In this paper the effects of inspector error on a cost-based quality control system are investigated. The system examined is of a single sampling plan design involving several cost components. Both type I and type I1 inspector errors are considered. The model employs a process distribution, thus assuming that a stochastic process of some kind governs the quality of incoming lots. Optimal plan design is investigated under both error-free and error-prone inspection procedures and some comparisons are made.

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Variables acceptance sampling plana ere designed under the assumption that measurement on the characteristic of interest may be performed without error. To the contrary. variables measurement tasks a.re often confounded by human inspection error and/or instrument test error. The net result may be characterized in terms of bias and imprecision, where (I) bias is the difference between the true dimension of a. unit of product and the average of a long series of repeated measurements on tha.t unit, and (2) imprecision is the dispersion of repea.ted meesuremente on the same unit of product.This paper considers the design of variables ecceptance uarnpling plans for a general lot distribution with known and oonstant variance and either an upper or lower specification limit. BiBS, imprecision, and their combined effects on the opera.ting characteristic curve are examined in detail and found to be quite significant. A method is then presented whereby the variable. sampling plan may be designed to explicitly compensate for measurement error and provide the desired operating characteristic curve. An example problem is used to illustrate the important results of the paper.