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Volume 13, Issue 3 (Iranian Journal of Ergonomics-In Press 2025)
Iran J Ergon 2025, 13(3): 0-0 |
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Nikray A R, Vesali Naseh M R, Mohammadi A. An Analytical Perspective on Human Reliability Assessment and Human Error Risk Studies (2010–2023): Exploring the Role of Artificial Intelligence and Lights-Out Manufacturing. Iran J Ergon 2025; 13 (3)
URL: http://journal.iehfs.ir/article-1-1099-en.html
URL: http://journal.iehfs.ir/article-1-1099-en.html
1- Department of Civil Engineering, Faculty of Engineering, Arak University, Arak, Iran
2- Safety & Protection Research Center (SPRC), University of Qom, Iran. ,mohammadi.a@qom.ac.ir
2- Safety & Protection Research Center (SPRC), University of Qom, Iran. ,
Abstract: (68 Views)
Objectives: As industrial systems become increasingly complex and technologically advanced, the human role in ensuring safety and efficiency remains indispensable. This study presents a comprehensive review of Human Reliability Assessment researches published between 2010-2023. It compares HRA methodologies with emerging technologies such as artificial intelligence and lights-out manufacturing, identifies existing research gaps, and analyzes both the analytical techniques employed and the industrial sectors addressed.
Methods: A systematic search of major scientific databases was conducted using domain-specific keywords, yielding over 230 publications. Following the removal of duplicates studies, 180 articles were selected for detailed analysis. Each article was evaluated based on methodology, industrial application, country and institutional affiliation, and publishing outlet.
Results: The results indicate that SHERPA, CREAM, and Fuzzy Mathematics are the most frequently applied approaches in HRA research. The United States, China, and South Korea emerged as leading contributors to the field. The findings reveal that neither qualitative nor quantitative methods alone are sufficient to fulfill the three core objectives of HRA: error identification, probability estimation, and control design. A hybrid approach is therefore recommended through the integration of SHERPA and TESEO. SHERPA offers comprehensive coverage of error identification and designing effective control measures, while TESEO facilitates rapid and conservative probability estimation. Together, these methods provide a practical and efficient framework for achieving HRA objectives within operational constraints. Additionally, ten key research gaps were identified.
Conclusion: The SHERPA–TESEO hybrid framework presents a viable strategy for achieving the core goals of HRA. Nonetheless, in the context of smart environments and operator-free production, the shift from static to dynamic and data-driven models is necessary. Recommended developments include revising SHERPA’s cognitive task classifications, recalibrating TESEO’s adjustment factors, and integrating real-time data with human–AI interaction. These advancements are expected to significantly enhance real-time prediction of human-error-risks and support timely intervention strategies.
Methods: A systematic search of major scientific databases was conducted using domain-specific keywords, yielding over 230 publications. Following the removal of duplicates studies, 180 articles were selected for detailed analysis. Each article was evaluated based on methodology, industrial application, country and institutional affiliation, and publishing outlet.
Results: The results indicate that SHERPA, CREAM, and Fuzzy Mathematics are the most frequently applied approaches in HRA research. The United States, China, and South Korea emerged as leading contributors to the field. The findings reveal that neither qualitative nor quantitative methods alone are sufficient to fulfill the three core objectives of HRA: error identification, probability estimation, and control design. A hybrid approach is therefore recommended through the integration of SHERPA and TESEO. SHERPA offers comprehensive coverage of error identification and designing effective control measures, while TESEO facilitates rapid and conservative probability estimation. Together, these methods provide a practical and efficient framework for achieving HRA objectives within operational constraints. Additionally, ten key research gaps were identified.
Conclusion: The SHERPA–TESEO hybrid framework presents a viable strategy for achieving the core goals of HRA. Nonetheless, in the context of smart environments and operator-free production, the shift from static to dynamic and data-driven models is necessary. Recommended developments include revising SHERPA’s cognitive task classifications, recalibrating TESEO’s adjustment factors, and integrating real-time data with human–AI interaction. These advancements are expected to significantly enhance real-time prediction of human-error-risks and support timely intervention strategies.
Keywords: Human Reliability, Human Error, Hybrid Framework, Artificial Intelligence, Lights-Out Manufacturing
Type of Study: Systematic Review |
Subject:
Other Cases
Received: 2025/08/4 | Accepted: 2025/09/27 | ePublished: 2025/09/27
Received: 2025/08/4 | Accepted: 2025/09/27 | ePublished: 2025/09/27
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
