Business Process Optimization, Cognitive Decision-Making Algorithms, and Artificial Intelligence Data-driven Internet of Things Systems in Sustainable Smart Manufacturing.

AuthorWilliams, Arthur
  1. Introduction

    Groundbreaking sensor technologies are harnessed in manufacturing systems to boost data visibility and system controllability. (Yang et al., 2019) Real-world applications integrate computation, interaction, sensing, and actuation (Nica et al., 2014) to supervise and remotely control the operations. (de Matos et al., 2020) The transmission level of Internet of Things sensing networks is confined by the spectrum resource deficiencies. (Liu et al., 2020a)

  2. Conceptual Framework and Literature Review

    Inspection, modeling, and assessment of data (Chuanyue, 2019; Kovacova et al., 2019; Lazaroiu et al., 2020; Peters et al., 2020) are pivotal in configuring significant insights. (de Matos et al., 2020) Industrial Internet of Things nodes generally have inadequate computational capability and limited battery, and thus cannot be deployed to perform large-scale computing tasks. (Xie et al., 2020) Data collection on the Internet of Things requires context-aware routing protocols (Andrei et al., 2020; Kliestik et al., 2020a, b) to meet the demands for first-rate quality of services. (Yousefi et al., 2020)

  3. Methodology and Empirical Analysis

    Using and replicating data from Accenture, Bain, Capgemini, CompTIA, IW Custom Research, Kronos, McKinsey, Microsoft, PAC, PwC, and Software AG, we performed analyses and made estimates regarding the link between business process optimization, cognitive decision-making algorithms, and artificial intelligence data-driven Internet of Things systems. Data were analyzed using structural equation modeling.

  4. Results and Discussion

    Cloud computing can surpass the inadequacies of Industrial Internet of Things nodes. (Xie et al., 2020) Internet of Things can articulate connected, universal, and smart nodes networking independently while displaying services (Mabodi et al., 2020), as physical, digital, and virtual entities throughout cyber-physical manufacturing systems can sense, detect and process (Kliestik et al., 2018; Krizanova et al., 2019; Lazaroiu et al., 2019; Majerova et al., 2020; Popescu et al., 2020) by use of devices online so as to carry out the assigned tasks. (Diene et al., 2020) Single mode manufacturing is progressively substituted by wide-ranging collaborative production through cognitive decision-making algorithms in Industry 4.0. (Liu et al., 2020b) (Tables 1-14)

    Application developers harness context-aware systems to convert the collected data into contextual information, enabling the applications to operate cognitively. (de Matos et al., 2020) With the incessant advancement of Internet of Things and the fulminant increase of big data, edge computing functions as a coherent computing mode for swift information handling, circumventing the limitations of network bandwidth and shaping interconnected applications. (Ning et al., 2020) By deploying groundbreaking data technology in sustainable smart manufacturing, the advancement of cyber-physical production systems is furthered in connection with cognitive decision-making algorithms, Internet of Things sensing networks, and business process optimization. (Liu et al., 2020b) Blockchain enhances output and operational coherence of Industrial Internet of Things by use of smart contract, towards self-governing machines connected through sensors. (Wang et al., 2020)

    Context sharing platforms are instrumental in transferring related data, consequently facilitating interoperability. (de Matos et al., 2020) Harnessing the wireless sensor networks associated with sustainable Internet of Things-based manufacturing systems and the radio frequency detection technology to the processing plant of the discrete production sector, the instantaneous status of the business unit can be digitally determined, articulating effective decision-making grounds for the controlling planning management section. (Chen, 2020) The growing intricacy of cyber-physical production networks necessitates groundbreaking system control techniques to preserve significant levels of adjustability. (Nikolakis et al., 2020) Maintenance constitutes an important lever for sustainable smart manufacturing by supplying organizations with the capacity to maintain its production system coherent and its product at first-rate quality through business process optimization. (Franciosi et al., 2020) Industry 4.0 enablers shape Internet of Things-based real-time production logistics and deep learning-assisted smart process planning. (Bag et al., 2020)

  5. Conclusions and Implications

    Context sharing platforms are pivotal in articulating context data interoperability across Internet of Things settings. (de Matos et al., 2020) The demand for personalized goods by adopting heterogeneous production resources boosts the variety of sustainable smart manufacturing systems, and thus their redesign is intricate and inefficient. (Nikolakis et al., 2020) Maintenance impacts production volume and related expenses, asset operation cycle, equipment convenience, final...

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