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On Designing Non-Parametric EWMA Sign Chart under Ranked Set Sampling Scheme with Application to Industrial Process

Saber Ali (), Zameer Abbas (), Hafiz Zafar Nazir (), Muhammad Riaz (), Xingfa Zhang () and Yuan Li ()
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Saber Ali: School of Economics and Statistics, Guangzhou University, Guangzhou 510000, China
Zameer Abbas: Department of Statistics, Government Ambala Muslim College Sargodha, Sargodha 40100, Pakistan
Hafiz Zafar Nazir: Departemnt of Statistics, University of Sargodha, Sargodha 40100, Pakistan
Muhammad Riaz: Department of Mathematics and Statistics, King Fahad University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Xingfa Zhang: School of Economics and Statistics, Guangzhou University, Guangzhou 510000, China
Yuan Li: School of Economics and Statistics, Guangzhou University, Guangzhou 510000, China

Mathematics, 2020, vol. 8, issue 9, 1-20

Abstract: Statistical process control (SPC) tools are used for the investigation and identification of unnatural variations in the manufacturing, industrial, and service processes. The control chart, the basic and the most famous tool of SPC, is used for process monitoring. Generally, control charts are constructed under normality assumption of the quality characteristic of interest, but in practice, it is quite hard to hold the normality assumption. In such situations, parametric charts tend to offer more frequent false alarms and invalid out-of-control performance. To rectify these problems, non-parametric control charts are used, as these have the same in-control run length properties for all the continuous distributions and are known as in-control robust. This study intends to develop a new non-parametric exponentially weighted moving average (NPEWMA) chart based on sign statistics under a ranked set sampling scheme that is hereafter named (NPREWMA-SN). The run-length profiles of the NPREWMA-SN chart are computed using the Monte Carlo simulation method. The proposed scheme is compared with NPEWMA-SN and classical EWMA- X ¯ charts, using different run length measures. The comparison reveals the in-control robustness and superiority of the proposed scheme over its competitors in detecting all kinds of shifts in the process location. A practical application related to the substrate manufacturing process is included to show the demonstration of the proposed chart.

Keywords: average run length; control chart; sign test; in-control robustness; manufacturing process; non-parametric; ranked set scheme (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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