An attribute chart for monitoring the process mean and variance

This article proposes an attribute chart for variables (AFV chart) that employs an attribute inspection (checking whether a unit is conforming or nonconforming) to monitor not only the mean but also the variance of a variable x. The salient feature of the AFV chart is its ability to determine the process status (i.e. in control or out of control) by applying the very simple attribute inspection to a single unit. By selecting its inspection limits appropriately, the AFV chart usually outperforms the joint & R and & S charts from an overall viewpoint under different circumstances. The AFV chart has the advantage of being extremely simple in design and implementation, and having a very low cost for operation. In particular, the AFV chart uses a single-attribute inspection for each sample, works as a leading indicator of trouble and allows operators to take the proper corrective action before any defective is actually produced. Since the AFV chart is simpler, more effective and less costly than the & R and & S charts, it may be highly preferred for many statistical process control applications, in which both the mean and variance of a variable need to be monitored.

[1]  Yu Tian,et al.  Optimization design of the charts for monitoring process capability , 2002 .

[2]  J. Bossert AMERICAN SOCIETY FOR QUALITY CONTROL , 2003 .

[3]  Joseph J. Pignatiello,et al.  Estimating the time of step change with Poisson CUSUM and EWMA control charts , 2011 .

[4]  Zhang Wu,et al.  Optimal average sample number of the SPRT chart for monitoring fraction nonconforming , 2013, Eur. J. Oper. Res..

[5]  Zhang Wu,et al.  The X control chart for monitoring process shifts in mean and variance , 2012 .

[6]  Marion R. Reynolds,et al.  Should Observations Be Grouped for Effective Process Monitoring? , 2004 .

[7]  M. Shamsuzzaman,et al.  An optimisation design of the combined np-CUSUM scheme for attributes , 2013 .

[8]  Antonio Fernando Branco Costa,et al.  Joint X̄ and R Charts with Variable Sample Sizes and Sampling Intervals , 1999 .

[9]  Erwin M. Saniga,et al.  Economic Statistical Control-Chart Designs With an Application to and R Charts , 1989 .

[10]  Michael B. C. Khoo,et al.  An np control chart for monitoring the mean of a variable based on an attribute inspection , 2009 .

[11]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[12]  Clifford W. b. Kennedy Inspection and Gaging , 1987 .

[13]  Arthur B. Yeh,et al.  The density control chart: a general approach for constructing a single chart for simultaneously monitoring multiple parameters , 2012 .

[14]  Ross Sparks,et al.  CUSUM Charts for Signalling Varying Location Shifts , 2000 .

[15]  Gyo-Young Cho,et al.  CUSUM charts with variable sampling intervals , 2009 .

[16]  Marion R. Reynolds,et al.  Control Charts and the Efficient Allocation of Sampling Resources , 2004, Technometrics.

[17]  Antonio Fernando Branco Costa,et al.  Monitoring a wandering mean with an np chart , 2011 .

[18]  Marion R. Reynolds,et al.  CUSUM Control Charts with Variable Sample Sizes and Sampling Intervals , 2001 .

[19]  M. A. Rahim,et al.  Economic Design of and R Charts Under Weibull Shock Models , 2013 .

[20]  Marion R. Reynolds,et al.  Combinations of Multivariate Shewhart and MEWMA Control Charts for Monitoring the Mean Vector and Covariance Matrix , 2008 .

[21]  Zhang Wu,et al.  A control chart for monitoring process mean based on attribute inspection , 2008 .

[22]  Wu Zhang,et al.  Optimization of joint X and S control charts with asymmetric control limits , 1997 .