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Specialized Control Charts |

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
approaches:^{*}

- 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 across products. - 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.

- 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.

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