3.
Production
Process Characterization
3.1.
Introduction to Production Process
Characterization
3.1.3.

Terminology/Concepts



There are just a few fundamental concepts needed for PPC.
This section will review these ideas briefly and provide links to other
sections in the Handbook where they are covered in more detail. 
Distribution(location,
spread, shape) 
For basic data analysis, we will need to understand how
to estimate location, spread and shape from the data. These three
measures comprise what is known as the distribution of the data.
We will look at both graphical and numerical techniques. 
Process variability 
We need to thoroughly understand the concept of process
variability. This includes how variation explains the possible range of
expected data values, the various classifications of variability, and the
role that variability plays in process stability and capability.

Error propagation 
We also need to understand how variation propagates through
our manufacturing processes and how to decompose the total observed variation
into components attributable to the contributing sources. 
Populations and sampling 
It is important to have an understanding of the various
issues related to sampling. We will define a population and discuss
how to acquire representative random samples from the population of interest.
We will also discuss a useful formula for estimating the number of
observations required to answer specific questions.

Modeling 
For modeling, we will need to know how to identify important
factors and responses. We will also need to know how to graphically
and quantitatively build models of the relationships between the factors
and responses. 
Experiments 
Finally, we will need to know about the basics of designed
experiments including screening designs and response surface designs so
that we can quantify these relationships. This topic will receive only
a cursory treatment in this chapter. It is covered in detail in the process
improvement chapter. However, examples of its use are in the case
studies.
