Summary

The C chart is a statistical process control (SPC) tool used to plot the number of defects in each sample over time. Use C charts for processes in which the measurement system is only capable of counting the number of defects in a sampled unit. The C chart is an industry standard for monitoring and controlling process outputs over time. The C chart assumes a constant sample size; for samples of different sizes, use the U chart.

Answers the questions:
  • How much common-cause variation does the process exhibit?
  • Is the process stable over time?
  • Did special causes exist during the timeframe of the plotted data?
  • Does evidence suggest something has changed or the process is performing differently than expected?
  • Does the defect rate of the process change at different levels of a process input?
  • Do the dynamic patterns of the process output change at different levels of a process input?
When to Use Purpose
Pre-project Assist in project selection by identifying the process steps that exhibit high defect rates, unstable variation, or other symptoms that indicate the need for improvement.
Start of project Verify the process stability when performing a baseline capability analysis.
Mid-project Investigate the effects of the input variables on the process output over time.
Mid-project Verify the process stability when performing confirmation runs after implementing improvements.
End of project Verify the process stability after implementing controls to obtain a final assessment of process capability.
End of project Graphically compare the pre-project process dynamic behavior to the post-improvement dynamic behavior.
Post-project Control the inputs to the improved process after the project is complete.
Post-project Monitor the output of the improved process after the project is complete.

Data

Discrete numeric Y (number of defects)

How-To

  1. Define what is and what is not a defect.
  2. Verify you can accurately assess each unit (that is, verify the measurement system).
  3. Establish a data collection strategy to define how you will sample units over time.
  4. In Minitab, enter the number of observed defects from each sample in one column. Do not enter a sample size; the C chart assumes it is constant. Use the U chart for unequal sample sizes.
  5. You can also use an historical record of process defects per unit as the basis for establishing control limits.
  6. Optionally, Minitab can evaluate four rules to determine if special causes are present.
  7. Optionally, you can identify meaningful process stages and input conditions in the chart by entering a categorical variable into an additional column. Stages have different center lines and control limits that help you make comparisons across stages. For example, you can examine changes in the process defect rate before, during, and after the implementation of a new procedure on one C chart.

Guidelines

  • A defect is any nonconformance of a product or service. To count defects or defectives, you must have a clear definition as to what does and does not constitute a defect.
  • The defect rate is the average number of defects per sample.
  • You can use a categorical variable with the C chart to show the effects of different input conditions, which Minitab refers to as stages. For example, if you want to examine the defect rate of a forms processing operation (Y) to see the differences between three shifts, you can use the shift as the stage variable to see whether changes occurred in the defect rate, variation, or within-shift patterns between the three shifts.
  • When using a categorical variable to set up stages for the C chart, you should have at least 30 observations in each stage. The C chart requires enough data within each stage to reliably estimate the within-stage process defect rate.
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