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
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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:
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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
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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
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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
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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
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Flow Mapping
Waste Elimination
Cycle Time
WIP Reduction
Operations and
Design
DMAIC
ELIMINATE
DEFECTS,
REDUCE
VARIABILIT
Y
Capable
Variation Reduction
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Predictability
Feasibility
Efficiency
Capability
Accuracy
The “I” in DMAIC may become DFSS
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DFSS Matches Customer Needs
with Capability
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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 processesremove 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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