Defining the best quality-control systems by design and inspection.
Not all of the many approaches to quality control are equally effective. Nonconformities in laboratory testing are caused basically by excessive process variation and mistakes. Statistical quality control can effectively control process variation, but it cannot detect or prevent most mistakes. Because mistakes or blunders are frequently the dominant source of nonconformities, we conclude that statistical quality control by itself is not effective. I explore the 100% inspection methods essential for controlling mistakes. Unlike the inspection techniques that Deming described as ineffective, the new "source" inspection methods can detect mistakes and enable corrections before nonconformities are generated, achieving the highest degree of quality at a fraction of the cost of traditional methods. Key relationships between task complexity and nonconformity rates are also described, along with cultural changes that are essential for implementing the best quality-control practices.
Hinckley
C M
CM
Assured Quality, Manteca, CA 95336, USA. cmhinck@sandia.gov
D002624:Chemistry, Clinical / Q000592:standards / Q000706:statistics & numerical data; D003951:Diagnostic Errors; D006801:Humans; D011786:Quality Control; D013223:Statistics as Topic