Design and Analysis of Engineering Experiments
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Transcript Design and Analysis of Engineering Experiments
IE340, I&IE Dept., COE UT
PROCESS IMPROVEMENT
THROUGH PLANNED
EXPERIMENTATION
Dr. Xueping Li
University of Tennessee
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Design of Engineering Experiments
Part 1 – Introduction
Chapter 1, Text
Why is this trip necessary? Goals of
the course
An abbreviated history of DOX
Some basic principles and
terminology
The strategy of experimentation
Guidelines for planning, conducting
and analyzing experiments
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Introduction to DOX
An experiment is a test or a series of
tests
Experiments are used widely in the
engineering world
Process characterization & optimization
Evaluation of material properties
Product design & development
Component & system tolerance determination
“All experiments are designed
experiments, some are poorly designed,
some are well-designed”
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Engineering Experiments
Reduce time to
design/develop new
products & processes
Improve performance
of existing processes
Improve reliability and
performance of products
Achieve product &
process robustness
Evaluation of
materials, design
alternatives, setting
component & system
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tolerances, etc.
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Four Eras in the History of DOX
The agricultural origins, 1918 – 1940s
R. A. Fisher & his co-workers
Profound impact on agricultural science
Factorial designs, ANOVA
The first industrial era, 1951 – late 1970s
Box & Wilson, response surfaces
Applications in the chemical & process
industries
The second industrial era, late 1970s – 1990
Quality improvement initiatives in many
companies
Taguchi and robust parameter design, process
robustness
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The modern era, beginning
circa 1990
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The Basic Principles of DOX
Randomization
Running the trials in an experiment in random
order
Notion of balancing out effects of “lurking”
variables
Replication
Sample size (improving precision of effect
estimation, estimation of error or background
noise)
Replication versus repeat measurements? (see
page 13)
Blocking
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Dealing with nuisance factors
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Strategy of Experimentation
“Best-guess” experiments
Used a lot
More successful than you might suspect, but
there are disadvantages…
One-factor-at-a-time (OFAT)
experiments
Sometimes associated with the “scientific” or
“engineering” method
Devastated by interaction, also very inefficient
Statistically designed experiments
Based on Fisher’s factorial concept
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Factorial Designs
In a factorial
experiment, all
possible
combinations of factor
levels are tested
The golf experiment:
Type of driver
Type of ball
Walking vs. riding
Type of beverage
Time of round
Weather
Type of golf spike
Etc, etc, etc…
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Factorial Design
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Factorial Designs with Several
Factors
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Factorial Designs with Several
Factors
A Fractional Factorial
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Planning, Conducting &
Analyzing an Experiment
1. Recognition of & statement of
problem
2. Choice of factors, levels, and ranges
3. Selection of the response variable(s)
4. Choice of design
5. Conducting the experiment
6. Statistical analysis
7. Drawing conclusions,
recommendations
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Planning, Conducting &
Analyzing an Experiment
Get statistical thinking involved early
Your non-statistical knowledge is crucial
to success
Pre-experimental planning (steps 1-3) vital
Think and experiment sequentially (use
the KISS principle)
See Coleman & Montgomery (1993)
Technometrics paper + supplemental text
material
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Six Sigma
Use of statistics & other analytical tools has
grown steadily for over 80 years
Statistical quality control (origins in 1920,
explosive growth during WW II, 1950s)
Operations research (1940s)
FDA, EPA in the 1970’s
TQM (Total Quality Management) movement in
the 1980’s
Reengineering of business processes (late
1980’s)
Six-Sigma (origins at Motorola in 1987,
expanded impact during 1990s to present)
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Focus of Six Sigma is on Process Improvement
with an Emphasis on Achieving Significant
Business Impact
A process is an organized sequence of activities
that produces an output that adds value to the
organization
All work is performed in (interconnected)
processes
Easy to see in some situations
(manufacturing)
Harder in others
Any process can be improved
An organized approach to improvement is
necessary
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The process focus is essential to Six Sigma
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Why “Quality Improvement” is
Important: A Simple Example
• A visit to a fast-food store: Hamburger (bun, meat, special sauce,
cheese, pickle, onion, lettuce, tomato), fries, and drink.
• This product has 10 components - is 99% good okay?
P{Single meal good} (0.99)10 0.9044
Family of four, once a month: P{All meals good} (0.9044)4 0.6690
P{All visits during the year good} (0.6690)12 0.0080
P{single meal good} (0.999)10 0.9900, P{Monthly visit good} (0.99)4 0.9607
P{All visits in the year good} (0.9607)12 0.6186
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Six Sigma Focus
Initially in manufacturing
Commercial applications
Banking
Finance
Public sector
Services
DFSS – Design for Six Sigma
Only so much improvement can be wrung out of
an existing system
New process design
New product design (engineering)
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Some Commercial Applications
Reducing average and variation of days outstanding
on accounts receivable
Managing costs of consultants (public accountants,
lawyers)
Skip tracing
Credit scoring
Closing the books (faster, less variation)
Audit accuracy, account reconciliation
Forecasting
Inventory management
Tax filing
Payroll accuracy
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Six Sigma
A disciplined and analytical approach to process and
product improvement
Specialized roles for people; Champions, Master Black
belts, Black Belts, Green Belts
Top-down driven (Champions from each business)
BBs and MBBs have responsibility (project definition,
leadership, training/mentoring, team facilitation)
Involves a five-step process (DMAIC) :
Define
Measure
Analyze
Improve
Control
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What Makes it Work?
Successful implementations
characterized by:
Committed leadership
Use of top talent
Supporting infrastructure
Formal project selection process
Formal project review process
Dedicated resources
Financial system integration
Project-by-project improvement
strategy (borrowed from Juran)
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The Process Improvement Triad: DFSS, Lean, and DMAIC
OVERALL PROGRAMS
•
•
•
•
DFSS
Lean
DESIGN
PREDICTIVE
QUALITY INTO
PRODUCTS
ELIMINATE
WASTE,
IMPROVE
CYCLE TIME
Robust
Lead-time
Design for Six Sigma
LEAN
Requirements allocation
Capability assessment
Robust Design
Predictable Product Quality
•
•
•
•
•
Flow Mapping
Waste Elimination
Cycle Time
WIP Reduction
Operations and
Design
DMAIC
ELIMINATE
DEFECTS,
REDUCE
VARIABILIT
Y
Capable
Variation Reduction
•
•
•
•
•
Predictability
Feasibility
Efficiency
Capability
Accuracy
The “I” in DMAIC may become DFSS
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DFSS Matches Customer Needs
with Capability
Mean and variability affects product performance and cost
Designers can predict costs and yields in the design phase
Consider mean and variability in the design phase
Establish top level mean, variability and failure rate targets
for
a design
Rationally allocate mean, variability, and failure rate targets
to subsystem and component levels
Match requirements against process capability and identify
gaps
Close gaps to optimize a producible design
Identify variability drivers and optimize designs or make
designs robust to variability
Process capability impact design decisions
DFSS enhances product design methods.
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Lean Focuses on Waste
Elimination
Definition
A set of methods and tools used to
eliminate waste in a process
Lean helps identify anything not absolutely
required to deliver a quality product on
time.
Benefits of using Lean
Lean methods help reduce inventory, lead
time, and cost
Lean methods increase productivity, quality,
on time delivery, capacity, and sales
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DMAIC Solves Problems by Using
Six Sigma Tools
DMAIC is a problem solving
methodology
Use this method to solve problems:
Define problems in processes
Measure performance
Analyze causes of problems
Improve processesremove variations
and nonvalue-added activities
Control processes so problems do not
recur
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Six Sigma
DMAIC is closely related to the
Shewhart cycle (variously called the
Deming cycle, or the PDCA cycle)
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