SLIM-MAUD

Embry, D. E. et al., SLIM-MAUD an Approach to Assessing Human Error Probabihues Using Structured Expert Judgment, BNL, March 1984. 

Embrey, D. E., Kirwan, B., Rea, K., Humphreys, P., Rosa, E. A. (1984). SLIM-MAUD. An approach to Assessing Human Error Probabilities Using Structured Expert judgment Vols. I and II. Washington, DC NUREG/CR—3518 US Nuclear Regulatory Commission. 

SLIM-MAUD (Embrey 1984) implements a related approach in which expert ratings are used to estimate human error probabilities (HEPs) in various environments. The experts first rate a set of tasks in terms of performance-shaping factors (PSFs) that are present. Tasks with known HEPs are used as upper and lower anchor values. The experts also judge the importance of individual PSFs. A subjective likelihood index (SLI) is then calculated for each task in terms of the PSFs. A logarithmic relationship is assumed between the HEP and SLI, allowing calculation of the human error probability for task j (HEPj) from the subjective likelihood index assigned to task j (SLIj). More specifically ... 

SKU (stock-keeping unit), 2087 Slackness, complementruy, 2554 Slat conveyor, 1515, 1517 SLDs (second-level domains), 243 Sleeping, day, 1367 SLIM-MAUD, 2192... 

Expert judgment There is another category of HRA which utilizes expert judgment, for example, SLIM-MAUD. 

SLIM-MAUD SLIM (as well as FLIM) method requires expert judgment and when they are used with an interactive computer program called multi-attribute utility decomposition (MAUD). It is called SLIM-MAUD. 

SLIM Success likelihood index method. It is an HRA quantification technique by which HEPs are quantified. For taking actions, this may be utilized in conjunction with multi-attribute utility decomposition (MAUD), discussed later. Here, SLI (Ref Clause 6.2.2) is calibrated. It should actually be considered under expert judgment type. It has wider application as it is somewhat generic. 

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