c-chart | |
---|---|
Originally proposed by | Walter A. Shewhart |
Process observations | |
Rational subgroup size | n > 1 |
Measurement type | Number of nonconformances in a sample |
Quality characteristic type | Attributes data |
Underlying distribution | Poisson distribution |
Performance | |
Size of shift to detect | ≥ 1.5σ |
Process variation chart | |
Not applicable | |
Process mean chart | |
Center line | |
Control limits | |
Plotted statistic |
In statistical quality control, the c-chart is a type of control chart used to monitor "count"-type data, typically total number of nonconformities per unit.[1] It is also occasionally used to monitor the total number of events occurring in a given unit of time.
The c-chart differs from the p-chart in that it accounts for the possibility of more than one nonconformity per inspection unit, and that (unlike the p-chart and u-chart) it requires a fixed sample size. The p-chart models "pass"/"fail"-type inspection only, while the c-chart (and u-chart) give the ability to distinguish between (for example) 2 items which fail inspection because of one fault each and the same two items failing inspection with 5 faults each; in the former case, the p-chart will show two non-conformant items, while the c-chart will show 10 faults.
Nonconformities may also be tracked by type or location which can prove helpful in tracking down assignable causes.
Examples of processes suitable for monitoring with a c-chart include:
The Poisson distribution is the basis for the chart and requires the following assumptions[2]:
The control limits for this chart type are where is the estimate of the long-term process mean established during control-chart setup.