A review and critique of auxiliary information-based process monitoring methods

References

• Abbas, N. (2018). Homogeneously weighted moving average control chart with an application in substrate manufacturing process. Computers & Industrial Engineering, 120(June), 460–470. https://doi.org/10.1016/j.cie.2018.05.009
   Web of Science ®Google Scholar
• Abbas, N., Riaz, M., & Does, R. J. M. M. (2014). An EWMA-type control chart for monitoring the process mean using auxiliary information. Communications in Statistics - Theory and Methods, 43(16), 3485–3498. https://doi.org/10.1080/03610926.2012.700368
   Web of Science ®Google Scholar
• Abbasi, S. A. (2020). Efficient control charts for monitoring process CV using auxiliary information. IEEE Access, 8, 46176–46192. https://doi.org/10.1109/ACCESS.2020.2977833
   Web of Science ®Google Scholar
• Abbasi, S. A., & Adegoke, N. A. (2020). Auxiliary-information-based efficient variability control charts for Phase I of SPC. Quality and Reliability Engineering International, 36(7), 2322–2337. https://doi.org/10.1002/qre.2699
   Web of Science ®Google Scholar
• Abbasi, S. A., & Haq, A. (2019a). Enhanced adaptive CUSUM charts for process mean. Journal of Statistical Computation and Simulation, 89(13), 2562–2582. https://doi.org/10.1080/00949655.2019.1625902
   Web of Science ®Google Scholar
• Abbasi, S. A., & Haq, A. (2019b). Optimal CUSUM and adaptive CUSUM charts with auxiliary information for process mean. Journal of Statistical Computation and Simulation, 89(2), 337–361. https://doi.org/10.1080/00949655.2018.1548619
   Web of Science ®Google Scholar
• Abbasi, S. A., & Haq, A. (2020). New adaptive CUSUM charts for process mean. Communications in Statistics - Simulation and Computation, 49(11), 2944–2962. https://doi.org/10.1080/03610918.2018.1530786
   Web of Science ®Google Scholar
• Abbasi, S. A., & Riaz, M. (2013). On enhanced control charting for process monitoring. International Journal of Physical Sciences, 8(17), 759–775. https://doi.org/10.5897/IJPS12.252
   Google Scholar
• Abbasi, S. A., & Riaz, M. (2016). On dual use of auxiliary information for efficient monitoring. Quality and Reliability Engineering International, 32(2), 705–714. https://doi.org/10.1002/qre.1785
   Web of Science ®Google Scholar
• Abbasi, S. A., Riaz, M., Ahmad, S., Sanusi, R. A., & Abid, M. (2020). New efficient exponentially weighted moving average variability charts based on auxiliary information. Quality and Reliability Engineering International, 36(7), 2203–2224. https://doi.org/10.1002/qre.2692
   Web of Science ®Google Scholar
• Adegoke, N. A., Riaz, M., Sanusi, R. A., Smith, A. N., & Pawley, M. D. (2017). EWMA- type scheme for monitoring location parameter using auxiliary information. Computers & Industrial Engineering, 114(December), 114–129. https://doi.org/10.1016/j.cie.2017.10.013
   Web of Science ®Google Scholar
• Adegoke, N. A., Smith, A. N. H., Anderson, M. J., Sanusi, R. A., & Pawley, M. D. M. (2019). Efficient homogeneously weighted moving average chart for monitoring process mean using an auxiliary variable. IEEE Access, 7(July), 94021–94032. https://doi.org/10.1109/ACCESS.2019.2926533
   Web of Science ®Google Scholar
• Ahmad, S., Abbasi, S. A., Riaz, M., & Abbas, N. (2014a). On efficient use of auxiliary information for control charting in SPC. Computers & Industrial Engineering, 67(January), 173–184. https://doi.org/10.1016/j.cie.2013.11.004
   Web of Science ®Google Scholar
• Ahmad, S., Lin, Z., Abbasi, S. A., & Riaz, M. (2012). On efficient monitoring of process dispersion using interquartile range. Open Journal of Applied Sciences, 2(4), 39–43. https://doi.org/10.4236/ojapps.2012.24B010
   Google Scholar
• Ahmad, S., Riaz, M., Abbasi, S. A., & Lin, Z. (2013). On monitoring process variability under double sampling scheme. International Journal of Production Economics, 142(2), 388–400. https://doi.org/10.1016/j.ijpe.2012.12.015
   Web of Science ®Google Scholar
• Ahmad, S., Riaz, M., Abbasi, S. A., & Lin, Z. (2014b). On efficient median control charting. Journal of the Chinese Institute of Engineers, 37(3), 358–375. https://doi.org/10.1080/02533839.2013.781794
   Web of Science ®Google Scholar
• Ahmad, S., Riaz, M., Abbasi, S. A., & Lin, Z. (2014c). On median control charting under double sampling scheme. European Journal of Industrial Engineering, 8(4), 478–512. https://doi.org/10.1504/EJIE.2014.064755
   Web of Science ®Google Scholar
• Ahmad, S., Riaz, M., Hussain, S., & Abbasi, S. A. (2019). On auxiliary information-based control charts for autocorrelated processes with application in manufacturing industry. The International Journal of Advanced Manufacturing Technology, 100(5–8), 1965–1980. https://doi.org/10.1007/s00170-018-2671-9
   Web of Science ®Google Scholar
• Ahmad, Z., Shahbaz, M. Q., & Hanif, M. (2013). Two phase sampling. Cambridge Scholars Publishing.
   Google Scholar
• Alt, F. B. (2004). Multivariate quality control. In S. Kotz, C. B. Read, N. Balakrishnan, B. Vidakovic, & N. L. Johnson (Eds.), Encyclopedia of statistical sciences. John Wiley & Sons, Inc.
   Google Scholar
• Amir, M. W., Rani, M., Abbas, Z., Nazir, H. Z., Riaz, M., & Akhtar, N. (2021a). Increasing the efficiency of double moving average chart using auxiliary variable. Journal of Statistical Computation and Simulation, 91(14), 2880–2898. https://doi.org/10.1080/00949655.2021.1909588
   Web of Science ®Google Scholar
• Amir, M. W., Raza, Z., Abbas, Z., Nazir, H. Z., Akhtar, N., Riaz, M., & Abid, M. (2021b). On increasing the sensitivity of moving average control chart using auxiliary variable. Quality and Reliability Engineering International, 37(3), 1198–1209. https://doi.org/10.1002/qre.2790
   Web of Science ®Google Scholar
• Anwar, S. M., Aslam, M., Ahmad, S., & Riaz, M. (2020a). A modified-mxEWMA location chart for the improved process monitoring using auxiliary information and its application in wood industry. Quality Technology & Quantitative Management, 17(5), 561–579. https://doi.org/10.1080/16843703.2019.1696011
   Web of Science ®Google Scholar
• Anwar, S. M., Aslam, M., Riaz, M., & Zaman, B. (2020b). On mixed memory control charts based on auxiliary information for efficient process monitoring. Quality and Reliability Engineering International, 36(6), 1949–1968. https://doi.org/10.1002/qre.2667
   Web of Science ®Google Scholar
• Anwar, S. M., Aslam, M., Zaman, B., & Riaz, M. (2022). An enhanced double homogeneously weighted moving average control chart to monitor process location with application in automobile field. Quality and Reliability Engineering International, 38(1), 174–194. https://doi.org/10.1002/qre.2966
   Web of Science ®Google Scholar
• Anwar, S. M., Aslam, M., Zaman, B., & Riaz, M. (2021). Mixed memory control chart based on auxiliary information for simultaneously monitoring of process parameters: An application in glass field. Computers & Industrial Engineering, 156(June), 107284. https://doi.org/10.1016/j.cie.2021.107284
   Web of Science ®Google Scholar
• Arshad, W., Abbas, N., Riaz, M., & Hussain, Z. (2017). Simultaneous use of runs rules and auxiliary information with exponentially weighted moving average control charts. Quality and Reliability Engineering International, 33(2), 323–336. https://doi.org/10.1002/qre.2007
   Web of Science ®Google Scholar
• Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 23(5), 517–543. https://doi.org/10.1002/qre.829
   Web of Science ®Google Scholar
• Chen, J.-H., & Lu, S.-L. (2020). An enhanced auxiliary information-based EWMA-t chart for monitoring the process mean. Applied Sciences, 10(7), 2252. https://doi.org/10.3390/app10072252
   Google Scholar
• Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.
   Google Scholar
• Cochran, W. G. (1978). Laplace’s ratio estimator. In H. A. David (Ed.), Contributions to survey sampling and applied statistics (pp. 3–10). Elsevier. https://doi.org/10.1016/b978-0-12-204750-3.50008-3
   Google Scholar
• Constable, G. K., Cleary, M. J., Tickel, C., & Zhang, G. (1988). Use of cause-selecting charts in the auto industry. ASQC Quality Congress Transactions, 43(10), 597–602.
   Google Scholar
• Constable, G. K., Cleary, M. J., & Zhang, G. (1987). Cause-selecting control charts: A new type of quality control charts. ASQC Quality Congress Transactions, 597–602.
   Google Scholar
• de Laplace, P. S. M. (1820). A philosophical essay on probabilities. Translated from the sixth French edition. John Wiley & Sons;Chapman & Hall.
   Google Scholar
• Deming, W. E. (1953). On the distinction between enumerative and analytic surveys. Journal of the American Statistical Association, 48(262), 244–255. https://doi.org/10.1080/01621459.1953.10483470
   Web of Science ®Google Scholar
• Deming, W. E. (1993). The new economics for Industry, Government, Education. Cambridge MA: MIT Center for Advanced Engineering Study. https://mitpress.mit.edu/books/new-economics-industry-government-education-second-edition
   Google Scholar
• Graunt, J. (1662). Natural and political observations made upon the bills of mortality (Reprint ed., pp. 1939). by Johns Hopkins Press.
   Google Scholar
• Haq, A. (2013). A new hybrid exponentially weighted moving average control chart for monitoring process mean. Quality and Reliability Engineering International, 29(7), 1015–1025. https://doi.org/10.1002/qre.1453
   Web of Science ®Google Scholar
• Haq, A. (2017a). New EWMA control charts for monitoring process dispersion using auxiliary information. Quality and Reliability Engineering International, 33(8), 2597–2614. https://doi.org/10.1002/qre.2220
   Web of Science ®Google Scholar
• Haq, A. (2017b). A new maximum EWMA control chart for simultaneously monitoring process mean and dispersion using auxiliary information. Quality and Reliability Engineering International, 33(7), 1577–1587. https://doi.org/10.1002/qre.2126
   Web of Science ®Google Scholar
• Haq, A. (2017c). New synthetic CUSUM and synthetic EWMA control charts for monitoring the process mean using auxiliary information. Quality and Reliability Engineering International, 33(7), 1549–1565. https://doi.org/10.1002/qre.2124
   Web of Science ®Google Scholar
• Haq, A. (2018). A new adaptive EWMA control chart using auxiliary information for monitoring the process mean. Communications in Statistics – Theory and Methods, 47(19), 4840–4858. https://doi.org/10.1080/03610926.2018.1448417
   Web of Science ®Google Scholar
• Haq, A. (2020). A nonparametric EWMA chart with auxiliary information for process mean. Communications in Statistics - Theory and Methods, 49(5), 1232–1247. https://doi.org/10.1080/03610926.2018.1554140
   Web of Science ®Google Scholar
• Haq, A., & Abidin, Z. U. (2019). An enhanced CUSUM-t chart for process mean Quality and Reliability Engineering International, 35 (7), 2067– 2080. https://doi.org/10.1002/qre.2490
   Google Scholar
• Haq, A., Abidin, Z. U., & Khoo, M. B. C. (2019). An enhanced EWMA-t control chart for monitoring the process mean. Communications in Statistics - Theory and Methods, 48(6), 1333–1350. https://doi.org/10.1080/03610926.2018.1429631
   Web of Science ®Google Scholar
• Haq, A., Akhtar, S3. (2022). Auxiliary information based maximum EWMA and DEWMA charts with variable sampling intervals for process mean and variance. Communications in Statistics-Theory and Methods, 51(12), 3985–4005. https://doi.org/10.1080/03610926.2020.1805766
   Web of Science ®Google Scholar
• Haq, A., Akhtar, S., & Khoo, M. B. C. (2021a). Adaptive CUSUM and EWMA charts with auxiliary information and variable sampling intervals for monitoring the process mean. Quality and Reliability Engineering International, 37(1), 47–59. https://doi.org/10.1002/qre.2719
   Web of Science ®Google Scholar
• Haq, A., & Bibi, L. (2022). The dual CUSUM charts with auxiliary information for process mean. Communications in Statistics-Simulation and Computation, 51(1), 164–189. https://doi.org/10.1080/03610918.2019.1648824
   Google Scholar
• Haq, A., Ejaz, S., Lee, M. H., & Khan, M. (2021b). A new double EWMA-t chart with auxiliary information for the process mean. Quality and Reliability Engineering International, 37(8), 3381–3394. https://doi.org/10.1002/qre.2923
   Web of Science ®Google Scholar
• Haq, A., Gulzar, R., & Khoo, M. B. C. (2018). An efficient adaptive EWMA control chart for monitoring the process mean. Quality and Reliability Engineering International, 34(4), 563–571. https://doi.org/10.1002/qre.2272
   Web of Science ®Google Scholar
• Haq, A., & Khoo, M. B. C. (2016). A new synthetic control chart for monitoring process mean using auxiliary information. Journal of Statistical Computation and Simulation, 86(15), 3068–3092. https://doi.org/10.1080/00949655.2016.1150477
   Web of Science ®Google Scholar
• Haq, A., & Khoo, M. B. C. (2018). A new double sampling control chart for monitoring process mean using auxiliary information. Journal of Statistical Computation and Simulation, 88(5), 869–899. https://doi.org/10.1080/00949655.2017.1408111
   Web of Science ®Google Scholar
• Haq, A., & Khoo, M. B. C. (2019a). Memory-type multivariate control charts with auxiliary information for process mean. Quality and Reliability Engineering International, 35(1), 192–203. https://doi.org/10.1002/qre.2391
   Web of Science ®Google Scholar
• Haq, A., & Khoo, M. B. C. (2019b). A synthetic double sampling control chart for process mean using auxiliary information. Quality and Reliability Engineering International, 35(6), 1803–1825. https://doi.org/10.1002/qre.2477
   Web of Science ®Google Scholar
• Haq, A., & Khoo, M. B. C. (2021). Memory-type control charts with multiple auxiliary information for process mean. Quality and Reliability Engineering International, 37(6), 2348–2364. https://doi.org/10.1002/qre.2861
   Web of Science ®Google Scholar
• Haq, A., Khoo, M. B. C., Lee, M. H., & Abbasi, S. A. (2021c). Enhanced adaptive multivariate EWMA and CUSUM charts for process mean. Journal of Statistical Computation and Simulation, 91(12), 2361–2382. https://doi.org/10.1080/00949655.2021.1894564
   Web of Science ®Google Scholar
• Haq, A., Munir, T., & Shah, B. A. (2020). Dual multivariate CUSUM charts with auxiliary information for process mean. Quality and Reliability Engineering International, 36(3), 861–875. https://doi.org/10.1002/qre.2604
   Web of Science ®Google Scholar
• Hawkins, D. M. (1991). Multivariate quality control based on regression-adjusted variables. Technometrics, 33(1), 61–75. https://doi.org/10.1080/00401706.1991.10484770
   Web of Science ®Google Scholar
• Hawkins, D. M. (1993). Regression adjustment for variables in multivariate quality control. Journal of Quality Technology, 25(3), 170–182. https://doi.org/10.1080/00224065.1993.11979451
   Web of Science ®Google Scholar
• Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart. M. W. Hastay. and W. A.Wallis (Eds.), Techniques of statistical analysis (pp. 111–184). McGraw Hill.
   Google Scholar
• Hussain, S., Mei, S., Riaz, M., & Abbasi, S. A. (2020). On phase-I monitoring of process location parameter with auxiliary information-based median control charts. Mathematics, 8(5), 706. https://doi.org/10.3390/math8050706
   Web of Science ®Google Scholar
• Hussain, S., Song, L., Ahmad, S., & Riaz, M. (2018). On auxiliary information based improved EWMA median control charts. Scientia Iranica, 25(2), 954–982. https://doi.org/10.24200/sci.2017.4432
   Web of Science ®Google Scholar
• Hussain, S., Song, L., Ahmad, S., & Riaz, M. (2019a). A new auxiliary information based cumulative sum median control chart for location monitoring. Frontiers of Information Technology & Electronic Engineering, 20(4), 554–570. https://doi.org/10.1631/FITEE.1700428
   Web of Science ®Google Scholar
• Hussain, S., Song, L., Ahmad, S., & Riaz, M. (2019b). New interquartile range EWMA control charts with applications in continuous stirred tank rector (sic) process. Arabian Journal for Science and Engineering, 44(3), 2467–2485. https://doi.org/10.1007/s13369-018-3162-x
   Web of Science ®Google Scholar
• Jackson, J. E. (1985). Multivariate quality control. Communications in Statistics - Theory and Methods, 14(11), 2657–2688. https://doi.org/10.1080/03610928508829069
   Web of Science ®Google Scholar
• Javaid, A., Noor-ul-Amin, M., & Hanif, M. (2020). A new max-HEWMA control chart using auxiliary information. Communications in Statistics - Simulation and Computation, 49(5), 1285–1305. https://doi.org/10.1080/03610918.2018.1494282
   Web of Science ®Google Scholar
• Javaid, A., Noor-ul Amin, M., & Hanif, M. (2021). Maximum hybrid exponentially weighted moving average control chart in the presence of measurement error by using auxiliary information. Quality and Reliability Engineering International, 37(8), 3262–3281. https://doi.org/10.1002/qre.2907
   Web of Science ®Google Scholar
• Kang, L., & Albin, S. L. (2000). On-line monitoring when the process yields a linear profile. Journal of Quality Technology, 32(4), 418–426. https://doi.org/10.1080/00224065.2000.11980027
   Web of Science ®Google Scholar
• Khan, N., Aslam, M., & Jun, C.-H. (2017). Design of a control chart using a modified EWMA statistic. Quality and Reliability Engineering International, 33(5), 1095–1104. https://doi.org/10.1002/qre.2102
   Web of Science ®Google Scholar
• Knoth, S. (2016). The case against the use of synthetic control charts. Journal of Quality Technology, 48(2), 178–195. https://doi.org/10.1080/00224065.2016.11918158
   Web of Science ®Google Scholar
• Knoth, S., Saleh, N. A., Mahmoud, M. A., Woodall, W. H., & Tercero-Gomez, V. G. (2022a). A critique of a variety of ”memory-based” process monitoring methods. Journal of Quality Technology. https://doi.org/10.1080/00224065.2022.2034487
   Google Scholar
• Knoth, S., Tercero-Gómez, V. G., Khakifirooz, M., & Woodall, W. H. (2021). The impracticality of homogeneously weighted moving average and progressive mean control chart approaches. Quality and Reliability Engineering International, 37(8), 3779–3794. https://doi.org/10.1002/qre.2950
   Web of Science ®Google Scholar
• Knoth, S., Woodall, W. H., & Tercero-Gómez, V. G. (2022b). The case against generally weighted moving average (GWMA) control charts. Quality Engineering, 34(1), 75–81. https://doi.org/10.1080/08982112.2021.2002359
   Web of Science ®Google Scholar
• Lee, H., Aslam, M., Shakeel, Q., Lee, W., & Jun, C.-H. (2015). A control chart using an auxiliary variable and repetitive sampling for monitoring process mean. Journal of Statistical Computation and Simulation, 85(16), 3289–3296. https://doi.org/10.1080/00949655.2014.970553
   Web of Science ®Google Scholar
• Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810. https://doi.org/10.1080/07408179508936797
   Web of Science ®Google Scholar
• Lu, S.-L., Chen, J.-H., & Yang, S.-F. (2021). Auxiliary information-based maximum generally weighted moving average chart for simultaneously monitoring process mean and variability. Quality and Reliability Engineering International, 37(8), 3242–3261. https://doi.org/10.1002/qre.2906
   Web of Science ®Google Scholar
• Mahmoud, M. A., & Woodall, W. H. (2010). An evaluation of the double exponentially weighted moving average control chart. Communications in Statistics - Simulation and Computation, 39(5), 933–949. https://doi.org/10.1080/03610911003663907
   Web of Science ®Google Scholar
• Maleki, M. R., Amiri, A., & Castagliola, P. (2018). An overview on recent profile monitoring papers (2008–2018) based on conceptual classification scheme. Computers & Industrial Engineering, 126(December), 705–728. https://doi.org/10.1016/j.cie.2018.10.008
   Web of Science ®Google Scholar
• Mandel, B. J. (1969). The regression control chart. Journal of Quality Technology, 1(1), 1–9. https://doi.org/10.1080/00224065.1969.11980341
   Google Scholar
• McIntyre, G. A. (1952). A method for unbiased selective sampling, using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390. https://doi.org/10.1071/AR9520385
   Google Scholar
• Mehmood, R., Riaz, M., Mahmood, T., Abbasi, S. A., & Abbas, N. (2017). On the extended use of auxiliary information under skewness correction for process monitoring. Transactions of the Institute of Measurement and Control, 39(6), 883–897. https://doi.org/10.1177/0142331215622248
   Web of Science ®Google Scholar
• Nahar, S., Rahman, M. S., & Laskar, M. R. (1993). Selection of a better ratio estimator. Journal of Statistical Studies, 13, 10–16.
   Google Scholar
• Naik, V. D., & Gupta, P. C. (1991). A general class of estimators for estimating population mean using auxiliary information. Metrika, 38(1), 11–17. https://doi.org/10.1007/BF02613594
   Google Scholar
• Ng, P. S., Khoo, M. B., Saha, S., & Yeong, W. C. (2022). Economic and economic-statistical designs of auxiliary information based Xˉ, synthetic and EWMA charts. Communications in Statistics-Simulation and Computation, 51(3), 1157–1185. https://doi.org/10.1080/03610918.2019.1664575
   Google Scholar
• Ng, P. S., Khoo, M. B. C., Yeong, W. C., & Lim, S. L. (2020). A variable sampling interval EWMA Xˉ chart for the mean with auxiliary information. In R. Alfred, Y. Lim, H. Haviluddin, C. On (Eds.), Computational Science and Technology. Lecture Notes in Electrical Engineering (vol. 603, pp. 113–122). Singapore: Springer.
   Google Scholar
• Noor-ul-Amin, M., Javaid, A., Hanif, M., & Dogu, E. (2022). Performance of maximum EWMA control chart in the presence of measurement error using auxiliary information. Communications in Statistics-Simulation and Computation, 1–25. https://doi.org/10.1080/03610918.2020.1772301
   Google Scholar
• Noor-ul-Amin, M., Khan, S., & Aslam, M. (2018). An EWMA control chart using two parametric ratio estimator. Journal of Industrial and Production Engineering, 35(5), 298–308. https://doi.org/10.1080/21681015.2018.1476414
   Web of Science ®Google Scholar
• Noor-ul-Amin, M., Khan, S., & Sanaullah, A. (2019a). HEWMA control chart using auxiliary information. Iranian Journal of Science and Technology, Transactions A: Science, 43(3), 891–903. https://doi.org/10.1007/s40995-018-0585-x
   Web of Science ®Google Scholar
• Noor-ul-Amin, M., Tariq, S., & Hanif, M. (2019b). Control charts for simultaneously monitoring of process mean and coefficient of variation with and without auxiliary information. Quality and Reliability Engineering International, 35(8), 2639–2656. https://doi.org/10.1002/qre.2546
   Web of Science ®Google Scholar
• Noorossana, R., Saghaei, A., & Amiri, A. (2011). Statistical analysis of profile monitoring. John Wiley & Sons.
   Google Scholar
• Nuriman, M. A., Mashuri, M., & Ahsan, M. (2021). Auxiliary information based generally weighted moving coefficient of variation (AIB-GWMCV) control chart. IOP Conference Series: Materials Science and Engineering, 1115(1), 012033. https://doi.org/10.1088/1757-899X/1115/1/012033
   Google Scholar
• Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1–2), 100–115. https://doi.org/10.1093/biomet/41.1–2.100
   Web of Science ®Google Scholar
• Prabhu, S. S., Montgomery, D. C., & Runger, G. C. (1994). A combined adaptive sample size and sampling interval Xˉ control scheme. Journal of Quality Technology, 26(3), 164–176. https://doi.org/10.1080/00224065.1994.11979524
   Web of Science ®Google Scholar
• Raza, S. M. M., Sial, M. H., Haider, M., & Butt, M. M. (2019). Hybrid exponentially weighted moving average (HEWMA) control chart based on exponential type estimator of mean. Journal of Reliability and Statistical Studies, 12(2), 187–198. https://doi.org/10.13052/jrss2229-5666.12214
   Web of Science ®Google Scholar
• Reynolds, M. R., Amin, R. W., Arnold, J. C., & Nachlas, J. A. (1988). Xˉ Charts with variable sampling intervals. Technometrics, 30(2), 181–192. https://doi.org/10.1080/00401706.1988.10488366
   Web of Science ®Google Scholar
• Riaz, A., Noor-ul-Amin, M., Shehzad, M. A., & Ismail, M. (2019). Auxiliary information based mixed EWMA-CUSUM mean control chart with measurement error. Iranian Journal of Science and Technology, Transactions A: Science, 43(6), 2937–2949. https://doi.org/10.1007/s40995-019-00774-6
   Web of Science ®Google Scholar
• Riaz, M. (2008a). Monitoring process mean level using auxiliary information. Statistica Neerlandica, 62(4), 458–481. https://doi.org/10.1111/j.1467-9574.2008.00390.x
   Web of Science ®Google Scholar
• Riaz, M. (2008b). Monitoring process variability using auxiliary information. Computational Statistics, 23(2), 253–276. https://doi.org/10.1007/s00180-007-0084-6
   Web of Science ®Google Scholar
• Riaz, M. (2011). An improved control chart structure for process location parameter. Quality and Reliability Engineering International, 27(8), 1033–1041. https://doi.org/10.1002/qre.1193
   Web of Science ®Google Scholar
• Riaz, M. (2015a). Control charting and survey sampling techniques in process monitoring. Journal of the Chinese Institute of Engineers, 38(3), 342–354. https://doi.org/10.1080/02533839.2014.970355
   Web of Science ®Google Scholar
• Riaz, M. (2015b). On enhanced interquartile range charting for process dispersion. Quality and Reliability Engineering International, 31(3), 389–398. https://doi.org/10.1002/qre.1598
   Web of Science ®Google Scholar
• Riaz, M., Abbasi, S. A., Ahmad, S., & Zaman, B. (2014). On efficient phase II process monitoring charts. The International Journal of Advanced Manufacturing Technology, 70(9–12), 2263–2274. https://doi.org/10.1007/s00170-013-5418-7
   Web of Science ®Google Scholar
• Riaz, M., & Does, R. J. M. M. (2009). A process variability control chart. Computational Statistics, 24(2), 345–368. https://doi.org/10.1007/s00180-008-0122-z
   Web of Science ®Google Scholar
• Riaz, M., Mehmood, R., Abbas, N., & Abbasi, S. A. (2016). On effective dual use of auxiliary information in variability control charts. Quality and Reliability Engineering International, 32(4), 1417–1443. https://doi.org/10.1002/qre.1848
   Web of Science ®Google Scholar
• Riaz, M., Mehmood, R., Ahmad, S., & Abbasi, S. A. (2013). On the performance of auxiliary- based control charting under normality and nonnormality with estimation effects. Quality and Reliability Engineering International, 29(8), 1165–1179. https://doi.org/10.1002/qre.1467
   Web of Science ®Google Scholar
• Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250. https://doi.org/10.1080/00401706.1959.10489860
   Google Scholar
• Sachlas, A., Bersimis, S., & Psarakis, S. (2019). Risk-adjusted control charts: Theory, methods, and applications in health. Statistics in Biosciences, 11(3), 630–658. https://doi.org/10.1007/s12561-019-09257-z
   Web of Science ®Google Scholar
• Saghir, A., Ahmad, L., Aslam, M., & Jun, C.-H. (2019). A EWMA control chart based on an auxiliary variable and repetitive sampling for monitoring process location. Communications in Statistics - Simulation and Computation, 48(7), 2034–2045. https://doi.org/10.1080/03610918.2018.1433837
   Web of Science ®Google Scholar
• Saha, S., Khoo, M. B. C., Lee, M. H., & Haq, A. (2019). A variable sample size and sampling interval control chart for monitoring the process mean using auxiliary information. Quality Technology & Quantitative Management, 16(4), 389–406. https://doi.org/10.1080/16843703.2018.1430523
   Web of Science ®Google Scholar
• Sanusi, R. A., Abbas, N., & Riaz, M. (2018). On efficient CUSUM-type location control charts using auxiliary information. Quality Technology & Quantitative Management, 15(1), 87–105. https://doi.org/10.1080/16843703.2017.1304039
   Web of Science ®Google Scholar
• Sanusi, R. A., Abujiya, M. R., Riaz, M., & Abbas, N. (2017). Combined Shewhart CUSUM charts using auxiliary variable. Computers & Industrial Engineering, 105(March), 329–337. https://doi.org/10.1016/j.cie.2017.01.018
   Web of Science ®Google Scholar
• Sen, A. R. (1993). Some early developments in ratio estimation. Biometrical Journal, 35(1), 1–13. https://doi.org/10.1002/bimj.4710350102
   Web of Science ®Google Scholar
• Shamma, S. E., Amin, R. W., & Shamma, A. K. (1991). A double exponentially weighted moving average control procedure with variable sampling intervals. Communications in Statistics - Simulation and Computation, 20(2–3), 511–528. https://doi.org/10.1080/03610919108812969
   Web of Science ®Google Scholar
• Shamma, S. E., & Shamma, A. K. (1992). Development and evaluation of control charts using double exponentially weighted moving averages. International Journal of Quality & Reliability Management, 9(6), 18–25. https://doi.org/10.1108/02656719210018570
   Google Scholar
• Shen, X., Zou, C., Jiang, W., & Tsung, F. (2013). Monitoring Poisson count data with probability control limits when sample sizes are time varying. Naval Research Logistics (NRL), 60(8), 625–636. https://doi.org/10.1002/nav.21557
   Web of Science ®Google Scholar
• Shewhart, W. A. (1924). Some applications of statistical methods to the analysis of physical and engineering data. Bell System Technical Journal, 3(1), 43–87. https://doi.org/10.1002/j.1538-7305.1924.tb01347.x
   Google Scholar
• Shi, J. (2006). Stream of variation modeling and analysis for multistage manufacturing processes. CRC press.
   Google Scholar
• Singh, R., & Mangat, N. S. (1996). Regression method of estimation. In Elements of Survey Sampling. Kluwer Texts in the Mathematical Sciences (Vol 15, pp. 197–220). Dordrecht, Netherlands: Springer. https://doi.org/10.1007/978-94-017-1404-4_8
   Google Scholar
• Singh, S. (2003). Advanced sampling theory with applications. Springer Netherlands.
   Google Scholar
• Steiner, S. H., Cook, R. J., Farewell, V. T., & Treasure, T. (2000). Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics, 1(4), 441–452. https://doi.org/10.1093/biostatistics/1.4.441
   PubMedGoogle Scholar
• Tian, W., Sun, H., Zhang, X., & Woodall, W. H. (2015). The impact of varying patient populations on the in-control performance of the risk-adjusted cusum chart. International Journal for Quality in Health Care, 27(1), 31–36. https://doi.org/10.1093/intqhc/mzu092
   Web of Science ®Google Scholar
• Umar, A. A., Khoo, M. B. C., Saha, S., & Chong, Z. L. (2021). Auxiliary information based variable sampling interval EWMA chart for process mean using expected average time to signal. International Journal of Mathematics and Computer Science, 16(3), 935–948. http://ijmcs.future-in-tech.net
   Web of Science ®Google Scholar
• Umar, A. A., Khoo, M. B. C., Saha, S., & Haq, A. (2020). A combined variable sampling interval and double sampling control chart with auxiliary information for the process mean. Transactions of the Institute of Measurement and Control, 42(6), 1151–1165. https://doi.org/10.1177/0142331219885525
   Web of Science ®Google Scholar
• Wade, M. R., & Woodall, W. H. (1993). A review and analysis of cause-selecting control charts. Journal of Quality Technology, 25(3), 161–169. https://doi.org/10.1080/00224065.1993.11979450
   Web of Science ®Google Scholar
• Woodall, W. H. (1986). Weaknesses of the economic design of control charts. Technometrics, 28(4), 408–409. https://doi.org/10.2307/1269000
   Web of Science ®Google Scholar
• Woodall, W. H. (2007). Current research on profile monitoring. Produção, 17(3), 420–425. https://doi.org/10.1590/S0103-65132007000300002
   Google Scholar
• Woodall, W. H. (2008). Profile monitoring. In F. Ruggeri, F.W, Faltin, R.S. Kenett (Eds.), entry in Volume 3 of Encyclopedia of statistics in quality and reliability (pp. 1507–1512). John Wiley & Sons, Inc.
   Google Scholar
• Woodall, W. H., & Faltin, F. W. (2019). Rethinking control chart design and evaluation. Quality Engineering, 31(4), 596–605. https://doi.org/10.1080/08982112.2019.1582779
   Web of Science ®Google Scholar
• Woodall, W. H., Fogel, S. L., & Steiner, S. H. (2015). The monitoring and improvement of surgical-outcome quality. Journal of Quality Technology, 47(4), 383–399. https://doi.org/10.1080/00224065.2015.11918141
   Web of Science ®Google Scholar
• Woodall, W. H., Spitzner, D. J., Montgomery, D. C., & Gupta, S. (2004). Using control charts to monitor process and product quality profiles. Journal of Quality Technology, 36(3), 309–320. https://doi.org/10.1080/00224065.2004.11980276
   Web of Science ®Google Scholar
• Wu, Z., & Spedding, T. A. (2000). A synthetic control chart for detecting small shifts in the process mean. Journal of Quality Technology, 32(1), 32–38. https://doi.org/10.1080/00224065.2000.11979969
   Web of Science ®Google Scholar
• Yang, K., & Qiu, P. (2021). Adaptive process monitoring using covariate information. Technometrics, 63(3), 313–328. https://doi.org/10.1080/00401706.2020.1772115
   Web of Science ®Google Scholar
• Zhang, G. (1984). A new type of control charts and a theory of diagnosis with control charts. In World Quality Congress, London, U.k.
   Google Scholar
• Zhang, G. (1985). Cause-selecting control charts – A new type of quality control charts. QR Journal, 12(4), 21–25.
   Google Scholar
• Zhang, G. (1990). A new diagnosis theory with two kinds of quality. Total Quality Management, 1(2), 249–258. https://doi.org/10.1080/09544129000000029
   Google Scholar
• Zhang, G. (1992). Cause-selecting control chart and diagnosis. theory and practice. Aarhus School of Business, Department of Total Quality Management.
   Google Scholar
• Zhang, L., & Chen, G. (2005). An extended EWMA mean chart. Quality Technology & Quantitative Management, 2(1), 39–52. https://doi.org/10.1080/16843703.2005.11673088
   Google Scholar
• Zhang, L., Chen, G., & Castagliola, P. (2009). On t and EWMA t charts for monitoring changes in the process mean. Quality and Reliability Engineering International, 25(8), 933–945. https://doi.org/10.1002/qre.1012
   Web of Science ®Google Scholar
• Zhang, X., & Woodall, W. H. (2015). Dynamic probability control limits for risk-adjusted Bernoulli cusum charts. Statistics in Medicine, 34(25), 3336–3348. https://doi.org/10.1002/sim.6547
   PubMed Web of Science ®Google Scholar
• Zichuan, M., Arslan, M., Abbas, Z., Abbasi, S. A., & Nazir, H. Z. (2020). Improving the performance of EWMA mean chart with use of two auxiliary variables. Revista Argentina de Clínica Psicológica, 29(5), 2016–2024. https://doi.org/10.24205/03276716.2020.1196
   Google Scholar