Nonstationarity in statistical process control — issues, cases, ideas
Article first published online: 27 JUL 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry
Volume 27, Issue 4, pages 367–376, July/August 2011
How to Cite
Ketelaere, B. D., Mertens, K., Mathijs, F., Diaz, D. S. and Baerdemaeker, J. D. (2011), Nonstationarity in statistical process control — issues, cases, ideas. Appl. Stochastic Models Bus. Ind., 27: 367–376. doi: 10.1002/asmb.911
- Issue published online: 17 AUG 2011
- Article first published online: 27 JUL 2011
- Manuscript Revised: 26 JUN 2011
- Manuscript Accepted: 26 JUN 2011
- Manuscript Received: 20 MAY 2011
- statistical process control;
- synergistic control
Statistical process control (SPC) is a powerful framework that is used in many industries to decrease process variability and to pinpoint special cause variation. Although a broad range of techniques have been developed to do so, often the real-life situation does not fully comply with the basic assumptions that are made in SPC resulting in poor results. One of the main violations against the assumptions is the fact that industrial processes rarely behave in a stationary manner — this is evidently the case for biological processes but is also an important issue when monitoring industrial processes. Besides, the ever increasing amount of data, with a clear shift towards multivariate and even multiway quality control, makes the classical univariate approach not feasible anymore. These two observations pose important challenges to statisticians to develop novel SPC algorithms that are broadly applicable in modern industries. In this contribution we discuss both issues and use two very different case studies to show the reader recent directions and developments in the SPC landscape. Copyright © 2011 John Wiley & Sons, Ltd.