What is it?
A c-chart is an attributes control chart used with data collected in subgroups that are the same size. C-charts show how the process, measured by the number of nonconformities per item or group of items, changes over time. Nonconformities are defects or occurrences found in the sampled subgroup. They can be described as any characteristic that is present but should not be, or any characteristic that is not present but should be. For example a scratch, dent, bubble, blemish, missing button, and a tear would all be nonconformities. C-charts are used to determine if the process is stable and predictable, as well as to monitor the effects of process improvement theories. C-charts can be created using software products like SQCpack.
What does it look like?
The c-chart shows the number of nonconformities in subgroups of equal size.
When is it used?
A c-chart is particularly useful when the item is too complex to be ruled as simply conforming or nonconforming. For example, an automobile could have hundreds of possible defects yet still not be considered defective. A c-chart could also be used to monitor the number of phone calls received in a given period of time such as an hour or a day.
Use c-charts when you can answer "yes" to these questions:
Getting the most
Collect as many subgroups as possible before calculating control limits. With smaller amounts of data, the c-chart may not represent variability of the entire system. The more subgroups you use in control limit calculations, the more reliable the analysis will be. Typically, 20 to 25 subgroups will be used in control limit calculations.
C-charts have several applications. When you begin improving a system, use them to assess the system’s stability .
After the stability has been assessed, determine if you need to stratify the data. You may find entirely different results between shifts, among workers, among different machines, among lots of materials, etc. To see if variability on the c-chart is caused by these factors, collect and enter data in a way that lets you stratify by time, location, symptom, operator, and lots.
You can also use c-charts to analyze the results of process improvements. Here you would consider how the process is running and compare it to how it ran in the past. Are there fewer nonconforming units?
Finally, use c-charts for standardization. This means you should continue collecting and analyzing data throughout the process operation. If you made changes to the system and stopped collecting data, you would have only perception and opinion to tell you whether the changes actually improved the system. Without a control chart, there is no way to know if the process has changed or to identify sources of process variability.