iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S13198-024-02360-5
Enhancing process mean monitoring efficiency using extended-EWMA control chart with auxiliary information | International Journal of System Assurance Engineering and Management Skip to main content
Springer Nature Link
Log in

Enhancing process mean monitoring efficiency using extended-EWMA control chart with auxiliary information

  • ORIGINAL ARTICLE
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

For monitoring the outputs of production processes, statistical process control (SPC) is widely used. The control chart (CC) stands as the most important SPC tool for differentiating between unnatural and natural sources of variation. Unnatural sources typically result in changes in the value of process parameters, which are categorized into three levels: minor, moderate, and high. While memory-less CCs excel in identifying large shifts, memory-based CCs such as exponentially weighted moving average (EWMA) and extended exponentially weighted moving average (EEWMA) CCs are optimal for detecting minor-to-moderate shifts in process parameter values. Detection ability of CCs is enhanced by using auxiliary variable. This study is conducted to propose an EEWMA CC using auxiliary information named as MyEEWMA CC. The proposed MyEEWMA chart is evaluated through average run length criterion. In the practical industrial applications with the density and stiffness of particle board data, the MyEEWMA CC detected process deviations earlier than existing EEWMA, and MyEWMA CCs. Specifically, the MyEEWMA chart signaled an issue by the 25th sample, whereas the EEWMA, and MyEWMA CCs did not detect any deviations even after the 25th sample. This tangible evidence underscores the superior efficiency of the proposed MyEEWMA CC in industrial settings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abbas N, Riaz M, Does RJMM (2011) Enhancing the performance of EWMA charts. Qual Reliab Eng Int 27(6):821–833

    Google Scholar 

  • Abbas N, Riaz M, Does RJMM (2013) Mixed exponentially weighted moving average–cumulative sum charts for process monitoring. Qual Reliab Eng Int 29(3):345–356

    Google Scholar 

  • Abbas N, Riaz M, Does RJMM (2014) An EWMA-type control chart for monitoring the process mean using auxiliary information. Commun Stat Theory Methods 43(16):3485–3498

    MathSciNet  Google Scholar 

  • Abbasi SA, Adegoke NA (2020) Auxiliary-information-based efficient variability control charts for phase I of SPC. Qual Reliab Eng Int 36:2322–2337

    Google Scholar 

  • Ahmad S, Abbasi SA, Riaz M, Abbas N (2014) On efficient use of auxiliary information for control charting in SPC. Comput Ind Eng 67:173–184

    Google Scholar 

  • Ahmed A, Sanaullah A, Hanif M (2020) A robust alternate to the HEWMA control chart under non-normality. Qual Technol Quant Manag 17(4):423–447

    Google Scholar 

  • Alevizakos V, Chatterjee K, Koukouvinos C (2021) The triple exponentially weighted moving average control chart. Qual Technol Quant Manag 18(3):267–395

    Google Scholar 

  • Anwer F, Sanaullah A, Ahmad A, Asghar A (2022) An improved mixed-homogeneously weighted moving average-CUSUM control chart for efficient monitoring of a process mean. J Stat Comput Simul 93(10):1644–1666

    MathSciNet  Google Scholar 

  • Arslan M, Ashraf MA, Anwar SM, Rasheed Z, Hu X, Abbasi SA (2022) Novel mixed EWMA dual-Crosier CUSUM mean charts without and with auxiliary information. Math Probl Eng 2022:1362193

    Google Scholar 

  • Arslan M, Anwar S, Gunaime NM, Shahab S, Lone SA, Rasheed Z (2023) An improved charting scheme to monitor the process mean using two supplementary variables. Symmetry 15:482. https://doi.org/10.3390/sym15020482

    Article  Google Scholar 

  • Chaudhary AM, Sanaullah A, Hanif M, Almazah MMA, Albasheir NA, Al-Duais FS (2023) Efficient monitoring of a parameter of non-normal process using a robust efficient control chart: a comparative study. Mathematics 11:4157

    Google Scholar 

  • Chen A, Guo RS (2001) Age-based double EWMA controller and its application to CMP processes. IEEE Trans Semicond Manufact 14(1):11–19

    Google Scholar 

  • Cochran WG (1977) Sampling techniques, 3rd edn. John Wiley and Sons, New York

    Google Scholar 

  • Deming WE (1982) Out of the crisis. Center for Advanced Engineering Study, Massachusetts Institute of Technology, Cambridge, Massachusetts

  • Haq A (2017) New EWMA control charts for monitoring process dispersion using auxiliary information. Qual Reliab Eng Int 33(8):2597–2614

    Google Scholar 

  • Haq A, Khoo MBC (2017) A new double sampling control chart for monitoring process mean using auxiliary information. J Stat Comput Simul 88:869–899

    MathSciNet  Google Scholar 

  • Kasse T (2004) Practical insight into CMMI. Artech House, London

    Google Scholar 

  • Lee S-H, Jun C-H (2012) A process monitoring scheme controlling false discovery rate. Commun Stat Simul Comput 41(10):1912–1920

    MathSciNet  Google Scholar 

  • Lee S-H, Park J-H, Jun C-H (2014) An exponentially weighted moving average chart controlling false discovery rate. J Stat Comput Simul 84(8):1830–1840

    MathSciNet  Google Scholar 

  • Lucas JM, Saccucci MS (1990) Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1):1–12

    MathSciNet  Google Scholar 

  • Mandel BJ (1969) The regression control chart. J Qual Technol 1(1):1–9

    Google Scholar 

  • Montgomery DC (2009) Introduction to statistical quality control. John Wiley and Sons, New York

    Google Scholar 

  • Naveed M, Azam M, Khan N, Aslam M (2018) Design of a control chart using extended EWMA statistic. Technologies 6(4):108. https://doi.org/10.3390/technologies6040108

    Article  Google Scholar 

  • Nuriman MA, Mashuri M, Ahsan M (2021) Auxiliary information based generally weighted moving coefficient of variation (AIB-GWMCV) control chart. IOP Conf Ser Mater Sci Eng 1115:012033

    Google Scholar 

  • Page ES (1954) Continuous inspection schemes. Biometrika 41(1–2):100–115

    MathSciNet  Google Scholar 

  • Riaz M (2008a) Monitoring process mean level using auxiliary information. Stat Neerl 62(4):458–481

    MathSciNet  Google Scholar 

  • Riaz M (2008b) Monitoring process variability using auxiliary information. Comput Stat 23(2):253–276

    MathSciNet  Google Scholar 

  • Riaz M (2015) Control charting and survey sampling techniques in process monitoring. J Chin Inst Eng 38(3):342–354

    Google Scholar 

  • Riaz M, Abbas N, Does RJMM (2011) Improving the performance of CUSUM charts. Qual Reliab Eng Int 27(4):415–424

    Google Scholar 

  • Riaz A, Noor-ul-Amin M, Shehzad MA, Ismail M (2019) Auxiliary information based mixed EWMA–CUSUM mean control chart with measurement error. Iran J Sci Technol Trans Sci 43(3):2937–2949

    MathSciNet  Google Scholar 

  • Roberts S (1959) Control chart tests based on geometric moving averages. Technometrics 1:239–250

    Google Scholar 

  • Saghir A, Lin Z (2014) A flexible and generalized exponentially weighted moving average control chart for count data. Qual Reliab Eng Int 30(8):1427–1443

    Google Scholar 

  • Sanusi RA, Abbas N, Riaz M (2017a) On efficient CUSUM-type location control charts using auxiliary information. Qual Technol Quant Manag 15(1):87–105

    Google Scholar 

  • Sanusi RA, Abujiya MR, Riaz M, Abbas N (2017b) Combined Shewhart CUSUM charts using auxiliary variable. Comput Ind Eng 105:329–337

    Google Scholar 

  • Sanusi RA, Riaz M, Adegoke NA, Xie M (2017c) An EWMA monitoring scheme with a single auxiliary variable for industrial processes. Comput Ind Eng 114:1–10

    Google Scholar 

  • Shewhart WA (1939) Statistical method from the viewpoint of quality control. Graduate School, Department of Agriculture, Washington

  • Singh HP, Tailor R (2003) Use of known correlation coefficient in estimating the finite population mean. Stat Trans 6(4):555–560

    Google Scholar 

  • Steiner SH (1999) EWMA control charts with time-varying control limits and fast initial response. J Qual Technol 31(1):75–86

    Google Scholar 

  • Xie M, Goh TN, Kuralmani V (2002) Statistical models and control charts for high-quality processes. Kluwer Academic Publishers, London

    Google Scholar 

  • Yan X, Su XG (2009) Linear regression analysis. World Scientific Inc, Singapore

    Google Scholar 

  • Yashchin E (1995) Estimating the current mean of a process subject to abrupt changes. Technometrics 37:311–323

    MathSciNet  Google Scholar 

  • Zaka A, Akhter AS, Jabeen R, Sanaullah A (2021) Control charts for the shape parameter of reflected power function distribution under classical estimators. Qual Reliab Eng Int 37:2458–2477

    Google Scholar 

  • B, Riaz M, Abbas N, Does RJMM (2014) Mixed cumulative sum-exponentially weighted moving average control charts: an efficient way of monitoring process location. Qual Reliab Eng Int 31(8):1407–1421

    Google Scholar 

  • Zhang G (1985) Cause-selecting control charts–a new type of quality control charts. QR J 12(4):221–225

    MathSciNet  Google Scholar 

  • Zhang L, Chen G (2005) An extended EWMA mean chart. Qual Technol Quant Manag 2:39–52

    MathSciNet  Google Scholar 

  • Zichuan M, Arslana M, Abbasb Z, Abbasic SA, Zafar H (2020) Improving the performance of EWMA mean chart using two auxiliary variables. Rev Argent Clin Psicol 29:2016–2024

    Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Aamir Sanaullah & Aqsa Hussain

  2. Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, India

    Prayas Sharma

Authors
  1. Aamir Sanaullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aqsa Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prayas Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prayas Sharma.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanaullah, A., Hussain, A. & Sharma, P. Enhancing process mean monitoring efficiency using extended-EWMA control chart with auxiliary information. Int J Syst Assur Eng Manag 15, 3522–3537 (2024). https://doi.org/10.1007/s13198-024-02360-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-024-02360-5

Keywords

Search

Navigation