Chapter 12
Design for
Six Sigma
1
DFSS Activities
Four Principal Activities




Concept development, determining product
functionality based upon customer requirements,
technological capabilities, and economic realities
Design development, focusing on product and
process performance issues necessary to fulfill the
product and service requirements in manufacturing
or delivery
Design optimization, seeking to minimize the impact
of variation in production and use, creating a
“robust” design
Design verification, ensuring that the capability of
the production system meets the appropriate sigma
level
Key Idea
Like Six Sigma itself, most tools for DFSS have
been around for some time; its uniqueness lies in
the manner in which they are integrated into a
formal methodology, driven by the Six Sigma
philosophy, with clear business objectives in
mind.
Tools for Concept
Development

Concept development – the process of
applying scientific, engineering, and
business knowledge to produce a basic
functional design that meets both customer
needs and manufacturing or service delivery
requirements.
– Quality function deployment (QFD)
– Concept engineering
Key Idea
Concept Development
Developing a basic functional design involves
translating customer requirements into
measurable technical requirements and,
subsequently, into detailed design
specifications.
Key Idea
QFD
QFD benefits companies through improved
communication and teamwork between all
constituencies in the value chain, such as
between marketing and design, between
design and manufacturing, and between
purchasing and suppliers.
House of Quality
Interrelationships
Technical requirements
Voice of
the
customer
Customer
requirement
priorities
Relationship
matrix
Technical requirement
priorities
Competitive
evaluation
7
Quality Function
Deployment
technical
requirements
component
characteristics
process
operations
quality plan
9
Building the House of
Quality
1.
2.
3.
4.
5.
6.
Identify customer requirements.
Identify technical requirements.
Relate the customer requirements to the
technical requirements.
Conduct an evaluation of competing
products or services.
Evaluate technical requirements and
develop targets.
Determine which technical requirements to
deploy in the remainder of the
production/delivery process.
Tools for Design
Development



Tolerance design
Design failure mode and effects
analysis
Reliability prediction
Key Idea
Tools for Design Development
Manufacturing specifications consist of nominal
dimensions and tolerances. Nominal refers to
the ideal dimension or the target value that
manufacturing seeks to meet; tolerance is the
permissible variation, recognizing the difficulty
of meeting a target consistently.
Tolerance Design


Determining permissible variation in a
dimension
Understand tradeoffs between costs
and performance
Key Idea
Tolerance Design
Tolerances are necessary because not all
parts can be produced exactly to nominal
specifications because of natural variations
(common causes) in production processes
due to the “5 Ms”: men and women,
materials, machines, methods, and
measurement.
DFMEA

Design failure mode and effects analysis
(DFMEA) – identification of all the ways in
which a failure can occur, to estimate the effect
and seriousness of the failure, and to
recommend corrective design actions.
DFMEA





Failure modes
Effect of the failure on the customer
Severity, likelihood of occurrence, and
detection rating
Potential causes of failure
Corrective actions or controls
Reliability Prediction

Reliability
– Generally defined as the ability of a
product to perform as expected over
time
– Formally defined as the probability that a
product, piece of equipment, or system
performs its intended function for a
stated period of time under specified
operating conditions
17
Types of Failures
Functional failure – failure that
occurs at the start of product life
due to manufacturing or material
detects
 Reliability failure – failure after
some period of use

Types of Reliability
Inherent reliability – predicted by
product design
 Achieved reliability – observed
during use

Reliability Measurement
Failure rate (l) – number of
failures per unit time
 Alternative measures

– Mean time to failure (MTTF)
– Mean time between failures (MTBF)
Cumulative Failure Rate
Curve
Failure Rate Curve
“Infant
mortality
period”
Average Failure Rate
Key Idea
Reliability Prediction
Many electronic components commonly
exhibit a high, but decreasing, failure rate
early in their lives (as evidenced by the steep
slope of the curve), followed by a period of a
relatively constant failure rate, and ending
with an increasing failure rate.
Product Life Characteristic
Curve

Three distinct time period
– Early failure
– Useful life
– Wearout period
Predicting System Reliability



Series system
Parallel system
Combination system
Series Systems
1
2
n
RS = R1 R2 ... Rn
27
Parallel Systems
1
2
n
RS = 1 - (1 - R1) (1 - R2)... (1 - Rn)
28
Series-Parallel Systems
C
RA
RB
A
B
RC
RD
D
C
RC

Convert to equivalent series system
RA
RB
A
B
RD
C’
D
RC’ = 1 – (1-RC)(1-RC)
Tools for Design
Optimization


Taguchi loss function
Optimizing reliability
Key Idea
Tools for Design Optimization
Design optimization includes setting
proper tolerances to ensure maximum
product performance and making
designs robust, that is, insensitive to
variations in manufacturing or the use
environment.
Loss Functions
Traditional
View
loss
no loss
loss
nominal
tolerance
Taguchi’s
View
loss
loss
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Loss function
Taguchi Loss Function
No strict cut-off point divides good
quality from poor quality. Rather,
losses can be approximated by a
quadratic function so that larger
deviations from target correspond to
increasingly larger losses.
Optimizing Reliability



Standardization—use components with
proven track records
Redundancy—provide backup
components
Physics of failure—understand physical
properties of materials
Tools for Design Verification



Reliability testing
Measurement systems evaluation
Process capability evaluation
Key Idea
Tools for Design Verification
Design verification is necessary to
ensure that designs will meet customer
requirements and can be produced to
specifications.
Reliability testing





Life testing
Accelerated life testing
Environmental testing
Vibration and shock testing
Burn-in (component stress testing)
Measurement System
Evaluation

Whenever variation is observed in
measurements, some portion is due to
measurement system error. Some errors
are systematic (called bias); others are
random. The size of the errors relative to
the measurement value can significantly
affect the quality of the data and
resulting decisions.
Metrology - Science of
Measurement


Accuracy - closeness of agreement
between an observed value and a
standard – can lead to systematic bias.
Precision - closeness of agreement
between randomly selected individual
measurements – can lead to random
variation.
Accuracy vs. Precision
Repeatability and
Reproducibility


Repeatability (equipment variation) –
variation in multiple measurements by
an individual using the same
instrument.
Reproducibility (operator variation) variation in the same measuring
instrument used by different
individuals
Key Idea
Calibration
One of the most important functions of
metrology is calibration—the
comparison of a measurement device
or system having a known relationship
to national standards against another
device or system whose relationship to
national standards is unknown.
Process Capability


The range over which the natural
variation of a process occurs as
determined by the system of common
causes
Measured by the proportion of output
that can be produced within design
specifications
44
Process Capability Study
Typical Questions Asked





Where is the process centered?
How much variability exists in the
process?
Is the performance relative to specs
acceptable?
What proportion of output will be
expected to meet the specs?
What factors contribute to variability?
Types of Capability Studies



Peak performance study - how a process
performs under ideal conditions
Process characterization study - how a
process performs under actual operating
conditions
Component variability study - relative
contribution of different sources of
variation (e.g., process factors,
measurement system)
Process Capability
(a)
specification
natural variation
(c)
specification
natural variation
(b)
specification
natural variation
(d)
specification
natural variation
47
Process Capability
Nominal
value
Process distribution
Upper
specification
Lower
specification
20
25
Process is capable
30
Minutes
Process Capability
Nominal
value
Process distribution
Upper
specification
Lower
specification
20
25
Process is not capable
30
Minutes
Effects of Reducing
Variability on Process Capability
Nominal value
Six sigma
Four sigma
Two sigma
Lower
specification
Upper
specification
Mean
Key Idea
Process Capability
The process capability index, Cp
(sometimes called the process potential
index), is defined as the ratio of the
specification width to the natural
tolerance of the process. Cp relates the
natural variation of the process with the
design specifications in a single,
quantitative measure.
Process Capability Index
Cp = UTL - LTL
6s
Cpu = UTL - m
3s
Cpl = m - LTL
3s
Cpk = min{ Cpl, Cpu }
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