Success likelihood index method

Success Likelihood Index Method (SLIM) is a technique used in the field of Human reliability Assessment (HRA), for the purposes of evaluating the probability of a human error occurring throughout the completion of a specific task. From such analyses measures can then be taken to reduce the likelihood of errors occurring within a system and therefore lead to an improvement in the overall levels of safety. There exist three primary reasons for conducting an HRA; error identification, error quantification and error reduction. As there exist a number of techniques used for such purposes, they can be split into one of two classifications; first generation techniques and second generation techniques. First generation techniques work on the basis of the simple dichotomy of ‘fits/doesn’t fit’ in the matching of the error situation in context with related error identification and quantification and second generation techniques are more theory based in their assessment and quantification of errors. ‘HRA techniques have been utilised in a range of industries including healthcare, engineering, nuclear, transportation and business sector; each technique has varying uses within different disciplines. SLIM is a decision-analytic approach to HRA which uses expert judgement to quantify Performance Shaping Factors (PSFs); factors concerning the individuals, environment or task, which have the potential to either positively or negatively affect performance e.g. available task time. Such factors are used to derive a Success Likelihood Index (SLI), a form of preference index, which is calibrated against existing data to derive a final Human Error Probability (HEP). The PSF’s which require to be considered are chosen by experts and are namely those factors which are regarded as most significant in relation to the context in question. The technique consists of two modules: MAUD (multi-attribute utility decomposition) which scales the relative success likelihood in performing a range of tasks, given the PSFs probable to affect human performance; and SARAH (Systematic Approach to the Reliability Assessment of Humans) which calibrates these success scores with tasks with known HEP values, to provide an overall figure.