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The History of Statistics in Mortgage Quality Control

August 9, 2010 By Cogent QC

cogent_qcsystems_logo-2.jpg

That red symbol at the heart of Cogent’s logo is the Greek letter sigma, which is the mathematical symbol for the statistical concept of “standard deviation”.   This is a reminder that statistical methods are central to Cogent QC Systems’ approach to improving the quality control process. In a nutshell, these methods enable quality control professionals to more efficiently identify and correct significant defects in loan production and servicing processes.

Statistical methods have been used in quality control since the 1920’s. In fact, one of the most powerful reports available in Cogent QC Systems, the control chart, was invented by Walter Shewhart, an engineer at Bell Laboratories, in 1924.  Statistical methods were also emphasized by W. Edwards Deming and Joseph Juran, the fathers of modern quality control, who (separately) took these methods to Japan in the 1950’s, contributing to the “Japanese miracle” of manufacturing quality and economic growth in the 1960’s and beyond.

Here’s brief timeline of the history of statistical quality control (and its acronyms): SPC >> TQM >> 6 Sigma

1920’s:  Walter Shewhart (Bell Labs) invents statistical control charts, pioneers methods of SPC (statistical process control)

1950’s:  Deming and Juran bring statistical QC to Japan; Deming coins the acronyms TQM (total quality management) and PDCA (plan-do-check-act)

1960’s-70’s:  Japan’s major corporations implement statistical QC, leading to the “Japanese Miracle”

1981:  Motorola incorporates statistical QC into a new quality management program called 6 Sigma; coins the acronym DMAIC (define-measure-analyze-improve-control)

1980’s-present:  “Quality Revolution” brings statistical QC methods to U.S. manufacturing, health care, and financial services

Since the mid-1990’s, when Cogent pioneered the use of statistical sampling and reporting methods in mortgage quality control, the use of statistics has become recognized as key to efficient and effective process improvement.  And yet, widespread understanding and adoption of robust statistical methods is not complete.  Cogent’s goal is to facilitate this adoption and to design systems that make the job as easy as possible.

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