Reduction of control-chart signal variablity for high-quality processes

The design of a control chart is often based on the statistical measure of average run length (ARL). A longer in-control ARL is ensured by the design, but the variance run length distribution may also be large for such a design. In practical terms, the variability in false alarms and true signals may be large. If the sample size for plotting a point is not constant, then the focus is on the average number inspected as against the ARL. This article considers two well-known attribute control chart procedures for monitoring high quality based on the number inspected, and shows how the variability in false alarms and correct signals can be reduced.

[1]  Patrick D. Bourke,et al.  Detecting a shift in fraction nonconforming using runlength control charts with 100% inspection , 1991 .

[2]  Neil C. Schwertman,et al.  Designing Accurate Control Charts Based on the Geometric and Negative Binomial Distributions , 2005 .

[3]  Song Huat Yeo,et al.  A comparative study of the CRL-type control charts , 2000 .

[4]  Thong Ngee Goh,et al.  A quality monitoring and decision‐making scheme for automated production processes , 1999 .

[5]  Loon Ching Tang,et al.  Cumulative conformance count chart with sequentially updated parameters , 2004 .

[6]  K. Govindaraju,et al.  Run Length Variability and Three Sigma Control Limits , 2004 .

[7]  Eli A. Glushkovsky.  ‘On-line’ G-control chart for attribute data , 1994 .

[8]  James C. Benneyan,et al.  Statistical Control Charts Based on a Geometric Distribution , 1992 .

[9]  Charles W. Champ,et al.  The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters , 2001, Technometrics.

[10]  Patrick D. Bourke,et al.  The RL2 chart versus the np chart for detecting upward shifts in fraction defective , 2006 .

[11]  Zhang Wu,et al.  Evaluation of ATS for CRL Control Chart , 1999 .

[12]  Min Xie,et al.  Statistical Models and Control Charts for High-Quality Processes , 2002 .

[13]  Charles P. Quesenberry,et al.  Geometric Q Charts for High Quality Processes , 1995 .

[14]  H. Lenz,et al.  Frontiers in Statistical Quality Control 2. , 1984 .

[15]  T. P. Ryan.  Efficient Estimation of Control Chart Parameters , 1997 .

[16]  Charles W. Champ,et al.  The Run Length Distribution of the CUSUM with Estimated Parameters , 2004 .

[17]  Thong Ngee Goh,et al.  Some procedures for decision making in controlling high yield processes , 1992 .

[18]  Gemai Chen,et al.  THE MEAN AND STANDARD DEVIATION OF THE RUN LENGTH DISTRIBUTION OF X̄ CHARTS WHEN CONTROL LIMITS ARE ESTIMATED Gemai Chen , 2003 .

[19]  William H. Woodall,et al.  Controversies and Contradictions in Statistical Process Control , 2000 .

[20]  J C Benneyan,et al.  Number-Between g-Type Statistical Quality Control Charts for Monitoring Adverse Events , 2001, Health care management science.

[21]  Jing Sun,et al.  Control charts based on the number of consecutive conforming items between two successive nonconforming items for the near zero-nonconformity processes , 2000 .

[22]  Lloyd S. Nelson,et al.  A Control Chart for Parts-Per-Million Nonconforming Items , 1994 .

[23]  T. Calvin,et al.  Quality Control Techniques for "Zero Defects" , 1983 .

[24]  Zhang Wu,et al.  Design of the sum-of-conforming-run-length control charts , 2001, Eur. J. Oper. Res..

[25]  G. Barrie Wetherill,et al.  Statistical Process Control , 1991 .

[26]  Abdur Rahim,et al.  A CCC‐r chart for high‐yield processes , 2001 .

[27]  Trevor A Spedding,et al.  A Synthetic Control Chart for Detecting Small Shifts in the Process Mean , 2000 .

[28]  D. Coleman.  Statistical Process Control—Theory and Practice , 1993 .