Big Data Analytics in Industry 4.0: Sustainable Industrial Value Creation, Manufacturing Process Innovation, and Networked Production Structures.

Author:Gradeck, Jaime
 
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  1. Introduction

    Industrial cyber-physical systems constitute the main setting up technology for Industry 4.0, that network established industrial and supervision engineering with cutting-edge technological patterns (e.g. big data, artificial intelligence, machine learning, and Internet of Things), to develop cognitive and self-designing factories competent enough for carrying out first-rate production advances. (O'Donovan et al., 2019)

  2. Conceptual Framework and Literature Review

    Big data can be brought about incessantly by any nearby smart object. Every digital operation and social media transfer generate information. Systems, sensors and mobile devices exchange big data that is materializing from various sources at a formidable swiftness, quantity, and diversity. To derive relevant value from big data, top-notch processing capacity, analytics performance, and information management expertise are required. (Oztemel and Gursev, 2018) Industry 4.0 will assist multinational organizations by improving the data processing capability (Campbell et al., 2017; Meila, 2018; Peters, 2017; Sion, 2018; Szkutnik and Szkutnik, 2018) and satisfying the information filtering demands under an unstable, intricate, and indefinite setting (Giroux, 2018; Nica, 2018; Popescu et al., 2018; Stroe, 2019; Vochozka et al., 2018), diminishing the degree of supply and requirement unpredictability in the large-scale worldwide business network, and being instrumental in process optimization. (Telukdarie et al., 2018)

  3. Methodology and Empirical Analysis

    The data used for this study was obtained and replicated from previous research conducted by BDO, Capgemini, CIO, Deloitte, EY, McKinsey, OSF, and PwC. We performed analyses and made estimates regarding manufacturers who have an ongoing smart factory initiative (%), importance of big data and public sector open data for enterprises' business activity (%), and trends regarding new business models that exploit opportunities (%). The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 5,700 respondents.

  4. Results and Discussion

    Data can be gathered via smart devices and sensor networks, and can be inspected and assessed through the agency of cognitive aid systems. (Rauch et al., 2019) The digital design of Industry 4.0 can assist employees in operating in and with cutting-edge architectures to make manufacturing or logistical service performance smarter and more streamlined. (Wilkesmann and Wilkesmann (2018) (Tables 1-8)

  5. Conclusions and Implications

    The cyber-physical systems encompass every feature from customer demands to product design and manufacturing of the fabricated commodities. The initial phase in continuous engineering assimilation is...

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