Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

Sustainable Manufacturing and Supply Chain in the Context of Industry 4.0: Challenges and Opportunities

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 16714

Share This Special Issue

Special Issue Editors

Dr. Olivier Cardin

E-Mail Website
Guest Editor

LS2N UMR CNRS 6004, University of Nantes, 44470 Carquefou, France
Interests: holonic manufacturing systems; digital twin; cyber-physical production systems

Prof. Dr. Damien Trentesaux

E-Mail Website
Guest Editor

LAMIH-UMR CNRS, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France
Interests: Industry 4.0; sustainable/energy aware scheduling; intelligent/active product; physical Internet; manufacturing systems; transportation systems; logistics; supply-chains
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Adriana Giret

E-Mail Website
Guest Editor

Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain
Interests: multiagent systems; intelligent manufacturing systems; agent-supported simulation for manufacturing systems; applications of multiagent systems; sustainable intelligent manufacturing and logistics systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The hyper-connectivity of future industrial devices has led to the development of the so-called Industry 4.0 paradigm. Among the main pillars of this paradigm, sustainability is meant to lower the impact of industry on its surrounding environment and enhance the working conditions. With the advances in the industrial internet of things, edge and fog computing, the growing amount of data extracted from the shop floor motivates the research for new information, control, and communication technologies such as digital twin, big data analysis, cloud services, and artificial intelligence, applied for agility, reality awareness, optimization, and a shift from diagnosis to prognosis in controlling the consumption and cost of energy in manufacturing. The perimeter of the sustainability pillar encompasses both the intra-workshop activities and the extra-workshop activities, typically the associated supply chain. This Special Issue will emphasize the impact of the development of innovative Industry 4.0-oriented paradigms, among which cyber-physical production systems or cobotics, on the sustainability of manufacturing management and control. Sustainability is considered here in all its facets: efficient use of energy and resources, adaptability to energy volatility, impact of Industry 4.0 on health and security of the workers, on inbound and outbound logistics, etc.

This Special Issue will provide knowledge and experience on the recent advancements of sustainable manufacturing and supply chain. It will address all aspects of industrial systems management, such as the control and management of production, supply chain, logistics, transportation, after-sales services, among others, industrial and after-sale services and maintenance operations, management and control of energy consumption and distribution, and humans in Industry 4.0

We welcome papers on the following topics for this Special Issue:

Innovative supply chain organization and control for sustainability objectives;
Workshop control or scheduling including energy constraints, changing of consumption patterns;
Planning and management of health and safety workshops for workers;
Industrial sustainability business models;
Human-machine cooperation in the context of cyber-physical production systems, including ethics and acceptability issues.

Dr. Olivier Cardin
Prof. Dr. Damien Trentesaux
Prof. Dr. Adriana Giret
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

research field:
- waste/energy management,
- scheduling, planning, control,
- maintenance,
- anthropocentric industry, human 4.0, human machine cooperation,
- cyber-physical production systems
application:
- manufacturing, production,
- transportation, supply chain

Published Papers (5 papers)

Download All Papers

Research

Jump to: Review

35 pages, 14313 KiB

Open AccessArticle

Enhancing the Decision-Making Process through Industry 4.0 Technologies

by Frédéric Rosin, Pascal Forget, Samir Lamouri and Robert Pellerin

Sustainability 2022, 14(1), 461; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010461 - 01 Jan 2022

Cited by 17 | Viewed by 5131

Abstract

In order to meet the increasingly complex expectations of customers, many companies must increase efficiency and agility. In this sense, Industry 4.0 technologies offer significant opportunities for improving both operational and decision-making processes. These developments make it possible to consider an increase in the level of operational systems and teams’ autonomy. However, the potential for strengthening the decision-making process by means of these new technologies remains unclear in the current literature. To fill this gap, a Delphi study using the Régnier Abacus technique was conducted with a representative panel of 24 experts. The novelty of this study was to identify and characterize the potential for enhancing the overall decision-making process with the main Industry 4.0 groups of technologies. Our results show that cloud computing appears as a backbone to enhance the entire decision-making process. However, certain technologies, such as IoT and simulation, have a strong potential for only specific steps within the decision-making process. This research also provides a first vision of the manager’s perspectives, expectations, and risks associated with implementing new modes of decision-making and cyber-autonomy supported by Industry 4.0 technologies. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Supply Chain in the Context of Industry 4.0: Challenges and Opportunities)

► Show Figures

Figure 1

36 pages, 2490 KiB

Open AccessArticle

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

by Rami Naimi, Maroua Nouiri and Olivier Cardin

Sustainability 2021, 13(23), 13016; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313016 - 24 Nov 2021

Cited by 6 | Viewed by 2254

Abstract

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Supply Chain in the Context of Industry 4.0: Challenges and Opportunities)

► Show Figures

Figure 1

19 pages, 6231 KiB

Open AccessArticle

Development of Multi-Disciplinary Green-BOM to Maintain Sustainability in Reconfigurable Manufacturing Systems

by Kezia Amanda Kurniadi and Kwangyeol Ryu

Sustainability 2021, 13(17), 9533; https://0-doi-org.brum.beds.ac.uk/10.3390/su13179533 - 24 Aug 2021

Cited by 7 | Viewed by 2979

Abstract

The reconfigurable manufacturing system (RMS) appears to be eco-friendly while coping with rapidly changing market demands. However, there remains a lack of discussion or research regarding sustainability or environment-friendly functions within RMS. In this study, the reconfiguration planning problem is introduced to represent the core issues within the RMS. Reconfiguration occurs depending on new demands or conditions in the company by reconfiguring machines, such as removing, adding, or changing parts, giving considerable consideration to arrangement of machines, known as configurations in RMS. Therefore, reconfiguration process is always strongly connected to cost, energy consumption, and, more importantly, data management. The complexity of reconfiguration, product variation, and development processes requires tools that are capable of managing multi-disciplinary bill-of-material(BOM) or product data and providing a better collaboration support for data/information tracking while maintaining sustainability. This paper proposes a multi-disciplinary green bill-of-material (MDG-BOM)—an improved Green-BOM concept—with an additional multi-disciplinary feature to minimize emissions and hazardous materials during product development, as well as manage product information across multiple disciplines during the reconfiguration process. A smart spreadsheet for managing MDG-BOM was developed to allow multiple departments to integrate multiple sources of CAD design data and monitor/track changes throughout each step of the process. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Supply Chain in the Context of Industry 4.0: Challenges and Opportunities)

► Show Figures

Figure 1

21 pages, 1497 KiB

Open AccessArticle

Industrial Performance: An Evolution Incorporating Ethics in the Context of Industry 4.0

by Lamia Berrah, Vincent Cliville, Damien Trentesaux and Claude Chapel

Sustainability 2021, 13(16), 9209; https://0-doi-org.brum.beds.ac.uk/10.3390/su13169209 - 17 Aug 2021

Cited by 16 | Viewed by 3065

Abstract

This article addresses the issue of the industrial performance model and its evolution to cope with the context of Industry 4.0. With its digitalisation, intelligent/autonomous systems and wealth of data, Industry 4.0 offers opportunities that can achieve objectives better. It also presents risks and uncertainties that question the autonomy of the systems, their interaction with humans and the use of available data. The hypothesis put forward in this work is that the efficiency–effectiveness–relevance performance triangle can no longer guarantee long-term performance under these conditions and needs to be associated with an ethical dimension that allows for the risks and uncertainties relating to Industry 4.0 to be considered. Ethics is therefore considered to extend the triangle to a tetrahedron. A brief analysis of current performance management will first show the limits of the current practice in the context of Industry 4.0. The frameworks that could overcome these limits in light of new needs are then recalled and discussed, leading to the choice of ethics, whose main definitions and use in the engineering field are also introduced. The proposed (efficiency–effectiveness–relevance–ethics tetrahedron-based methodology is illustrated through a case study related to an aeronautical supplier, regarding the consequences of the implementation of a MES (Manufacturing Execution System) in terms of product traceability and operator autonomy. The discussion and prospects finally conclude this study. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Supply Chain in the Context of Industry 4.0: Challenges and Opportunities)

► Show Figures

Figure 1

Review

Jump to: Research

20 pages, 1633 KiB

Open AccessReview

by Nicolas Murcia, Olivier Cardin, Abdelmoula Mohafid and Marie-Pascale Senkel

Sustainability 2021, 13(23), 13387; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313387 - 03 Dec 2021

Cited by 3 | Viewed by 1600

Abstract

Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers. Full article

(This article belongs to the Special Issue Sustainable Manufacturing and Supply Chain in the Context of Industry 4.0: Challenges and Opportunities)

► Show Figures