in statistical quality control a very widely used measure is average run length (arl) which may be worked out by different methods like integral equation, approximations, and monte carlo simulations. the arl measure and the other related measures are of major significance in every type of production process. an omission in its computation (and hence its related measures such as extra quadratic ...

In many processes in real practice at the start-up stages the process parameters are not known a priori and there are no initial samples or data for executing Phase I monitoring and estimating the process parameters. In addition, the practitioners are interested in using one control chart instead of two or more for monitoring location and variability of processes. In this paper, we consider a s...

Statistical process control methods for monitoring processes with univariate ormultivariate measurements are used widely when the quality variables fit to known probabilitydistributions. Some processes, however, are better characterized by a profile or a function of qualityvariables. For each profile, it is assumed that a collection of data on the response variable along withthe values of the c...

In many applications, quality of a process is best characterized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is used for checking the stability of this relationship over time. Control charts based on nonparametric regression are particularly useful when the in-control (IC) or out-of-control (OC) relationship is too complicat...

Along with the widespread use of Taguchi methods in product design, deenition of the loss function has been integrated with numerous models which require quality cost estimation. In this paper, the economic-statistical design of a variable sampling X-bar control chart is extended using the Taguchi loss function to improve chart eeectiveness from a quality cost point of view. The eeectiveness of...

Traditional statistical process control charts used to monitor key process variables are based on the assumption that measurements are independent and identically distributed about a target value. In practice they are not and often are actually correlated. Reliance on univariate charts can lead to misleading conclusions. This paper addresses the methods for improving the quality of industrial p...

Process control charts are proven techniques for improving quality. Specifying the control limits is the most important step in designing a control chart. The presence of outliers may extremely affect the estimates of parameters using classical methods. Robust estimators which are not affected by outliers or the small departures from the model assumptions are applied in this paper to specify th...