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 (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, SQC includes acceptance sampling where SPC does not.
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
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 Relationship Between Statistical Quality Control and 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 SPC 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.
A LASSO-Based Diagnostic Framework For Multivariate Statistical Process Control (Technometrics) Several statistical process control examples are presented to demonstrate the effectiveness of the adaptive LASSO variable selection method.
Rethinking Statistics For Quality Control (Quality Engineering) As methods used for statistical process control become more sophisticated, it becomes apparent that the required tools have not been included in courses that teach statistics in quality control. A basic description of these tools and their applications is provided, based on the ideas of Box and Jenkins and referenced publications.
Clearing SPC Hurdles (Quality Progress) Statistical process control has provided significant cost savings for companies that are fortunate enough to implement it fully. However, these six obstacles can waylay the best of intentions.
SPC: From Chaos to Wiping the Floor (Quality Progress) A history of statistical process control shows how it has gone from taming manufacturing processes to enabling all organizations to maintain their competitive edge.
Statistical Process Control For Monitoring Nonlinear Profiles: A Six Sigma Project On Curing Process (Quality Engineering) This article describes a successful Six Sigma project in the context of statistical engineering for integrating SPC to the existing practice of engineering process control (EPC) according to science.
Using Control Charts In A Healthcare Setting (PDF) This teaching case study features characters, hospitals, and healthcare data to help readers create a control chart, interpret its results, and identify situations that would be appropriate for control chart analysis.