Use a C chart, a statistical process control (SPC) tool, 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. It assumes a constant sample size; for samples of different sizes, use the U chart.
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. |
Your data must be discrete numeric Y (number of defects) values.
For more information, go to Insert an analysis capture tool.
An I-MR chart is an industry standard for monitoring and controlling process outputs over time. In manufacturing, an I-MR chart is generally used for low-volume production and destructive or expensive testing. Many situations exist in transactional or business processes in which an I-MR chart can be used (for example, sales and inventory data). Generally, if you can obtain rational subgroups, you should use the Xbar-R or Xbar-S chart; otherwise, use the I-MR chart.
When to Use | Purpose |
---|---|
Pre-project | Assist in project selection by identifying outputs that exhibit high common-cause variation, frequent special causes, unstable variation, or other symptoms that point to the need for improvement. |
Start of project | Verify process stability when performing a baseline capability analysis. |
Mid-project | Investigate effects of input variables on the process output over time. |
Mid-project | Verify process stability when performing confirmation runs after implementing improvements. |
End of project | Verify 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 inputs to the improved process after the project is complete. |
Post-project | Monitor output of the improved process after the project is complete. |
Your data must be values for continuous Y, but must not contain rational subgroups.
If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least 10 possible values, you can evaluate these data as if they are continuous.
For more information, go to Insert an analysis capture tool.
Use an NP chart, a statistical process control (SPC) tool, to plot the proportion defective per sample or subgroup over time. You can use an NP chart for processes where the measurement system is only capable of determining whether a unit is defective or not defective. An NP chart is an industry standard for monitoring and controlling process outputs over time. An NP chart can be used for samples of different sizes; however, it is best suited for a constant sample size. For different sized samples, try the P chart instead.
When to Use | Purpose |
---|---|
Pre-project | Assist in project selection by identifying process steps that exhibit high defect rates, unstable variation, or other symptoms that point to the need for improvement. |
Start of project | Verify process stability when performing a baseline capability analysis. |
Mid-project | Investigate effects of input variables on process output over time. |
Mid-project | Verify process stability when performing confirmation runs after implementing improvements. |
End of project | Verify 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 inputs to the improved process after project is completed. |
Post-project | Monitor output of the improved process after project is completed. |
Your data must be discrete numeric Y (number of defectives) values and the number of units sampled per lot.
For more information, go to Insert an analysis capture tool.
Use a P chart, a statistical process control (SPC) tool, to plot the proportion defective per sample or subgroup over time. Use a P chart for processes where the measurement system is only capable of determining whether a unit is defective or not defective. The P chart is an industry standard for monitoring and controlling process outputs over time and you can use it for samples of different sizes.
When to Use | Purpose |
---|---|
Pre-project | Assist in project selection by identifying process steps that exhibit high defect rates, unstable variation, or other symptoms that point to the need for improvement. |
Start of project | Verify process stability when performing a baseline capability analysis. |
Mid-project | Investigate the effects of input variables on the process output over time. |
Mid-project | Verify process stability when performing confirmation runs after implementing improvements. |
End of project | Verify 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 inputs to the improved process after the project is complete. |
Post-project | Monitor output of the improved process after the project is complete. |
Your data must be discrete numeric Y (number of defectives) values and the number of units sampled per lot.
For more information, go to Insert an analysis capture tool.
Use a U chart, a statistical process control (SPC) tool, to plot the number of defects in each sampled unit over time. You can use a U chart for processes where the measurement system is only capable of counting the number of defects in a sampled unit. The U chart is an industry standard for monitoring/ controlling process outputs over time and can be used for samples that are different sizes, for example, different sized units or different sized lots of like-sized units.
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 pointing to the need for improvement. |
Start of project | Verify process stability when performing a baseline capability analysis. |
Mid-project | Investigate effects of input variables on process output over time. |
Mid-project | Verify process stability when performing confirmation runs after implementing improvements. |
End of project | Verify 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 inputs to the improved process after the project is complete. |
Post-project | Monitor output of the improved process after the project is complete. |
Your data must be the discrete numeric Y (number of defects) values and the size of the inspected unit (for example, square inches of surface or words of text).
For more information, go to Insert an analysis capture tool.
Use a run chart to provide a graphical means for looking at data dynamically (over time). Run charts often show patterns that do not appear in static displays of your data, such as histograms. A run chart is a useful tool for discovering evidence of influences on the process, all of which can be used to make process improvements
When to Use | Purpose |
---|---|
Start of project | While performing a baseline analysis, you typically use a control chart to verify that the process was stable. A run chart is an additional tool that tests for patterns that may not be detected in a control chart, yet may often reveal clues for making process improvements. |
Mid-project | The first rule of data analysis is to graph the data before running statistical tests. Whenever you collect data over time, you should also graph the data over time to examine its dynamic behavior. A run chart provides tests for trends, cycles, and other patterns. |
Your data must be numeric (continuous or discrete) and collected over time.
The tests that appear in the run chart provide various insights into the process behavior over time.
You can use one of three ways to enter the data in Minitab:
For more information, go to Insert an analysis capture tool.
You can use an Xbar-R or Xbar-S chart for processes with continuous data. This chart is an industry standard for monitoring and controlling process outputs over time. While you usually use Xbar-R and Xbar-S charts with subgroups that are the same size, you can also use them with different size subgroups to accommodate botched measurements or missing data.
When to Use | Purpose |
---|---|
Pre-project | Assist in project selection by identifying outputs that exhibit high common-cause variation, frequent special causes, unstable variation, or other symptoms pointing to the need for improvement. |
Start of project | Verify process stability when performing a baseline capability analysis. |
Mid-project | Investigate effects of input variables on the process output over time. |
Mid-project | Verify process stability when performing confirmation runs after implementing improvements. |
End of project | Verify 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 inputs to the improved process after project is complete. |
Post-project | Monitor output of the improved process after project is complete. |
Your data must be continuous Y values collected in rational subgroups.
For more information, go to Insert an analysis capture tool.