Empirical evaluation of THERP, SLIM and ranking to estimate HEPs

Abstract

The most important techniques for assesing 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 judgements 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.