Statistical Process Control (SPC)
Quick facts
| Category | Data-driven process monitoring and variation control |
|---|---|
| Used by | Manufacturing, automotive, aerospace, pharmaceuticals and other industries with repetitive production processes |
| Also called | SPC |
| Related standards | IATF 16949, AIAG SPC reference manual |
| Related processes | Gage R&R, measurement system analysis, six sigma, continuous improvement |
| Semantic match | Statistical Process Control, SPC, control charts, common cause special cause variation |
What is Statistical Process Control (SPC)?
SPC plots process data over time using control charts, which display measurements alongside statistically calculated control limits, giving a visual, ongoing picture of whether a process is behaving predictably or has shifted in a way that requires attention.
A stable process shows only common cause variation, the natural, recurring variation inherent to the process itself, producing a consistent, predictable distribution over time. Special cause variation, sometimes called assignable variation, is intermittent and unpredictable, arising from a specific, identifiable issue and showing up as out-of-control signals on a control chart.
SPC tells an organization if and when something unusual has happened in a process, but it does not by itself explain why; when a special cause signal appears, the process must still be investigated to identify and address the underlying issue.
Why is Statistical Process Control (SPC) important?
SPC allows organizations to catch process drift before it produces enough defective output to become a costly quality problem, shifting from reactive inspection to proactive process monitoring.
By distinguishing common cause from special cause variation, SPC prevents two costly mistakes: overreacting to normal process variation as if it were a real problem, and underreacting to genuine special cause signals that actually require investigation.
Organizations that implement SPC effectively commonly see reduced variability, decreased scrap and rework, and improved throughput, since problems are caught and addressed earlier in the process.
How does Statistical Process Control (SPC) work?
A typical SPC implementation includes:
- Process selection. Identify critical processes where SPC will have the most impact.
- Data collection. Establish reliable systems for collecting and recording process data.
- Control chart creation. Plot data over time with calculated control limits.
- Variation classification. Distinguish common cause from special cause variation using established rules.
- Investigation. Investigate special cause signals to identify their underlying source.
- Ongoing monitoring. Continue tracking the process to confirm stability and catch future shifts.
Common Cause vs. Special Cause Variation
| Comparison | Common Cause Variation | Special Cause Variation |
|---|---|---|
| Nature | Natural, recurring, predictable | Intermittent, unpredictable, assignable |
| Response | Generally requires no action | Requires investigation and correction |
Real-world examples of Statistical Process Control (SPC)
An automotive plant implements SPC on a critical dimension, reducing defect rates significantly within months by catching and addressing process drift before it produced widespread nonconformances.
An IT director uses SPC control charts to track computer crash frequency, identifying and addressing a special cause signal before it become a recurring pattern.
A pharmaceutical company applies SPC to its most costly production line, using early wins to build momentum for broader SPC adoption across additional processes.
Regulations and standards related to Statistical Process Control (SPC)
IATF 16949 commonly expects SPC as part of an automotive supplier's process control toolkit, and the AIAG SPC reference manual provides widely used, standardized guidance for control chart selection and interpretation.
SPC is also a core tool within Lean Six Sigma methodology, used across all five DMAIC phases, from establishing a baseline understanding of process performance through ongoing control after improvements are implemented.
Required by
How QT9 helps with Statistical Process Control (SPC)
QT9 QMS SPC capabilities
- Support control chart creation and process data tracking.
- Connect SPC data to calibration and measurement system analysis records.
- Flag out-of-control signals for investigation and corrective action.
- Maintain historical process data for baseline and trend comparison.
- Link SPC findings directly to CAPA and nonconformance records.
- Provide real-time dashboards for ongoing process monitoring.
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Common mistakes with Statistical Process Control (SPC)
Common mistakes include reacting to every fluctuation in process data as if it were a special cause, when normal common cause variation should generally be left alone rather than adjusted.
Other problems include implementing SPC without first confirming the measurement system itself is reliable through Gage R&R or broader measurement system analysis, risking control charts built on flawed data.
Frequently asked questions
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