February 23, 2005
Advances in LargeScale Assembly
Six Sigma at National
Semiconductor
Leveraging Your ISO
9001 System for
Sarbanes-Oxley
Compliance
Out or In: The
Challenge of Calibration
Management
Design for Six Sigma
Demystified
2005 Six Sigma
Services Directory
by Phong Vu and Kempton Smith
Six Sigma’s define, measure, analyze, improve and control methodology is well
known as the process improvement program that “fixes” problems resulting from
variability in manufacturing, engineering or transactional processes. There are
times, however, when no fix will enable an existing process to meet customer
expectations. A new process is needed to replace the old one, which leads to the
question, “Can Six Sigma help design a new product or process?”
The answer is a resounding yes, with DMAIC’s companion methodology, known
generically as Design for Six Sigma (DFSS). As with Six Sigma DMAIC, the DFSS
methodology doesn’t actually design a new part or process since every enterprise
has a unique design process tailored to its own product or service. However, DFSS
can make these processes more robust and less costly. It delivers products and
processes that perform at higher quality levels than otherwise possible. In short,
while DMAIC can be loosely characterized as a find-and-fix methodology, DFSS
could be thought of as a preventive one.
DFSS methodology can help a design team fully understand its customer’s
requirements and predict if the design will meet those requirements at each phase
of the process. It’s this predictive ability that saves costs in design time, prototypes
and validation tests, which translates to a less expensive launch.
Comparing customer requirements and process capability
DFSS is defined as a systematic methodology, with tools, training and
measurements that enable us to design products and/or processes that can be
produced at the Six Sigma level. As such, DFSS users must begin by understanding
their customers, who will have many expectations of a product or service. Not all of
these requirements are equally important. The first task is to identify which of
these will be the focus of the DFSS effort. This involves listening to the voice of the
customer (VOC), prioritizing customer responses and, most important, identifying a
measurable target and range for these requirements. Hitting these targets with the
design and staying within an established range (i.e., minimum variability) will
ensure that customer expectations are met and also serve as a measure of the
design’s success.
The second important factor in DFSS is to understand the capability of the
processes. To do this, we must answer this question for each key process: “How
often will this process cause us to fail to meet customer requirements?” Comparing
customer requirements and process capability enables us to predict the level at
which we’ll be able to meet customer expectations.
The transfer function
As with Six Sigma’s DMAIC for process improvement, a key concept of DFSS is
understanding the relationship of inputs to outputs, the Y = f(x) relationship. This
is also known as a “transfer function” or “prediction equation.” Transfer functions
can be determined by several different methods. For the simplest processes, data
can be readily obtained from a process map or product drawings. In some cases,
they may be described from principles inherent in the design’s physics, chemistry
or geometry. In less obvious situations, we might be able to develop a model to
describe the relationship between inputs and outputs using design of experiments.
Designers have even conducted experiments on finite element models to obtain
these relationships.
Regardless of how the transfer function is obtained, knowing it allows us to predict
the quality level of any design before production. In other words, we’ll know in
advance whether a design will meet customer expectations and what we might
need to change.
Other useful information we can learn from the transfer function is exactly what
effect each input factor is likely to have on an output. Fully understanding these
relationships allows us to adjust a design to hit a target and to choose settings that
reduce variability. This is the concept of robust
design. In addition to designing for
performance targets and cost, we can now
design for a specific quality level and measure
progress toward that goal throughout the
process.
DFSS case study
A manufacturer of portable power tools wishes
to improve customer satisfaction with a certain
tool by reducing the amount of noise generated
during its operation. The designers have
determined that a primary source of noise is
bearing slap in the conversion of rotary to
reciprocal motion. The design change is
intended to reduce this clearance. When
presented with this task, most designers
intuitively observe that the clearance can be reduced by increasing the ball
diameter, decreasing the angle of the raceway, increasing the width of the slider or
some combination of these three. Without understanding the relationships
embodied in the transfer function, however, these conclusions will lead to a lessthan-optimal solution.
From the geometry shown on the drawings in the figure to the right, the designer
was able to describe the clearance between the ball bearing and the slider
dimensions in terms of the components’ dimensions. Using this approach along
with DFSS tools, a solution was found that simultaneously achieved the desired
target for the clearance and minimized the inherent variability from the
manufacturing process.
That solution called for a reduced ball diameter (a), an increased raceway angle (a)
and increased raceway width (b). The underlying reasons for this can be seen in
the analytic description shown in the figure below. All four factors have an effect on
the clearance, but two of them affect variability as well. The optimum design
strategy used the “shrink” factors to reduce the variability and the “shift” factors to
put the design on target. This case illustrates that design solutions to reduce
variability often aren’t intuitive and are missed in the traditional design focus on
achieving a single-point design.
Prerequisites for success
There are some important prerequisites for successfully implementing DFSS design
principles. The first and most important is stability in critical processes. Prediction
is the essence of DFSS, and prediction relies on understanding process capability.
Second, DFSS is inherently a cross-functional activity. Process stability might be
the domain of operations, but customer requirements must come from marketing
and be communicated in quantitative and measurable terms. Achieving designs
that are robust to the inherent variation of key processes requires optimizing
design parameters through engineering. Finding those optimums is the business of
design. None of these activities can be accomplished in the absence of the others.
Finally, it’s critical that DFSS is implemented in an environment of accountability.
Clear targets for performance, quality, cost and delivery must be established at the
outset and rewards (or lack thereof) for the design team should be based on
measurable achievement at the end.
Transactional processes
DFSS isn’t just for engineering or manufacturing processes. It’s equally useful in
designing new transactional processes. DFSS for transactional processes is easier
to apply and produces results more quickly than for many engineering or
manufacturing applications. The method follows the same flow as that for
manufacturing applications; however, each step can be easier to accomplish
because the laws of physics or chemistry usually aren’t involved.
For example, a call center operation can be designed using DFSS as follows:
Understand VOC (e.g., use a Pareto chart to organize the types of inquiries,
survey to identify the acceptable wait time, etc.)
Classify the inquiries into those that do and don’t require operators’ assistance
Identify critical functions that must be performed by the process
Draw a process map that brings the information to the customers accurately and
efficiently to meet or exceed their targets.
Identify key steps that must be robust and design them accordingly
Predict the performance of the new process using existing process data,
simulations or other techniques
Design a test to validate the new process and demonstrate that it meets
customers’ expectations as predicted before fully implementing the solution
This example is highly simplified; however, the entire system can be designed and
implemented within a few months with great improvement in speed, accuracy and
customer satisfaction, as well as a reduction in labor costs.
Many successful DFSS implementations have begun with pilot projects. Training in
the tools and disciplines of DFSS is best accomplished with cross-functional design
teams focused on a single project. It’s best to begin using DFSS early in the design
process to take full advantage of the methodology. Having the guidance of a DFSS
practitioner who has completed projects is an enormous help.
About the authors
Phong Vu is CEO of the Dr. Mikel J. Harry Six Sigma Management Institute
(www.ss-mi.com), which has partnered with Arizona State University to provide Six
Sigma Generation III and business leadership training for corporations globally.
Kempton Smith is a managing partner with Mosaica Partners LLC
(www.mosaica.cc), which developed and taught the first DFSS course for DuPont
and has since fostered Six Sigma implementations at numerous companies.
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