Estimating capability index in multivariate processes using bootstrap sequential sampling procedures

Capability indices in both univariate and multi-variate processes are extensively employed in quality control to assess the quality status of production batches before their release for operational use. It is traditionally a mesure of the rtio of the allowable process spread and the actual spread. In this paper, we will adopt a bootstrap and sequential sampling procedures to determine the optimal sample size for estimating a multivariate capability index introduced by Pearns et. al. [12]. Bootstrap techniques have the distinct advantage of placing very minimum requirement on the distributions of the underlying quality characteristics, thereby rendering them more relevant under a wide variety of situations. Finally, we provide several numerical examples where the sequential sampling procedures are evaluated and compared.