Statistical Process Control (SPC) is a set of methods first generated by Walter A. Shewhart at Bell Laboratories in the early 1920s. Shewhart is credited with first inventing the concept of controlling manufacturing processes to detect issues before defects happen, but lean manufacturing popularized the notion that defects should be spotted as soon as possible.
Then, W. Edwards Deming adapted SPC for the American industry during WWII and later introduced it to Japan. Statistical Process Control later became a tenet of Six-Sigma and lean manufacturing. This process evaluates the product of processes, pinpointing changes in data that can be used to ultimately prevent defects. In manufacturing, this means that operators can use it to lower waste, rework, and scrap.
Statistical Process Control’s impact on industry has been enormous. It has branched out beyond the manufacturing realm and can be applied to any process with quantifiable outputs. For example, service industries and healthcare fields use it.
So how does SPC work? SPC uses statistical methods to oversee and control process outputs. Operators often use run charts and control charts, as well as special software, to manage it.
SPC is carried out in two phases: To establish a process, and then to monitor that process. The goal is to reduce and eliminate process variation, which can increase production costs and defects. Statistical Process Control reduces such variation by empowering businesses to supervise real-time production processes and confirm that they are operating at maximum potential while minimizing waste. Digital SPC solutions can help manufacturers to:
- Pinpoint process trends;
- Analyze defects;
- Acquire variable and defect data; and
- Monitor indexes in real-time, among other features.
In the century since SPC was first introduced, who would have thought that it would come this far?
