Product quality has a relationship with customer satisfaction. Therefore, the company always strives to maintain the product quality. The effort can be made by the company to maintain product quality is monitoring the production process. One of the tools used to monitor the production process is the control chart. In process monitoring, there are two parameters are observed in the process that are mean and variance. The univariate process monitoring is only carried out on a study variable and only using the information on the corresponding variable. However, in this study, need to monitor process mean and variance simultaneously on a study variable that requires information on study variable and information on auxiliary variable using Auxiliary Information Based Maximum Generally Weighted Moving Average (AIB-MaxGWMA). Besides that, in this study, compare AIB-MaxGWMA with the MaxGWMA control chart to know the effect of adding an auxiliary variable in process monitoring. The performance of these control chartss is evaluated using out of control Average Run Length (OC ARL), where OC ARL is the average number of samples needed to detect a particular shift. The result of this study is AIB-MaxGWMA control chart has a smaller OC ARL than the MaxGWMA control chart which showed that the AIB-MaxGWMA control chart is more sensitive or faster than MaxGWMA control chart to detect a shift. In further study, we recommended to enhance the performance of the AIB-MaxGWMA control chart by extending the current work to the Auxiliary Information Based Maximum Multivariate Generally Weighted Moving Average (AIB-Max MGWMA) control chart, so it is possible to monitor the mean and variance process simultaneously in multivariate case (at least two quality characteristics).
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 5 Jun 2020|
|Event||2019 International Conference on Science Education and Technology, ICOSETH 2019 - Surakarta, Central Java, Indonesia|
Duration: 23 Nov 2019 → …