Statistical process control

This helps to ensure that the process operates efficiently, producing more specification-conforming products with less waste scrap.

[4] Along with a team at AT&T that included Harold Dodge and Harry Romig he worked to put sampling inspection on a rational statistical basis as well.

Shewhart consulted with Colonel Leslie E. Simon in the application of control charts to munitions manufacture at the Army's Picatinny Arsenal in 1934.

That successful application helped convince Army Ordnance to engage AT&T's George D. Edwards to consult on the use of statistical quality control among its divisions and contractors at the outbreak of World War II.

Deming was an important architect of the quality control short courses that trained American industry in the new techniques during WWII.

[5][6] Shewhart read the new statistical theories coming out of Britain, especially the work of William Sealy Gosset, Karl Pearson, and Ronald Fisher.

The application of SPC to non-repetitive, knowledge-intensive processes, such as research and development or systems engineering, has encountered skepticism and remains controversial.

[9][10][11] In No Silver Bullet, Fred Brooks points out that the complexity, conformance requirements, changeability, and invisibility of software[12][13] results in inherent and essential variation that cannot be removed.

From an SPC perspective, if the weight of each cereal box varies randomly, some higher and some lower, always within an acceptable range, then the process is considered stable.

The degraded functionality of the cams and pulleys may lead to a non-random linear pattern of increasing cereal box weights.

If, however, all the cereal boxes suddenly weighed much more than average because of an unexpected malfunction of the cams and pulleys, this would be considered a special cause variation.

The tools used in these extra activities include: Ishikawa diagram, designed experiments, and Pareto charts.

Designed experiments are a means of objectively quantifying the relative importance (strength) of sources of variation.

Steps to eliminating a source of variation might include: development of standards, staff training, error-proofing, and changes to the process itself or its inputs.

Digital control charts use logic-based rules that determine "derived values" which signal the need for correction.