Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective

Bojana Bajic, Nikola Suzic, Slobodan Moraca, Miladin Stefanović, Milos Jovicic and Aleksandar Rikalovic ()
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Bojana Bajic: Department of Industrial Engineering and Management, University of Novi Sad, 21000 Novi Sad, Serbia
Nikola Suzic: Department of Industrial Engineering, University of Trento, 38123 Trento, Italy
Slobodan Moraca: Department of Industrial Engineering and Management, University of Novi Sad, 21000 Novi Sad, Serbia
Miladin Stefanović: Center for Quality, Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Milos Jovicic: Institute for Artificial Intelligence Research and Developments of Serbia, 21000 Novi Sad, Serbia
Aleksandar Rikalovic: Department of Industrial Engineering and Management, University of Novi Sad, 21000 Novi Sad, Serbia

Sustainability, 2023, vol. 15, issue 7, 1-19

Abstract: In the last decade, researchers have focused on digital technologies within Industry 4.0. However, it seems the Industry 4.0 hype did not fulfil industry expectations due to many implementation challenges. Today, Industry 5.0 proposes a human-centric approach to implement digital sustainable technologies for smart quality improvement. One important aspect of digital sustainability is reducing the energy consumption of digital technologies. This can be achieved through a variety of means, such as optimizing energy efficiency, and data centres power consumption. Complementing and extending features of Industry 4.0, this research develops a conceptual model to promote Industry 5.0. The aim of the model is to optimize data without losing significant information contained in big data. The model is empowered by edge computing, as the Industry 5.0 enabler, which provides timely, meaningful insights into the system, and the achievement of real-time decision-making. In this way, we aim to optimize data storage and create conditions for further power and processing resource rationalization. Additionally, the proposed model contributes to Industry 5.0 from a social aspect by considering the knowledge, not only of experienced engineers, but also of workers who work on machines. Finally, the industrial application was done through a proof-of-concept using manufacturing data from the process industry, where the amount of data was reduced by 99.73% without losing significant information contained in big data.

Keywords: human-cyber-physical systems (HCPS); big data analytics (BDA); Industrial Internet of Things (IIoT); smart quality management; digital sustainability; data optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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