Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 269 - Statistical Process Monitoring Methods
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Quality and Productivity Section
Abstract #320811
Title: Adaptive Process Monitoring Using Covariate Information
Author(s): Kai Yang*
Companies: Medical College of Wisconsin
Keywords: Auxiliary variables; Covariates; Exponentially weighted moving average charts; Regression modeling; Statistical process control; Weighting function
Abstract:

Statistical process control charts provide a powerful tool for monitoring production lines, internet traffic flows, disease incidences, health care systems and more. In practice, quality variables are often affected by many covariates. Intuitively, a control chart could be improved by using information in covariates. However, because of the complex relationship between the quality variables and the covariates, shifts in the quality variables could be due to certain covariates whose data cannot be collected, and shifts in some observable covariates may not necessarily cause shifts in the quality variables. Thus, it is quite challenging to properly use covariate information for process monitoring. This talk suggests a method to handle this problem. An effective exponentially weighted moving average chart is developed, in which its weighting parameter is chosen large if the related covariates included in the collected data tend to have a shift and small otherwise. Because the covariate information is used in the weighting parameter only, the chart is designed solely for detecting shifts in the quality variables, and it can react to a future shift in the quality variables quickly.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program