Volume 11, Issue 1 (Occupational Medicine Quarterly Journal 2019)
tkj 2019, 11(1): 42-58 |
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afshari D, jafarzadeh Z, mosavianasl Z, jahani F. Identification and Evaluation of Human Errors Using Human Factor Analysis and Classification System based on Fuzzy Hierarchy Theory: A Case Study in the Cement Industry. tkj 2019; 11 (1) :42-58
URL: http://tkj.ssu.ac.ir/article-1-963-en.html
URL: http://tkj.ssu.ac.ir/article-1-963-en.html
Department of Safety Engineering, Lamerd Higher Education Center, Lamerd, Iran. , jahani.f71@gmail.com
Abstract: (3224 Views)
Introduction: Human factors studies have shown that about 80% of the root causes of major accidents affecting safety, the environment or ergonomics are related to human error. The purpose of this study was to identify human errors using the HFACS method and the FAHP theory in cement industry.
Methods: The present study was a retrospective study carried out in the cement industry. At first, RCA analysis of 95 incidents occurred during the past six years. Then, by integrating the human factors analysis and classification system technique ) HFACS(and the theory of fuzzy analytic hierarchy process ) FAHP( with 4 safety engineers The accident factory was analyzed.
Results: The results of this study show that the highest errors are related to the first level with a weight of 0.272, that is, the errors caused by unsafe acts, at this level of the subgroup "skill-based error" with a weight of 0.269, the 2nd level of the sub-group "industrial environment" with a weight of 0.155 , The 3rd subgroup of "inadequate supervision" with the weight of 0.352 and the 4th sub-category "resource management" with a weight of 0.393 have the highest impact on the levels.
Conclusion: The results showed that errors have various causes, including individual, activity, situational, and organizational errors that require careful planning and management to eliminate or reduce these errors. This error reduction, according to safety expert theory, Health and environment of the cement plant including the promotion and effectiveness of staff training, safety guidelines, improvement of staff performance monitoring, identification, evaluation and elimination of high-risk risks, improvement of management and equipment systems, changing safety attitudes, as well as enhancing safety attitudes. Awareness can help reduce the likelihood of human error in the organization.
Methods: The present study was a retrospective study carried out in the cement industry. At first, RCA analysis of 95 incidents occurred during the past six years. Then, by integrating the human factors analysis and classification system technique ) HFACS(and the theory of fuzzy analytic hierarchy process ) FAHP( with 4 safety engineers The accident factory was analyzed.
Results: The results of this study show that the highest errors are related to the first level with a weight of 0.272, that is, the errors caused by unsafe acts, at this level of the subgroup "skill-based error" with a weight of 0.269, the 2nd level of the sub-group "industrial environment" with a weight of 0.155 , The 3rd subgroup of "inadequate supervision" with the weight of 0.352 and the 4th sub-category "resource management" with a weight of 0.393 have the highest impact on the levels.
Conclusion: The results showed that errors have various causes, including individual, activity, situational, and organizational errors that require careful planning and management to eliminate or reduce these errors. This error reduction, according to safety expert theory, Health and environment of the cement plant including the promotion and effectiveness of staff training, safety guidelines, improvement of staff performance monitoring, identification, evaluation and elimination of high-risk risks, improvement of management and equipment systems, changing safety attitudes, as well as enhancing safety attitudes. Awareness can help reduce the likelihood of human error in the organization.
Type of Study: Research |
Subject:
Safety and occupational accidents
Received: 2018/08/13 | Accepted: 2020/04/14 | Published: 2020/04/14
Received: 2018/08/13 | Accepted: 2020/04/14 | Published: 2020/04/14
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