Process capability index C_pm under autoregressive process AR (2)

Abstract

Purpose

The paper aims to identify the effect of ignorance of correlatedness among process observations and to implement new sampling schemes; skip and mixed sampling, in order to reduce the effect of autocorrelation on process capability index (PCI) Cpm.

Design/methodology/approach

Autocorrelated observations are generated using autoregressive process of order two (AR (2)) using Monte Carlo simulations. The PCI is computed based on these observations assuming the independence. The skip and mixed sampling schemes are then used to form sub-groups among correlated observations. The PCI obtained using sub-groups from skip and mixed sampling schemes are assessed using sample mean and sample standard deviation.

Findings

The paper provides empirical insights into how the effect of autocorrelation decreases in the estimated value of PCI Cpm. The use of new sampling schemes, skip and mixed sampling, reduces the effect of autocorrelation on estimates of PCI Cpm.

Originality/value

This paper fulfills an identified need to study how to reduce the effect of autocorrelation on PCI Cpm.

Citation

Ghute, V. and Deshpande, M. (2024), "Process capability index C_pm under autoregressive process AR (2)", International Journal of Quality & Reliability Management, Vol. 41 No. 4, pp. 1130-1141. https://doi.org/10.1108/IJQRM-02-2023-0053