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Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 78309

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Dr. Antonella Petrillo

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Department of Engineering, University of Naples “Parthenope”, 80143 Naples, Italy
Interests: green transition; digitalization; operations management; industrial plant
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fabio De Felice

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Department of Engineering, University of Napoli “Parthenope”, 80143 Naples, Italy
Interests: operations management; smart manufacturing; digitalization; sustainability
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Special Issue Information

Dear Colleagues,

In recent years, the focus on smart manufacturing systems has been pushing companies toward a new variety of highly specific technical solutions. These solutions are characterized by an integrated approach to manufacturing termed “digital manufacturing”. In fact, digital manufacturing systems often incorporate optimization capabilities to reduce time and cost and improve the efficiency of most processes. The digital revolution is now our “present” and not the future. There are many different tooling processes that digital manufacturing utilizes, such as artificial intelligence, automation and robotics, additive technology, and human–machine interaction. These tools are unleashing innovations that will change the nature of manufacturing itself.

Industry and academic leaders agree that digital-manufacturing technologies will transform every link in the manufacturing value chain, from research and development, supply chain, and factory operations to marketing, sales, and service.

Furthermore, recently, some studies have identified several interlinks between smart manufacturing and sustainability. Emerging academic research is concerned with how the principles, practices, and enabling technologies of industry 4.0 might unlock the potentials of circular economy (CE) and sustainable manufacturing. Digitalization and the use of big data are seen as key enablers for increased sustainability and for the implementation of a circular economy.

Promoting research for innovation, sustainable solutions, and sustainable lifestyles in a new digitalized society and business sector, as well as facilitating them by financial measures and social measures, are the key tasks of this Special Issue.

This Special Issue is will collect a high-quality selection of contemporary research articles on the topic of “Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities”.

We are particularly interested in publishing articles not only from a traditional point of view but also from new emerging trends in order to meet practitioners’ needs and make theoretical contributions. This call is also aimed at collecting contributions that explore policies and practices adopted in different countries/regions in the field of smart manufacturing, as well as on the results obtained.

Prof. Dr. Antonella Petrillo
Prof. Fabio De Felice
Guest Editors

Manuscript Submission Information

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Keywords

Smart manufacturing: Economic and technology development
Sustainable manufacturing and industrial policy
Circular economy and blue economy (best practices for the transition toward a CE, CE policies impact assessments)
Process digitalization, rethink robotics, cobot, and advanced manufacturing solution
Enabling technology (IoT, simulation and digital twin, additive manufacturing, big data, cyber-physical systems, augmented reality, horizontal and vertical system integration, autonomous robot, virtual reality, machine learning, etc.)
5G and smart manufacturing
Innovation and new business model
Product life-cycle management to support industry 4.0
Life cycle assessment and environmental impacts of industry 4.0
Climate change
Design for environment
Innovative software
Decision analysis
Decision support systems applications
Optimization and management in manufacturing
Strategies for emerging technologies and strategic sectors

Published Papers (14 papers)

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Editorial

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3 pages, 182 KiB

Open AccessEditorial

Special Issue “Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities”

by Fabio De Felice and Antonella Petrillo

Appl. Sci. 2023, 13(11), 6397; https://doi.org/10.3390/app13116397 - 24 May 2023

Viewed by 896

Abstract

In recent years, the focus on digital transformation has begun to push companies and society towards an epochal change that will require a complete overhaul of the culture, operations, technologies and value delivery of individual companies and of the entire society worldwide [...] [...] Read more.

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

Research

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19 pages, 1479 KiB

Open AccessArticle

Smart Learning Technologization in the Economy 5.0—The Polish Perspective

by Łukasz Sułkowski, Katarzyna Kolasińska-Morawska, Robert Seliga and Paweł Morawski

Appl. Sci. 2021, 11(11), 5261; https://doi.org/10.3390/app11115261 - 05 Jun 2021

Cited by 13 | Viewed by 3290

Abstract

Contemporary higher education is gradually transforming. Meetings of teachers and students from lecture halls are increasingly moving into the digital space of the Internet, adopting the formula of distance learning. The advent of Society 5.0 and Economy 5.0 will imply further changes. The necessity to integrate the real and virtual world, increased demand for information, limited time resources, and the need to combine professional work with education will cause higher education, in order to prepare future citizens to function in the area of sharing resources, to be forced to further adaptive transformations. The subject of this article is the analysis of the impact of technology on changes in higher education with an indication of the model of future paths of education in the Economy 5.0 trend. The source of the article was exploratory research of secondary sources, including books, articles, and reports, which were subjected to a critical analysis of the content. The obtained results made it possible to design and implement an explanatory study among students based on the CAWI methodology. The collected material became the basis for the authors to prepare a proposal for a model of future educational paths in accordance with the Economy 5.0 trend in which the flexibility of place and time, customization of the offer, cooperation, adaptability of teaching methods and instruments, and the proactive role of the teacher as a mentor and trainer constitute a set of set guidelines in the teaching model of the future. This model will be able to be used by universities and training institutions in the field of professionalization of the management of teaching and organizational processes. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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27 pages, 3063 KiB

Open AccessArticle

A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

by Miguel Ortíz-Barrios, Antonella Petrillo, Fabio De Felice, Natalia Jaramillo-Rueda, Genett Jiménez-Delgado and Luz Borrero-López

Appl. Sci. 2021, 11(11), 5107; https://doi.org/10.3390/app11115107 - 31 May 2021

Cited by 12 | Viewed by 2270

Abstract

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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27 pages, 22244 KiB

Open AccessArticle

Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems

by Emilia Brad and Stelian Brad

Appl. Sci. 2021, 11(10), 4446; https://doi.org/10.3390/app11104446 - 13 May 2021

Cited by 6 | Viewed by 2256

Abstract

In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise’s business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment’s lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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30 pages, 6469 KiB

Open AccessArticle

Methodology for Data-Informed Process Improvement to Enable Automated Manufacturing in Current Manual Processes

by Mumtahina Mahajabin Adrita, Alexander Brem, Dominic O’Sullivan, Eoin Allen and Ken Bruton

Appl. Sci. 2021, 11(9), 3889; https://doi.org/10.3390/app11093889 - 25 Apr 2021

Cited by 7 | Viewed by 5588

Abstract

Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization. Data that are analyzed or entered manually are subject to human error. Digitization can remove human error, when dealing with data, via automatic extraction and data conversion. This paper presents methodology to identify automation opportunities and eliminate manual processes via digitized data analyses. The method uses a hybrid combination of Lean Six Sigma (LSS), CRISP-DM framework, and “pre-automation” sequence, which address the gaps in each individual methodology and enable the identification and analysis of processes for optimization, in terms of automation. The results from the use case validates the novel methodology, reducing the implant manufacturing process cycle time by 3.76%, with a 4.48% increase in product output per day, as a result of identification and removal of manual steps based on capability studies. This work can guide manufacturing industries in automating manual production processes using data digitization. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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15 pages, 1872 KiB

Open AccessArticle

The Impact of Additive Manufacturing on the Flexibility of a Manufacturing Supply Chain

by Ageel Abdulaziz Alogla, Martin Baumers, Christopher Tuck and Waiel Elmadih

Appl. Sci. 2021, 11(8), 3707; https://doi.org/10.3390/app11083707 - 20 Apr 2021

Cited by 25 | Viewed by 4083

Abstract

There is an increasing need for supply chains that can rapidly respond to fluctuating demands and can provide customised products. This supply chain design requires the development of flexibility as a critical capability. To this end, firms are considering Additive Manufacturing (AM) as one strategic option that could enable such a capability. This paper develops a conceptual model that maps AM characteristics relevant to flexibility against key market disruption scenarios. Following the development of this model, a case study is undertaken to indicate the impact of adopting AM on supply chain flexibility from four major flexibility-related aspects: volume, mix, delivery, and new product introduction. An inter-process comparison is implemented in this case study using data collected from a manufacturing company that produces pipe fittings using Injection Moulding (IM). The supply chain employing IM in this case study shows greater volume and delivery flexibility levels (i.e., 65.68% and 92.8% for IM compared to 58.70% and 75.35% for AM, respectively) while the AM supply chain shows greater mix and new product introduction flexibility, indicated by the lower changeover time and cost of new product introduction to the system (i.e., 0.33 h and €0 for AM compared to 4.91 h and €30,000 for IM, respectively). This work will allow decision-makers to take timely decisions by providing useful information on the effect of AM adoption on supply chain flexibility in different sudden disruption scenarios such as demand uncertainty, demand variability, lead-time compression and product variety. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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29 pages, 11657 KiB

Open AccessArticle

Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System

by Ana Pamela Castro-Martin, Horacio Ahuett-Garza, Darío Guamán-Lozada, Maria F. Márquez-Alderete, Pedro D. Urbina Coronado, Pedro A. Orta Castañon, Thomas R. Kurfess and Emilio González de Castilla

Appl. Sci. 2021, 11(3), 1312; https://doi.org/10.3390/app11031312 - 01 Feb 2021

Cited by 11 | Viewed by 3132

Abstract

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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25 pages, 4217 KiB

Open AccessArticle

A Method for Building Service Process Value Model Based on Process Mining

by Xuequan Zhou, Gregory Zacharewicz, David Chen and Dianhui Chu

Appl. Sci. 2020, 10(20), 7311; https://doi.org/10.3390/app10207311 - 19 Oct 2020

Cited by 8 | Viewed by 2405

Abstract

With the emergence and development of servitization, more and more enterprises are turning from product focus to service focus. Service is customer-oriented, and driven by customer requirements. Value is the goal pursued by all actors in the service. In order to analyze the mechanism of multi-actor collaborative value creation in the service process, this paper proposes a method for building a service process value model, based on process mining. Driven by the raw data and an event log of service activities and processes in the real world, stored in the service system, the method uses process mining techniques and combines domain knowledge to describe the construction steps of the service process value model at the conceptual level. We focus on the specific processes and activities in the service, and mainly consider the value creation of the activity. The model proposed in this paper aims, to reflect how service actors co-create value in the actual execution of service processes, and to help service actors achieve their value goals. We use a case study inspired by an industrial case to validate our idea. Moreover, we develop a new plug-in, based on the α-algorithm for ProM, to realize the model construction in the case study. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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23 pages, 1192 KiB

Open AccessArticle

The Construction of an Intelligent Risk-Prevention System for Marine Silk Road

by Xingqun Xue, Xiaochen Ma, Mingnan Jiang, Yang Gao and Sae Woon Park

Appl. Sci. 2020, 10(15), 5044; https://doi.org/10.3390/app10155044 - 22 Jul 2020

Cited by 4 | Viewed by 2076

Abstract

The purpose of this study is to explore how to effectively prevent risks in the Marine Silk Road. This paper establishes a hierarchical theoretical framework by using the interpretive structural modeling (ISM) and explores an application system for intelligent prevention. The fuzzy set theory is also used to screen out the unnecessary attributes, and a decision-making and trial evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes. Finally, we suggest an applicable risk-prevention system for the Marine Silk Road. Our results: (1) the solution to international political and trade risks is the most critical for the risk prevention; (2) the solution to marine meteorological risks relies mainly on the improvement of ocean information sharing mechanism driven by big data which needs international cooperation in terms of information and technology; (3) the solution to marine energy and environmental risks also requires active international cooperation; (4) the application system should be built based on three levels, including the international level, the government level, and the company level. This theoretical hierarchical framework aims to guide the countries alongside the road to effectively prevent the risks on the Marine Silk Road, promote the sustainable development of the Marine Silk Road, and develop the transnational economies and cultures. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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24 pages, 251 KiB

Open AccessFeature PaperArticle

Industry 4.0 and World Class Manufacturing Integration: 100 Technologies for a WCM-I4.0 Matrix

by Lorenzo D’Orazio, Roberto Messina and Massimiliano M. Schiraldi

Appl. Sci. 2020, 10(14), 4942; https://doi.org/10.3390/app10144942 - 18 Jul 2020

Cited by 22 | Viewed by 5819

Abstract

In the last decade, technological progress has profoundly influenced the industrial world and all industrial sectors have been confronted with a change in technological paradigms. In such a context, this study aims to analyze the synergies between the technological world of Industry 4.0 and the purely organizational and managerial domain of World Class Manufacturing, a model of Operational Excellence. The objective is relating the driving dimensions of the World Class Manufacturing (WCM) system to the technological macrocategories of Industry 4.0: this would allow the identification of which technological solution to leverage on, aiming at optimization in a given World Class Manufacturing pillar. The result is a “WCM-I4.0 matrix”: a proposal to reconcile, exploit and trace the relations between the two complex concepts. The WCM-I4.0 matrix includes, by now, 100 Industry 4.0 technologies that best suits with the World Class Manufacturing pillars. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

25 pages, 6202 KiB

Open AccessArticle

Value-Oriented and Ethical Technology Engineering in Industry 5.0: A Human-Centric Perspective for the Design of the Factory of the Future

by Francesco Longo, Antonio Padovano and Steven Umbrello

Appl. Sci. 2020, 10(12), 4182; https://doi.org/10.3390/app10124182 - 18 Jun 2020

Cited by 248 | Viewed by 18992

Abstract

Although manufacturing companies are currently situated at a transition point in what has been called Industry 4.0, a new revolutionary wave—Industry 5.0—is emerging as an ‘Age of Augmentation’ when the human and machine reconcile and work in perfect symbiosis with one another. Recent years have indeed assisted in drawing attention to the human-centric design of Cyber-Physical Production Systems (CPPS) and to the genesis of the ‘Operator 4.0’, two novel concepts that raise significant ethical questions regarding the impact of technology on workers and society at large. This paper argues that a value-oriented and ethical technology engineering in Industry 5.0 is an urgent and sensitive topic as demonstrated by a survey administered to industry leaders from different companies. The Value Sensitive Design (VSD) approach is proposed as a principled framework to illustrate how technologies enabling human–machine symbiosis in the Factory of the Future can be designed to embody elicited human values and to illustrate actionable steps that engineers and designers can take in their design projects. Use cases based on real solutions and prototypes discuss how a design-for-values approach aids in the investigation and mitigation of ethical issues emerging from the implementation of technological solutions and, hence, support the migration to a symbiotic Factory of the Future. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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15 pages, 3575 KiB

Open AccessArticle

Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators

by Piotr Barosz, Grzegorz Gołda and Adrian Kampa

Appl. Sci. 2020, 10(8), 2862; https://doi.org/10.3390/app10082862 - 21 Apr 2020

Cited by 42 | Viewed by 11321

Abstract

The problem of production flow and evaluation of productivity in the manufacturing line is analysed. Machines can be operated by humans or by robots. Since breakdowns and human factors affect the destabilization of the production processes, robots are preferred. The main problem is a proper methodology—how can we determine the real difference in work efficiency between human and robot at the design stage? Therefore, an analysis of the productivity and reliability of the machining line operated by human operators or industrial robots is presented. Some design variants and simulation models in FlexSim have been developed, taking into consideration the availability and reliability of the machines, operators and robots. Traditional productivity metrics, such as the throughput and utilization rate, are not very helpful for identifying the underlying problems and opportunities for productivity improvement in a manufacturing system, therefore we apply the OEE (overall equipment effectiveness) metric to present how the availability and reliability parameters influence the performance of the workstation, in the short and long terms. The implementation results of a real robotic line from industry are presented with the use of the overall factory efficiency (OFE) metric. The analysis may help factories achieve the level of world class manufacturing. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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28 pages, 5477 KiB

Open AccessArticle

Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach

by Dionicio Neira-Rodado, Miguel Ortíz-Barrios, Sandra De la Hoz-Escorcia, Cristiano Paggetti, Laura Noffrini and Nicola Fratea

Appl. Sci. 2020, 10(5), 1792; https://doi.org/10.3390/app10051792 - 05 Mar 2020

Cited by 38 | Viewed by 4572

Abstract

Product design has become a critical process for the healthcare technology industry, given the ever-changing demands, vague customer requirements, and interrelations among design criteria. This paper proposed a novel integration of fuzzy Kano, Analytic Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Quality Function Deployment (QFD) to translate customer needs into product characteristics and prioritize design alternatives considering interdependence and vagueness. First, the customer requirements were established. Second, the fuzzy KANO was applied to calculate the impact of each requirement, often vague, on customer satisfaction. Third, design alternatives were defined, while the requirements’ weights were calculated using AHP. DEMATEL was later implemented for evaluating the interdependence among alternatives. Finally, QFD was employed to select the best design. A hip replacement surgery aid device for elderly people was used for validation. In this case, collateral issues were the most important requirement, while code change was the best-ranked design. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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Review

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22 pages, 4406 KiB

Open AccessEditor’s ChoiceReview

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

by Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, Antonella Petrillo and Adele Parmentola

Appl. Sci. 2020, 10(8), 2897; https://doi.org/10.3390/app10082897 - 22 Apr 2020

Cited by 61 | Viewed by 8563

Abstract

Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable principles that are based on the concept of circular economy. Digital innovation enables a circular economy model, promoting the use of solutions like digital platforms, smart devices, and artificial intelligence that help to optimize resources. Thus, the purpose of the research is to present a systematic literature review on what enabling technologies can promote new circular business models. A total of 31 articles were included in the study. Our results showed that realization of the circular economy involved two main changes: (i) managerial changes and (ii) legislative changes. Furthermore, the creation of the circular economy can certainly be facilitated by innovation, especially through the introduction of new technologies and through the introduction of digital innovations. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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