Motorola's Six Sigma Program

by Thomas Pyzdek

In 1988, Motorola Corp. became one of the first companies to receive the Malcolm Baldrige National Quality Award. The award strives to identify those  excellent firms that are worthy role models for other businesses. One of Motorola's innovations that attracted a great deal of attention was its Six Sigma program. Six Sigma is, basically, a process quality goal. As such, it falls into the category of a process capability (Cp) technique.

The traditional quality paradigm defined a process as capable if the process's natural spread, plus and minus three sigma, was less than the engineering tolerance. Under the assumption of normality, this translates to a process yield of 99.73 percent. A later refinement considered the process location as well as its spread (Cpk) and tightened the minimum acceptable so that the process was at least four sigma from the nearest engineering requirement. Motorola's Six Sigma asks that processes operate such that the nearest engineering requirement is at least plus or minus six sigma from the process mean.

Motorola's Six Sigma program also applies to attribute data. This is accomplished by converting the Six Sigma requirement to equivalent conformance levels (see Figure 1).

One of Motorola's most significant contributions was to change the discussion of quality from one where quality levels were measured in percentages (parts per hundred) to a discussion of parts per million or even parts per billion. Motorola correctly pointed out that modern technology was so complex that old ideas about acceptable quality levels were no longer acceptable.

One puzzling aspect of the "official" Six Sigma literature is that it states that a process operating at Six Sigma levels will produce 3.4 parts-per-million nonconformances. However, if a normal distribution table is consulted (very few go out to six sigma), one finds that the expected nonconformances are 0.002 parts per million (two parts per billion). The difference occurs because Motorola presumes that the process mean can drift 1.5 sigma in either direction. The area of a normal distribution beyond 4.5 sigma from the mean is indeed 3.4 parts per million. Because control charts will easily detect any process shift of this magnitude in a single sample, the 3.4 parts per million represents a very conservative upper bound on the nonconformance rate.

Excerpted from The Complete Guide to the CQE by Thomas Pyzdek. 1996. Tucson: Quality Publishing Inc.