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Future Industrial Systems: Opportunities and Challenges
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Future Industrial Systems: Opportunities and Challenges

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (23 September 2022) | Viewed by 25412

Special Issue Editors

Prof. Dr. Maurizio Bevilacqua

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Guest Editor
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Ancona, Italy
Interests: operation management; industrial plants services; industrial production management; industrial logistics; project management; supply chain management; industrial safety management
Dr. Sara Antomarioni

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Ancona, Italy
Interests: operations management; industrial logistics; production management; industrial safety and maintenance; data mining applications to manufacturing; life cycle assessment
Dr. Giovanni Mazzuto

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Ancona, Italy
Interests: digital twin and cyber physical system; industrial plants smart services; artificial intelligence for industrial plant management; industrial plant modeling and simulation; Industrial Safety Management 4.0; smart devices
Dr. Giulio Marcucci

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Ancona, Italy
Interests: supply chain resilience; system dynamics; management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scientific community has become increasingly interested and focused on the challenges associated with Industry 4.0. Indeed, it encompasses future trends in industry development towards smarter production processes, including the reliance on Cyber-Physical Systems (CPS), the construction of Cyber-Physical Production Systems (CPPS), and the implementation and operation of smart factories. Thus, the adoption of intelligent manufacturing technologies is nowadays perceived as the key to industrial success, given the chance of interconnecting machines and tools to improve their performance and efficiency. However, the introduction of new industrial paradigms is not a panacea since it is necessary to sustain such innovations with support shared by the entire company to lead to the expected success and further development. 

The main points of this strategy can be identified in: building a specialized network for the connection of physical and digital world; researching how to connect smart factory and intelligent production; realizing horizontal, vertical, and end-to-end integration; and achieving eight Industry 4.0 planning objectives, such as standardization, safety and security, and creation of a complete and reliable industrial broadband infrastructure.

In this regard, the proposed Special Issue aims to collect and analyze how such change can be implemented over time to understand the challenges and opportunities for future industrial systems. With future goals focused on environmental sustainability and a circular economy, among these, the development and improvement of smart devices, the analysis and processing of big data and digital production, and the creation of a network environment are of particular interest. Researchers and practitioners dealing with frameworks for the sustainable development of future industrial systems, case studies, surveys, and literature reviews critically exploring these topics are invited to submit their contributions. 

Relevant papers may focus on—but are not limited to—the adoption of the following paradigms:

  • Smart devices;
  • Network environment;
  • Big data analysis and processing;
  • Digital Manufacturing;
  • Cyber-physical system and cyber-physical production systems;
  • Digital twin;
  • Cloud computing technology;
  • Mobile internet and Internet of Things technologies;
  • Circular economy;
  • System sustainability;
  • Safety, security and cyber security;
  • Cyber resilience;
  • Standardization;
  • Artificial intelligence in a smart factory;
  • Staff training and continuing professional development.

Prof. Dr. Maurizio Bevilacqua
Dr. Sara Antomarioni
Dr. Giovanni Mazzuto
Dr. Giulio Marcucci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • smart devices
  • network environment
  • big data analysis and processing
  • digital manufacturing
  • cyber physical system
  • cyber-physical production systems
  • digital twin
  • cloud computing technology
  • mobile internet and internet of things technologies
  • circular economy
  • sustainability
  • system sustainability
  • safety
  • security
  • cyber security
  • cyber resilience
  • standardization
  • artificial intelligence
  • smart factory
  • staff training
  • continuing professional development

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

32 pages, 9845 KiB  
Article
New Product Development of a Robotic Soldering Cell Using Lean Manufacturing Methodology
by Emanuela Pop, Emilia Campean, Ion Cristian Braga and Darius Ispas
Sustainability 2022, 14(21), 14057; https://doi.org/10.3390/su142114057 - 28 Oct 2022
Viewed by 3842
Abstract
With the advent of manufacturing in Industry 4.0 and consumer demand, there has been a trend of mass customization of products. This customization requirement can only be achieved through the flexibility of manufacturing processes that are tailored to meet the quality standards of [...] Read more.
With the advent of manufacturing in Industry 4.0 and consumer demand, there has been a trend of mass customization of products. This customization requirement can only be achieved through the flexibility of manufacturing processes that are tailored to meet the quality standards of customers and the large volume of production in a short time. The increase of the production capacity is achieved through the processes of industrial automation of the manufacture which maintains the increased efficiency for the series production. This study was based on the Design for Six Sigma methodology (DMADV—Define, Measure, Analyze, Design and Verify) in order to determine the soldering process characteristics and how the soldering process can be automatized. When planning the implementation of a collaborative robot in a workstation in the production plant, the following must be taken into account: steps in operations that require the most time for the worker and/or that represent factors of physical and moral overload for him; the use of adequate precision fixing devices, delimitation of work areas, sensors as well as spaces for connecting the workstation to the electrical, hydraulic/pneumatic network and constant cycle time. The proposed solution can improve the productivity of the process by integrating advanced robotics and smart devices into the soldering line. Full article
(This article belongs to the Special Issue Future Industrial Systems: Opportunities and Challenges)
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21 pages, 2477 KiB  
Article
Industry 4.0 as an Opportunity and Challenge for the Furniture Industry—A Case Study
by Luboš Červený, Roman Sloup, Tereza Červená, Marcel Riedl and Petra Palátová
Sustainability 2022, 14(20), 13325; https://doi.org/10.3390/su142013325 - 17 Oct 2022
Cited by 15 | Viewed by 7351
Abstract
The aim of the document is to provide effective guidelines and recommendations for the effective design of the implementation process of Industry 4.0 in the furniture sector and to provide managers with effective guidance in this context. The primary data sources are semi-structured [...] Read more.
The aim of the document is to provide effective guidelines and recommendations for the effective design of the implementation process of Industry 4.0 in the furniture sector and to provide managers with effective guidance in this context. The primary data sources are semi-structured expert interviews and questionnaire surveys. Based on the structured interviews with executives of furniture companies in 2021 and 2022, the main drivers necessary for the implementation of Industry 4.0 in the furniture industry were identified both from the internal company environment perspective using a 7S analysis and from the technological perspective using Industry 4.0 building blocks applied to individual examples in the furniture industry. The respondents agree that the current state of the sector is generally at the Industry 2.0 level. They also recommend SMEs establish inter-company cooperation in production and development, which will enable the involvement of small and medium enterprises in buyer–supplier linkages. They further stress that the application of Industry 4.0 has led to rapid shifts in terms of: an increase in the operational efficiency in a range of 30–50%, a reduction in communication flow, errors and repetitive operations, and thus has directly contributed to the realisation of sustainable production. Full article
(This article belongs to the Special Issue Future Industrial Systems: Opportunities and Challenges)
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13 pages, 3380 KiB  
Article
Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction
by Seokho Moon, Hansam Cho, Eunji Koh, Yong Sung Cho, Hyoung Lok Oh, Younghoon Kim and Seoung Bum Kim
Sustainability 2022, 14(19), 12357; https://doi.org/10.3390/su141912357 - 28 Sep 2022
Cited by 3 | Viewed by 2116
Abstract
Remanufacturing has emerged as a way to solve production problems, as raw material costs increase and environmental pollution caused by discarded equipment occurs. The process can extend product lifetime and prevent waste of resources. In particular, it has economical efficiency for large equipment [...] Read more.
Remanufacturing has emerged as a way to solve production problems, as raw material costs increase and environmental pollution caused by discarded equipment occurs. The process can extend product lifetime and prevent waste of resources. In particular, it has economical efficiency for large equipment such as GIS (Gas Insulated Switchgear). The crucial points in remanufacturing are determining replaceable parts and economic valuation. To address these issues, we propose a framework for remanufacturing GIS with remaining lifetime prediction. We construct a regression model for remaining useful life (RUL) in the proposed framework using GIS sensor data. The cost of the replacement parts is estimated with the selected sensors. To validate the effectiveness of the proposed framework, we conducted accelerated life testing on a GIS for data acquisition and applied our framework. The experimental results demonstrate that the tree-based RUL regression model outperforms the others in prediction accuracy. In the simulation of part replacement, the important sensor-based decision-making improves RUL significantly. Full article
(This article belongs to the Special Issue Future Industrial Systems: Opportunities and Challenges)
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26 pages, 5848 KiB  
Article
SaaMES: SaaS-Based MSA/MTA Model for Real-Time Control of IoT Edge Devices in Digital Manufacturing
by Sanghoon Do, Woohang Kim, Huiseong Cho and Jongpil Jeong
Sustainability 2022, 14(16), 9864; https://doi.org/10.3390/su14169864 - 10 Aug 2022
Cited by 2 | Viewed by 3158
Abstract
As a software delivery model, Software as a Service (SaaS) has attracted considerable attention from software providers and users. Most traditional companies are shifting their businesses to an SaaS model. SaaS development is a very complicated process and its success depends on architectural [...] Read more.
As a software delivery model, Software as a Service (SaaS) has attracted considerable attention from software providers and users. Most traditional companies are shifting their businesses to an SaaS model. SaaS development is a very complicated process and its success depends on architectural design and development. A Manufacturing Execution System (MES) was used at the expense of licensing fees for features not used in the On-Premise environment, although the features used vary depending on the manufacturing environment. In an SaaS environment, MES is applied with a function-specific container approach through a Microservice Architecture (MSA) to select and employ only the necessary functions. Furthermore, as the number of customers of virtualized applications increases in SaaS-based services, complexity and operating costs increase; thus, Multi-tenancy Architecture (MTA) technology, which serves all customers through a single instance of the application is crucial. Thus, in this study, we investigate the MTA approach and propose a suitable MTA for the manufacturing execution system. Real-time response is crucial to achieving a cyber-physical system of digital manufacturing in SaaS-based MES. Furthermore, SaaS-based big data analytics and decision-making cannot meet the needs of numerous applications in real-time sensitive workplaces. In this study, we propose an SaaS-based MSA/MTA model for real-time control of Internet of Things (IoT) Edge in digital manufacturing (SaaMES), an architecture of SaaS-based MES with MSA and MTA to meet vulnerable workplaces and real-time responses in Cloud environments. The analysis is used by applying the Autoencoder and Generic Adversarial Networks analysis model to IoT Edge for the connection between the Cloud environment and work site to enable real-time response and decision-making through communication using OPC-UA and small-scale analysis. Full article
(This article belongs to the Special Issue Future Industrial Systems: Opportunities and Challenges)
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22 pages, 4004 KiB  
Article
Learning-by-Doing Safety and Maintenance Practices: A Pilot Course
by Giovanni Mazzuto, Sara Antomarioni, Giulio Marcucci, Filippo Emanuele Ciarapica and Maurizio Bevilacqua
Sustainability 2022, 14(15), 9635; https://doi.org/10.3390/su14159635 - 5 Aug 2022
Cited by 6 | Viewed by 1920
Abstract
This paper presents an educational approach for teaching Industry 4.0 concepts to maintenance and safety operators involved in industrial processes. A Learning-by-doing approach was introduced to assess the impact of learning by doing and knowledge sharing on designing maintenance and safety solutions based [...] Read more.
This paper presents an educational approach for teaching Industry 4.0 concepts to maintenance and safety operators involved in industrial processes. A Learning-by-doing approach was introduced to assess the impact of learning by doing and knowledge sharing on designing maintenance and safety solutions based on Industry 4.0 concepts to build experience and improve decision-making skills. To this end, we proposed a pilot course to train industrial operators in the field of new technologies so that they could continue their work effectively. Specifically, the development of the course began with a needs assessment of the perspective participants, followed by an outline of the objectives and course structure. The course was adapted to the different educational and technical backgrounds of the participants (i.e., experienced operators who were digital immigrants and non-experienced operators who were digital natives). The results of the course were assessed through a survey, which allowed us to evaluate the operators’ perception of the learning approach and the contribution to improving the operators’ competencies and abilities. The results highlighted that the educational approach facilitated the teaching of maintenance and safety principles, promoting operators’ attention and participation. The difference in the learning level that we observed between the younger and older operators was also highlighted by the survey results. A dichotomy was revealed between the younger operators, who showed a greater understanding of the explained technologies, and the older operators, who required longer learning times. In this way, both types of participant could benefit from mutual collaboration and teamwork to improve their respective weaknesses. Full article
(This article belongs to the Special Issue Future Industrial Systems: Opportunities and Challenges)
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43 pages, 2620 KiB  
Article
Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing
by Łukasz Paśko, Maksymilian Mądziel, Dorota Stadnicka, Grzegorz Dec, Anna Carreras-Coch, Xavier Solé-Beteta, Lamprini Pappa, Chrysostomos Stylios, Daniele Mazzei and Daniele Atzeni
Sustainability 2022, 14(6), 3312; https://doi.org/10.3390/su14063312 - 11 Mar 2022
Cited by 17 | Viewed by 5715
Abstract
Knowledge and skills in the field of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC) are more and more important for industry. Therefore, it is crucial to know what current students and future employees can offer to the industry. University [...] Read more.
Knowledge and skills in the field of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC) are more and more important for industry. Therefore, it is crucial to know what current students and future employees can offer to the industry. University students develop their knowledge and skills to support the industry in implementing modern technologies in the future. It can be expected that the first source of information for students will be lectures and other activities at the university. However, they may obtain knowledge from other sources. This article presents the results of research conducted among students assessing their own knowledge and skills in the field of IoT, AI, and EC. The research was preceded by an analysis of curricula at selected universities in terms of topics related to AI, IoT, and EC. Based on the results of the analysis, survey questions were prepared. The developed questionnaire was made available to students. The research sample for the survey participants was 563 students. The results obtained were analyzed. The results of the analysis show which issues are better known to students and which are worse. The information presented in this paper can be a source of information for the industry that can assess the competences that are or will be available on the labor market in the near future. Additionally, universities can obtain information on the areas in which there are competency gaps and which methods of teaching AI, IoT, and EC are better perceived by students. Full article
(This article belongs to the Special Issue Future Industrial Systems: Opportunities and Challenges)
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