Statistical Process Control
Also known as Statistical Quality Control (SQC) or control charts. This approach uses statistical methods to monitor and control a process. It helps you identify the stability of a process, baseline historical performance and variation, and identify new trends, shifts and outliers in the process.
The key to SPC charts or control charts is the identification of variation over time that falls into two categories:
- Common Cause – variation that exists within the natural variation of the process, based on recurring sources or factors that consistently acts on the process. These types of causes produce a stable and repeatable distribution over time.
- Special Cause – also known as assignable variation. They are often intermittent and unpredictable, and due to a specific problem or issue that has arisen. It can be identified by looking for out of control conditions as defined by the Nelson Rules.
In a given process, there may be signals that need to be responded to (the process is going “out of control”) and there are signals that aren’t present (everything is stable and “in control”). Without control charts, we might react when there is no signal, or fail to react when there is a signal. Separating common from special cause variation helps minimize the wrong reaction.
When a real special cause is identified, the process should be stopped right away, so problems can be found and resolved, and the problem doesn’t continue to generate cost issues (scrap and rework) or customer complaints, which lead to negative publicity or lost sales.
