Wednesday, April 17, 2013

The Analyzation of SPC Data

Stanley Deming, Ph.D., President, Statistical Designs recently sat down with us to go over some of the topics he'll be presenting on during the Development, Validation and Maintenance of Biological Assays event taking place this coming May.

Today, Dr. Deming answers the question:
Are there differences in how SPC data should be analyzed if one is interested in spotting emerging trends rather than determining the acceptability of a particular assay?

This is, in part, a trick question: SPC data should never be used to determine the acceptability of a particular assay. Concepts like “system suitability criteria” and “assay acceptance criteria” are essentially “specifications” (whether we call them that or not), and it is a commonplace that “specifications should be based on fitness for use”, not on the behavior of the assay. 
Statistical process control limits can be thought of as a visualization of the voice of the process (assay) talking to us and telling us how it is behaving. Specification limits can be thought of as a visualization of our voice talking to the process and trying to tell it how we want it to behave. Unfortunately, the process could care less about what we want – it is not going to listen to us, it is just going to do what it does. 
Setting specifications based on statistical process control limits is misguided thinking – control limits and specification limits are totally separate concepts. For example, a process can be out of statistical control and still be within specifications (i.e. fit for use). Statistical process control limits should never be used to set specification limits – specification limits should be based on fitness for use. 
Getting back to the first part of the question: The “rule of eight” (or, as I prefer, the “rule of ten”) states that if eight (or ten) or more consecutive data points fall on the same side of the center line on either the x-bar or r chart, then the process is out of control in the sense that it is drifting off in one direction. This indicates an ‘emerging trend” and should be brought to the attention of the assayist. 
As a final point, sometimes “emerging trends” are not necessarily bad. As an example, if the r-chart has eight (or ten) or more consecutive data points below the centerline, this is an indication of improved precision. This suggests that there is an opportunity to discover why the precision has improved, and to then implement that discovery so the precision can remain improved in the future.

Dr. Deming will be leading the workshop Process Capability and Variance Components Analysis on May 14, 2013.  For more information on this session and the rest of the program, download the Bioassays agenda. If you'd like to join us May 14-16, 2013 in Seattle, as a reader of this blog, when you register to join us and mention code IBA13JP, you'll save 20% off the standard rate! Have any questions? Feel free to email Jennifer Pereira.

Share this article with your social network, just click below to share now!

No comments :

Post a Comment