Economic–statistical design of Bayesian $\bar{\mathit{X}}$ control chart based on the predictive distribution

Abstract

Purpose

The purpose of this paper is to present an economic–statistical design (ESD) for the Bayesian X‾ control chart based on predictive distribution with two types of informative and noninformative prior distributions.

Design/methodology/approach

The design used in this study is based on determining the control chart of the predictive distribution and then its ESD. The new proposed cost model is presented by considering the conjugate and Jeffrey's prior distribution in calculating the expected total cycle time and expected cost per cycle, and finally, the optimal design parameters and related costs are compared with the fixed ratio sampling (FRS) mode.

Findings

Numerical results show decreases in costs in this Bayesian approach with both Jeffrey's and conjugate prior distribution compared to the FRS mode. This result shows that the Bayesian approach which is based on predictive density works better than the classical approach. Also, for the Bayesian approach, however, there is no significant difference between the results of using Jeffrey's and conjugate prior distributions. Using sensitivity analysis, the effect of cost parameters and shock model parameters and deviation from the mean on the optimal values of design parameters and related costs have been investigated and discussed.

Practical implications

This research adds to the body of knowledge related to quality control of process monitoring systems. This paper may be of particular interest to quality system practitioners for whom the effect of the prior distribution of parameters on the quality characteristic distribution is important.

Originality/value

economic statistical design (ESD) of Bayesian control charts based on predictive distribution is presented for the first time.

Citation

Seirani, R., Torabian, M., Behzadi, M.H. and Seif, A. (2023), "Economic–statistical design of Bayesian $\bar{\mathit{X}}$ control chart based on the predictive distribution", International Journal of Quality & Reliability Management, Vol. 40 No. 8, pp. 1925-1939. https://doi.org/10.1108/IJQRM-06-2022-0170