Model-based process monitoring using robust generalized linear models

D. Jearkpaporn, Douglas Montgomery, George Runger, C. M. Borror

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Model-based process-monitoring procedures are extremely useful in situations where an output variable of interest is impacted by one or more inputs to the process, and where there are multistage processes with multiple inputs and outputs. To build the model relating input and output variables, the procedure uses historical data, which often contain outliers. To accommodate the presence of these outliers, a robust fitting scheme is introduced for the Generalized Linear Model in process monitoring. Robust deviance residuals are defined and used as the basis of the monitoring procedure. An example and a simulation study for a gamma-distributed response are included. The average run length performance reveals that the procedure is effective for detecting small process shifts when outliers are present.

Original languageEnglish (US)
Pages (from-to)1337-1354
Number of pages18
JournalInternational Journal of Production Research
Issue number7
StatePublished - Apr 1 2005


  • Gamma-distributed data
  • M-estimator
  • Robust deviance
  • Robust generalized linear models (GLMs)

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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