|Specialized Control Charts
Short Run Process Control
See SHWSRUN in the SAS/QC Sample Library
When conventional Shewhart charts are used to
establish statistical control, the initial control
limits are typically based on 25 to 30 subgroup samples.
Often, however, this amount of
data is not available in manufacturing situations
where product changeover occurs frequently or
production runs are limited.
A variety of methods have been introduced for analyzing
data from a process that is alternating between short
runs of multiple products. The methods commonly used
in the United States are variations of two basic
These approaches are highlighted in this section because
of their popularity, but two alternatives that are
technically more sophisticated are worth noting.
- the difference from nominal approach.
A product-specific nominal value is subtracted
from each measured value, and the differences
(together with appropriate control limits) are
charted. Here it is assumed that the nominal
value represents the central location of the
process (ideally estimated with historical data)
and that the process variability is constant
- the standardization approach. Each
measured value is standardized with a
product-specific nominal and standard deviation
values. This approach is followed when the
process variability is not constant across products.
SAS examples illustrating these alternatives are
provided in the SAS/QC sample library and are
described by Rodriguez and Bynum (1992).
- Hillier (1969) provided a method for modifying the
usual control limits for and R
charts in startup situations where fewer than 25
subgroup samples are available for estimating the
process mean and standard deviation ; also refer to Quesenberry (1993).
- Quesenberry (1991a, 1991b) introduced the so-called
Q chart for short (or long) production runs,
which standardizes and normalizes the data using
probability integral transformations.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.