IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i19p4157-d1253045.html
   My bibliography  Save this article

Efficient Monitoring of a Parameter of Non-Normal Process Using a Robust Efficient Control Chart: A Comparative Study

Author

Listed:
  • Aamir Majeed Chaudhary

    (Department of Statistics, National College of Business Administration and Economics, Lahore 54660, Pakistan)

  • Aamir Sanaullah

    (Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan)

  • Muhammad Hanif

    (Department of Statistics, National College of Business Administration and Economics, Lahore 54660, Pakistan)

  • Mohammad M. A. Almazah

    (Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil 61421, Saudi Arabia)

  • Nafisa A. Albasheir

    (Department of Mathematics, College of Sciences and Arts (Majardah), King Khalid University, Magardah 61937, Saudi Arabia)

  • Fuad S. Al-Duais

    (Mathematics Department, College of Humanities and Science, Prince Sattam Bin Abdulaziz University, Al Aflaj 16278, Saudi Arabia)

Abstract

The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special cause variations and guiding the process back to statistical control. While Shewhart control charts excel at detecting significant shifts, EWMA and CUSUM charts are better suited for detecting smaller to moderate shifts. However, the effectiveness of all these control charts is compromised when the underlying distribution deviates from normality. In response to this challenge, this study introduces a robust mixed EWMA-CUSUM control chart tailored for monitoring processes characterized via symmetric but non-normal distributions. The key innovation of the proposed approach lies in the integration of a robust estimator, based on order statistics, that leverages the generalized least square (GLS) technique developed by Lloyd. This integration enhances the chart’s robustness and minimizes estimator variance, even in the presence of non-normality. To demonstrate the effectiveness of the proposed control chart, a comprehensive comparison is conducted with several well-known control charts. Results of the study clearly show that the proposed chart exhibits superior sensitivity to small and moderate shifts in process parameters when compared to its predecessors. Through a compelling illustrative example, a real-life application of the enhanced performance of the proposed control chart is provided in comparison to existing alternatives.

Suggested Citation

  • Aamir Majeed Chaudhary & Aamir Sanaullah & Muhammad Hanif & Mohammad M. A. Almazah & Nafisa A. Albasheir & Fuad S. Al-Duais, 2023. "Efficient Monitoring of a Parameter of Non-Normal Process Using a Robust Efficient Control Chart: A Comparative Study," Mathematics, MDPI, vol. 11(19), pages 1-30, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4157-:d:1253045
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/19/4157/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/19/4157/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lucas O. F. Sales & André L. S. Pinho & Marcelo Bourguignon & F. Moisés C. Medeiros, 2022. "Control charts for monitoring the median in non-negative asymmetric data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 1037-1068, October.
    2. Tao Zan & Zhihao Liu & Hui Wang & Min Wang & Xiangsheng Gao, 2020. "Control chart pattern recognition using the convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 703-716, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed Maged & Min Xie, 2023. "Recognition of abnormal patterns in industrial processes with variable window size via convolutional neural networks and AdaBoost," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1941-1963, April.
    2. Pei-Hsi Lee & Shih-Lung Liao, 2023. "Residual Control Chart Based on a Convolutional Neural Network and Support Vector Regression for Type-I Censored Data with the Weibull Model," Mathematics, MDPI, vol. 12(1), pages 1-14, December.
    3. Nompilo Moyo & Martin Jones & Shaun Dennis & Karan Sharma & Michael McKeown & Richard Gray, 2023. "The Association between Nursing Skill Mix and Patient Outcomes in a Mental Health Setting: An Observational Feasibility Study," IJERPH, MDPI, vol. 20(3), pages 1-11, February.
    4. Chuen-Sheng Cheng & Pei-Wen Chen & Yu-Chin Hsieh & Yu-Tang Wu, 2023. "Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-26, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4157-:d:1253045. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.