Mean and Variance Adjustment of the Average Control Chart by Shape Parameter Using Bayesian Estimation of the Inverse Gaussian Distribution
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Abstract
This research aims to develop the average control chart ( ) using the shape parameter of the Inverse Gaussian Distribution by Bayesian Estimation for estimating mean and variance, and to compare the process potential capability (Cp) and the actual process capability index (Cpk) for Monte Carlo simulation with 10,000 replications assuming that the specification is 0.001. The result shows that the process potential capability (Cp) and the actual process capability index (Cpk) of the Adjusted
using Bayesian Estimation of the shape parameter of the Inverse Gaussian Distribution for estimating mean and variance have more capability than the
under the normal distribution when the sample size is less than 30. For the sample size of 30, the two control charts have the indifferent capability process.
Keywords: Adjusted ; Bayesian Estimation; shape parameter; Inverse Gaussian Distribution
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