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Assignable Causes and Autocorrelation: Control Charts for Observations or Residuals?

Summary: [This abstract is based on the author's abstract.] In many industrial processes, the disturbance generated by an assignable cause is affected by the same inertial elements as the observations from the common-cause system. A control chart based on the observations can be effective for statistical process control, but its success depends on the relationship of the time-series model produced by the inertial elements to the magnitude of the disturbance in the input. Characteristics of industrial processes, including the types of assignable causes, are critical for design of the appropriate control chart. Simple guidelines are developed for selecting one approach over another in application. A physically realistic disturbance model is demonstrated that differs from that in the majority of SPC autocorrelation research. While it is reasoned that a control chart based on residuals is preferred in general for this model, there are cases in which a control chart based on raw data can be nearly as effective.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Common causes,Design of control charts,Time series,Statistical process control (SPC),Systematic assignable cause,Exponentially weighted moving average control charts (EWMA)
  • Author: Runger, George C.
  • Journal: Journal of Quality Technology