SPC for the Process Industries

Process industries such as paper and chemical present rather unique problems for SPC implementation.  Namely that several assumptions of X-Bar charts are violated:


  • Samples or subgroups are usually limited to a size of one
  • A control chart with a sample size of one is not sensitive to small process shifts.   It would take on average over 16 samples to detect just a one and a half sigma shift in the process
  • Successive data points in continuous processes are often autocorrelated, i.e. related in value.  For example in the diagram below successive samples will be correlated do to mixing in the tank


  • Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) Charts provide an increased sensitivity to small shifts
  • Autoregressive Integrated Moving Average (ARIMA) models provide a means to remove the noise in the data caused by autocorrelation.  These tools are mentioned in the course outline on the SPC page