Explicit Formulas of Average Run Length for Mixed Moving Average – Exponentially Weighted Moving Average Control Chart

Main Article Content

Sunisa Wiwek
Saowanit Sukparungsee
Suganya Phantu

Abstract


The objective of this research was to propose an estimation method of Average Run Length (ARL) via explicit formulas for mixed Moving Average - Exponentially Weighted Moving Average (MA-EWMA) control chart. The performance detection of MA-EWMA control charts measured by the Average Run Length (ARL) was compared with Moving Average (MA) chart, Exponentially Weighted Moving Average (EWMA) chart, and Shewhart chart. The in control Average Run Length (ARL0) was given to 370 and the processes possessed normal, exponential, gamma and Laplace distributions while the ARLs were estimated from the explicit formulas. It was found that the performance of MA-EWMA control chart performed more efficiently in detecting process dispersion than other control charts for all magnitudes of λ = 0.01


Article Details

Section
Applied Science Research Articles

References

W. A. Shewhart, Economic Control of Quality of Manufactured Product, Van Nostrand: Princeton, 1931.

S. W. Roberts, “Control chart tests based on geometric moving average,” Techmometrics, vol. 42, no. 1, pp. 239–250, 1959.

M. Khoo, “Poisson moving average versus c chart for nonconformities,” Quality Engineering, vol. 16, pp. 525-534, 2004.

R. Taboran, S. Sukparungsee, and Y. Areepong, “Mixed moving average-exponentially weighted moving average control charts for monitoring of parameter change,” in Proceedings of the International MultiConference of Engineers and Computer Scientists 2019, 2019.

Y. Areepong, “Explicit formulas of average run length for a moving average control chart for monitoring the number of defective product,” International Journal of Pure and Applied Mathematics, vol. 80, no. 3, pp. 331–343, 2012.

C. Chananet, Y. Areepong, and S. Sukparungsee, “An approximate formula for ARL in moving average chart with ZINB data,” Journal of Thai Statistical Association, vol. 13, no. 2, pp. 209–222, 2015.

A. Prarisudtipong, Y. Areepong, and S. Sukparungsee, “An approximation of average run length for nonparametric arcsine ewma sign control chart using markov chain approach,” The Journal of KMUTNB, vol. 27, no. 1, pp. 139–146, 2017.

N. Ngamsopasirisakun, Y. Areepong, and S. Sukparungsee, “A markov chain approach for evaluation characteristics of ewma chart for lognormal observation,” The Journal of KMUTNB, vol. 22, no. 3, pp. 661–668, 2012.

N. Thongrong, Y. Areepong, and S. Sukparungsee, “Evaluation of average run length of nonparametric EWMA sign control chart by markov chain approach,” The Journal of KMUTNB, vol. 26, no. 3, pp. 487–497, 2016.

S. Phantu and S. Sukparungsee, “Robustness of χ CUSUM and CP CUSUM using fast initial response in detecting of process dispersion,” The Journal of KMUTNB, vol. 30, no. 2, pp. 291–203, 2020.

R. Thitisoowaranon, Y. Areepong, and S. Sukparungsee, “A mixed cumulative sum-Tukey’s control chart for detecting process dispersion,” The Journal of KMUTNB, vol. 29, no. 3, pp. 507–517, 2019.