A Bayesian approach for assessing process precision based on multiple samples

W.l. Pearn*, Chien Wei Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Using process capability indices to quantify manufacturing process precision (consistency) and performance, is an essential part of implementing any quality improvement program. Most research works for testing the capability indices have focused on using the traditional distribution frequency approaches. Cheng and Spiring [IIE Trans. 21 (1) 97] proposed a Bayesian procedure for assessing process capability index Cp based on one single sample. In practice, manufacturing information regarding product quality characteristic is often derived from multiple samples, particularly, when a routine-based quality control plan is implemented for monitoring process stability. In this paper, we consider estimating and testing Cp with multiple samples using Bayesian approach, and propose accordingly a Bayesian procedure for capability testing. The posterior probability, p, for which the process under investigation is capable, is derived. The credible interval, a Bayesian analogue of the classical lower confidence interval, is obtained. The results obtained in this paper, are generalizations of those obtained in Cheng and Spiring [IIE Trans. 21 (1), 97]. Practitioners can use the proposed procedure to Cheng and Spiring determine whether their manufacturing processes are capable of reproducing products satisfying the preset precision requirement.

Original languageEnglish
Pages (from-to)685-695
Number of pages11
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - 16 Sep 2005


  • Bayesian approach
  • Credible interval
  • Decision making
  • Posterior probability
  • Process capability indices
  • Quality control

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