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Sustainability
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Industry 4.0 in Support of Process Transformation
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Industry 4.0 in Support of Process Transformation

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 16566

Special Issue Editors

Prof. Dr. Samir Lamouri

E-Mail Website
Guest Editor
Arts et Métiers Sciences et Technologies, Institute of Technology, 75013 Paris, France
Interests: supply chain; Industry 4.0; SME; production planning and control; industrial management
Prof. Dr. Robert Pellerin

E-Mail Website
Guest Editor
Department of Mathematics and Industrial Engineering, École Polytechnique de Montréal, Montréal, QC H3T 1J4, Canada
Interests: supply chain; Industry 4.0; SME; production planning and control; industrial management
Prof. Dr. Pascal Forget

E-Mail Website
Guest Editor
Département de génie industriel, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
Interests: lean management; performance indicators; transportation; simulation; production planning

Special Issue Information

Dear Colleagues,

Despite strong consensus on principles, it is not easy to provide a unanimous definition of Industry 4.0.  In 2013, the German telecommunications association BITKOM found more than 100 different definitions of the concept of Industry 4.0 used worldwide [1]. For example, ref. [2] defined Industry 4.0 as a general concept to make manufacturing smarter using techniques and technologies such as the Internet of Things, cloud computing, and big data. For [3], Industry 4.0 refers to recent technological advances in which the internet and technologies serve as the basic support for integrating physical objects, human actors, smart machines, product lines, and processes across organizational boundaries to form a new type of smart network and agile value chains.  In a more general way, the CEFRIO research group defines Industry 4.0 as a set of initiatives for improving products, services, and processes that enable decentralized decisions based on real-time data acquisition [4].  Based on the latter definition and the development of new products and services studied in recent years, this Special Issue calls for applied research papers that present innovative and advanced Industry 4.0 practices aimed at improving business processes.

Research papers should present and discuss how Industry 4.0 technologies can be used and combined to improve (continuous improvement) or completely transform (reengineering) business or decision-making processes. These topics can be addressed by review papers proposing guidelines for process design and/or management frameworks or approaches, research papers demonstrating the benefits and limitations of the proposed strategies or methods, or case study papers exploring and validating the applications of tools, techniques, and models in transforming processes in organizations.

References

  1. Bidet-Mayer, T. Industrie du Future: Une Compétition Mondiale; Presses des MINES, Paris, France, 2016.
  2. Trappey, A.J.C.; Trappey, C.V.; Hareesh Govindarajan, U.; Chuang, A.C.; Sun, J.J. A Review of Essential Standards and Patent Landscapes for the Internet of Things: A Key Enabler for Industry 4.0. Adv. Eng. Inform. 2016, 33, 208–229. https://doi.org/10.1016/j.aei.2016.11.007.
  3. Schumacher, A.; Erol, S.; Sihn, W.; A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP 2016, 52, 161–166. https://doi.org/10.1016/j.procir.2016.07.040.
  4. Danjou, C.; Rivest, L.; Pellerin, R. Industrie 4.0: Des Pistes Pour Aborder l’ère du Numérique et de la Connectivité; CEFRIO: Québec, QC, Canada, 2017; 27p.
  5. Moeuf A.; Pellerin R.; Lamouri S.; Tamayo S.; Barbaray R. The industrial management of SMEs in the era of Industry 4.0. Int. J. Prod. Res. 2018, 56, 1118–1136.
  6. Moeuf A.; Lamouri S.; Pellerin R.; Tamayo S.; Tobon Valencia E. Identification of Critical Success Factors, Risks and Opportunities of Industry 4.0 in SMEs. Int. J. Prod. Res. 2020, 58, 1384–1400. https://doi.org/10.1080/00207543.2019.1636323
  7. Rosin, F.; Forget P.; Lamouri, S.; Pellerin, R. Impacts of Industry 4.0 technologies on Lean principles. Int. J. Prod. Res. 2020, 58, 1644–1661. https://doi.org/10.1080/00207543.2019.1672902.

Prof. Dr. Samir Lamouri
Prof. Dr. Robert Pellerin
Prof. Dr. Pascal Forget
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.

Published Papers (6 papers)

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Research

18 pages, 1983 KiB  
Article
Digital Twin: An Added Value for Digital CONWIP in the Context of Industry 4.0
by Latifa Benhamou, Samir Lamouri, Patrick Burlat and Vincent Giard
Sustainability 2023, 15(13), 9874; https://doi.org/10.3390/su15139874 - 21 Jun 2023
Cited by 1 | Viewed by 1095
Abstract
Despite technological progress and a large amount of research on Industry 4.0, digital transformation remains a complex process that most manufacturers are hesitant to invest in. Interest in digital Kanban, for example, remains low compared with traditional Kanban, which is widely used. This [...] Read more.
Despite technological progress and a large amount of research on Industry 4.0, digital transformation remains a complex process that most manufacturers are hesitant to invest in. Interest in digital Kanban, for example, remains low compared with traditional Kanban, which is widely used. This applies to the other card-based production control systems, including CONstant Work-In-Process (CONWIP), which is the focus of this paper. In an industrial context where digitization and Industry 4.0 are the main trends, one may wonder why traditional CONWIP is preferred to digital CONWIP. Following a praxeological approach (i.e., study of practice and instrumentation), this article explores the strengths and weaknesses of the CONWIP practice, in both its paper and electronic versions, while taking into account the human dimension. The aim is to motivate potential CONWIP users to implement it in its digital mode and to show them how a Digital Twin-based solution can overcome the managerial problems that arise with digitization while enabling improved performance. As an illustration, experience feedback from several companies using Digital Twin with CONWIP is provided. Full article
(This article belongs to the Special Issue Industry 4.0 in Support of Process Transformation)
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24 pages, 1970 KiB  
Article
A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem
by Ali Fırat İnal, Çağrı Sel, Adnan Aktepe, Ahmet Kürşad Türker and Süleyman Ersöz
Sustainability 2023, 15(10), 8262; https://doi.org/10.3390/su15108262 - 18 May 2023
Cited by 4 | Viewed by 1894
Abstract
In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and [...] Read more.
In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in–first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 × 3, 5 × 5, and 10 × 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time. Full article
(This article belongs to the Special Issue Industry 4.0 in Support of Process Transformation)
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13 pages, 1061 KiB  
Article
An Agent-Based Architecture of the Digital Twin for an Emergency Department
by Thierry Moyaux, Yinling Liu, Guillaume Bouleux and Vincent Cheutet
Sustainability 2023, 15(4), 3412; https://doi.org/10.3390/su15043412 - 13 Feb 2023
Cited by 6 | Viewed by 1925
Abstract
The concept of Digital Twin (DT) seems promising to improve the management of patient pathways in Emergency Departments (EDs). This article proposes an agent-based architecture of a DT designed for that purpose. The core of this DT is its Information System (IS), which [...] Read more.
The concept of Digital Twin (DT) seems promising to improve the management of patient pathways in Emergency Departments (EDs). This article proposes an agent-based architecture of a DT designed for that purpose. The core of this DT is its Information System (IS), which is regularly synchronised on the IS of the Physical Twin (PT). The agents model the ED’s resources (equipment and staff) and patients in the DT and update this information in the DT’s IS. This article shows how such a DT may operate in three modes: (0) “Digital Shadow” to monitor the ED’s current state in real time, (1) “Synchronised DT” to monitor the ED’s current and future states according to a predictive simulation, and (2) “Exploratory DT” in order to perform Monte Carlo simulations of various future states. Mode (1) is the main contribution. This proposition is illustrated in a simulation of the ED in order to demonstrate the capabilities. Full article
(This article belongs to the Special Issue Industry 4.0 in Support of Process Transformation)
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28 pages, 9600 KiB  
Article
Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0
by Estefania Tobon-Valencia, Samir Lamouri, Robert Pellerin and Alexandre Moeuf
Sustainability 2022, 14(19), 12562; https://doi.org/10.3390/su141912562 - 02 Oct 2022
Cited by 6 | Viewed by 2606
Abstract
The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as [...] Read more.
The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as a starting point toward digitization. The current state of this process reveals a lack of tools to support decision making and the need to increase the reliability of input data and to make the process more agile. Industry 4.0 technologies and process modeling could increase agility in the planning process. However, the digital transformation of medium-term planning activities in SMEs has not been studied. To fill this gap, a group of six experts was consulted. The novelty of this study was to identify the Industry 4.0 technologies that could improve medium-term planning and integrate them into a standardized MPS process model. This model is an ultimate point of digitization that cannot be achieved immediately by any SME, but only after several cycles of planning, deployment, and improvement. Therefore, this research also provides a method to help SMEs determine how to start the digitization of their MPS process. Full article
(This article belongs to the Special Issue Industry 4.0 in Support of Process Transformation)
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20 pages, 6089 KiB  
Article
Automated Storage and Retrieval Systems: An Attractive Solution for an Urban Warehouse’s Sustainable Development
by Aurélie Edouard, Yves Sallez, Virginie Fortineau, Samir Lamouri and Alexandre Berger
Sustainability 2022, 14(15), 9518; https://doi.org/10.3390/su14159518 - 03 Aug 2022
Cited by 3 | Viewed by 4134
Abstract
In recent years, there has been an increasing awareness of sustainable development issues. Supply chain actors have become more and more aware of this situation, especially since the regulatory, social and societal pressures in this area are becoming more and more numerous. This [...] Read more.
In recent years, there has been an increasing awareness of sustainable development issues. Supply chain actors have become more and more aware of this situation, especially since the regulatory, social and societal pressures in this area are becoming more and more numerous. This state of affairs is not without consequences on company practices and economic. As a result, the urban warehouse model is emerging as one of the solutions studied in the urban logistics context. The characteristics, constraints and challenges of this model are presented in this article in order to define this new logistics facility. Secondly, automated storage and retrieval systems (AS/RS), today considered as a solution from Industry 4.0, are studied through a case study in order to determine their potential to meet the challenges of urban warehouses. Their ability to optimize available surfaces by densifying stocks in limited spaces is particularly demonstrated. Full article
(This article belongs to the Special Issue Industry 4.0 in Support of Process Transformation)
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35 pages, 25629 KiB  
Article
Operationalization of Critical Success Factors to Manage the Industry 4.0 Transformation of Manufacturing SMEs
by Jonathan Brodeur, Robert Pellerin and Isabelle Deschamps
Sustainability 2022, 14(14), 8954; https://doi.org/10.3390/su14148954 - 21 Jul 2022
Cited by 3 | Viewed by 3742
Abstract
As an increasing number of manufacturing small and medium enterprises (SMEs) tackle their digital transformation toward Industry 4.0, the need for a methodology to manage this transformation, tailored to their particular context, becomes apparent. Since recent studies have identified critical success factors (CSFs) [...] Read more.
As an increasing number of manufacturing small and medium enterprises (SMEs) tackle their digital transformation toward Industry 4.0, the need for a methodology to manage this transformation, tailored to their particular context, becomes apparent. Since recent studies have identified critical success factors (CSFs) for the Industry 4.0 transformation of manufacturing SMEs, this paper aims to operationalize these CSFs and propose an Industry 4.0 transformation management methodology. This research is based on an extensive literature review on CSFs for Industry 4.0 transformation, followed by a Delphi–Régnier survey with a panel of Industry 4.0 experts. For each CSF, specific actions to perform at different stages of the Industry 4.0 transformation were defined and validated by experts. Based on a proposed Industry 4.0 transformation process, not all CSFs have to be managed at every phase and step of the transformation process. Each CSF must be supported by different actions positioned within each Industry 4.0 transformation process step. The results of this research are particularly relevant for manufacturing SME managers and consultants managing Industry 4.0 transformation. By performing these actions, they can ensure the achievement of multiple CSFs during their digital transformation projects and, thus, ensure their success. This research combines the academic and professional domains by proposing a way for theoretical findings to be translated into clear actions. The proposed model allows all the actors involved in manufacturing SMEs’ digital transformation projects to understand the actions needed to achieve a successful transformation. Full article
(This article belongs to the Special Issue Industry 4.0 in Support of Process Transformation)
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