Abstract
This paper presents a statistical analysis control chart for nonconforming units in quality control. In many situations the Shewhart control charts for nonconforming units may not be suitable or cannot be used, as for many processes, the assumptions of binomial distribution may deviate or may provide inadequate model. In this Study we propose a new control chart based on regression estimator of proportion based on single auxiliary variable, namely the Pr chart and compared its performance with P and Q chart with probability to signal as a performance measure. It has been observed that the proposed chart is superior to the P and Q chart. This study will help quality practitioners to choose an efficient alternative to the classical P and Q charts for monitoring nonconforming units in industrial process.
Highlights
Control Charts are commonly used in monitoring and detecting shifts in the production processes
In this paper the performance of the Pr chart is compared with P and Q chart for binomial data developed by Quesenberry (1991)
The attribute control charts are useful in the service industries and in nonconfirming quality improvement efforts
Summary
Control Charts are commonly used in monitoring and detecting shifts in the production processes. An item that does not satisfy those specifications is called a defective or a non-conforming item These defects lead to rework or they are characterized as scrap or second quality product. The quality of a chemical process may be a function of process temperature, pressure, and flow rates, all of which need to be monitored in a situation where some correlation may exist between any two of them. In these cases, if we want to monitor these quality characteristics separately, there will be some error associated with the out of control detection procedure”. We will propose a proportion control chart namely the Pr chart, based on Das (1982) based estimate of proportion
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