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Industry 4.0: Quality Management and Technological Innovation

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (19 September 2023) | Viewed by 15131

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Special Issue Editors

Prof. Dr. Slavko Arsovski

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Guest Editor

Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Interests: quality; quality management; innovation

Prof. Dr. Miladin Stefanović

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Guest Editor

Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, Kragujevac, 34000, Serbia
Interests: quality; information systems
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Special Issue Information

Dear Colleagues,

Advances in technology that form the foundation for Industry 4.0 will transform production toward fully integrated, automated, and optimized production flow, leading to greater efficiencies and changing traditional production relationships among suppliers, producers, customers and, between humans and machines. Questions of quality and sustainability will be increasingly important.

These new technologies that form the main pillars of Industry 4.0 include: Big Data and analytics, autonomous robots, simulation, horizontal and vertical system integration, the Industrial Internet of Things, cybersecurity, the cloud, additive manufacturing, and augmented reality.

Industry 4.0 will have a large impact on different areas, leading to concepts such as Quality 4.0, Maintenance 4.0, Safety 4.0, Cybersecurity 4.0, Operator 4.0, and Logistics 4.0, as well as influences and connections with lean supply chain management.

We would like to explore quality management in the concept of sustainability using the toolbox of Industry 4.0 and technological innovation. Quality management and the ISO 9001 standard have a great impact in three key aspects of Industry 4.0 (vertical, horizontal and end-to-end engineering integration). It is also clear that a number of the suggested pillars of Industry 4.0 could be employed and used for the improvement of the practice and concept of quality management and sustainability. Having clear and precise documentation that supports digital quality management systems (DQMSs) is important for all quality management system, and the concept of DQMS has been changing and evolving through the use of new solutions and new environments such as Industry 4.0 pillars (i.e., Big Data and analytics and the usage of cloud systems).

For this Special Issue, authors are encouraged to consider how technological innovation, especially in the context of Industry 4.0, affects quality management and sustainability; what kind of new opportunities, risks, and problems may arise for managers and employees; the way the economy and industry will change, evolve and sustain; the need for new innovation, software and technology for quality management and sustainability; the trends in education and training that will arise; and what kinds of specialists will be in demand. We encourage contributions on the roles of Quality 4.0 and innovation in sustainable Industry 4.0, as well as those on the impact of Quality 4.0 and technological innovation on the smart sustainability of Industry 4.0.

We look forward to receiving your contributions.

Prof. Dr. Slavko Arsovski
Prof. Dr. Miladin Stefanović
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

quality management
innovation
sustainability
Industry 4.0

Published Papers (5 papers)

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Research

22 pages, 5871 KiB

Open AccessArticle

Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT

by Dalibor Dobrilovic, Jasmina Pekez, Eleonora Desnica, Ljiljana Radovanovic, Ivan Palinkas, Milica Mazalica, Luka Djordjević and Sinisa Mihajlovic

Sustainability 2023, 15(9), 7440; https://doi.org/10.3390/su15097440 - 30 Apr 2023

Cited by 3 | Viewed by 2462

Abstract

In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established. Full article

(This article belongs to the Special Issue Industry 4.0: Quality Management and Technological Innovation)

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19 pages, 2696 KiB

Open AccessArticle

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

by Bojana Bajic, Nikola Suzic, Slobodan Moraca, Miladin Stefanović, Milos Jovicic and Aleksandar Rikalovic

Sustainability 2023, 15(7), 6032; https://doi.org/10.3390/su15076032 - 30 Mar 2023

Cited by 7 | Viewed by 2637

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. Full article

(This article belongs to the Special Issue Industry 4.0: Quality Management and Technological Innovation)

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29 pages, 4448 KiB

Open AccessArticle

Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

by Milorad K. Banjanin, Mirko Stojčić, Dejan Danilović, Zoran Ćurguz, Milan Vasiljević and Goran Puzić

Sustainability 2022, 14(24), 17053; https://doi.org/10.3390/su142417053 - 19 Dec 2022

Cited by 7 | Viewed by 2190

Abstract

The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of QoE, this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal–regulatory, technological–process, content-formatted and performative, contextual–relational and subjective user-influencing factors on QoE sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark QoE as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of QoE. The hypothetical setting is that the individual user’s curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable QoE model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&H. By treating the data with mathematical and machine learning models, the QoE assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of QoE_i. The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%. Full article

(This article belongs to the Special Issue Industry 4.0: Quality Management and Technological Innovation)

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23 pages, 1345 KiB

Open AccessArticle

Soft and Hard Total Quality Management Practices Promote Industry 4.0 Readiness: A SEM-Neural Network Approach

by Kashif Ali, Satirenjit Kaur Johl, Amgad Muneer, Ayed Alwadain and Rao Faizan Ali

Sustainability 2022, 14(19), 11917; https://doi.org/10.3390/su141911917 - 21 Sep 2022

Cited by 17 | Viewed by 3845

Abstract

Industry 4.0 (I4.0) is a technological development in the manufacturing industry that has revolutionized Total Quality Management (TQM) practices. There has been scant empirical research on the multidimensional perspective of TQM. Thus, this study aims to empirically examine the effect of the multidimensional view of TQM (soft and hard) on I4.0 readiness in small and medium-sized (SMEs) manufacturing firms. Based on the sociotechnical systems (STS) theory, a framework has been developed and validated empirically through an online survey of 209 Malaysian SMEs manufacturing firms. Unlike the existing TQM studies that used structural equation modeling (SEM), a two-stage analysis was performed in this study. First, the SEM approach was used to determine which variable significantly affects I4.0 readiness. Second, the artificial neural network (ANN) technique was adopted to rank the relative influence of significant predictors obtained from SEM. The results show that the soft and hard TQM practices have supported the I4.0 readiness. Moreover, the results highlight that hard TQM practices have mediating role between soft TQM practices and I4.0 readiness. The ANN results affirmed that customer focus is considered an important TQM factor for I4.0 managerial readiness, advanced manufacturing technology for operational readiness and top management commitment for technology readiness. In a nutshell, the SEM-ANN approach uniquely contributes to the TQM and I4.0 literature. Finally, the findings can help managers to prioritize firms’ soft and hard quality practices that promote I4.0 implementation, especially in emerging economies. Full article

(This article belongs to the Special Issue Industry 4.0: Quality Management and Technological Innovation)

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22 pages, 3347 KiB

Open AccessArticle

Creating Quality-Based Smart Sustainable Public Parking Enterprises: A Methodology to Reframe Organizations into Smart Organizations

by Gordana Todorović, Hrvoje Puskarić, Yury Klochkov, Vladimir Simić, Zorica Lazić and Aleksandar Đorđević

Sustainability 2022, 14(11), 6641; https://doi.org/10.3390/su14116641 - 28 May 2022

Cited by 4 | Viewed by 1841

Abstract

Enterprise sustainability is a key aim in the fourth industrial revolution era, requiring a new approach based on intelligent technologies that considers the new roles of leadership and sustainability as well as the new trends in emerging smart technologies, with a new focus on Society 5.0. Smart parking has a significant role in fostering the determinants of sustainability in public parking enterprises and achieving adequate mobility in smart cities. Thus, smart parking is the subject of the research presented in this paper. This study defines the vital processes, including leadership processes and technologies needed for smart parking, managed by innovative public parking enterprises. Having this in mind, trends, key facts, the results of present innovative technology enterprises, and methodologies for designing and establishing smart public parking enterprises are analyzed. This paper aims to determine the sustainability of parking enterprises in their current states by developing a MORSO methodology. The MORSO methodology includes independent variables, including the leadership level of the intelligent technologies used, quality of the business processes, and risk related to the business processes, and a dependent variable, the sustainability of smart public parking enterprises. The MORSO methodology also includes steps for the definition of indices related to variables that could be assessed by appropriate techniques such as using questionnaires. Finally, the MORSO methodology introduces steps by which statistical approaches and artificial neural networks (ANN) are applied to test hypotheses regarding correlations between independent and dependent variables. The results of the presented model case study application show that there are strong correlations between smart sustainability and leadership (0.769), quality (0.904), and risk (−0.884), respectively. Additionally, at the level of the presented case study, the results of the application of the ANN indicate that the values of the dependent variable in the following time period can be determined with high accuracy, based on the knowledge of the values from the previous period, with a regression coefficient value of R = 0.99482. Finally, in this way, the transition from existing public enterprises to sustainable smart public parking enterprises is envisioned. Full article

(This article belongs to the Special Issue Industry 4.0: Quality Management and Technological Innovation)

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