A Case Study on Six Sigma-Based Bayesian Control Charts under Gamma Prior Distribution

Abstract

Statistical process control, or SPC, is a proactive strategy that uses statistical methodology to monitor a process control.In contrast to acceptance sampling plans, which are typically employed after nonconformity products have already occurred in the process, SPC is used to identify process abnormalities once they spiral out of control and after appropriate preventive measures have been made to manage the production process. In particular, it is the use of statistical techniques to gather data, chart the data, and then track the variability of a specific process of interest over a period of time in relation to the lower and upper control limits, which are typically set at three standard deviations above and below the process target line.The Bayesian approach is a novel and intriguing method in industrial statistical quality control (SQC) that relies on the subjective likelihood of past information about the process. In this research paper, the construction of six sigma-based control chart is developed by using Gamma prior distribution along with Bayesian approach.