the interconnected network of Internet of Things devices. (Holman et al., 2020) As limitations, this article focuses only on cognitive decision-making algorithms, Internet of Things...

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