Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.9
4.1
Journals
Sustainability
Special Issues
Industry 4.0 and Application of Artificial Intelligence System in Operation...
sustainability-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Industry 4.0 and Application of Artificial Intelligence System in Operation Management

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 15136

Editor

Prof. Dr. Jose Antonio Marmolejo-Saucedo

E-Mail Website
Guest Editor
Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo León, Mexico
Interests: large-scale optimization; high-performance computing; digital twins; integer programming; global optimization; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue of the journal Sustainability, which focuses on the critical intersection and the application of artificial intelligence in operation management, life cycle analysis, lean manufacturing, and green optimization in the pursuit of sustainable manufacturing practices. As industries face increasing pressure to reduce their environmental footprints while maintaining economic viability, the integration of these methodologies has become essential. Life cycle analysis (LCA) provides a comprehensive framework for assessing the environmental impacts of products throughout their entire life cycle, from raw material extraction to disposal. Lean manufacturing principles emphasize waste reduction and efficiency, which align perfectly with sustainability goals. Meanwhile, green optimization techniques aim to enhance resource utilization and minimize negative environmental impacts. This research area is of paramount importance as it not only addresses the urgent need for sustainable development but also fosters innovation and competitive advantage in manufacturing considering Industry 4.0.

This Special Issue aims to gather a diverse array of research contributions that advance the understanding and implementation of sustainable practices in manufacturing through the lenses of life cycle analysis, lean manufacturing, and green optimization. By focusing on these interconnected themes, we seek to provide insights that are directly relevant to the scope of the journal Sustainability, which aims to promote multidisciplinary discussions on environmental, social, and economic sustainability. We aspire to compile research that explore both theoretical frameworks and practical applications, and if the number of submissions allows, the Special Issue may be published in book form. Contributions that offer novel methodologies, case studies, or empirical research will be particularly valuable in enriching this discourse.

In this Special Issue, original research articles and comprehensive reviews are welcome. We encourage submissions that explore, but are not limited to, the following research areas:

  • Digital Twins Development
  • Life Cycle Analysis Applications in Manufacturing
  • Industry 4.0 and Sustainable Manufacturing
  • Application of Artificial Intelligence in Operation Management
  • Lean Manufacturing Techniques for Sustainable Production
  • Green Optimization Strategies
  • Circular Economy Models in Manufacturing
  • Case Studies of Sustainable Manufacturing Practices
  • Quantitative and Qualitative Metrics for Sustainability Assessment
  • Barriers and Drivers of Sustainable Manufacturing Adoption
  • Technological Innovations Supporting Sustainable Practices

I look forward to receiving your contributions and fostering a rich dialogue on sustainable practices in manufacturing. Together, we can advance knowledge and foster actionable insights that contribute to a more sustainable future. Join us in this important endeavor to promote sustainability within the manufacturing sector.

Prof. Dr. Jose Antonio Marmolejo-Saucedo
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • digital twins
  • Industry 4.0
  • life cycle analysis
  • lean manufacturing
  • green optimization
  • sustainable practices
  • circular economy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

21 pages, 743 KB  
Article
Norwegian Construction Leaders’ Views on Society 5.0 and Industry 5.0: Reality or Utopia? An Empirical Study Involving 70 Leaders in Norway’s Construction Industry
by Arne Ronny Sannerud, Roger Drange and Atle Solbakken
Sustainability 2026, 18(12), 5963; https://doi.org/10.3390/su18125963 - 10 Jun 2026
Viewed by 512
Abstract
This article examines how managers in the Norwegian construction industry—represented by bachelor’s students in construction site management—perceive and understand the concepts of Industry 5.0 and Society 5.0. The study focuses on participants’ interpretations, reflections, and expectations regarding these emerging frameworks. Using qualitative data, [...] Read more.
This article examines how managers in the Norwegian construction industry—represented by bachelor’s students in construction site management—perceive and understand the concepts of Industry 5.0 and Society 5.0. The study focuses on participants’ interpretations, reflections, and expectations regarding these emerging frameworks. Using qualitative data, the research draws on insights from 70 part-time students with full-time industry positions, organised into 15 interdisciplinary groups. The findings show that participants were familiar with the core ideas of Industry 5.0 and Society 5.0 and were able to identify both opportunities and challenges. They emphasised the increasing importance of human roles in Industry 5.0 compared with Industry 4.0. A successful transition toward these paradigms will require strengthened competence, self-directed learning, and investment in both skills and technology, aligning with the needs of a knowledge-intensive and sustainability-oriented society. Participants highlighted the Norwegian working life model as an advantage, noting its compatibility with the human-centred principles of Industry 5.0. The article contributes to understanding how future construction managers interpret these concepts and offers an analysis of resilience, including vulnerability and capacity, within a Norwegian context. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

37 pages, 3624 KB  
Article
An Integrated Lean–QMS–SPC Analytical Framework for Process Stability and Sustainable Manufacturing
by Mariusz Niekurzak and Jerzy Mikulik
Sustainability 2026, 18(11), 5324; https://doi.org/10.3390/su18115324 - 25 May 2026
Viewed by 654
Abstract
This study addresses the growing need to integrate operational excellence with sustainability objectives in manufacturing systems. Despite extensive research on Lean Management and Quality Management Systems (QMSs), their combined impact on process performance and resource efficiency remains insufficiently explored, particularly in real industrial [...] Read more.
This study addresses the growing need to integrate operational excellence with sustainability objectives in manufacturing systems. Despite extensive research on Lean Management and Quality Management Systems (QMSs), their combined impact on process performance and resource efficiency remains insufficiently explored, particularly in real industrial contexts. The aim of this study is to develop and apply an integrated Lean–QMS–SPC analytical framework linking process performance improvement with sustainability-related outcomes. A case study was conducted in a high-volume manufacturing environment. The study combined process analysis, system-level assessment, and root cause identification to support targeted improvement actions. The results indicate that the implementation of Lean-oriented practices and supporting methods was associated with improved process stability, reduced variability, and decreased occurrence of nonconformities. These improvements translate into enhanced operational performance and reduced resource consumption associated with rework and defects. A scenario-based estimation model, based on observed defect reduction, is used to assess the potential impact on energy consumption and CO2 emissions. The study contributes to the literature by operationally integrating SPC analysis, QMS assessment, root cause analysis, and Lean-oriented improvement activities within an industrial manufacturing context. The findings highlight that quality-driven process improvements may support operational efficiency while contributing to resource-efficiency performance. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

16 pages, 4480 KB  
Article
Color Vision in Digital Twin Creation Using Photogrammetry in Sustainable Agriculture 4.0
by Irena Drofova, Haozhou Wang, Wei Guo, Naoya Katsuhama, James Burridge, Pieter M. Blok and Milan Adamek
Sustainability 2026, 18(5), 2160; https://doi.org/10.3390/su18052160 - 24 Feb 2026
Viewed by 994
Abstract
The study proposes a methodological integration of machine vision and image processing based on color-based object detection. The primary goal of the study is to use the color vision method to simplify the process of transforming real objects into 3D digital twins for [...] Read more.
The study proposes a methodological integration of machine vision and image processing based on color-based object detection. The primary goal of the study is to use the color vision method to simplify the process of transforming real objects into 3D digital twins for application in Sustainable Agriculture 4.0. The experiment solves several related problems: (1) Color analysis and methodology for quantifying the color representation of a 3D model. Representation quality was determined using colorimetric methods with sRGB and L*a*b* models in relation to the D65 standard. Colors with accurate color values on the object surface and in the 3D model were identified. (2) The process of capturing and creating digital twins using the SfM method is time-consuming and requires manual work. The study solves this problem by partially automating the entire process. The proposed DSLR system with an automated method for capturing, storing, and sorting data significantly accelerates the entire process. (3) To create a digital color scale, it is necessary to define the color values of 3D digital twins. A color segmentation procedure based on points on the surface of a 3D model is proposed. These color values form a basic color form corresponding to the color value changes in the coloring process of a real object. The proposed procedure uniquely integrates methodologies and has potential for use in Sustainable Agriculture 4.0. The proposed colorimetric method quantifies representation quality and could be deployed in other 3D model digitization and automation processes, especially in image processing and computer vision. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

43 pages, 12935 KB  
Article
Engineering for Industry 5.0: Developing Smart, Sustainable Skills in a Lean Learning Ecosystem
by Eduard Laurenţiu Niţu, Ana Cornelia Gavriluţă, Nadia Ionescu, Maria Loredana Necşoi and Jeremie Schutz
Sustainability 2026, 18(4), 1855; https://doi.org/10.3390/su18041855 - 11 Feb 2026
Cited by 2 | Viewed by 1234
Abstract
As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean [...] Read more.
As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean methods with technologies such as simulation, robotics, and virtual reality in a modular educational environment. At the University Centre Pitești, six hands-on projects were implemented to guide students through key concepts, including production system layout, digital assistance, sustainability, and human–robot collaboration. Through experiential learning, students engage in iterative design, data analysis, and practical validation using real equipment and software platforms. The results indicate that the LLF effectively supports the development of technical, digital, transversal, and human-centred competencies aligned with EUR-ACE® standards. Students acquire skills in process optimisation, ergonomics, and sustainable production, while also reflecting on the ethical and social implications of automation. The study concludes that the LLF model provides a scalable and adaptable framework for engineering education. It fosters competence-based learning and prepares students for the demands of Industry 5.0. This paper contributes a replicable educational approach that blends Lean efficiency, digital transformation, and human-centred values into a cohesive learning ecosystem. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

35 pages, 1049 KB  
Article
Strategic Human Resource Development for Industry 4.0 Readiness: A Sustainable Transformation Framework for Emerging Economies
by Kwanchanok Chumnumporn Vong, Kalaya Udomvitid, Yasushi Ueki, Nuchjarin Intalar, Akkaranan Pongsathornwiwat, Warut Pannakkong, Somrote Komolavanij and Chawalit Jeenanunta
Sustainability 2025, 17(15), 6988; https://doi.org/10.3390/su17156988 - 1 Aug 2025
Cited by 16 | Viewed by 8652
Abstract
Industry 4.0 represents a significant transformation in industrial systems through digital integration, presenting both opportunities and challenges for aligning the workforce, especially in emerging economies like Thailand. This study adopts a sequential exploratory mixed-method approach to investigate how strategic human resource development (HRD) [...] Read more.
Industry 4.0 represents a significant transformation in industrial systems through digital integration, presenting both opportunities and challenges for aligning the workforce, especially in emerging economies like Thailand. This study adopts a sequential exploratory mixed-method approach to investigate how strategic human resource development (HRD) contributes to sustainable transformation, defined as the enduring alignment between workforce capabilities and technological advancement. The qualitative phase involved case studies of five Thai manufacturing firms at varying levels of Industry 4.0 adoption, utilizing semi-structured interviews with executives and HR leaders. Thematic findings informed the development of a structured survey, distributed to 144 firms. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to test the hypothesized relationships among business pressures, leadership support, HRD preparedness, and technological readiness. The analysis reveals that business pressures significantly influence leadership and HRD, which in turn facilitate technological readiness. However, business pressures alone do not directly enhance readiness without the support of intermediaries. These results underscore the critical role of integrated HRD and leadership frameworks in enabling sustainable digital transformation. This study contributes to theoretical perspectives by integrating HRD, leadership, and technological readiness, offering practical guidance for firms aiming to navigate the complexities of Industry 4.0. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

21 pages, 950 KB  
Article
A Fuzzy Unit Commitment Model for Enhancing Stability and Sustainability in Renewable Energy-Integrated Power Systems
by Sukita Kaewpasuk, Boonyarit Intiyot and Chawalit Jeenanunta
Sustainability 2025, 17(15), 6800; https://doi.org/10.3390/su17156800 - 26 Jul 2025
Cited by 8 | Viewed by 1455
Abstract
The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in [...] Read more.
The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

Other

Jump to: Research

39 pages, 2037 KB  
Systematic Review
Sustainable Maintenance 4.0 Enhanced by Digital Twins: A Systematic Literature Review and Conceptual Model Proposal
by David Mendes, Vítor Alcácer, Rui Ferreira, Elena Terradillos, Olga Costa and Helena V. G. Navas
Sustainability 2026, 18(11), 5718; https://doi.org/10.3390/su18115718 - 4 Jun 2026
Viewed by 529
Abstract
Industrial maintenance has increasingly evolved into a strategic function for improving asset reliability, extending asset lifecycle, and supporting sustainability objectives. However, the literature remains fragmented, with limited integration between digital twins, maintenance practices, and sustainability-oriented decision-making. To address this gap, the study performs [...] Read more.
Industrial maintenance has increasingly evolved into a strategic function for improving asset reliability, extending asset lifecycle, and supporting sustainability objectives. However, the literature remains fragmented, with limited integration between digital twins, maintenance practices, and sustainability-oriented decision-making. To address this gap, the study performs a systematic review of 49 publications indexed in Scopus and Web of Science and introduces an integrative conceptual framework for Sustainable Maintenance 4.0. The analysis explores the role of digital twins, as a key enabling technology within the Industry 4.0 landscape, in supporting the shift from reactive and schedule-based maintenance toward predictive and prescriptive strategies. The findings suggest that digital twins can enhance maintenance decision-making, improve asset reliability, and contribute to lifecycle optimization. The reviewed studies also report improvements in operational and energy performance, although these effects vary according to digital maturity, system configuration, and implementation scope. In addition, digital twins may support safer operations and workforce development through data-driven and immersive environments. Despite these benefits, challenges remain, including high investment requirements, interoperability limitations, cybersecurity risks, and the need for interdisciplinary skills. The proposed framework positions digital twins as a mediating element between physical assets, data acquisition, advanced analytics, maintenance services, and sustainability outcomes. Full article
(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop