In the manufacturing industry, many product characteristics are of one-sided tolerances. The process capability indices Cpu (u, v) and Cpl (u, v) can be used to measure process performance. Most research work related to capability indices assumes no gauge measurement errors. This assumption insufficiently reflects real situations even when advanced measuring instruments are used. In this paper we show that using a critical value without taking into account these errors, severely underestimates the α-risk which causes a less accurate testing capacity. In order to improve the results we suggest the use of an adjusted critical value, and we give a Maple program to get it. An example in a polymer granulates manufactory is presented to illustrate this approach.

Most research works related to process capability indices assume no gauge measurement errors. However, such an assumption inadequately reflects real situations even when advanced measuring instruments are employed. If we do not take into account these errors, conclusions drawn from process capability are therefore unreliable. In this paper we study the sampling distribution of capability indices Cp''(u,v) in the presence of measurements errors, and when small subsamples data are collected from past “in-control”. We show that using a critical value without taking into account these errors, severely underestimates the α-risk which causes a less accurate testing capacity. To improve the results we suggest the use of an adjusted critical value, and we give a Maple program to get it. An example in a nougat manufactory is presented to illustrate this approach.