Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring

Kai Le Goh; Wei Lin Teoh; Kai Lin Ong; Laila El-Ghandour; Zhi Lin Chong; Ming Ha Lee

doi:10.1007/978-981-95-0378-0_3

Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring

Kai Le Goh
, Wei Lin Teoh^*
, Kai Lin Ong
, Laila El-Ghandour
, Zhi Lin Chong
, Ming Ha Lee

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Conventional control charts in statistical process control are designed based on the presumption that the underlying process adheres to normal distribution. In practical scenarios, real-world processes often exhibit skewed distributions, such as the gamma distribution, leading to suboptimal performance of the standard control charts. This research proposes the variable sampling interval (VSI) exponentially weighted moving average (EWMA) X¯ chart specifically designed for the gamma distribution. Performance measures, including the average time to signal and standard deviation of the time to signal, are evaluated through the Monte Carlo simulations. Findings reveal significant performance deteriorations when the VSI EWMA X¯ chart is designed under the normal distribution model for monitoring the gamma process. New charting parameters are specially derived under the gamma distribution to ensure stable in-control performance and faster detection of mean shifts in gamma processes. A practical illustration is employed to demonstrate the implementation of the proposed chart.

Original language	English
Title of host publication	Information System Design
Subtitle of host publication	Sustainable Computing. ISDIA 2025
Editors	Vikrant Bhateja, Farhad Oroumchian, Jinshan Tang, Ahmad Taher Azar
Publisher	Springer
Pages	37-52
Number of pages	16
ISBN (Electronic)	9789819503780
ISBN (Print)	9789819503773
DOIs	https://doi.org/10.1007/978-981-95-0378-0_3
Publication status	Published - 2 Jan 2026
Event	9th International Conference on Information System Design and Intelligent Applications 2025 - University of Wollongong, Dubai, United Arab Emirates Duration: 3 Jan 2025 → 4 Jan 2025 Conference number: 9 https://isdia.org/

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1566
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th International Conference on Information System Design and Intelligent Applications 2025
Abbreviated title	ISDIA 2025
Country/Territory	United Arab Emirates
City	Dubai
Period	3/01/25 → 4/01/25
Internet address	https://isdia.org/

Keywords

EWMA X¯ chart
Gamma process
Monte Carlo simulation
Statistical process control
Variable sampling interval scheme

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-95-0378-0_3

Cite this

Goh, K. L., Teoh, W. L., Ong, K. L., El-Ghandour, L., Chong, Z. L., & Lee, M. H. (2026). Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring. In V. Bhateja, F. Oroumchian, J. Tang, & A. T. Azar (Eds.), Information System Design: Sustainable Computing. ISDIA 2025 (pp. 37-52). (Lecture Notes in Networks and Systems; Vol. 1566). Springer. https://doi.org/10.1007/978-981-95-0378-0_3

@inproceedings{dfd36ad5772a43c4992767aef23db08a,

title = "Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring",

abstract = "Conventional control charts in statistical process control are designed based on the presumption that the underlying process adheres to normal distribution. In practical scenarios, real-world processes often exhibit skewed distributions, such as the gamma distribution, leading to suboptimal performance of the standard control charts. This research proposes the variable sampling interval (VSI) exponentially weighted moving average (EWMA) X¯ chart specifically designed for the gamma distribution. Performance measures, including the average time to signal and standard deviation of the time to signal, are evaluated through the Monte Carlo simulations. Findings reveal significant performance deteriorations when the VSI EWMA X¯ chart is designed under the normal distribution model for monitoring the gamma process. New charting parameters are specially derived under the gamma distribution to ensure stable in-control performance and faster detection of mean shifts in gamma processes. A practical illustration is employed to demonstrate the implementation of the proposed chart.",

keywords = "EWMA X¯ chart, Gamma process, Monte Carlo simulation, Statistical process control, Variable sampling interval scheme",

author = "Goh, \{Kai Le\} and Teoh, \{Wei Lin\} and Ong, \{Kai Lin\} and Laila El-Ghandour and Chong, \{Zhi Lin\} and Lee, \{Ming Ha\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 9th International Conference on Information System Design and Intelligent Applications 2025, ISDIA 2025 ; Conference date: 03-01-2025 Through 04-01-2025",

year = "2026",

month = jan,

day = "2",

doi = "10.1007/978-981-95-0378-0\_3",

language = "English",

isbn = "9789819503773",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "37--52",

editor = "Vikrant Bhateja and Farhad Oroumchian and Jinshan Tang and Azar, \{Ahmad Taher\}",

booktitle = "Information System Design",

url = "https://isdia.org/",

}

Goh, KL, Teoh, WL , Ong, KL , El-Ghandour, L, Chong, ZL & Lee, MH 2026, Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring. in V Bhateja, F Oroumchian, J Tang & AT Azar (eds), Information System Design: Sustainable Computing. ISDIA 2025. Lecture Notes in Networks and Systems, vol. 1566, Springer, pp. 37-52, 9th International Conference on Information System Design and Intelligent Applications 2025, Dubai, United Arab Emirates, 3/01/25. https://doi.org/10.1007/978-981-95-0378-0_3

Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring. / Goh, Kai Le; Teoh, Wei Lin ; Ong, Kai Lin et al.
Information System Design: Sustainable Computing. ISDIA 2025. ed. / Vikrant Bhateja; Farhad Oroumchian; Jinshan Tang; Ahmad Taher Azar. Springer, 2026. p. 37-52 (Lecture Notes in Networks and Systems; Vol. 1566).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring

AU - Goh, Kai Le

AU - Teoh, Wei Lin

AU - Ong, Kai Lin

AU - El-Ghandour, Laila

AU - Chong, Zhi Lin

AU - Lee, Ming Ha

N1 - Conference code: 9

PY - 2026/1/2

Y1 - 2026/1/2

N2 - Conventional control charts in statistical process control are designed based on the presumption that the underlying process adheres to normal distribution. In practical scenarios, real-world processes often exhibit skewed distributions, such as the gamma distribution, leading to suboptimal performance of the standard control charts. This research proposes the variable sampling interval (VSI) exponentially weighted moving average (EWMA) X¯ chart specifically designed for the gamma distribution. Performance measures, including the average time to signal and standard deviation of the time to signal, are evaluated through the Monte Carlo simulations. Findings reveal significant performance deteriorations when the VSI EWMA X¯ chart is designed under the normal distribution model for monitoring the gamma process. New charting parameters are specially derived under the gamma distribution to ensure stable in-control performance and faster detection of mean shifts in gamma processes. A practical illustration is employed to demonstrate the implementation of the proposed chart.

AB - Conventional control charts in statistical process control are designed based on the presumption that the underlying process adheres to normal distribution. In practical scenarios, real-world processes often exhibit skewed distributions, such as the gamma distribution, leading to suboptimal performance of the standard control charts. This research proposes the variable sampling interval (VSI) exponentially weighted moving average (EWMA) X¯ chart specifically designed for the gamma distribution. Performance measures, including the average time to signal and standard deviation of the time to signal, are evaluated through the Monte Carlo simulations. Findings reveal significant performance deteriorations when the VSI EWMA X¯ chart is designed under the normal distribution model for monitoring the gamma process. New charting parameters are specially derived under the gamma distribution to ensure stable in-control performance and faster detection of mean shifts in gamma processes. A practical illustration is employed to demonstrate the implementation of the proposed chart.

KW - EWMA X¯ chart

KW - Gamma process

KW - Monte Carlo simulation

KW - Statistical process control

KW - Variable sampling interval scheme

UR - https://www.scopus.com/pages/publications/105027934163

U2 - 10.1007/978-981-95-0378-0_3

DO - 10.1007/978-981-95-0378-0_3

M3 - Conference contribution

AN - SCOPUS:105027934163

SN - 9789819503773

T3 - Lecture Notes in Networks and Systems

SP - 37

EP - 52

BT - Information System Design

A2 - Bhateja, Vikrant

A2 - Oroumchian, Farhad

A2 - Tang, Jinshan

A2 - Azar, Ahmad Taher

PB - Springer

T2 - 9th International Conference on Information System Design and Intelligent Applications 2025

Y2 - 3 January 2025 through 4 January 2025

ER -

Goh KL, Teoh WL , Ong KL , El-Ghandour L, Chong ZL, Lee MH. Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring. In Bhateja V, Oroumchian F, Tang J, Azar AT, editors, Information System Design: Sustainable Computing. ISDIA 2025. Springer. 2026. p. 37-52. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-95-0378-0_3

Using Variable Sampling Intervals in EWMA Control Charts for Effective Gamma Process Monitoring

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this