Open Access

ARTICLE

New Hybrid EWMA Charts for Efficient Process Dispersion Monitoring with Application in Automobile Industry

Xuechen Liu¹, Majid Khan², Zahid Rasheed³, Syed Masroor Anwar^4,*, Muhammad Arslan⁵
1 Faculty of Economics, Taiyuan Normal University, Taiyuan, 030619, China
2 Department of Mathematics and Statistics, Riphah International University, Islamabad, 44000, Pakistan
3 Department of Mathematics, Women University of Azad Jammu and Kashmir, Bagh, 12500, Pakistan
4 Department of Statistics, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Pakistan
5 School of Statistics, Shanxi University of Finance and Economics, Taiyuan, 030619, China
* Corresponding Author: Syed Masroor Anwar. Email:
(This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)

Computer Modeling in Engineering & Sciences 2022, 131(2), 1171-1195. https://doi.org/10.32604/cmes.2022.019199

Received 08 September 2021; Accepted 09 November 2021; Issue published 14 March 2022

Abstract

The EWMA charts are the well-known memory-type charts used for monitoring the small-to-intermediate shifts in the process parameters (location and/or dispersion). The hybrid EWMA (HEWMA) charts are enhanced version of the EWMA charts, which effectively monitor the process parameters. This paper aims to develop two new uppersided HEWMA charts for monitoring shifts in process variance, i.e., HEWMA1 and HEWMA2 charts. The design structures of the proposed HEWMA1 and HEWMA2 charts are based on the concept of integrating the features of two EWMA charts. The HEWMA1 and HEWMA2 charts plotting statistics are developed using one EWMA statistic as input for the other EWMA statistic. A Monte Carlo simulations method is used as a computational technique to determine the numerical results for the performance characteristics, such as average run length (ARL), median run length, and standard deviation run length (SDRL) for assessing the performance of the proposed HEWMA1 and HEWMA2 charts. In addition, to evaluate the overall performance of the proposed HEWMA1 and HEWMA2 charts, other numerical measures consisting of the extra quadratic loss (EQL), relative average run length (RARL), and performance comparison index (PCI) are also computed. The proposed HEWMA1 and HEWMA2 charts are compared to some existing charts, such as CH, CEWMA, HEWMA, AEWMA HHW1, HHW2, AIB-EWMA-I, and AIB-EWMA-II charts, on the basis aforementioned numerical measures. The comparison reveals that the proposed HEWMA1 and HEWMA2 charts achieve better detection ability against the existing charts. In the end, a real-life data application is also provided to enhance the implementation of the proposed HEWMA1 and HEWMA2 charts practically.

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Keywords

Average run length; extra quadratic loss; memory-type charts; Monte Carlo simulations; smoothing parameter

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