Quality teams use SPC because it stops two kinds of mistake: reacting to random noise as if it meant something, and missing a real shift until it becomes scrap. It is a standard tool in the control phase of DMAIC.
Where it comes from
Walter Shewhart developed the control chart at Bell Telephone Laboratories in the 1920s. W. Edwards Deming later taught SPC widely, including to Japanese industry after the Second World War.
At a glance
Noise or signal?
Swipe sideways to see the whole diagram.
How to apply Statistical process control
- 1
Choose what to measure
Pick a characteristic that matters to the customer or predicts quality downstream, such as a critical dimension, fill weight or cycle time.
- 2
Check the measurement system
Make sure the gauge is accurate and repeatable before charting. A noisy measurement hides the process.
- 3
Pick the chart
Use X-bar and R or individuals charts for measured data, and p, np, c or u charts for counts of defects or defectives.
- 4
Set control limits
Calculate the center line and control limits from a stable period of data, usually three standard deviations either side of the mean. Control limits come from the process, not from the specification.
- 5
Watch for signals
Look for points outside the limits and for patterns such as runs and trends, using a set of run rules. The Western Electric rules, for example, flag one point beyond the limits or eight in a row on one side of the center line.
- 6
Respond and record
When a signal appears, stop and find the special cause, fix it and note what happened on the chart. Leave the process alone when there is no signal.
Worked example (illustrative)
A filling line weighs five bottles every hour and charts the average. The center line is 502.0 g, the control limits are 499.5 g and 504.5 g, and the specification is 495 to 510 g. For weeks the averages wander inside the limits, so operators leave the filler alone. One morning eight averages in a row fall above the center line, between 502.6 and 503.4 g. None is outside the limits, but the run is a signal under the Western Electric rules. The team checks the filler, finds a worn valve seal and replaces it before any bottle goes out of specification, then notes the cause on the chart.
Common mistakes
- Using specification limits as control limits, which hides whether the process itself is stable.
- Adjusting the process after every point, which adds variation instead of removing it.
- Charting data nobody reviews, so signals appear but nobody acts.
When Statistical process control is not the right tool
SPC needs a stable, repeating process and enough data points. For one-off jobs or very low volumes, a checklist or first-article inspection may be more practical.
SPC and process capability
A control chart shows whether a process is stable. Process capability shows whether that stable process fits inside the customer's specification, using indices such as Cp and Cpk.
Check stability first. Capability figures from an unstable process do not predict what it will do next. The process capability calculator works out Cp and Cpk from your spec limits, mean and standard deviation.
Guides on this topic
- Lean vs Six Sigma: differences, history and when to use eachLean vs Six Sigma compared: what each method targets, where they came from, how they differ in manufacturing and how Lean Six Sigma combines them.Read the guide
- Six Sigma green belt: meaning, training and DMAIC toolsA six sigma green belt leads focused DMAIC projects. See what green belt training covers, the tools by phase, exam topics and whether it is worth it.Read the guide
- Six Sigma black belt: role, training and certificationA six sigma black belt runs complex improvement projects and coaches green belts. Learn the role, statistical tools, certification and the path from green belt.Read the guide
- Six sigma certification free: what is free and what countsSix sigma certification free routes explained: what free usually covers, how to judge recognition, and how to build real evidence with a project.Read the guide
Sources
Books and papers show their publication year. Web pages show the month we last checked them.
- 1.Walter A. Shewhart, Economic Control of Quality of Manufactured Product (D. Van Nostrand, 1931)
- 2.Douglas C. Montgomery, Introduction to Statistical Quality Control, 8th edition (Wiley, 2019)