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SUMMARY
This paper presents a statistical method for selecting the best inspector for visual inspection having the highest defect-finding ability among k(≥2) prospective inspectors.
A sample containing known number of defects are submitted to each of k prospective inspectors. As the sample size is increased, sampling variation decreases so that we have a better chance of selecting the truly best inspector, but at the same time the cost of sampling is also increased, The problem is how to determine the sample size required for selecting the best inspector with specified probability P*.
This problem is formulated from the indifference zone point of view with an application of the loss function which takes into consideration the opportunity cost of making a wrong selection and the cost of sampling. Minimax solution is obtained under the restriction that the best inspector is to be selected with the specified probability P*. The selection procedure is applied to the visual inspection of car body painting.