Developing new multivariate process capability indices for autocorrelated data

Jeh Nan Pan, Wendy K.C. Huang

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Traditionally, the process capability index is developed assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data have more than one quality characteristic and exhibit property of autocorrelation. We propose two novel multivariate process capability indices for autocorrelated data, NMACp and NMACpm, for the nominal-the-best case. For the smaller-the-better case, Γ(0) is used to modify the ND index and a new multivariate autocorrelated process capability index NMACpu is derived. Furthermore, a simulation study is conducted to compare the performance of the various multivariate autocorrelated indices. The simulation results show that the actual nonconforming rates can be correctly reflected by our proposed indices, which outperform the previous Cpm, MCp, MCpm, NMCp, NMCpm, and ND indices under different time series models. Thus, our proposed capability indices can be used in evaluating the performance of multivariate autocorrelated processes. Finally, a realistic example in hydrological application further demonstrates the usefulness of our proposed capability indices.

Original languageEnglish
Pages (from-to)431-444
Number of pages14
JournalQuality and Reliability Engineering International
Volume31
Issue number3
DOIs
Publication statusPublished - 2015 Apr 1

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Developing new multivariate process capability indices for autocorrelated data'. Together they form a unique fingerprint.

Cite this