Six Sigma Quality is a popular approach to process improvement,
particularly among technology driven companies such as Allied
Signal, General Electric, Kodak and Texas Instruments. Its
objective is to reduce output variability through process
improvement, and/or to increase customer specification limits
through design for producibility (DfP), so that these specification
limits lie at more than ±"six" standard
deviations, or s's, from the process mean (I'll explain
the quotation marks later). In this way, defect levels should
be below 3.4 "defects per million opportunities"
for a defect, or "dpmo" for short.
Although originally introduced by Motorola in 1986 as a
quality performance measurement, 6s has evolved into a statistically
oriented approach to process improvement. It is deployed
throughout an organization using an army of champions and
experts called "black belts," a title borrowed
from their martial arts counterparts. They command a rank-and-file
made up of teams focusing on the improvement of the organization's
processes. Just search the internet for "six sigma"
and you'll come up with several informative descriptions
of its history and current practice. The Six Sigma Academy,
a Motorola spin-off, provides consulting service to many
of the leading practitioners of this approach. What I want
to focus on here though, is the 6s metric itself, not the
concept or the approach.
I don't like the 6s metric. As you'll see, it fails to
pass many of the tests that I've previously established
for "good" metrics and described in Part 1 of
Metrics for the Order Fulfillment Process. In particular,
it's neither simple to understand nor, in most applications,
an effective proxy for customer satisfaction. It does not
have an optimum value of zero. And, its definition is ambiguous
and therefore easily gamed because there is no accepted
test for what to include as an "opportunity" for
a defect.
What is an "opportunity"?
I've trained improvement teams, team leaders, and black
belts for one of the aforementioned companies in their 6s
metrics module. Once they get through the distinction between
defects vs. defectives and attribute vs. variable data the
greatest difficulty that the trainees encounter is in determining
what constitutes an opportunity for a defect. Obviously,
by increasing the number of opportunities (the denominator
of dpmo), you can improve the metric, particularly if you
include opportunities that are not important to customers
and consequently are not routinely checked for conformance,
thereby allowing their defects to go uncounted.
This weakness can be overcome (but seldom is in practice)
by applying an objective weighting for defect severity in
counting both opportunities and actual defects. For example,
critical defects, ones that make the output unusable by
the customer, get a weighting of one while inconsequential
defects get a weighting of zero. Cosmetic defects or ones
that can be corrected or compensated for have values in
between, depending on the relative cost of correction or
their likely impact on the customer's repurchase decision.
A similar approach is taken in Failure Mode and Effect Analysis
(FMEA) where improvement priorities are set based on a combination
of frequency of occurrence, severity and detectability of
candidate failure modes. I understand that the TI flavor
of 6s does include this type of logic. Where should the
weightings come from? The customer of the process, of course
(but, more about this in a future installment in this series,
if there's sufficient interest). Current practice usually
leaves the choice of what constitutes an opportunity for
a defect as a subjective, not objective decision. This has
proven to be a poor standard for good metrics.
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