Statistical Process Control (SPC) is a quality method, founded by Walter A. Shewhart and disseminated by W. Edwards Deming, for the systematic statistical monitoring of running processes. Using control charts and detection rules such as the Nelson Rules, it separates inherent, predictable common cause variation from assignable special cause variation that calls for intervention. A process showing only common causes is in control, and SPC secures improvements in the Control phase of DMAIC.

SPC (Statistical Process Control)

Details

Full Name: Statistical Process Control

Core Concepts:

Process monitoring: Systematic statistical monitoring of running processes
Common Cause Variation: Inherent, random fluctuation — stable and predictable
Special Cause Variation: Assignable cause — unstable, correctable
Control Charts: Central visual tool (see dedicated anchor)
Detection rules: Nelson Rules, Western Electric Rules (see dedicated anchors)
Process Capability: Indices Cp, Cpk (short-term) and Pp, Ppk (long-term)
In-Control: Process exhibits only Common Cause Variation
Out-of-Control: Special Cause detected — intervention required
Continuous Improvement: SPC as a foundation for ongoing process improvement
DMAIC Control Phase: SPC tools secure improvements within Six Sigma
Key Proponents: Walter A. Shewhart (founder), W. Edwards Deming (dissemination), Western Electric Company

Relationship to Other Anchors:

Control Chart (Shewhart): Central tool within SPC
Nelson Rules: Detection rules for Special Causes on Control Charts
Six Sigma: Uses SPC particularly in the Control phase
Testing Pyramid: Conceptual parallel — different levels of quality assurance

When to Use:

Monitoring manufacturing and business processes
Quality management per ISO 9001, IATF 16949, Six Sigma
Pharmaceutical Continuous Process Verification (CPV)
Conceptual foundation for anomaly detection in IT systems
When the question is: "Is my process stable, or has something changed?"