DMAIC 1-Day Training
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1
Prepared by Dr. Leonard R. Hepp
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What is Six Sigma?
A Methodology … For Continuous Improvement
Six Sigma is a highly disciplined data-based methodology of
problem solving leveraging tools & techniques where appropriate.
Six Sigma follows two rigorous approaches:
DMAIC Methodology …for improving EXISTING processes
Define
Measure
Improve
Analyze
Control
DMADOV Methodology …for CREATING a new product or process
Define
Measure
Analyze
Design
Optimize
Verify
Let’s Look At Each Method
2
Prepared by Dr. Leonard R. Hepp
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3
Prepared by Dr. Leonard R. Hepp
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Variation & Defects are the Enemy
Every Human Activity Has Variability...
Customer
Specification
X
X
XXX
1sX
X XXX
XXXXXXX X
XXXXXXXXXX
X XXXXXXXXXXXXXX
defects
Target
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Prepared by Dr. Leonard R. Hepp
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Reliability thru Variance Reduction
A 3s process because 3 standard deviations
fit between target and spec
Target
Customer
Specification
1s
2s
3s
Customer
Specification
Target
6s
1s
2s
3s
4s
5s
6s
“No Defects”
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Prepared by Dr. Leonard R. Hepp
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And … What About Discrete Data?
We have an Invoice Payment Process of 45 Days or less and
recent data for last month shows that of 100 invoices, 92 were
paid in 45 days or less.
92% “On Time”
Question…How many were paid in 30 days? Between 40-45
Days? What was my shortest payment cycles?
With Discrete Data you’d have to go back and re-measure!
And you can’t model new performance limits. And…
Prepared by Dr. Leonard R. Hepp
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6
What is Six Sigma Quality?
Customer
Specification
Average
Considerations
67,000 Defects per
Million Opportunities
3s
• Customer Specification
3s
• Average
• Variation
Customer
Specification
Average
Six Sigma - A Stretch Goal
For many processes
BUT
Not Good Enough for Some!
6s
6s
3.4 Defects per
Million Opportunities
_________________________________________________________________________
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Prepared by Dr. Leonard R. Hepp
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What is Six Sigma Quality?
Sigma is a statistical unit of measure that reflects
process capability
s
DPMO
6
3.4
99.9997%
5
233
99.98%
4
6,210
99%
3
66,807
93%
2
308,537
69%
Process
Capability
Defects Per Million
Opportunities
%
Percentage
Good
s Increase Requires Exponential DPMO Reduction
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Prepared by Dr. Leonard R. Hepp
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Sigma Quality Level - Examples
IRS Tax Advice
(phone in)
1,000,000
Order Write-up
Doctor Prescription Writing
Restaurant Bills
10,000
Defects
per
Million
Airline Baggage Handling
1,000
“Average”
Industrial
Company
100
10
Best-in-Class
Industrial Company
1
2
3
4
5
Domestic Airline
Fatality Rate
(0.43 PPM)
6
7
Sigma Scale of Measure
“Typical” Service Industry Processes are 1.5s to 3s
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Six Sigma DMAIC Process
Define
Characterization
Measure
Analyze
6s
Improve
Optimization
Control
10
Prepared by Dr. Leonard R. Hepp
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Six Sigma DMAIC Overview
Problem
Solving
Flow
Need
What’s
The Problem?
Practical
Problem
Define
Practical
Practical Problem
Problem
Measure
Statistical
Statistical Problem
Problem
Analyze
Statistical
Statistical Solution
Solution
Improve
Practical
Practical Solution
Solution
Control
Do
Need
Do
Need
Do
Need
Practical Problem: Low Yield
Statistical Problem: Mean Off Target
Statistical Solution: Isolate Key Variables
Practical Solution: Install Automatic Controller
11
Prepared by Dr. Leonard R. Hepp
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DMAIC – The 12 + 3 Steps
Six Sigma DMAIC
The 12+3 Step DMAIC Strategy
Formulating the Practical Problem
DMAIC
Step 0: Build the House of Quality
How do my customers look at me?
A. Identify Needs B. Team Charter C. Process/SIPOC
DMAIC
DMAIC
DMAIC
Step 1: Select the CTQ Characteristic
Step 2: Define Performance Standards
Step 3: Validate MSA and Data Collection
Changing to a Statistical Problem
DMAIC
DMAIC
DMAIC
Step 4: Establish Process Capability
Step 5: Define Performance Objectives
Can I trust the output data?
How good am I today?
How good do I need to be?
What factors make a difference?
What’s at the root of the problem?
Step 7: Screen Potential Causes
How can I predict the output?
How tight does the control have to be?
Step 8: Discover Variable Relationships
Step 9: Establish Operating Tolerances
Implementing the Practical Solution
DMAIC
DMAIC
DMAIC
What’s the best way to measure?
Step 6: Identify Variation Sources
Developing a Statistical Solution
DMAIC
DMAIC
DMAIC
What do I want to improve?
Can I trust the in-process data?
Have I reached my goal?
How can I sustain the improvement?
Step 10: Validate MSA on the Xs
Step 11: Determine Process Capability
Step 12: Implement Process Controls
12
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Define
Measure
Analyze
Improve
Control
Define The Problem
Define
A. Customer
FocusCTQs
Focus-Needs
• Collect VOC
• Define Customer Needs
Measure
Analyze
B. Team
Charter
• Business Case
• Problem & Goal
Statements
• Project Timeline
• Team Members
Project Scope
Improve
Control
C. Process
Mapping
• 5-7 High Level
Steps
• Validated by
Process Owner
13
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When To Use DMADOV
DMAIC/DMADOV Transition Points:
Define
Measure
Yes
Does a
Process
Exist?
No
Define
Analyze
Is
Incremental
Improvement
Enough?
Improve
Yes
Is the
Improvement
a New or
Redesigned
Product/
Service?
No
Measure
Analyze
Control
No
Yes
Design
Optimize
Verify
14
Prepared by Dr. Leonard R. Hepp
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DMADOV – The 5 Phases and 14 Steps
Define
Measure
Analyze
Design
Verify
Optimize
Design
Design For
For Six
Six Sigma
Sigma
Initiate,
Scope,
And Plan
The
Project
Define
Define
Understand
Customer
Needs And
Specify
CTQs
Measure
Measure
Develop
Design
Concepts
And HighLevel
Design
Analyze
Analyze
Develop
Detailed
Design And
Control/Test
Plan
Design
Design
Test Design,
Optimize and
validate
performance
to CTQs
Optimize
Optimize
Implement
Full-Scale
Processes
and
Document
Control
Plans
Verify
Verify
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The DMADOV Methodology – 14 Steps
D e fi n e
M e a s u re
A n a ly ze
D e s ig n
O p ti m i z e
V e ri fy
1. Identify customer needs (CTQ’s) and set performance goals.
2. Perform QFD/CTQ flowdown…Needs to Design Requirements
3. Establish measurement system capability.
4. Develop conceptual designs
5. Reliability Analysis of Designs
6. Build Scorecard of Customer Needs (CTQ’s)
7. Perform risk assessment
8. Generate and validate models - Identify transfer functions.
9. Capability flow-up utilizing scorecards…watch for:
• Low Zst on scorecard.
• Lack of transfer function.
• Unknown process capability.
10. Optimize design
• Statistical analysis of variance drivers
• Robustness
• Error proofing
11. Generate process specs and verify measurement system X’s
DMADOV
Fundamentals
(Key Concepts)
QFD-CTQ
Flow-Down
FMEA
Business Model
(Transfer Function)
Scorecards
12. Statistically confirm predictions.
13. Develop control plan for CTQ’s (mean and variance).
14. Document the effort and results.
QUALITY BY DESIGN!
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Prepared by Dr. Leonard R. Hepp
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Define
Measure
Design
Analyze
DMADOV
DESIGN
DMADOV
DEFINE
Step 8: Generate & Validate Models
• Develop Transfer Function
Step 1: Identify Customer CTQ’s and Set Quality Goals
Step 9: Capability Flow-Up Utilizing Scorecards
• Low Zst on Scorecard
Key Focus Areas
Plan &
Manage the
Project
ID Service or
Product Issue
Business Case
Problem/Goal
Statement
Project
Leadership/Team
Initiate the
Project
Project
Management
Approach
ID Customers
Gather
Customer
Needs
Project Plan
and Timelines
Organization
Challenges
Key Focus Areas
Scope the
Project
Watch for These!
• Lack of Transfer Function
• Unknown Process Capability
ID Customers
& Gather
Needs
Rank/Prioritize
Needs
Develop Detailed
Design
Project Scope
Review
MGPP
Resources/
Team
Assessment
Design Elements to
meet Functional CTQs
Detailed Process
Elements
Evaluate Detailed
Design Capability
Predict & Improve
Design Capability
Prepare Control &
Verification Plan
Control Strategy Plan
Test & Validate
Control Plan
Pilot Process Review
Design Reviews &
Risk Assessment
Determine & Measure
CTPs
Step 10: Optimize Design
• Statistical Analysis of Variance Drivers
• Robustness
• Error Proofing
Step 2: Perform QFD/CTQ Flowdown
Step 3: Establish Measurement System Capability & Baseline
Step 11: Generate Process Specs & Verify MSA on the X’s
Key Focus Areas
Perform CTQ Flow-Down
Organize and Prioritize
Customer Needs
Key Focus Areas
Establish MSA and Baseline
Determine Project CTQ’s
Flow-Down of CTQ’s by
QFD
Optimize Design
MSA Analysis
•
•
DMADOV
OPTIMIZE
DMADOV
MEASURE
Verify
Optimize
•
•
GR&R – Precision
Accuracy
Linearity
Stability
Baseline-Sigma and/or
Zscores
Legal and Regulatory
Process Specs and MSA
Pilot & Assess Variance
Business Model (Transfer
Function)
Robustness & Error
Proofing
MSA on System X’s
Pilot Process & Test to
ScoreCard
DMADOV
VERIFY
DMADOV
ANALYZE
Step 12: Statistically Determine Predictions
Step 13: Develop Control; Plans for CTQs (Mean & Variance)
Step 4: Develop Conceptual Designs
Step 14: Document Efforts and Results
Step 5: Reliability Analysis of Designs
Key Focus Areas
Step 6: Build Scorecards of CTQ’s
Step 7: Perform Risk Analysis
Execute Pilot and
Analyze Results
Key Focus Areas
Develop Design
Concepts
Develop High Level
Designs
Functional Analysis
High Level Design
and Process Focus
Alternate Design
Reviews
Requirements for
Process, IT, Facilities,
HR, etc.
Evaluate High Level
Design Capability
Assess Capability via
CTQ Flow-Down &
Flow-Up
Select Best-Fit Design
Customer Feedback
Risk & Patent
Reviews
Implement
Production Process
Transition to
Process Owners
Build Pilot Scale
Process
Build Full Scale
Process
Turnover to Ops &
Maintenance
Pilot Test & Evaluate
Scale-Up & Test
Transition to Process
Owners
Implement Planning
Verify Performance
Project Close
17
Prepared by Dr. Leonard R. Hepp
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DMADOV Project Progress Overview
Define
Project Charter
Problem Statement
& Goal
Project Scope
Project Milestones
(with firm dates for
DMA, targets for
DOV)
High-Level CTQ’s
Project Team
(Leader, Champion,
Sponsor, Black Belt,
Master Black Belt,
Team Members,
Other Resources)
Internal
Communication Plan
Business Case
Project Risk Assessment
(FMEA or written
assessment)
Multi-Generational Plan
Cost-Benefit Analysis
Measure
Identify, segment &
prioritize customer
User Profiling (how
many, how often, from
where)
Identify & prioritize
CTQ’s
Interviews,
surveys, or focus
groups
Measurement Plan
Acceptance Criteria
As-Is Process
Documentation
QFD to determine how
to satisfy CTQ’s
Analyze
Benchmarking (within
your team, within
company, external)
To-Be Process
Map/High-Level
Solution
Make vs. Buy Analysis
Vendor/Technology
Selection
Detailed Functional
Specification
Prototype (use to
iteratively refine
functional specification)
Define Test Cases
Final Project Schedule
and Project Plan
Phased Rollout
Schedule
End-User
Communication and
Marketing Plan
Design
Technical Specification
Interface Design
Application
Architecture
Information
Architecture (DB)
Server
Architecture
Code Reuse Strategy
System FMEA
Security Plan (engage
SSO team)
Optimize
Purchase Hardware and
Software
Schedule Stress Test and
Security Review
Code Application
Develop User & Training
Documentation
System Documentation
Application Kit (for
Production Support)
Help Desk Documentation
Unit Test
Backup & DR Plan
Integration Test
Monitoring Solution
Browser Lab Test
Peer Technical Reviews
Peer Code Review
Packaged Software
Customization Review
Freeze Code
Help Desk Strategy
Performance & Load Test
Security Code Review
Verify
Launch Monitoring Tools
User Training
Production Pilot
Update System FMEA
Transition to Production
Support
Transition to Help Desk
GO LIVE
Performance and Usage
Monitoring
Issues Log
Feedback Management
Bug Fixes and Further
Optimization
Final Acceptance
Testing and CTQ
Measurement
Document & Share Best
Practices
Data Migration
Production Deployment
Preliminary Acceptance
Testing
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Prepared by Dr. Leonard R. Hepp
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