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Using Factor Effects Analysis to Improve Statistical Process Control

Summary: [This abstract is based on the author's abstract.] The value of factor effects analysis on improving the diagnostic capability of the practitioner is significant, but it relies on the adequacy of the model. Quality practitioners know they are to take action when a control chart shows an out-of-control condition, but they may not know what action is appropriate. Several methods can be employed to generate the process model require in multivariate factor effects analysis. The best approach may be an amalgam of several techniques. A model based on design of experiments (DOE) helps develop a better understanding of the process and is valuable for both process control and improvement. A model involving the interpretation of physical laws governing the behavior of a system or deductive reasoning is frequently used because of its speed, simplicity, and cost. Yet another approach is to build a model based on analysis of observed process data. Despite the inherent risk of unknown and uncontrolled variables, this methodology has proved valuable in prescreening variables for more formal DOE. As Dr. Box noted, "All models are wrong, but some are useful." A useful model can add substantially to the practitioner's diagnostic tool kit.

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  • Topics: Design of Experiments, Statistical Process Control (SPC)
  • Keywords: Cause and effect diagram, Design of experiments (DOE), Multivariate control charts, Statistical process control (SPC)
  • Author: Flaig, John J.
  • Journal: Quality Engineering