Smart Connected Sensors, Industrial Big Data, and Real-Time Process Monitoring in Cyber-Physical System-based Manufacturing.

AuthorNica, Elvira
  1. Introduction

    In cyber-physical systems, the concrete component has a data access function with sensors and communication networks to gather instantaneous information (Nica, 2018; Popescu, 2018) and transmit it to computation modules that inspect and report the outcomes to the related concrete component by use of various feedback loops. (Chen et al., 2020a) Cyber-physical systems can clarify the congruity and coherence matter of wide-reaching elaborate massive systems in an intricate heterogeneous setting. (Yao et al., 2019)

  2. Conceptual Framework and Literature Review

    Optimizing big data analytics capabilities should be a business urgency for the purpose of adequately set up competitive digitally-enabled sustainable supply chains. (Chiappetta Jabbour et al., 2020) Industry 4.0 technologies configure cutting-edge manufacturing approaches that necessitate personalized supply network monitoring (Kliestik et al., 2018; Lazaroiu and Adams, 2020; Lyons and Lazaroiu, 2020; Popescu Ljungholm, 2020; Roth, 2019), the consolidation of resilient companies to handle risks, advancements in management decision-support systems (Kliestik et al., 2020a, b, c; Popescu, 2014; Popescu et al., 2018; Wilson et al., 2020), standardization and supervision of robust and digital production systems, and joint control. (Panetto et al., 2019)

  3. Methodology and Empirical Analysis

    Building our argument by drawing on data collected from Capgemini, Catapult, CGI, Deloitte, Eclipse Foundation, PAC, PwC, SME, Software AG, and ZDNet, we performed analyses and made estimates regarding the link between smart connected sensors, industrial big data, and real-time process monitoring. The structural equation modeling technique was used to test the research model.

  4. Results and Discussion

    System integration, information analytics, performance prediction, sustain-ability, resource distribution and hardware constitute determinants of big data applications in smart production. (Cui et al., 2020) Cyber-physical systems facilitate enhanced assimilation of shared heterogeneous devices and systems shaping the business process management. (Yao et al., 2019) Using big data approaches is pivotal for adjustable, intelligent, and resilient cyber-physical systems in Industry 4.0. (Xu and Duan, 2019) (Tables 1-13)

    Smart factory is developed on cyber-physical systems by deploying digital twin, big data analytics, virtual-real mapping and fusion, and edge-to-cloud service technology to the production networks. (Chen et al., 2020a) Cyber-physical system-based manufacturing facilitates first-rate monitoring of production operations by harnessing Internet of Things smart devices to enhance model-based state assessment and variability recognition, and to perform data-driven tasks with first-rate quality and adjustability. (Yao et al., 2019) Sustainable Internet of Things-based manufacturing systems should control physical processes, configure a digital twin of the physical realm, and make big data-driven decisions by use of instantaneous interaction between smart machines. (Panetto et al., 2019) System infrastructures use connectivity to provide real-time networking between facilities and smart devices, and big data analytics aims to optimize product customization and resource coherence in Industry 4.0-based manufacturing systems. (Xu and Duan, 2019)

    Big data produced by cyber-physical production systems is pivotal in adopting groundbreaking manufacturing patterns. (Yao et al., 2019) Favorable outcomes throughout supply chain service competition are significantly determined by big analytics algorithms related to increase in efficiency and simulation modeling. (Panetto et al., 2019) Cyber-physical systems in Industry 4.0 entail large volumes of physical facilities monitored by computing devices to aggregately network by use of smart connected sensors. (Xu and Duan, 2019) Cyber-physical systems and real-time big data analytics are pivotal in the configuration of smart manufacturing. (Cui et al., 2020) By harnessing professional information technologies to sense and gather instantaneous manufacturing data, smart and computerized planning and monitoring systems can be adopted effortlessly. (Chen et al., 2020b)

  5. Conclusions and Implications

    With the massive volumes of information produced by cyber-physical systems across heterogeneous devices, big data approaches are decisive in smart manufacturing. (Xu and Duan, 2019) As an essential technology for developing operations in smart manufacturing, a digital twin represents a groundbreaking virtual factory technology harnessing simulation as its pivotal technical functionality (Dusmanescu et al., 2016; Kovacova et al., 2019; Lazaroiu et al., 2020a, b; Peters et al., 2020; Popescu et al., 2017; Popescu et al., 2019) that performs in the type and instance phases of the physical asset. (Park et al., 2020) The...

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