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An SPC chart for analyzing process behavior of data collected in proportions or percentages.

A p-chart is an attributes control chart used with data collected in subgroups of varying sizes. Because the subgroup size can vary, it shows a proportion on nonconforming items rather than the actual count. P-charts show how the process changes over time. The process attribute (or characteristic) is always described in a yes/no, pass/fail, go/no go form. For example, use a p-chart to plot the proportion of incomplete insurance claim forms received weekly. The subgroup would vary, depending on the total number of claims each week. P-charts are used to determine if the process is stable and predictable, as well as to monitor the effects of process improvement theories.

A p-chart should be used instead of a u-chart when the result is expressed in a proportion. For example, the number of items found to be defective during an inspection.

A u-chart shows process changes over time by looking at the number of nonconformities produced by the process. The plotted values represent a ratio. The subgroup size can vary, so control limits can adjust to reflect this, just as p-charts do.

Another guideline: If the items being counted can never be larger than the item being inspected, a p-chart is recommended. For example, if you are inspecting a dozen labels, at most 12 can have a missing label.

When to use classification (attributes) control charts:

1. When you want to see if your process is stable and predictable;

2. When you want to see how a planned change affects the process;

3. When the data you are examining represents counts of some type;

4. When the time order of the data is preserved.

This chart is part of PI VIZpack™ by PQ Systems, a collection of eight process improvement charts for Power BI®. They provide a clear way to visualize and evaluate process behavior and performance. Get started with a free two-week trial today. After the trial, you may continue using the basic features or upgrade to the premium version. To learn more visit our website.

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