Page 112 - Six Sigma Demystified

Chapter 5 m e a s u r e s tag e 93

These processes have different outcomes, yet the yield metric fails to dis-
criminate between them. In this production process, some units initially with
errors can be reworked and sold as new. For example, units with unacceptable
paint finish might be repaired and repainted. Likewise in a service process, a
customer initially dissatisfied with the service may be directed to a manager for
repair of the situation, resulting in an ultimately satisfied customer.
In terms of the metric, if the reworked units are treated the same as nonre-
worked units, information is lost. This simplistic yield metric obscures the “hid-
den factory” responsible for rework and process variation. While the hidden
factory provides a useful and necessary service to the customers, its use comes
at the price of increased process cycle times and costs.
A solution to this limitation is offered in the throughput yield metric.
Throughput yield measures the ability of the process to produce error- free units
(or error- free service) in the first attempt: the average percentage of units (or
instances of service) with no errors. Throughput yield Y is calculated by sub-
t
tracting the defects per unit (DPU) percentage from 100 percent.
For example, process A (described above) has a DPU of 200/4,000 = 0.05,
so its throughput yield is 95 percent, the same as the yield calculated earlier.
Process B has a DPU of 600/4,000 = 0.15 (a throughput yield of 85 percent).
In this case, the throughput yield reflects the cost of the multiple errors in some
of the sample units. Finally, process C has a DPU of 1,000/4,000 = 0.25 (a
throughput yield of 75 percent). In each case, the throughput yield is consider-
ably less than the calculated first- pass yield.
Rolled throughput yield Y is calculated as the expected quality level after
rt
multiple steps in a process. If the throughput yield for n process steps is Y , Y ,
t2
t1
Y , . . . , Y ,then
t3 tn
Y = Y , × Y × Y × . . . × Y tn
t2
t1
t3
rt
For example, suppose that there are six possible CTQ steps required to pro-
cess a customer order, with their throughput yields calculated as 0.997, 0.995,
0.95, 0.89, 0.923, and 0.94. The rolled throughput yield then is calculated as
Y = 0.997 × 0.995 × 0.95 × 0.89 × 0.923 × 0.94 = 0.728
rt
Thus only 73 percent of the orders will be processed error- free. It’s interesting
to see how much worse the rolled throughput yield is than the individual
throughput yields. As processes become more complex (i.e., involve more CTQ
steps), the combined error rates can climb rather quickly. This should serve as
a warning to simplify processes, as suggested in Chapter 4.