Smart Sustainable Data-driven Manufacturing: Cyber-Physical Production Systems and Internet of Things Sensing Networks.

Author:Fielden, Anna
  1. Introduction

    The quantity of information produced and gathered throughout the manufacturing process is persistently increasing, and big data have to be transferred from input resources to a fog/cloud platform. (Tao et al., 2019) Smart manufacturing necessitates adjustable production organization and administration to deal with the shifting customer demands swiftly and easily: in smart factories, physical resources have intelligence features, e.g. self-perception and self-decision-making. (Ding et al., 2020)

  2. Conceptual Framework and Literature Review

    Manufacturing machines are gradually equipped with sensors and interaction capabilities. (Wang et al., 2019) Cyber-physical production systems are viable (Androniceanu and Popescu, 2017; Kliestik et al., 2018; Lazaroiu, 2018; Nica, 2015; Popescu et al., 2017; Radulescu, 2018) due to the synergy between Internet of Things and control systems. (Rojas and Rauch, 2019) The Internet of Things and cyber-physical systems technologies (e.g. radio-frequency identification and sensor networks) provide cutting-edge supervision of efficient operations at an outstanding scale. (Yao et al., 2019) Smart manufacturing systems are coherent, requiring uninterrupted input flow (Kanovska, 2018; Krizanova et al., 2019; Nica et al., 2014; Nica, 2018; Popescu et al., 2018; Shang et al., 2018) in enterprise information systems, awareness, and big data-driven decision making thoroughly. (Qu et al., 2019)

  3. Methodology and Empirical Analysis

    Building our argument by drawing on data collected from Deloitte, Forbes, Management Events, PwC, and World Economic Forum, we performed analyses and made estimates regarding development priorities in marketing and customer experience management (%), current development projects in marketing organizations (%), current development priorities in sales organizations (%), future workforce strategies, industries overall (%), and how industrial companies are getting closer to customers (%). The structural equation modeling technique was used to test the research model.

  4. Results and Discussion

    In a smart factory, manufacturing resources endowed intelligence and self-governance are separated as cyber-physical system entities, interacting without assistance to make appropriate production decisions in conformity with the operational status of the industrial unit. (Ding et al., 2019) Big data analytics is instrumental in smart manufacturing decision making. (Zhang et al., 2019) As Internet of Things and big data make possible cyber-physical manufacturing systems, the concrete realm is reproduced in simulated reality via data-driven information computing, modeling, and analysis. (Yang et al., 2019) (Tables 1-9)

  5. Conclusions and Implications

    Smart production systems can diagnose their soundness unassisted and design prolonged enhancement projects unsupervised, resulting in the expected output improvement. (Alavian et al., 2019) The fastness of technological change will reinforce the widespread reach of the Internet with additional capacity, precisely to monitor the physical realm, encompassing the machines, industrial equipments and settings that define breakthrough technology. (Seetharaman et al., 2019) Assessing and harnessing the data can bolster judicious decision making at various phases of...

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