A statistical process control (SPC) tool that plots the proportion defective per sample or subgroup over time. You can 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.

Answers the questions:

- How much common-cause variation does the process exhibit?
- Is the process stable over time?
- Did any special causes exist during the timeframe of the plotted data?
- Does any evidence suggest something has changed or the process is performing differently than expected?
- Does the rate of defectives for 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 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. |

Discrete numeric Y (number of defectives), number of units sampled per lot

- Define what is and what is not a defective unit.
- Verify you can accurately assess each unit (verify the measurement system).
- Establish a data collection strategy to define how you will sample lots over time.
- In Minitab, enter the count of defective products or services into one column in and the sample or subgroup sizes in another column. If the samples are all the same size, you can specify a constant instead of using a separate column.
- You can also use a historical process defective rate as the basis for establishing control limits.
- Optionally, Minitab can evaluate four rules to determine if special causes are present.
- 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 can help you make comparisons across stages. For example, you can examine changes in the process proportion defective before, during, and after the implementation of a new procedure on one P chart.

- If your sample sizes are different, your calculated control limits will also be different. However, if the largest or smallest sample size do not differ from the average by more than 25%, you should use a constant sample size, which results in constant control limits and makes the chart more easily understood.
- A defective is a process output (product or service) that has one or more defects.
- You can use a categorical variable with the P chart to show the effects of different input conditions. Minitab refers to this as stages. For example, if you want to examine the rate of defectives for a forms processing operation (Y) to see if differences exist between three different processing sites, you can use the site as the stage variable to see whether the mean, variation, or within-shift patterns change between the three cites.
- When using a categorical variable to set up stages for the P chart, you should have at least 30 observations in each stage. The P chart requires enough data within each stage to reliably estimate the within-stage process defective rate.