Year-end Sale % OFF 
Abstract
ABSTRACTThis study introduces a Bayesian adaptive exponentially weighted moving average (AEWMA) control chart designed to monitor process dispersion in case of normal distribution by integrating various loss functions. It exhibits robust performance in identifying shifts in process dispersion across different scales. Evaluation involves Monte Carlo simulations to calculate run length characteristics and a comprehensive comparative analysis against existing charts. Our findings emphasize the increased sensitivity of the Bayesian AEWMA control chart to shifts of different magnitudes. Furthermore, an experiment was performed in the context of another field, specifically semiconductor manufacturing, to compare the performance of the proposed Bayesian control chart using different loss functions. It showed that the suggested chart was much better than the existing control charts in terms of observing out‐of‐control signals. In summary, this article develops an innovative approach with various loss function approaches and improves the accuracy and efficiency depending on the dispersion change in the Phase II process, thus it is a valuable contribution to the further development of the quality control and monitoring process.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
