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IIE Transactions Volume 34, 2002 - Issue 12
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ORIGINAL ARTICLES

A conditional decision procedure for high yield processes

V. KURALMANI Institute of High Performance Computing, 89C Science Park Drive, 118261, Singapore
,
M. XIE Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore
,
T.N. GOH Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore
&
F.F. GAN Department of Statistics and Applied Probability, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore E-mail: [email protected]
Pages 1021-1030 | Received 01 Mar 2000, Accepted 01 Nov 2001, Published online: 17 Apr 2007

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Mohammad Rasouli, Hamid Shahriari & Yaser Samimi. (2022) Monitoring a bi-attribute high-quality process using a mixture probability distribution. Communications in Statistics - Simulation and Computation 51:12, pages 7427-7443.
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V. Golbafian, M. S. Fallahnezhad & Y. Zare Mehrjerdi. (2017) A new economic scheme for CCC charts with run rules based on average number of inspected items. Communications in Statistics - Theory and Methods 46:24, pages 12023-12044.
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Jing-Er Chiu & Chih-Hsin Tsai. (2015) Monitoring high-quality processes with one-sided conditional cumulative counts of conforming chart. Journal of Industrial and Production Engineering 32:8, pages 559-565.
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Ming Ha Lee & Michael B. C. Khoo. (2015) Variable Sampling Interval Cumulative Count of Conforming Chart with Runs Rules. Communications in Statistics - Simulation and Computation 44:9, pages 2410-2430.
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Baocai Guo, Bing Xing Wang & Min Xie. (2014) ARL-unbiased control charts for the monitoring of exponentially distributed characteristics based on type-II censored samples. Journal of Statistical Computation and Simulation 84:12, pages 2734-2747.
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Shuguang He, Shijie Li & Zhen He. (2014) A Combination of CUSUM Charts for Monitoring a Zero-Inflated Poisson Process. Communications in Statistics - Simulation and Computation 43:10, pages 2482-2497.
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S. K. Khilare & D. T. Shirke. (2014) The Steady-State Performance of Cumulative Count of a Conforming Control Chart Based On Runs Rules. Communications in Statistics - Theory and Methods 43:15, pages 3135-3147.
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CesarA. Acosta-Mejia. (2012) Two-Sided Charts for Monitoring Nonconforming Parts per Million. Quality Engineering 25:1, pages 34-45.
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Cai Wen Zhang, Min Xie & Tongdan Jin. (2012) An improved self-starting cumulative count of conforming chart for monitoring high-quality processes under group inspection. International Journal of Production Research 50:23, pages 7026-7043.
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Pei-Wen Chen & Chuen-Sheng Cheng. (2011) On Statistical Design of the Cumulative Quantity Control Chart for Monitoring High Yield Processes. Communications in Statistics - Theory and Methods 40:11, pages 1911-1928.
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Tee Chin Chang & Fah Fatt Gan. (2007) Modified Shewhart Charts for High Yield Processes. Journal of Applied Statistics 34:7, pages 857-877.
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Ling-Yau Chan, Jintao Ouyang & Henry Ying-Kei Lau. (2007) A Two-Stage Cumulative Quantity Control Chart for Monitoring Poisson Processes. Journal of Quality Technology 39:3, pages 203-223.
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Arne Johannssen, Nataliya Chukhrova, Giovanni Celano & Philippe Castagliola. (2022) A number‐between‐events control chart for monitoring finite horizon production processes. Quality and Reliability Engineering International 38:4, pages 2110-2138.
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Nataliya Chukhrova & Arne Johannssen. (2019) Improved control charts for fraction non-conforming based on hypergeometric distribution. Computers & Industrial Engineering 128, pages 795-806.
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Sajid Ali, Antonio Pievatolo & Rainer Göb. (2016) An Overview of Control Charts for High-quality Processes. Quality and Reliability Engineering International 32:7, pages 2171-2189.
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Peipei Deng, Thong Ngee Goh, Yuan Cheng & Min Xie. (2015) Circle Chart for the Monitoring of Maxima in Periodic Processes. Quality and Reliability Engineering International 31:8, pages 1437-1447.
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Baocai Guo, Bing Xing Wang & Yuan Cheng. (2015) Optimal design of a synthetic chart for monitoring process dispersion with unknown in-control variance. Computers & Industrial Engineering 88, pages 78-87.
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Seyed Taghi Akhavan Niaki & Majid Khedmati. (2014) Change point estimation of high-yield processes experiencing monotonic disturbances. Computers & Industrial Engineering 67, pages 82-92.
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Jung-Tai Chen. (2013) Design of cumulative count of conforming charts for high yield processes based on average number of items inspected. International Journal of Quality & Reliability Management 30:9, pages 942-957.
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Silvia Joekes & Emanuel Pimentel Barbosa. (2013) An improved attribute control chart for monitoring non-conforming proportion in high quality processes. Control Engineering Practice 21:4, pages 407-412.
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Yan-Kwang Chen. (2013) Cumulative conformance count charts with variable sampling intervals for correlated samples. Computers & Industrial Engineering 64:1, pages 302-308.
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Cesar A. Acosta-Mejia. (2011) On the performance of the conditional decision procedure in geometric charts. Computers & Industrial Engineering 61:4, pages 905-910.
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John L. SzarkaIIIIII & William H. Woodall. (2011) A Review and perspective on surveillance of Bernoulli processes. Quality and Reliability Engineering International 27:6, pages 735-752.
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Jung‐Tai Chen. (2009) A new approach to setting control limits of cumulative count of conforming charts for high‐yield processes. Quality and Reliability Engineering International 25:8, pages 973-986.
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Hsiuying Wang. (2009) Comparison of control charts for low defective rate. Computational Statistics & Data Analysis 53:12, pages 4210-4220.
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J. J. Lyu & M.N. Chen. (2008) On evaluating the measurement capability of high-quality processes. On evaluating the measurement capability of high-quality processes.
Rassoul Noorossana, Abbas Saghaei, Kamran Paynabar & Yaser Samimi. (2007) On the conditional decision procedure for high yield processes. Computers & Industrial Engineering 53:3, pages 469-477.
Crossref
M Xie, T N Goh & V KuralmaniM. Xie, T. N. Goh & V. Kuralmani. 2002. Statistical Models and Control Charts for High-Quality Processes. Statistical Models and Control Charts for High-Quality Processes 237 261 .

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