G. A. Shirali, E. Karami, Z. Goodarzi,
Volume 3, Issue 3 (12-2013)
Abstract
Introduction: Although risk assessment and accident prevention program have been widely used in industries such as steel industry, there are still numerous accidents in these industries. Hence, applying an accident analysis method can identify the root causes and casual factors of accidents and causal factors. Human Factors Analysis and Classification System can identify human errors in the steel industry by using an analysis of past events. The aim of this study was to identify the human errors in the steel industry using the HFACS methodology.
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Material and Method: In this study first, incident reports of industries with high risk, such as Ahvaz steel and pipe industries existing in the department of work and social security was gathered. Then, an analysis of accident was done based on HFACS model. This model has 4 levels and 18 categories which are 1 - unsafe acts of operators (that includes four subtypes) 2 - pre-conditions for unsafe acts (with seven categories) 3 - unsafe supervision (includes four categories) and 4 - the effect of association (with three categories).
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Result: In this study, 158 reports of accident in Ahvaz steel industry were analyzed by HFACS technique. This analysis showed that most of the human errors were: in the first level was related to the skill-based errors, in the second to the physical environment, in the third level to the inadequate supervision and in the fourth level to the management of resources.
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Conclusion: Studying and analyzing of past events using the HFACS technique can identify the major and root causes of accidents and can be effective on prevent repetitions of such mishaps. Also, it can be used as a basis for developing strategies to prevent future events in steel industries.
Rouhalah Fooladi, Ali Karimi, Adel Mazloumi, Mohsen Sharif Rohani, Rajabali Hokmabadi,
Volume 12, Issue 4 (12-2022)
Abstract
Introduction: Human factor analysis has been identified as the most common cause of accidents in natural gas transportation and distribution facilities. The occurrence of accidents at these systems, especially gas reduction stations located in residential and industrial areas, has had catastrophic consequences. Therefore, this study aimed at analyzing critical tasks and human error assessment using the system for predictive error analysis and reduction (SPEAR) method and providing the appropriate framework for error management in the operation and maintenance of city gate stations.
Material and Methods: This descriptive cross-sectional study was conducted using the SPEAR framework and safety critical task analysis guideline to evaluate errors in gas pressure reduction stations. First, critical tasks were screened and evaluated, followed by performing task analysis by the hierarchical task analysis and detecting performance-influencing factors (PIF). Then, human errors were predicted and assessed based on the predictive human error analysis. Finally, error management was developed at three process, equipment, and training improvement levels.
Results: In general, out of 23 operations and 164 sub-tasks, 12 critical tasks were identified based on the results. Criticality level percentages were about 67% high risk, 25% moderate, and 8% low risk. In addition, 134 errors were identified which were mostly related to action (42.53%) and checking (39.55%) errors, respectively. Eventually, communication, retrieval, and selection errors were 8.96, 5.22, and 3.74%, respectively.
Conclusion: The results revealed that action and checking errors had the highest percentages. This method can be applied to appropriate the systems approach to error reduction using the PIF assessment output. The privilege affecting factors include preparing standard operation procedures, implementing a comprehensive training program, and controlling environmental hazards.
