Quality Advisor

A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.

SPC DEMO

Minimize Production Costs, Quickly Detect Issues, and Optimize Your Product Quality

Don’t miss out! Book a demo of our specialized SPC software and unlock immediate improvements in your processes.

Book a Demo

• Quality Advisor
• Data Collection Tools
• Data Analysis Tools
• Formulas and Tables
• Glossary
• Additional Resources

Control Charts

Control charts: Does your data represent a process that is stable and in control?

Control charts are the best way to learn how a process is running. They are used to reduce the chance of making one of two kinds of mistakes:

1. Overcontrolling
   Also called a Type I error, this refers to adjusting the process when nothing out of the ordinary has occurred.
2. Undercontrolling
   Also called a Type II error, this refers to the failure to adjust the process when something out of the ordinary has occurred

If you are not sure which chart to use for your data, review the brief descriptions on data analysis tools.

Frequently-asked questions about control charts

1. Which control chart should you use?
2. Multiple control limits: A long shot, or just a bad slice?
3. When should you recalculate limits?
4. Testing a theory about your data

For more on control charts and how they can help you, view a 4-minute video.

---

Additional reference material

Additional sections from legacy control-chart-selection:

Which control chart should you use?

Correct control chart selection is a critical part of creating a control chart. If the wrong control chart is selected, the control limits will not be correct for the data. The type of control chart required is determined by the type of data to be plotted and the format in which it is collected. Data collected is either in variables or attributes format, and the amount of data contained in each sample (subgroup) collected is specified.

Variables data is defined as a measurement such as height, weight, time, or length. Monetary values are also variables data. Generally, a measuring device such as a weighing scale, vernier, or clock produces this data. Another characteristic of variables data is that it can contain decimal places e.g. 3.4, 8.2.

Attributes data is defined as a count such as the number of employees, the number of errors, the number of defective products, or the number of phone calls. A standard is set, and then an assessment is made to establish if the standard has been met. The number of times the standard is either met or not is the count. Attributes data never contains decimal places when it is collected, it is always whole numbers, e.g. 2, 15.

Sample or subgroup size is defined as the amount of data collected at one time. This is best explained through examples.

• When assessing the temperature in a vat of liquid, the reading is measured once hourly; therefore the sample size is one per hour.
• When measuring the height of parts, a sample of five parts is taken and measured every 15 minutes; therefore the sample size is five.
• When checking the number of phone calls that ring more than three times before being answered, the sample size is the total number of phone calls received, which will vary.
• When checking 10 invoices per day for errors, the sample size is 10.

More information on types of data, sample sizes, and how to select them is given in Practical Tools for Continuous Improvement which is available from PQ Systems. Once the type of data and the sample size are known, the correct control chart can be selected. Use the following “Control chart selection flow chart” to choose the most appropriate chart.

Once you’ve determined which control chart is appropriate, software like SQCpack can be used to create the chart.

Additional sections from legacy control-chart-recalculate:

When should you recalculate limits?

Eventually, everyone using SPC charts will have to decide whether they should change the control limits or leave them alone. There are no hard and fast rules, but here are some thoughts to help you make your decision.

The purpose of any control chart is to help you understand your process well enough to take the right action. This degree of understanding is only possible when the control limits appropriately reflect the expected behavior of the process. When the control limits no longer represent the expected behavior, you have lost your ability to take the right action. Merely recalculating the control limits, however, is no guarantee that the new limits will properly reflect the expected behavior of the process either.

1. Have you seen the process change significantly, i.e., is there an assignable cause present?
2. Do you understand the cause for the change in the process?
3. Do you have reason to believe that the cause will remain in the process?
4. Have you observed the changed process long enough to determine if newly-calculated limits will appropriately reflect the behavior of the process?

You should ideally be able to answer yes to all of these questions before recalculating control limits.

To create control charts and easily recalculate control limits, try software products like SQCpack.

Additional sections from legacy control-chart-test-theory:

Testing a theory about your data

If your theory is concerned with different results coming from different shifts, operators, or equipment, try separating the data. For example, you might suspect that one machine is the source of more scrap than another machine. If you are considering process improvements, one way to test a theory is to make a change in the process and track the effects. To do this, isolate data.

1. If you are collecting data from multiple lines or shifts, you might make a change on one shift or line, and stratify data for analysis. If you are using SQCpack, the filter function can help create a subset of data from the process you have changed.
2. Create a control chart, histogram, or run chart, or perform capability analysis with data collected after the change. Compare charts or capability indices created before and after the change.
3. Create a control chart showing data collected before and after the change. You can create a separate set of control limits for each group of data. Has the process improved? Stayed the same? Worsened?