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Area of Science:

  • Statistical Process Control
  • Industrial Engineering
  • Quality Management

Background:

  • Adaptive EWMA (AEWMA) control charts are recognized for monitoring production over various shifts.
  • Their proficiency stems from adapting computational statistics to system shifts.
  • Monitoring the variance-covariance matrix is crucial for process stability.

Purpose of the Study:

  • To propose a function-based AEWMA multivariate control chart for monitoring the variance-covariance matrix stability.
  • To estimate process shifts in real-time and adapt the smoothing constant using a continuous function.
  • To evaluate the proposed chart's performance against existing methods.

Main Methods:

  • Utilizing an unbiased estimator with EWMA statistics for real-time shift estimation.
  • Developing a continuous function to adapt the smoothing/weighting constant.
  • Employing Monte Carlo simulations to assess the chart's shift detection capabilities.
  • Comparing results with existing EWMA and AEWMA charts.

Main Results:

  • The proposed AEWMA chart demonstrated superior performance in detecting process shifts.
  • It provided quicker detection across various shift sizes compared to existing charts.
  • The chart effectively monitored the stability of the variance-covariance matrix.

Conclusions:

  • The function-based AEWMA multivariate control chart is a proficient tool for monitoring covariance matrix changes.
  • It offers practical advantages for statistical process control in industries like the bimetal thermostat manufacturing.
  • The research contributes a valuable method for enhancing real-time process monitoring and quality assurance.