Spatial control charts for the mean

Scott D. Grimshaw, Natalie J. Blades, Michael P. Miles

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Developments in metrology provide the opportunity to improve process monitoring by obtaining many measurements on each sampled unit. Increasing the number of measurements may increase the sensitivity of control charts to detection of flaws in local regions; however, the correlation between spatially proximal measurements may introduce redundancy and inefficiency in the test. This paper extends multivariate statistical process control to spatial-data monitoring by recognizing the spatial correlation between multiple measurements on the same item and replacing the sample covariance matrix with a parameterized covariance based on the semivariogram. The properties of this control chart for the mean of a spatial process are explored with simulated data and the method is illustrated with an example using ultrasonic technology to obtain nondestructive measurements of bottle thickness.

Original languageEnglish (US)
Pages (from-to)130-148
Number of pages19
JournalJournal of Quality Technology
Issue number2
StatePublished - Apr 2013
Externally publishedYes


  • Hotelling T control chart
  • Multivariate EWMA
  • Optimal allocation of sample resources
  • Semivariogram
  • Spatial covariance models

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research


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