What is Statistical Process Control?
Statistical process control (SPC) is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help you monitor process behavior, discover issues in internal systems, and find solutions for production issues. Statistical process control is often used interchangeably with statistical quality control (SQC).
A popular SPC tool is the control chart, originally developed by Walter Shewhart in the early 1920s. A control chart helps one record data and lets you see when an unusual event, such as a very high or low observation compared with "typical" process performance, occurs.
Control charts attempt to distinguish between two types of process variation:
- Common cause variation, which is intrinsic to the process and will always be present
- Special cause variation, which stems from external sources and indicates that the process is out of statistical control
Various tests can help determine when an out-of-control event has occurred. However, as more tests are employed, the probability of a false alarm also increases.
Statistical Quality Control Versus Statistical Process Control
Statistical quality control (SQC) is defined as the application of the 14 statistical and analytical tools (7-QC and 7-SUPP) to monitor process outputs (dependent variables). Statistical process control (SPC) is the application of the same 14 tools to control process inputs (independent variables). Although both terms are often used interchangeably, statistical quality control includes acceptance sampling where statistical process control does not.
The 7 Quality Control (7-QC) Tools
In 1974, Dr. Kaoru Ishikawa brought together a collection of process improvement tools in his text Guide to Quality Control. Known around the world as the seven quality control (7-QC) tools, they are:
- Cause-and-effect diagram (also called Ishikawa diagram or fishbone diagram)
- Check sheet
- Control chart
- Pareto chart
- Scatter diagram
The 7 Supplemental (7-SUPP) Tools
In addition to the basic 7-QC tools, there are also some additional statistical quality tools known as the seven supplemental (7-SUPP) tools:
- Data stratification
- Defect maps
- Events logs
- Process flowcharts
- Progress centers
- Sample size determination
The figure below portrays the relationship between SQC and SPC:
The History of Statistical Process Control
A marked increase in the use of control charts occurred during World War II in the United States to ensure the quality of munitions and other strategically important products. The use of statistical process control methods diminished somewhat after the war, though was subsequently taken up with great effect in Japan and continues to the present day. (For more information, see the History of Quality.)
Many SPC techniques have been adopted by organizations throughout the globe in recent years, especially as a component of quality improvement initiatives like Six Sigma. The widespread use of control charting procedures has been greatly assisted by statistical software packages and sophisticated data collection systems.
Additional process-monitoring tools include:
- Cumulative Sum (CUSUM) charts: The ordinate of each plotted point represents the algebraic sum of the previous ordinate and the most recent deviations from the target.
- Exponentially Weighted Moving Average (EWMA) charts: Each chart point represents the weighted average of current and all previous subgroup values, giving more weight to recent process history and decreasing weights for older data.
Stastistical Process Control Articles and Books
Quality Essentials: A Reference Guide from A to Z (ASQ Quality Press)
SPC: From Chaos to Wiping the Floor (PDF) A history of statistical process control shows how it has gone from taming manufacturing processes to enabling all organizations to maintain their competitive edge.
Chicken Soup for Processes (PDF) Understanding process variation is a prerequisite to Using SPC.
Controversies and Contradictions in Statistical Process Control (PDF) Areas of controversy in SPC include the relationship between hypothesis testing and control charting, the role of theory and the modeling of control chart performance, the relative merits of competing methods, the relevance of research on SPC, and even the relevance of SPC itself.