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Abstract
The existing process capability indices (PCI's) assume that the distribution of the process being investigated is normal. For non-normal distributions, PCI's become unreliable in that PCI's may indicate the process is capable when in fact it is not. In this paper, we propose a new index which can be applied to any distribution. The proposed indexCf:, is directly related to the probability of non-conformance of the process. For a given random sample, the estimation of Cf boils down to estimating non-parametrically the tail probabilities of an unknown distribution. The approach discussed in this paper is based on the works by Pickands (1975) and Smith (1987). We also discuss the construction of bootstrap confidence intervals of Cf: based on the so-called accelerated bias correction method (BC a:). Several simulations are carried out to demonstrate the flexibility and applicability of Cf:. Two real life data sets are analyzed using the proposed index.
