Monitoring process variation using modified EWMA

Monte Carlo simulation; Flowcharting; Graphic methods; Intelligent systems; Monte Carlo methods; Exponentially weighted moving average control charts; Monitoring process; Process variance; Process Variation; Run length; Sample sizes; Smoothing constant; Standard deviation; Control charts

Résumé :

[en] A new control chart, namely, modified exponentially weighted moving average (EWMA) control chart, for monitoring the process variance is introduced in this work by following the recommendations of Khan et al.15 The proposed control chart deduces the existing charts to be its special cases. The necessary coefficients, which are required for the construction of modified EWMA chart, are determined for various choices of sample sizes and smoothing constants. The performance of the proposed modified EWMA is evaluated in terms of its run length (RL) characteristics such as average RL and standard deviation of RL. The efficiency of the modified EWMA chart is investigated and compared with some existing control charts. The comparison reveals the superiority of proposal as compared with other control charts in terms of early detection of shift in process variation. The application of the proposal is also demonstrated using a real-life dataset. © 2019 John Wiley & Sons, Ltd.

Disciplines :

Méthodes quantitatives en économie & gestion

Auteur, co-auteur :

Saghir, Aamir; Department of Mathematics, Mirpur University of Science and Technology (MUST), AJK, Mirpur, 10250, Pakistan

Aslam, Muhammad; Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, 21551, Saudi Arabia

Faraz, Alireza ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations: Statistique appl. à la gest. et à l'économie

Ahmad, Liaquat; Department of Statistics and computer Science, UVAS Business School, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan

Heuchenne, Cédric ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations: Statistique appl. à la gest. et à l'économie

Langue du document :

Anglais

Titre :

Monitoring process variation using modified EWMA

Date de publication/diffusion :

2020

Titre du périodique :

Quality and Reliability Engineering International

ISSN :

0748-8017

eISSN :

1099-1638

Maison d'édition :

John Wiley and Sons Ltd

Volume/Tome :

Fascicule/Saison :

Pagination :

328-339

Peer reviewed :

Peer reviewed vérifié par ORBi

Disponible sur ORBi :

depuis le 27 janvier 2020

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74 (dont 8 ULiège)

Nombre de téléchargements

308 (dont 7 ULiège)

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Bibliographie

Montgomery DC. Introduction to statistical quality control. 7th ed. New York: John Wiley; 2012.
Balamurali S, Jun CH. Repetitive group sampling procedure for variables inspection. J Appl Stat. 2006;33(3):327-338.
Acosta-Mejia CA, Pignatiello JA, Rao BV. A comparison of control charting procedures for monitoring process dispersion. IIE Trans. 1999;31(6):569-579.
Lowry CA, Champ CW, Woodall WH. The performance of control charts for monitoring process variation. Commun Stat Simul Comput. 1995;24(2):409-437.
Roberts SW. A comparison of some control chart procedures. Dent Tech. 1966;8:411-430.
Abbasi SA. On the performance of EWMA chart in presence of two components of measurement error. Qual Eng. 2010;22(3):199-213.
Lucas JM, Saccucci MS. The exponentially weighted moving average control schemes: properties and enhancements (with discussion). Dent Tech. 1990;32:1-12.
Woodall WH. The use of control charts in health-care and public-health surveillance. J Qual Technol. 2006;38(2):89-104.
Crowder SV. A simple method for studying run length distributions of exponentially weighted moving average charts. Dent Tech. 1987;29:401-407.
Crowder SV. Design of exponentially weighted moving average schemes. J Qual Technol. 1989;21(3):155-162.
Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: properties and enhancements. Dent Tech. 1990;32(1):1-12.
Gan F. Monitoring observations generated from a binomial distribution using modified exponentially weighted moving average control chart. J Stat Comput Simul. 1990;37(1–2):45-60.
Gan F. Monitoring Poisson observations using modified exponentially weighted moving average control charts. Commun Stat Simul Comput. 1993;19(1):103-124.
Patel AK, Divecha J. Modified exponentially weighted moving average (EWMA) control chart for an analytical process data. J Chem Eng Mater Sci. 2011;2(1):12-20.
Khan N, Aslam M, Jun CH. Design of a control chart using a modified EWMA statistic. Qual Reliab Eng Int. 2017;33(5):1095-1104.
Schmid W. On EWMA charts for time series. In: Frontiers in Statistical Quality Control. Springer; 1997:115-137.
Aslam M, Saghir A, Ahmad L, Jun C-H, Hussain J. A control chart for COM-Poisson distribution using a modified EWMA statistic. J Stat Comput Simul. 2017;87(18):3491-3502.
Khan N, Yasmin T, Aslam M, Jun C-H. On the performance of modified EWMA charts using resampling schemes. Oper Res Dec. 2018;3:29-43.
Herdiani ET, Fandrilla G, Sunusi N. Modified exponential weighted moving average (EWMA) control chart on autocorrelation data. J Phys Conf Ser. 2018;979(1):012097.
Zhang G, Li N, Li S. A modified multivariate EWMA control chart for monitoring process small shifts. Proceedings of 2011 International Conference on Modelling, Identification and Control, Shanghai, China, June 26-29, 2011.
Lampreia SPGF, Requeijo JFG, Dias JAM, Vairinhos VM, Barbosa PIS. Condition monitoring based on modified CUSUM and EWMA control charts. J Qual Main Eng. 2018;24(1):119-132.
Lawless JF. Statistical models and methods for lifetime data. New Jersey: John Wiley and Sons; 2003.
Crowder S, Hamilton M. An EWMA for monitoring a process standard deviation. J Qual Technol. 1992;24(1):12-21.
Hussain S, Song L, Ahmad S, Riaz M. On auxiliary information based improved EWMA median control charts. Scientia Iranica. 2018;25(2):954-982.
Chananet C, Sukparungsee S, Areepong Y. The ARL of EWMA chart for monitoring ZINB model using Markov chain approach. Int J Appl Phys Math. 2014;4(4):236-249.
Li ZH, Zou CL, Gong Z, Wang ZJ. The computation of average run length and average time to signal: an overview. J Stat Comput Simul. 2014;84(8):1779-1802.
Chan LK, Zhang J. Cumulative sum control charts for covariance matrix. Stat Sincia. 2001;11(3):767-790.
Kim MJ. Number of replications required in control chart Monte Carlo simulation studies. Ph.D. dissertation, University of Northern Colorado, 2005.
Schaffer JR, Kim MJ. Number of replications required in control chart Monte Carlo simulation studies. Commun Stat Simul Comput. 2007;36(5):1075-1087.
Huwang L, Huang CJ, Wang YHT. New EWMA control charts for monitoring process dispersion. Comput Stat Data Anal. 2010;54:2328-2342.
Zou C, Tsung E. Likelihood ratio based distribution free EWMA control charts. J Qual Technol. 2010;42(2):174-196.
Jones-Farmer LA, Jordan A, Champ CW. Distribution-free phase I charts for subgroup location. J Qual Technol. 2009;41(3):304-316.
Abbasi SA, Miller A. On proper choice of variability control chart for normal and non-normal processes. Qual Reliab Eng Int. 2012;28:279-296.