The Smart Cyber-Physical Systems of Sustainable Industry 4.0: Innovation-driven Manufacturing Technologies, Creative Cognitive Computing, and Advanced Robotics.

Author:Cosgrave, Keith W.
 
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  1. Introduction

    Automatically gathered data by Industry 4.0 applications enable access to formerly non-digital centralized information. The exploration of the collected data furthers the advancement of cutting-edge Industry 4.0 applications and groundbreaking business patterns. (Wilkesmann and Wilkesmann, 2018) Firms should mainly grasp the characteristics and content of the Industry 4.0 for achievable transition from machine-prevalent production to digital manufacturing. (Oztemel and Gursev, 2018)

  2. Conceptual Framework and Literature Review

    Industry 4.0 is a full-scale digitalization and interconnection of production processes, evolving from the customer's order, via the setting up of manufacturing operations, to long-term product services, and thus chiefly self-organized value-creation networks (Blacker, 2018; Kanovska, 2018; Mircica, 2018; Nica, 2018; Pilkington, 2018; Sion, 2018) will bring about far-reaching alterations in economic interactions. (Wilkesmann and Wilkesmann, 2018) Industry 4.0 will facilitate performance measurement processes to take on project workforce from cross-functional and industrial spheres, customize performance measurement, coordinate performance measurement operations systematically (Chapman, 2018; Lazaroiu, 2017; Mutekwe, 2018; Nunes et al., 2018; Roberts and Marchais, 2018), and receive immediate assessment through technologies such as web-based systems, Internet of Things, and cloud-based infrastructure, therefore breaking the specific confines of the conventional and off-line-based performance measurement processes. (Yin and Qin, 2019)

  3. Methodology and Empirical Analysis

    Building my argument by drawing on data collected from Ad Hoc Research, BCG, BDO, CIO, DAA, EY, IoT Analytics GmbH, and McKinsey, I performed analyses and made estimates regarding qualifications needed more or less by employees in the future (%), top targets for supply chain improvement (%), and important factors to deploy disruptive technologies (%). The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 4,600 respondents.

  4. Results and Discussion

    Cloud computing and service-oriented frameworks display robust adoption, but their carrying out is often put into practice by employing information technology frames of mind that may neglect engineering, supervision, and Industry 4.0 design issues in respect of actual performance, soundness, or adjustability. (O'Donovan et al., 2019) Intelligent sensors are assimilated into...

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