EMPIRICAL-EVALUATION OF THERP, SLIM AND RANKING TO ESTIMATE HEPS

The most important techniques for assessing human reliability in risky technologies-such as chemical and manufacturing industries or nuclear power plants-all use human error data for tasks to estimate the overall reliability of the system. Because empirical data are not usually available, these assessment techniques use judgments made by subject-matter experts about the likelihood of human error in task performance. An experimental study was carried out to compare and evaluate three different estimation techniques: the technique for human error rate prediction (THERP), success-likelihood index methodology (SLIM) and a rank ordering procedure. Human error probabilities (HEPs) were derived empirically under 12 different task conditions in a batch manufacturing scenario. THERP was applied to estimate the overall failure probability. Results indicate a satisfactory match between empirical HEPs and THERP estimates. Six judges with backgrounds in human factors and/or mechanical engineering assessed the likelihood of failures, using SLIM and rank ordering. Results indicate a poor match of empirical HEPs and their estimates. The reasons are analyzed and suggestions for improved estimation techniques are outlined.