Abstract
AbstractMonitoring the multivariate coefficient of variation (MCV) is conducted when the multivariate process variation changes proportionally with the mean vector because under this situation the traditional multivariate charts for the mean and variance cannot work correctly. All existing MCV charts in the literature are implemented based on sample MCVs computed from samples of size larger than unity each. However, there are situations when forming samples (of ) is not feasible. To address the aforementioned limitation of existing MCV charts, in this paper, an EWMA chart based on individual observations for monitoring the MCV is developed. Numerical studies based on Monte Carlo simulations are conducted to investigate the run length properties of the proposed chart in detecting either increases or decreases in the MCV, for two and three quality characteristics and various parameter values. A real bivariate dataset on the diameter and length of dowel pins is adopted to illustrate the implementation of the proposed chart.
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.
