Applying analytic hierarchy process and failure likelihood index method (AHP-FLIM) to assess human reliability in critical and sensitive jobs of a petrochemical industry

Background: Given that human error is the most important cause of industrial accidents, it seems necessary to identify and analyze human error, assess human reliability, and reduce errors or prevent unfortunate consequences. This study aimed to evaluate human reliability in a petrochemical industry.Methods: Critical and sensitive jobs were identified by interviewing the staff and supervisors of the industry. Then, the most important human errors and Performance Shaping Factors (PSFs) in each job group were identified. Using the Analytic Hierarchy Process (AHP), PSFs and human errors were weighed and prioritized. Next, the Failure Likelihood Index (FLI) was calculated for each of the human errors identified in the selected tasks. Finally, by converting this index to human error, human reliability was calculated.Results: Firefighters, site men, and mechanical repairmen had the highest risk and were selected to evaluate human reliability by the AHP-FLI method. Accordingly, six PSFs (training, experience, instruction, stress, task complexity, and environmental conditions) were identified as the most important factors in human error occurrence. The average human error probability was calculated as 0.019, 0.018, and 0.013 in the three job groups of firefighters, site men, and mechanical repairmen, respectively.Conclusion: This study showed that the AHP-FLI method could be a suitable method for assessing human reliability in the petrochemical industry since it could measure the impact of different PSFs on the probability of human error.

Automated summary

This study assessed human reliability in the petrochemical industry using the AHP-FLI method. The highest risk was found in firefighters, site men, and mechanical repairmen. By identifying the most important Performance Shaping Factors, the study calculated the average human error probability in different job groups.