Page 204 - Design for Six Sigma a Roadmap for Product Development

Design for Six Sigma Project Algorithm 177

and often, many can be relaxed at a savings. The quality loss function
is the basis for these decisions. The proposed process also identifies
key characteristics where functional criteria are met, but where
further variability reduction will result in corresponding customer
benefits.
When tolerances are not well understood, the tendency is to over-
specify with tight dimensional tolerances to ensure functionality and
thereby incur cost penalties. Traditionally, specification processes are
not always respected as credible. Hence, manufacturing and production
individuals are tempted to make up their own rules. Joint efforts
between design and process in the team help improve understanding of
the physical aspects of tolerance and thus result in tolerances that are
cross-functional and better balanced. This understanding will be
greatly enhanced by the previous steps in the DFSS algorithm and by
continuous employment of design axioms, QFD, the zigzagging process,
and other tools. The goal in the optimization step (Sec. 5.9) was to find
combinations of dimensions that inherently reduced FR variation.
Typically, further reduction in tolerances is necessary to meet the FR
Six Sigma targets. This can be accomplished best by the tolerance
design step.
Tolerance design can be conducted analytically on the basis of the val-
idated transfer function obtained in Sec. 5.9 or empirically via testing.
In either case, the inputs of this step are twofold—the DFSS team
should have a good understanding of the product and process require-
ments and their translation into product and process specifications
using the QFD. The going-in (initial) position in the DFSS algorithm is
to initially use tolerances that are as wide as possible for cost consider-
ations, then to optimize the function of the design and process through
a combination of suitable design parameters (DPs). Following this, it is
necessary to identify those customer-related FRs that are not met
through parameter design optimization methods. Tightening tolerances
and upgrading materials and other parameters will usually be required
to meet Six Sigma functional requirement targets. Systematic applica-
tion of DFSS principles and tools such as QFD allows the identification
of customer-sensitive characteristics and the development of target val-
ues for these characteristics to meet customer expectations. It is vital
that these characteristics be traced down to lowest-level mappings, and
that appropriate targets and ranges be developed.
Decisions regarding tolerance reduction are based on the quadratic
loss function, which suggests that loss to society is proportional to the
square of the deviation of a design characteristic (such as a dimension)
from its target value. The cost of being “out of specification” for the crit-
ical performance criteria must be estimated.