Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices

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 Engineering and Science".

Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 15356

Share This Special Issue

Special Issue Editors

Prof. Dr. Massimo Bertolini

E-Mail Website
Guest Editor

“Enzo Ferrari” Engineering Department, University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: operations management; supply chain management; project management; lean production; industrial logistics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mosè Gallo

E-Mail Website
Guest Editor

Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy
Interests: lean manufacturing; remanufacturing; Industry 4.0; supply chain management
Special Issues, Collections and Topics in MDPI journals

Dr. Mattia Neroni

E-Mail
Guest Editor

Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
Interests: Monte Carlo Simulations; Auto-Id technologies for Logistic and supply chain management; Artificial Intelligence for Logistics and supply chain management; lean manufacturing; project management; Digital Twin for Logistics and supply chain management

Dr. Patrick Dallasega

E-Mail Website
Guest Editor

Faculty of Science and Technology, Free University of Bozen, 39100 Bolzano, Italy
Interests: Industry 4.0; lean construction; lean manufacturing; supply chain management; production planning and control in MTO and ETO enterprises
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The pervasiveness of innovative digital technologies is profoundly transforming the way industrial systems are being operated. New production and logistics paradigms as well as more conscious and sustainable approaches to operating industrial systems are becoming standard for companies all over the world.

In line with these premises, the aim of this Special Issue is to solicit contributions investigating the interconnection of Lean Manufacturing methodologies, Industry 4.0 technologies and impacts on the Triple Bottom Line (TBL) of sustainability. Contributions, coming from both researchers and practitioners, may include mathematical models, theoretical approaches and frameworks, as well as successful implementations and applications of the role and potential of the new digital technologies, and their integration with lean practices, for the improvement of sustainable development.

Scientific works showing the enabling as well as empowering effects of Lean Manufacturing methodologies and/or Industry 4.0 concepts and technologies on economic, ecological and social sustainability aspects of Supply Chain Management and Manufacturing Management are highly appreciated.

References:

Agrawal S., Singh R.K., Murtaza Q., (2016), “Outsourcing decisions in reverse logistics: Sustainable balanced scorecard and graph theoretic approach”, Resources, Conservation and Recycling, Vol. 1, pp. 41-53. https://doi.org/10.1016/j.resconrec.2016.01.004
Darbari J.D., Kannan D., Agarwal V., Jha P.C., (2019), “Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem”, Annals of Operations Research, Vol. 273, Issue 1-2, pp. 693-738. https://doi.org/10.1016/j.ijpe.2017.02.020
Henao R., Sarache W., Gómez I., (2019), “Lean manufacturing and sustainable performance: Trends and future challenges”, Journal of Cleaner Production, Vol. 208, pp. 99-116. https://doi.org/10.1016/j.jclepro.2018.10.116
Kannan D., (2019), “Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process”, International Journal of Production Economics, Vol. 195, pp. 391-418. https://doi.org/10.1007/s10479-017-2701-2
Kiel D., Müller J., Arnold C., Voigt K.-I., (2017), “Sustainable industrial value creation: Benefits and challenges of industry 4.0”, International Journal of Innovation Management, Vol. 21, Issue 8. https://doi.org/10.1142/S1363919617400151
Kleindorfer P.R., Singhal K., Van Wassenhove L.N., (2005), “Sustainable operations management”, Production and Operations Management, Vol. 14, Issue 4, pp. 482-492. https://doi.org/10.1111/j.1937-5956.2005.tb00235.x
Rahimi M., Ghezavati V., (2018), ”Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste”, Journal of Cleaner Production, Vol. 172, pp. 1567-1581. https://doi.org/10.1016/j.jclepro.2017.10.240
Rajeev A., Pati R.K., Padhi S.S., Govindan K., (2017), “Evolution of sustainability in supply chain management: A literature review”, Journal of Cleaner Production, Vol. 20, pp. 299-314. https://doi.org/10.1016/j.jclepro.2017.05.026

Prof. Dr. Massimo Bertolini
Dr. Mosè Gallo
Dr. Mattia Neroni
Dr. Patrick Dallasega
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

Industry 4.0
lean manufacturing
manufacturing sustainability
Triple Bottom Line (TBL)
supply chain sustainability

Published Papers (5 papers)

Download All Papers

Research

Jump to: Other

17 pages, 3588 KiB

Open AccessEditor’s ChoiceArticle

Metrology Process to Produce High-Value Components and Reduce Waste for the Fourth Industrial Revolution

by Ahmad Junaid, Muftooh Ur Rehman Siddiqi, Sundas Tariq, Riaz Muhammad, Ubaidullah Paracha, Nasim Ullah, Ahmad Aziz Al Ahmadi, Muhammad Suleman and Tufail Habib

Sustainability 2022, 14(12), 7472; https://doi.org/10.3390/su14127472 - 19 Jun 2022

Cited by 1 | Viewed by 2594

Abstract

Conventionally, a manufactured product undergoes a quality control process. The quality control department mostly ensures that the dimensions of the manufactured products are within the desired range, i.e., the product either satisfies the defined conformity range or is rejected. Failing to satisfy the conformity range increases the manufacturing cost and harms the production rate and the environment. Conventional quality control departments take samples from the given batch after the manufacturing process. This, in turn, has two consequences, i.e., low-quality components being delivered to the customer and input energy being wasted in the rejected components. The aim of this paper is to create a high-precision measuring (metrology)-based system that measures the dimension of an object in real time during the machining process. This is accomplished by integrating a vision-based system with image processing techniques in the manufacturing process. Experiments were planned using an experimental design which included different lightning conditions, camera locations, and revolutions per minute (rpm) values. Using the proposed technique, submillimeter dimensional accuracy was achieved at all the measured points of the component in real time. Manual validation and statistical analysis were performed to check the validity of the system. Full article

(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)

► Show Figures

Figure 1

16 pages, 4447 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

by Ming Zhang, Yang Lu, Youxi Hu, Nasser Amaitik and Yuchun Xu

Sustainability 2022, 14(9), 5177; https://doi.org/10.3390/su14095177 - 25 Apr 2022

Cited by 18 | Viewed by 4409

Abstract

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods. Full article

(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)

► Show Figures

Figure 1

25 pages, 5891 KiB

Open AccessArticle

Analysis of Directional Activities for Industry 4.0 in the Example of Poland and Germany

by Tomasz Jałowiec and Henryk Wojtaszek

Sustainability 2022, 14(7), 3848; https://doi.org/10.3390/su14073848 - 24 Mar 2022

Cited by 2 | Viewed by 1766

Abstract

An analysis of directional activities in Poland and Germany towards the implementation of Industry 4.0 was carried out by comparing the common sustainable development features. The value of production sold along with the benefits of its implementation are presented. The transformation map was characterized along with development areas and potential directions of automation and robotization. Technological possibilities were assessed, considering the production of robots. The execution of activities aimed at implementing solutions in the field of Industry 4.0 in Poland was indicated. The key information gleaned in this study is the awareness of the implemented features proving the fulfillment of conditions relating to Industry 4.0. Action towards the sustainable replacement of machines that require repair or regeneration is significantly related to thinking towards rationalizing the actions taken and assessing the financial capabilities of companies so as not to lead to their collapse. The article presents original research on the characteristics of selected production companies in Poland and Germany striving for digital maturity and the results of our hypotheses. The key direction should be activities aimed at developing a coherent strategy, the proper selection and evaluation of managers, focusing on communication, and the pursuit of intelligent products by creating appropriate integration standards that facilitate the implementation of an innovative process generating modern technologies. Full article

(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)

► Show Figures

Figure 1

23 pages, 1155 KiB

Open AccessArticle

The Challenge of Deploying Failure Modes and Effects Analysis in Complex System Applications—Quantification and Analysis

by Mansoor Alruqi, Martin Baumers, David T. Branson and Sourafel Girma

Sustainability 2022, 14(3), 1397; https://doi.org/10.3390/su14031397 - 26 Jan 2022

Cited by 3 | Viewed by 2398

Abstract

Failure Modes and Effects Analysis (FMEA) is a systematic approach for evaluating failure modes in a system. However, its current implementation in complex systems is marred by high resource requirements, a lack of available data and difficulty of deployment. Consequently, attempts to integrate FMEA with other systematic methodologies have yielded unclear outcomes. Therefore, this paper used a score-based metric and applied the ordered probit model to empirically identify challenges associated with deploying FMEA and these attempts’ impact on FMEA applications as well as the influence of other organisational parameters. Our findings reveal that Fault Tree Analysis and Axiomatic Design methodologies reduced the perceived level of challenge significantly in the investigated sample. Our research outcome is of value to the practitioner community by showing that the level of challenge associated with FMEA deployment appears independent of organisational parameters, and that such a co-adoption of complementary methodologies in complex systems can reduce this challenge. Full article

(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)

► Show Figures

Figure 1

Other

Jump to: Research

25 pages, 1371 KiB

Open AccessCase Report

Application of the Maturity Model in Industrial Corporations

by Cihan Ünal, Cemil Sungur and Hakan Yildirim

Sustainability 2022, 14(15), 9478; https://doi.org/10.3390/su14159478 - 2 Aug 2022

Cited by 8 | Viewed by 2092

Abstract

Enterprises need to evaluate for themselves whether they are ready for Industry 4.0 to survive and develop in the era of the Fourth Industrial Revolution. Therefore, it is necessary to conceptualize or develop an Industry 4.0 readiness and maturity model with basic model dimensions. The present study aimed to review the maturity models available in the literature and to develop and implement a comprehensive maturity model that would eliminate the problems in the existing models. Most maturity models developed lack vital dimensions such as laws, incentives, and corporate culture. While developing the model, AHP and expert opinions were used to determine the dimension weights. The model was applied to 87 businesses in various industries at the Ankara Chamber of Industry Industrial Park in Turkey. The developed model calculates the maturity level of the enterprise for six dimensions. The data on 61 corporations where Industry 4.0 technologies were adopted were analyzed based on demographic variables such as the year of establishment, industry, size, capital, and turnover. These findings demonstrated that Industry 4.0 was introduced recently in Turkey and businesses are required to take further steps to keep up with the global digital transformation. Since the number of industries and corporations that are aware of the Industry 4.0 technologies is limited in Ankara, Turkey, only a few businesses adopted the Industry 4.0 technologies. This developed model will make an important contribution to the literature with its unique dimensions. It would pave the way for further research in various industries in Turkey and other nations where Industry 4.0 investments are new. Full article

(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)

► Show Figures