Maximum hybrid exponentially weighted moving average control chart in the presence of measurement error by using auxiliary information

Abstract

Hybrid control charts have become part of statistical process control (SPC) but still, need more emphasis. Researchers are developing charts for joint monitoring of process mean and variance shifts just like Max‐EWMA and their hybrid version using auxiliary information but are ignoring the effect of measurement error on the efficiency of charts. We propose maximum hybrid exponentially weighted moving average with measurement error using auxiliary information and name it Max‐HEWMAMEAI control chart. The efficiency of this chart is proved through calculations of average run lengths (ARLs) and standard deviations of run lengths (SDRLs) using the Monte Carlo simulations method whereas, ARLs and SDRLs are shown in tabular form. The effect of measurement error on the efficiency of the chart has been analyzed and the impact of multiple measurements to reduce the error effect has been studied using the covariate model. Real‐life application is also part of this article to support the simulation results.

Performance of Bayesian EWMA control chart with measurement error under ranked set sampling schemes with application in industrial engineering

“…Additionally, they investigated the implications of multiple measurements and a linearly increasing variance. Afterwards many authors studied the impact of ME on the efficiency of the CCs during industrial production e.g., Huwang and Hung 13 , Wu 14 , Hu et al 15 , 16 , Sabahno and Amiri 17 , Riaz et al 18 , Javaid et al 19 , Noor-ul-Amin et al 20 , 21 etc. Imran et al 22 examined the influence of ME on multivariate CUSUM CCs for compositional data (CoDa).…”

The effect of measurement error on the performance of the s chart

“…A linear covariate error model was put forth by 1 when control charts with measurement error were taken into account. In recent years, numerous scholars have employed this paradigm extensively; see 2,3,4,5,6,7,8 . All of these studies showed that measurement error had a negative impact on the performances of control charts, making control charts perform worse.…”