3. Production Process Characterization
3.1. Introduction to Production Process Characterization
3.1.3. Terminology/Concepts

3.1.3.6.

Experiments and Experimental Design

Factors and responses

Besides just observing our processes for evidence of stability and capability, we quite often want to know about the relationships between the various Factors and Responses.

We look for correlations and causal relationships

There are generally two types of relationships that we are interested in for purposes of PPC.  They are: 

Correlation

Two variables are said to be correlated if an observed change in the level of one variable is accompanied by a change in the level of another variable.  The change may be in the same direction (positive correlation) or in the opposite direction (negative correlation).

Causality

There is a causal relationship between two variables if a change in the level of one variable causes a change in the other variable.

Note that correlation does not imply causality.  It is possible for two variables to be associated with each other without one of them causing the observed behavior in the other.  When this is the case it is usually because there is a third (possibly unknown) causal factor. 

Our goal is to find causal relationships

Generally, our ultimate goal in PPC is to find and quantify causal relationships. Once this is done, we can then take advantage of these relationships to improve and control our processes.

Find correlations and then try to establish causal relationships

Generally, we first need to find and explore correlations and then try to establish causal relationships. It is much easier to find correlations as these are just properties of the data. It is much more difficult to prove causality as this additionally requires sound engineering judgment.  There is a systematic procedure we can use to accomplish this in an efficient manner. We do this through the use of designed experiments.

First we screen, then we build models

When we have many potential factors and we want to see which ones are correlated and have the potential to be involved in causal relationships with the responses, we use screening designs to reduce the number of candidates. Once we have a reduced set of influential factors, we can use response surface designs to model the causal relationships with the responses across the operating range of the process factors.

Techniques discussed in process improvement chapter

The techniques are covered in detail in the process improvement section and will not be discussed much in this chapter. Examples of how the techniques are used in PPC are given in the Case Studies.