A review and critique of auxiliary information-based process monitoring methods

ABSTRACT

We review the rapidly growing literature on auxiliary information-based (AIB) process monitoring methods. Under this approach, there is an assumption that the auxiliary variable, which is correlated with the quality variable of interest, has a known mean, or some other parameter, which cannot change over time. We demonstrate that violations of this assumption can have serious adverse effects both when the process is stable and when there has been a process shift. Some process shifts in the quality variable of interest can become undetectable. We also show that the basic AIB approach is a special case of simple linear regression profile monitoring. The AIB charting techniques require strong assumptions. Based on our results, we warn against the uncritical use of the AIB approach in quality control applications.

KEYWORDS:

• cause-selecting chart
• control chart
• profile monitoring
• regression-adjusted charts
• statistical process monitoring

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nesma A. Saleh

Nesma A. Saleh is an Assistant Professor of Statistics at the Department of Statistics, Faculty of Economics and Political Science, Cairo University. She holds her B.Sc. (2009), M.Sc. (2012), and PhD (2016) in statistics from Cairo University. Her main area of interest is statistical quality control.

Mahmoud A. Mahmoud

Mahmoud A. Mahmoud is Dean of the Faculty of Economics and Political Science, Cairo University. Prior to becoming Dean, he was the Vice Dean for Education and Students’ Affairs, and a Professor of Statistics at Cairo University, Faculty of Economics and Political Science. He holds his BS (1992) and MS (1997) in statistics from Cairo University, and PhD (2004) in statistics from Virginia Tech - USA. His primary area of interest is statistical quality control and improvement. He is a member of the Editorial Board of Quality and Reliability Engineering International, and Review of Economics and Political Science (REPS). He is a Deputy Editor-in-Chief in Journal of Humanities and Applied Social Sciences (JHASS).

William H. Woodall

William H. Woodall is an Emeritus Professor in the Department of Statistics at Virginia Tech. He is a former editor of the Journal of Quality Technology (2001–2003). He is the recipient of the Box Medal (2012), Shewhart Medal (2002), Hunter Award (2019), Youden Prize (1995, 2003), Brumbaugh Award (2000, 2006), Bisgaard Award (2012), Nelson Award (2014), Ott Foundation Award (1987), and best paper award for the IIE Transactions on Quality and Reliability Engineering (1997).He is a Fellow of the American Statistical Association, a Fellow of the American Society for Quality, and an elected member of the International Statistical Institute.

Sven Knoth

Sven Knoth is a Professor of Statistics in the Department of Mathematics and Statistics within the School of Economic and Social Sciences at the Helmut Schmidt University, Hamburg, Germany. Prior to that, he worked as a Senior SPC Engineer at Advanced Mask Technology Center (AMTC) Dresden, Germany from 2004 to 2009. He is an Associate Editor of Computational Statistics and member of the Editorial Board of Journal of Quality Technology and Quality Engineering.