Transcript Sunny Fresh Foods

Chapter 12
Design for
Six Sigma
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DFSS 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
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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.
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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
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Key Idea
Developing a basic functional design
involves translating customer
requirements into measurable technical
requirements and, subsequently, into
detailed design specifications.
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Quality Function Deployment
technical
requirements
component
characteristics
process
operations
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quality plan
6
Key Idea
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.
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House of Quality
Interrelationships
Technical requirements
Voice of
the
customer
Customer
requirement
priorities
Relationship
matrix
Technical requirement
priorities
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Competitive
evaluation
8
Building the House of Quality
1. Identify customer requirements.
2. Identify technical requirements.
3. Relate the customer requirements to the
technical requirements.
4. Conduct an evaluation of competing products or
services.
5. Evaluate technical requirements and develop
targets.
6. Determine which technical requirements to
deploy in the remainder of the
production/delivery process.
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Concept Engineering
•
•
•
•
•
Understanding the customer’s environment.
Converting understanding into requirements.
Operationalizing what has been learned.
Concept generation.
Concept selection.
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Tools for Design Development
• Tolerance design
• Design failure mode and effects analysis
• Reliability prediction
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Key Idea
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.
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Tolerance Design
• Determining permissible variation in a
dimension
• Understand tradeoffs between costs and
performance
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Key Idea
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.
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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.
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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
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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
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Types of Reliability
• Inherent reliability – predicted by product
design
• Achieved reliability – observed during use
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Reliability Measurement
• Failure rate (l) – number of failures per unit
time
• Alternative measures
– Mean time to failure
– Mean time between failures
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Cumulative Failure Rate Curve
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Key Idea
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.
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Failure Rate Curve
“Infant
mortality
period”
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Average Failure Rate
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Reliability Function
• Probability density function of failures
f(t) = le-lt for t > 0
• Probability of failure from (0, T)
F(t) = 1 – e-lT
• Reliability function
R(T) = 1 – F(T) = e-lT
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Series Systems
1
2
n
RS = R1 R2 ... Rn
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Parallel Systems
1
2
n
RS = 1 - (1 - R1) (1 - R2)... (1 - Rn)
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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)
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Tools for Design Optimization
• Taguchi loss function
• Optimizing reliability
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Key Idea
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.
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Loss Functions
Traditional
View
loss
no loss
loss
nominal
tolerance
Taguchi’s
View
loss
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loss
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Taguchi Loss Function
Calculations
Loss function: L(x) = k(x - T)2
Example: Specification = .500  .020. Failure
outside of the tolerance range costs $50 to
repair. Thus, 50 = k(.020)2. Solving for k
yields k = 125,000. The loss function is:
L(x) = 125,000(x - .500)2
Expected loss = k(2 + D2)
where D is the deviation
from
the
target.
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Optimizing Reliability
• Standardization
• Redundancy
• Physics of failure
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Tools for Design Verification
• Reliability testing
• Measurement systems evaluation
• Process capability evaluation
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Key Idea
Design verification is necessary to
ensure that designs will meet customer
requirements and can be produced to
specifications.
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Reliability testing
•
•
•
•
•
Life testing
Accelerated life testing
Environmental testing
Vibration and shock testing
Burn-in (component stress testing)
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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.
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Metrology - Science of
Measurement
 Accuracy
- closeness of agreement
between an observed value and a
standard
 Precision
- closeness of agreement
between randomly selected individual
measurements
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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
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Repeatability & Reproducibility Studies
• Quantify and evaluate the capability of a
measurement system
– Select m operators and n parts
– Calibrate the measuring instrument
– Randomly measure each part by each operator
for r trials
– Compute key statistics to quantify repeatability
and reproducibility
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Spreadsheet Template
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R&R Evaluation
• Under 10% error - OK
• 10-30% error - may be OK
• over 30% error - unacceptable
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Key Idea
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.
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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
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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)
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Process Capability Study
1.
2.
3.
4.
5.
6.
7.
8.
Choose a representative machine or process
Define the process conditions
Select a representative operator
Provide the right materials
Specify the gauging or measurement method
Record the measurements
Construct a histogram and compute
descriptive statistics: mean and standard
deviation
Compare results with specified tolerances
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Process Capability
(a)
specification
natural variation
(b)
specification
natural variation
(c)
specification
(d)
specification
natural variation
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natural variation
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Key Idea
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.
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Process Capability Index
Cp = UTL - LTL
6
Cpu = UTL - m
3
Cpl = m - LTL
3
Cpk = min{ Cpl, Cpu }
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Spreadsheet Template
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