Page 113 - Six Sigma Demystified

94 Six SigMa DemystifieD

Conversely, the normalized yield may be used as a baseline for process steps
when a required rolled throughput yield is defined for the process series. The
normalized yield is calculated as the nth root of the rolled throughput yield. For
example, if the desired rolled throughput yield is 73 percent for a process with six
1/6
steps, then the normalized yield for each step of the process is (0.73) = 0.95
because 0.95 raised to the sixth power is approximately equal to 0.73. The nor-
malized yield provides the minimum throughput yield for each step of the process
to achieve a given rolled throughput yield. Of course, if some process steps cannot
meet this normalized yield level, then the rolled throughput yield could be less.
From a quality perspective, these throughput yields are an improvement
from the simple first- pass yield, but they still lack a fundamental quality: They
cannot provide immediate information to prevent errors. Each of these metrics
relies on attribute (i.e., count) data, where the numerical value of the attribute
count is incremented based on the property of each sample relative to a quality
specification. For example, the metric may be the count of errors in a given
sample of deposits from a banking process: The count is incremented only
when an error is observed in one of the deposit records. Attribute data have less
resolution than measurement (variables) data because a count is registered only
if an error occurs. In a health care process, for example, the number of patients
with a fever (attributes data) could be counted or the measured temperature
of the patients (variables data) recorded. There is clearly more informational
content in the variables measurement because it indicates how good or how bad,
rather than just good (no fever) or bad (fever). This lack of resolution in attri-
butes data will prevent detection of trends toward an undesirable state.
In addition to the lack of resolution, the data are tainted by the criteria from
which they were derived. The count of errors is based on a comparison of the

process measurement relative to a specification. The customer specification
may be unilateral (one sided, with either a minimum or maximum) or bilateral
(two sided, with both a minimum and a maximum). All values within the
specifications are deemed of equal (maximum) value to the customer (i.e., they
pass), and all values outside the specifications are deemed of zero value to the
customer (i.e., they fail), as discussed in Chapter 3.
In most industries, the specifications provide reasonable guidelines, but they
are hardly black- and- white indicators of usability or acceptability of a product
or service. As a unit of product or service approaches a specification, the usabil-
ity becomes gray and is subject to other mitigating concerns such as delivery
dates and costs of replacement. This practicality is not surprising, considering
the rather subjective manner in which specifications are often developed. Even