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Leveraging Digital Transformation for Enhanced Occupational Health and Safety in Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 1302

Special Issue Editors


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Guest Editor
Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
Interests: safety; resilience; smart factory; supply chain; digitalization
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
Interests: smart factory; Industry 4.0; Industry 5.0; large language models; safety; cybersecurity

Special Issue Information

Dear Colleagues,

The integration of digital technologies is revolutionizing the field of occupational health and safety. This Special Issue explores the industrial impact of enabling technologies such as virtual reality, augmented reality, and generative artificial intelligence, focusing on occupational health and safety (OHS). In high-risk, complex manufacturing environments, the use of innovative tools can significantly improve safety standards, reduce hazards, and promote a culture of preventive maintenance and risk management. Here, the use of digital technologies—such those cited—enables the development of novel tools that promote learning through actions, storytelling, and a first-person experiences-based approach. Furthermore, it permits the integration of non-traditional training content and modes, thereby addressing the inherent variability in digitized industrial contexts. In such a context, it is not sufficient to learn rules and behaviors that are required. Ad-hoc adaptation and response to unpredictable conditions must also be learned. Therefore, in the field of safety management, a new level of expertise is needed. The Skill–Rule–Knowledge framework contains an exemplary level of knowledge. This framework helps one to understand the different levels of conscious effort that workers must apply to industrial tasks and how this affects decision-making. In the event of a unique and unfamiliar situation, the decision is not automatic and reflexive (skill-based), and there are no rules to guide the decision maker (rule-based). So, it is evident that a knowledge-based decision is needed, i.e., the creation of plans and responses based on personal knowledge and experience. In the pursuit of a sustainable working environment, all these concerns must be addressed. So, this Special Issue explores how these technologies can support training, enhance safety protocols, and improve incident management processes. Submissions are invited to present frameworks, models, experimental studies, and practical applications that integrate technologies to support OHS and enhance the social, economic, and environmental sustainability of industrial environments.

Topics of interest include but are not limited to the following: Innovative uses of immersive technologies for safety training, demonstrating impacts on workers' skills and capabilities; Development and application of AI models for the analysis of safety incident narratives and protocols to identify trends and preventive measures; Comparative analyses of industrial safety performance before and after the adoption of technologies, highlighting their effectiveness and potential improvement.

Prof. Dr. Francesco Costantino
Dr. Silvia Colabianchi
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. Applied Sciences 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

  • training
  • safety management
  • smart manufacturing
  • sustainable digitization
  • virtual reality
  • augmented reality
  • generative artificial intelligence

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Published Papers (1 paper)

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Research

21 pages, 2265 KiB  
Article
A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety
by Andrea Falegnami, Andrea Tomassi, Giuseppe Corbelli, Francesco Saverio Nucci and Elpidio Romano
Appl. Sci. 2024, 14(24), 11586; https://doi.org/10.3390/app142411586 - 11 Dec 2024
Viewed by 647
Abstract
This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. [...] Read more.
This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow, iterative cycles of empirical data collection and analysis, which can be both time-intensive and costly. In contrast, our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse, realistic data sets on demand. This capability allows for more flexible and accelerated experimentation, enhancing the efficiency and scalability of safety science research. By detailing an application case, we demonstrate the practical implementation and advantages of our framework, such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies. Full article
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