Open AccessHuman error probability analysis using Success Likelihood Index Method (SLIM) approach in grinding activities
Author(s) -
Indri Santiasih,
Ratna Ayu Ratriwardhani
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1072/1/012027
Subject(s) - human error , task (project management) , grinding , index (typography) , computer science , value (mathematics) , statistics , process (computing) , reliability engineering , mathematics , machine learning , engineering , mechanical engineering , systems engineering , world wide web , operating system
Grinding activities produced 31% accident cases that occurred repeatedly and had the same cause. Most of them were investigated and were proved to be caused by human error. The study aims to obtain the value of Human Error Probability (HEP), thus, it could be employed as prevention and controlling consideration. HEP value was obtained by finding the value of. Success Likelihood Index (SLI). SLI value could be obtained using questionnaires and Performance Shaping Factor (PSF) assessment which had been filled by expert judgment. The scenarios were developed to predict and reduce the occurrence of human error as applied to Success Likelihood Index Method (SLIM). This research illustrated the factors that affected errors in the grinding process were procedure, fatigue, complexity, training, and experience. The task that contained the highest error value was in the first task (use PPE following predetermined). While the task which included the lowest HEP was task 2 (prepare documents according to pre-defined). As part of efforts to reduce the error probability values grinding process, it recommended reducing the probability of error by increasing the value of PSF, ranging from improved procedures, reduction of fatigue of the workers, the reduction of the complexity of the job, increased training for workers, and increased workers’ qualification through work experience history.