A Bayesian Approach to Verify A Sudden Reduction of Process Variance

This research is an effort to introduce an effective tool for evaluating process adjustments aimed at causing a sudden reduction in the process variance. This isusually encountered in scenarios where the process is found to be stable and centered but with high variability. In these scenarios, a number of corrective changes must be made and tested to reduce the process variance. Typically, it is desired to evaluate the effects of these changes and take appropriateactions. A number of sampling techniques can be used to quantify the impact of engineering efforts and verify theireffects on the process performance. This paper proposes a Bayesian approach for determining the maximum economic sample size required to verify such efforts. Mathematicalmodels are derived and loss function is utilized to develop implementation boundaries from an economic aspect. Inaddition, a numerical example demonstrates the steps involved in the model procedure and the economic advantages of theproposed approach.

Keywords:

Statistical Process Control , Fully Bayesian Sample Size Determination , Traditional Methods , Process Variance