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D
M
A
I
C
SIX SIGMA TRAINING
FOR GREEN BELTS
WEEK 1
Six Sigma Operational Excellence
Literature PN 1654520
402-108
16Feb04 Rev CEC 0990-0196-04
Tyco Electronics
Six Sigma Operational Excellence
2100 Paxton Street
Harrisburg, PA 17111 USA
All rights reserved.
This material is “company confidential” and is intended
for internal use in Tyco Electronics only.
Revision C, February 2004
Changes are made periodically to this document.
Changes and technical updates will be added in
subsequent editions.
Rev C February 16, 2004
Order from Literature Distribution Phone 717-558-1495 Document # 402-108 Literature Distribution # 1654520
INTRODUCTION
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-1
IntroductionIntroduction
Process Improvement Methodology Process Improvement Methodology Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-2
SPACERSPACER
S - Safety
P - Purpose
A - Agenda
C - Code of Conduct
E - Expectations
R - Roles and Responsibilities
INTRODUCTION
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-3
SafetySafety
Safety is always our first priority !
Please be careful of all work hazards, trip hazards, and bio-hazards!
Specific safety issues including:
Safety equipment
Emergency exit procedures
Any other safety related issues
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-4
PurposePurpose
Provide an overview of the Green Belt training program
Explore the Define, Measure, Analyze, Improve, Control (DMAIC) process improvement methodology.
Construct a plan and use the tools and methods on the project that has been assigned
INTRODUCTION
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-5
4:30 - 5:00
4:00 - 4:30
3:30 - 4:00 Basic Quality Tools and Using Minitab
3:00 - 3:30
2:30 - 3:00
Basic Statistics2:00 - 2:30
1:30 - 2:00Action PlanningProcess Mapping
And Value Stream
Mapping
1:00 - 1:30 Introduction to Minitab
12:30 - 1:00
LUNCHLUNCHLUNCHNoon - 12:30
LUNCH11:30 - Noon
11:00 - 11:30Team Building
Introduction to Lean10:30 - 11:00
InterpersonalManagement
Skillsand Dealing with
Resistance
10:00 - 10:30 Kappa Studies & Attribute MSA
Capability Studies
LUNCH
Introduction to SPC9:30 - 10:00 Introduction to Six Sigma
9:00 - 9:30
Use of the Reference Guides8:30 - 9:00 Sampling
FMEAMeasurement
System Analysis
Basic Quality Tools and Using Minitab
(cont.)
Introduction8:00 - 8:30
FridayThursdayWednesdayTuesdayMonday
Effective Team Meetings and Presentation Skills
Introduction to Multi Vari Analysis
C&E Matrix
Summary Week 1
Capability Studies(cont.)
AgendaAgenda –– 11stst SessionSession
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-6
AgendaAgenda –– 22ndnd SessionSession
Multi – Vari Analysis
Central Limit Theorem and Minitab
Confidence Intervals
Focused Area Improvement Event
Hypothesis Testing
t – Tests
ANOVA
Chi-Square
Correlation and Regression
Control Plans
Quick Changeover
Project Management
Introduction to Design of Experiments
Techniques for Preparing Presentations
Error Proofing
INTRODUCTION
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-7
Guidelines / Code of ConductGuidelines / Code of Conduct
Try to visualize what the DMAIC process should look like for your project issues
If you have a question at any time - ask!
Listen as an ally - “How can I apply the DMAIC effectively in my process?”
Parking Lot will be set up to capture ideas
There will be a lot of team activities - we’ll be moving fast!
Housekeeping
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB101-8
Expectations & RolesExpectations & Roles
Expectations
You will have a better understanding of the DMAIC process and the utilization of the various statistical tools and Lean methodologies
You will have a plan for use of the tools in your project areas by Friday
What are your expectations?
Roles
Instructors:
Participants - You
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB102-1
QUALITY TOOLS AND
STATISTICS
REFERENCE GUIDE
402-105
Quality Tools and Statistics Quality Tools and Statistics Reference GuideReference Guide
GB102-2
Purpose of the Reference GuidePurpose of the Reference Guide
Provide a supplement to the Power Point slides that are used during the weeks / sessions of Belt training
Provide Tyco Electronics employees with a convenient reference resource for use when working independently or as a team
Provide consistent training and resource material
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
GB102-3
Obtaining Additional CopiesObtaining Additional Copies
Controlled through Tyco Electronics Literature Distribution
Ordering information:
Document Number 402-105
Literature Distribution Number 1307319
Maintained under “document control”
GB102-4
Major TopicsMajor Topics
There are 6 major sections within the Quality Tools andStatistics Reference Guide
The 8 – Discipline Model for Problem Solving
The DMAIC Process
Lean Techniques
Basic Tools
Variability
Glossary of Terms
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
GB102-5
The 8 The 8 –– Discipline Model Discipline Model
for Problem Solvingfor Problem Solving
What is 8-D?
A systematic process that describes, analyzes and identifies the root cause(s) of a problem
Most frequently prescribed format for responding to customer complaints
When is it used?
When the product or process does not meet the documented standards
When a process is operating “out of control”
To resolve incidents of customer reported failures
GB102-6
4Identify &
Verify RootCauses
3ContainProblem
2Describe
Opportunity/Problem
1Define concern,
Organize andPlan
7Prevent
Recurrence
8Celebrate andCommunicate
Success
5Develop
CorrectiveAction Plan
6Implement &
VerifyCorrective
Action
ProblemSolvingProcess
(8D)
PLANACT
DO
CH
EC
K
The 8 StepsThe 8 Steps
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
GB102-7
•STEP 1: Define concern, organize, and plan
1.If needed, assemble a small team of 4-6 people.
2.Ensure that the team has a designated champion.
3.Identify problem topic or opportunity.4.Define purpose, objective, and scope of
project.5.Reach consensus on:
- Charter/Key operating boundaries6.Gather team's ideas and opinions about
the problem area.7.Identify/define action plans.
1.Collect data on production, sales, employee/customer, surveys and feedback, reports, memos, etc. that would establish that a problem exists in a particular area. Determine if data indicates patterns or trends. Are the standards being adhered to?
2.Identify problem topic or opportunity. What is the issue?
3.Relate topic to objectives, business impacts and processes.
4.Define purpose, objective, and scope of project.
Organize and PlanDefine Concern
Reference Guide: Section 1-9Reference Guide: Section 1-9
88––D: Example DetailsD: Example Details
GB102-8
Use For ThisUse For This
Section on 8 Section on 8 -- DD
Can be used for training individuals or teams on this problem solving model
Provides detailed guidance for teams attempting to apply the process to resolve problems
Suggests various tools to apply within the 8 steps
Assists with selecting tools for data collection and decision making
Provides a series of questions to ask at the end of each step
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
GB102-9
DMAIC
The DMAIC model – Define, Measure, Analyze, Improve and Control is a rigorous, disciplined process used by Six Sigma Project Teams.
The DMAIC ProcessThe DMAIC Process
GB102-10
When To Use DMAICWhen To Use DMAIC
Black Belt Projects:
When there is a need to implement breakthrough improvement to a process of strategic importance to the Business
When there is a need to introduce a new product design
Green Belt Projects:
When there is a need to implement breakthrough improvement on projects identified by service or process inefficiencies or product defects
When there is a need for continual improvement on an established process
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
GB102-11
Step 1: DEFINEDescription: Leadership identifies a process that is not meeting the strategic objectives, establishes the scope and boundaries for the project and commissions a Black Belt to lead a Six Sigma project team.
Tasks to do / Questions to AskAre the wasteful processes defined as an output?Which process requires improvement the most?Who is the process owner?Who are the subject matter experts?Who should participate in the improvement effort?What are the boundaries for the process?Who are the customers for this process?What are the customers’ requirements?What is to be produced? Draft specific description of each requirement in terms of physical and measurable attributes of the output.Review agreed-upon customer requirements.Validate the detailed description with the customerDefine the project goals and savings.
Guidelines1.The process owner has
accountability and authority to make changes in the process.
2.Standards are being followed.
3.Identify the current state, the entitlement state, and GAP.
4.Team consists of a Black Belt, process employees, process owner and finance.
5.Process defined as an output statement. Boundaries should be established by identifying suppliers, inputs, customers, and output.
6.Identify internal customers by name.
Step 1: DefineStep 1: Define
GB102-12
What is the Result Of What is the Result Of
the Define Step?the Define Step?
Project commissioned by Executive Champion
Project Charter completed
Improvement opportunity identified
Goals and savings identified
Savings agreed to by Finance
Subject matter experts identified
The improvement team members identified
The customers of the process identified
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
GB102-13
ChecklistChecklist –– Define StepDefine Step
Is this process the most critical facing your organization?
Has the Project Charter been completed?
Has a Black Belt been assigned?
Has a process owner been identified?
Have the team members been identified?
Have the boundaries, inputs and outputs been identified for the project? (Initial Value Stream Map)
Has Finance endorsed the projected savings?
GB102-14
Commission Team1. Who is the Black Belt?2. Who are the subject matter experts?3. Who will be responsible for implementation of this
improvement?4. What are the team’s next steps?
Identify wasteful process1. How have you determined the boundaries to your
process?2. How does this process support external customer
requirements?3. What documented standards are being followed for
the wasteful process?
Define Step Define Step –– Leadership QuestionsLeadership Questions
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
GB102-15
Topics include:
Lean strategies and building blocks
8 wastes (value added vs. non-value added)
Continuous improvement teams (Kaizen)
Cycle time
One-piece flow (JIT)
Pull system (Kanban)
Quality at the source: error proofing (Poka Yoke)
Quick changeover
Reference Guide Reference Guide –– Lean TechnologiesLean Technologies
GB102-16
Topics include:
Standard work; standard operations
Takt time
Total productive maintenance (TPM)
Value Stream mapping
Work flow: visual control and cell design
Workplace organization: 5S
Reference Guide Reference Guide –– Lean Technologies, cont.Lean Technologies, cont.
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
GB102-17
The Basic Quality ToolsThe Basic Quality Tools
Tools for collecting, analyzing and displaying data
Tools for generating ideas
Tools for reaching consensus
Tools for documenting processes and planning actions
GB102-18
The Basic Concepts of StatisticsThe Basic Concepts of Statistics
Types of data
Managing a process
Process Control charts
Process Capability
Measurement System Analysis
STATISTICS REFERENCE GUIDE
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
GB102-19
Glossary of TermsGlossary of Terms
Provides a convenient reference to the terms that are used most frequently within the Six Sigma Performance Excellence initiative
Provides a guide to consistent use of terms
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB103-1
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Climbing to Operational Excellence:Black Belt Training: Introduction to Six Sigma
Training for Operations Training for Operations Green BeltsGreen Belts
GB103-2
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Introduction To Six SigmaIntroduction To Six Sigma
History of Six Sigma
Basic Definitions
What is Six Sigma?
Why is Tyco Electronics deploying Six Sigma?
What is the implementation strategy for Tyco Electronics?
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
GB103-3
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
So many choices,
too many buzz words
GB103-4
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
The History of Six SigmaThe History of Six Sigma
First publicized in the mid 1980’s by Motorola as a means of organizing their Malcolm Baldrige initiative
Jack Welch adopted in 1994 and implemented within GE
Gained notoriety in 1999 when GE reported that Six Sigma goals were tied to all executive compensation and that promotions within the executive ranks required the achievement of Black Belt status
1999 annual report stated that GE saved $2 Billion over 5 years because of Six Sigma projects
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
GB103-5
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Broad AcceptanceNew Technologies
Competitive Awareness
ResultsRefinementDesign
Merrill LynchECMWirecraft
Lockheed Martin
Avery Dennison
FordPolaroidSiebe
AMEXCraneNokiaGEMotorola
DupontSonyBombardierAllied SignalABBTI
1999 - 20021997 - 19981996 – 19971994 – 19951993 – 19941985 - 1992
The History of Six Sigma, cont.The History of Six Sigma, cont.
GB103-6
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
The History of Six Sigma, cont.The History of Six Sigma, cont.
Motorola
Texas Instruments
IBM / DEC / Kodak
ABB
Allied Signal
GE
Whirl Pool, Siebe, Polaroid
3M
Research & DevelopmentResearch & Development
ManufacturingManufacturing
ServiceService
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
GB103-7
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic DefinitionsBasic Definitions
Sigma
A mathematical term used to designate the distribution or spread of any process around the average (mean) as expressed in “standard deviations”
For a business or manufacturing process, the sigma value is a metric used to indicate how well the process is performing
GB103-8
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic Definitions, continuedBasic Definitions, continued
Six Sigma Process
A disciplined methodology of defining, measuring, analyzing, improving and controlling the quality of products, processes and transactions with the goal of eliminating virtually all defects
Six Sigma Process Improvement
A comprehensive, flexible, but essential system to make our business processes more responsive, competitive and profitable
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
GB103-9
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic Definitions, continuedBasic Definitions, continued
Entitlement
The optimal performance level that can be achieved by a process
How would the process operate if it was centered and did not move
Lean
A series of qualitative tools that focus on process optimization through cycle time reduction and the elimination of waste
DMAIC
Define, Measure, Analyze, Improve, Control: disciplined methodology utilized to manage Six Sigma projects
GB103-10
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic Definitions, continuedBasic Definitions, continued
Process
The defined way by which all work gets completed
Any activity or group of activities that takes an input, adds value and provides an output
Manufacturing Process
The defined way utilized to produce the physical product that must conform to a defined set of requirements
Transactional Process
All non-manufacturing processes
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
GB103-11
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic Definitions, continuedBasic Definitions, continued
Six Sigma Champions
Organizational executives who are responsible for:
Sponsoring the Six Sigma project
Launching the Six Sigma project
Making site visits to “see the team in action”
Attending the project review report-out meetings
Removing barriers that are impeding project success, such as
• Capital
• Human resources
• “Red tape”
GB103-12
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic Definitions, continuedBasic Definitions, continued
Master Black Belt
The highest level of technical and organizational proficiency
Provides the training for and assists the Black Belts
Black Belt
Key technical leaders responsible for organizational change and development – ideally this would be a full time position
Must have a comprehensive knowledge of statistical techniques, quality management systems, project management techniques and must have excellent problem solving and facilitation skills
Responsible for oversight of several Six Sigma projects
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
GB103-13
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Basic Definitions, continuedBasic Definitions, continued
Green Belt
Provide the leadership for the Six Sigma Project Teams from concept to completion - rotate back to “normal” assignment when the project is completed, or is assigned to another project
Must have statistical training
Must have good facilitation and problem solving skills
GB103-14
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Qualitative ToolsSix Sigma Qualitative Tools
Audits
Benchmarking
Brainstorming
Cause and Effect Diagrams
Design Review
FMEA
Housekeeping
Process Mapping
Quality Function Deployment
Total Employee Involvement
Workplace Organization
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
GB103-15
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Quantitative ToolsSix Sigma Quantitative Tools
Capability Analysis
Check Sheets
Control Plans
Design For Six Sigma
Design Of Experiments
Gage Reliability and Reproducibility Studies
Histograms
Lead Time Analysis
Pareto Analysis
Statistical Process Control
Statistical Tolerance Analysis
GB103-16
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Implementation ToolsSix Sigma Implementation Tools
Cellular Manufacturing
DMAIC
Design For Manufacturability
Focused Area Improvement Event
Just In Time (JIT)
Kan Ban
One Piece Flow
Poka Yoke (Error Proofing)
Predictive Maintenance
QOS – Quality Operating System Reviews
Quick Changeover
Supply Base Development
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
GB103-17
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
What is Six Sigma?What is Six Sigma?
An approach to sustainable improvement that:
Moves us toward our goal of being a world-class company
Fosters a common language and interplant cooperation
Uses a disciplined process to drive capability improvement and eliminate waste
Helps Tyco:
Develop expert personnel and future managers
Increase capacity with minimal capital
Meet productivity goals
Improve customer service
GB103-18
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Is…Six Sigma Is…
A Business Improvement Process That Is Lead By Organizational Executives and Senior Management
A Holistic Approach To Management
Must Be Applied In All Areas Of The Company
Manufacturing
Transactional
• Service / Administrative
• Engineering / Design
Communications Intensive
Fact Based And Results Oriented
6
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
GB103-19
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Six Sigma –– New and OldNew and Old
What’s new
Some analysis tools (C&E matrix, Multi-vari studies)
Software (Minitab)
DMAIC process integrates the use of the tools
Train - Apply - Review format
Application to Non-manufacturing and Design
What’s not
Some tools (DOE, Gage R&R)
Team structure
Continuous Improvement methods (Kaizan)
GB103-20
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Why Six Sigma FailsWhy Six Sigma Fails
Lack of visible support from Executive and Senior Managers
Lack of understanding of the Six Sigma process
Business metrics not clearly defined
Viewed as “another quality program” or “a manufacturing” program
Reward system not linked to the outcomes of Six Sigma
Inadequate / infrequent communications
Not requiring the disciplined process (shortcuts)
Insufficient resources
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
GB103-21
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Count the number of times the 6th letter of the alphabet appears in the following text:
The necessity of training farm hands for the first class farms in the fatherly handling of farm live stock is foremost in the eyes of the farm owners. Since the forefathers of the farm owners trained the farm hands for first class farms in the fatherly handling of farm live stock, the farm owners felt they should carry on with the family tradition of training farm hands of the first class farmers in the fatherly handling of farm live stock because they believe it is the basis of good fundamental farm management.
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
The Inspection ExerciseThe Inspection Exercise
GB103-22
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
No matter how good your inspection and test processes are, the more defects you create, the more defects escape to the customer
Total Defects Per Unit
Esc
ap
ing
Def
ects
Theory of Escaping DefectsTheory of Escaping Defects
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
GB103-23
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma …..Six Sigma …..
A ProcessA Process
ImprovementImprovement
MethodologyMethodology
Based onBased on
“Statistical“Statistical
Thinking”Thinking”
GB103-24
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Statistical ThinkingStatistical Thinking
A habitual way of looking at work that:
Recognizes all activities as processes
Recognizes that processes have inherent variability and probably have non-value adding steps
Uses data to understand variation / waste and to drive decisions to improve these processes
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
GB103-25
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
The “Normal Curve”
+/- 3 : The Traditional
Quality Standard
23 11 2 3
34.13% 34.13%
2.1% 2.1%13.6% 13.6%
68.3%
95.5%
99.7%
Six Sigma As a Statistical ConceptSix Sigma As a Statistical Concept
GB103-26
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Three-Sigma Process
LSL USL3 standard deviations
LSL USL4 standard deviations
LSL USL5 standard deviations
LSL USL6 standard deviations
“Sigma” refers to the number of standard deviations between the center of the process and
the nearest specification limit.
Four-Sigma Process
Five-Sigma Process
Six-Sigma Process
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
GB103-27
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
2 308,5373 66,8074 6,2105 2336 3.4
PPM
Process
Capability
Defects per
Million Opp.
The Sigma LevelThe Sigma Level
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB103-28
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Close your eyes and listen to a 6 sigma process, followed by a 4 sigma process.
The Sound of Quality…The Sound of Quality…
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
GB103-29
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
LSL USL
Defects
Process Capability
Inadequate DesignMargin
Inadequate Process
Capability
SuppliedMaterial Variation
Inadequate Measurement
Capability
Defects
Dissecting Process CapabilityDissecting Process Capability
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB103-30
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
ProcessImprovement That Is:
Methodical
Significant -essentially “error free”
Abundant
3.4
Why Deploy Six Sigma?Why Deploy Six Sigma?
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
GB103-31
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
6
5
4
3
2
5% 10% 15% 20% 25% 30%
Cost of Quality (% of Sales)
3 Cost of Quality : 10-15% of Sales3 Cost of Quality : 10-15% of Sales
What’s Your Cost Reduction Opportunity ?What’s Your Cost Reduction Opportunity ?
Sig
ma
Le
vel
Sigma Level and Cost of QualitySigma Level and Cost of Quality
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB103-32
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Why Deploy Six Sigma?Why Deploy Six Sigma?
Customers Want
Price
Delivery
Quality
We Focus On
Cost
Cycle Time
Reduction of Defects
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
GB103-33
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
The Basic ApproachThe Basic Approach
The Right Projects
+The Right People
= Better, Faster Results
+The Right Roadmap
and Tools
+The Right Support
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB103-34
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
A Proven ApproachA Proven ApproachAimed at infrastructure to produce institutionalization
Leadership training to create understanding, goals, process review
Champions form guiding coalition
Black Belts lead projects and become coaches and trainers
Well documented, step-by-step roadmap that works
Process analysis
Application of tools
Establishment of process control
Training with aggressive project selection and results
Action learning: “Plan - Train - Apply - Review” format
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
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GB103-35
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Practical ProblemPractical Problem Statistical ProblemStatistical Problem
Statistical SolutionStatistical SolutionPractical SolutionPractical Solution
),...,,( 21 kxxxfy
Overall ApproachOverall Approach
Rev. A Printed 3/10/2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB103-36
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Define
Measure
Analyze
Improve
Control
D
M
A
I
C
The Six Sigma ProcessThe Six Sigma Process
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
GB103-37
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 1: DefineStep 1: Define
Identify the “gap” in meeting the Business strategy or objective
Establish the scope and boundary for the project
Identify the Black Belt and the project team members
Establish the project goals and savings
Obtain approval of the Senior Leaders
GB103-38
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 2: MeasureStep 2: Measure
A functioning project team lead by a Black Belt
A detailed process map reflecting the key process variables
Measurement capability has been determined
Baseline short term and long term process capability has been determined
Initial determination of the appropriate qualitative, quantitative and implementation tools
Confirmation of the project goals and savings
Confirmation that the team consists of the right members
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
GB103-39
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 2: Measure Typical ActivitiesStep 2: Measure Typical Activities
Process mapping
Process FMEA
Evaluate / implement a data collection process
Evaluate / implement a control plan
Process characterization / identification of the primary sources of variation
Measurement R & R studies
Process capability determination
GB103-40
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 3: AnalyzeStep 3: Analyze
Comprehensive analysis of the process variables and the relationship to the product requirements
Understanding of which inputs affect which output
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 21402-108, Rev. C
GB103-41
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 3: Analyze Typical Step 3: Analyze Typical
Update process map, process FMEA and control plan
Identification of the root cause of the variation through
Pareto Charts that analyze the data from several perspectives
Cause and Effect Diagrams
SPC Charts
Multi-Vari Studies
Correlation and Regression
GB103-42
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 4: ImproveStep 4: Improve
Key process variables have been confirmed through the use of data and statistical experimentation
Trial solutions are identified – the efforts required and risks involved are documented
Trial solutions must attack the root cause
Solution must solve the problem / close the “gap”
Project plan to transfer the revised process into sustaining operation
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 22402-108, Rev. C
GB103-43
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 4: Improve Typical ActivitiesStep 4: Improve Typical Activities
Process optimization using implementation tools
Focused area improvement
One piece flow
Kan-Ban
Poka Yoke (Error Proofing)
Design of Experiments (DOE)
Determination of the new process capability
Updated process map, FMEA, and control plan
GB103-44
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 5: ControlStep 5: Control
The Six Sigma Operational Excellence project has been completed and has met the stated goals and savings
Long term capability has been established
Operational control plans have been developed and implemented
The process is returned to the process owner for sustained maintenance
Team final report includes identification of lessons learned and future improvement opportunities
Team recognition
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
GB103-45
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Step 5: Control Typical ActivitiesStep 5: Control Typical Activities
Update all required documentation to ensure compliance with Quality Management System requirements
Inspection plans / control plans
Process specifications
Quality specifications
Product prints
Workmanship documents
Local documents – work instructions
Routings
FMEA
On going measurement collection defined / implemented
GB103-46
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
SevenBasic
QualityTools
GreenBelts
StatisticalToolsFor
ProcessImprovement
BlackBelts
Design ForSix Sigma
MasterBlackBelts
Basic Tools Wall Design Wall
2 Sigma 3 Sigma 4 Sigma 5 Sigma 6 Sigma
Why Deploy Six Sigma?Why Deploy Six Sigma?
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 24402-108, Rev. C
GB103-47
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Improvement FocusSix Sigma Improvement Focus
People
Skills
Capabilities
Knowledge
Material
Reduce Variability
Improve Delivery
Reduce Cost
Processes
Reduce Variability
Improve Capability
Improve Cycle Time
Reduce Errors / Defects
Increase Yields
Match Process to Design
GB103-48
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Some Benefits of Six SigmaSome Benefits of Six Sigma
Cost Reduction
Productivity Improvement
Market Share Growth
Customer Retention
Cycle Time Reduction
Reduction in Errors / Defects
Culture Change
Improved Product and Service Development
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
GB103-49
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Why 3 Sigma Isn’t Good EnoughWhy 3 Sigma Isn’t Good Enough
20,000 lost articles of mail per hour
Unsafe drinking water for 15 minutes each day
2 unsafe landings at most major airports each day
5,000 incorrect surgical procedures each week
No electricity for almost 7 hours each month
22,000 checks deducted from the wrong account each hour
200,000 prescriptions filled incorrectly each year
50 newborns dropped at birth each day
» Source: QCI International
GB103-50
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Will Improve EBITSix Sigma Will Improve EBIT
Improve Rolled
ThroughputYield
LowerTotal
Cost ofQuality
Improve ThroughputVolume and
Delivery
Meet CustomerExpectationsAt A Lower
Cost
Increased RevenueIncreased Margins
Lower Cost ofCapital
ImprovedEBIT
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 26402-108, Rev. C
GB103-51
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Design
Manufacturing Defects
Scrap
Rework
Variance
Suppliers
Administrative Activities
Cycle Time
Reliability
Areas of Opportunity Areas of Opportunity
Within Tyco ElectronicsWithin Tyco Electronics
GB103-52
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
The “Program Du Jour”
An Easy Fix
A Broad Brush Approach
The Universal Answer To All Business Problems
A Self-sustaining Initiative
A Statistical Program
Free
What Six Sigma Is Not…What Six Sigma Is Not…
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 27402-108, Rev. C
GB103-53
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Integrating Six Sigma Integrating Six Sigma
Time
Op
era
tio
na
l E
xc
ell
en
ce
Breakthrough
Continuous Improvement
BreakthroughSix Sigma
Continuous Improvement
NewStandard
NewStandard
GB103-54
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Operational ExcellenceSix Sigma Operational Excellence
Along with recognized industry standards will provide the baseline for a common approach to Quality Management and continual improvement
Will provide increased emphasis on business process improvement during Operations reviews
Will allow us to “Plan For Quality”
Reduce the variability in our processes
Improve the design process
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 28402-108, Rev. C
GB103-55
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Operational ExcellenceSix Sigma Operational Excellence
Implementation Strategy: Phase 1Implementation Strategy: Phase 1
“Back To The Basics” – to drive immediate improvement
Preventative control plans for every major manufacturing process are to be reviewed, updated and enforced
Operator certification to be reviewed and updated as required
Utilize the Deployment Champions to manage the corrective action commitments for customer complaints
Formalize analysis of material returned due to a Tyco Electronics error
Implement a manufacturing process audit program
Implement a shipping / dock audit program
GB103-56
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Operational ExcellenceSix Sigma Operational Excellence
Implementation Strategy: Phase 2Implementation Strategy: Phase 2
“Launch Six Sigma Projects”
Completion of the Executive awareness training
Deploy Champion training on a Regional / Country level
Complete initial training sessions for Black Belts on a Regional level
Initial Six Sigma Black Belt projects completed and assessed
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 29402-108, Rev. C
GB103-57
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Operational ExcellenceSix Sigma Operational Excellence
Implementation Strategy: Phase 3Implementation Strategy: Phase 3
“Breakthrough Improvement”
Each Business achieves “self-sufficiency”
Master Black Belts within the business are leading the organization to higher levels of proficiency
Master Black Belts are training Black Belts
Black Belts are training Green Belts
Black Belts are managing multiple projects
Design for Six Sigma is developed and launched
GB103-58
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
ExecutivesProject Team
Members
Champions
Master BlackBelt
Black Belts
Green Belts
All Employees
•Lead change•Own strategy, direction, results
•Approve, launch, monitor projects•Responsible for project activities
•Train & coach•Tools expert
•Apply concepts within work area
•Work on assigned projects
•Work on assigned projects
•Key project leaders•Responsible for selecting the right tools
Six Sigma RolesSix Sigma Roles
And Responsibilities SummaryAnd Responsibilities Summary
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 30402-108, Rev. C
GB103-59
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Six Sigma Operational Six Sigma Operational
Excellence ConclusionsExcellence Conclusions
Remember -----
Six Sigma is more than the answer to a mathematical formula for calculating variability
Six Sigma is a comprehensive approach to Business Process Improvement
GB103-60
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Reduced process variation
Outputs match customer requirements and expectations
Measurable continual improvement
Reduced costs
Enhanced profitability
Primary Benefits of Six Sigma…Primary Benefits of Six Sigma…
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 31402-108, Rev. C
GB103-61
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Secondary Benefits of Six Sigma…Secondary Benefits of Six Sigma…
Quality of products and service is improved
Process investigation evaluates value-added status of each element (inputs, raw materials, operations)
Mistake-proofing added to processes
Better flow of inputs/materials toward output means less money tied up in inventory/WIP.
Company can react more quickly to customer requests
GB103-62
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Program launched with outward blessing of management
Provision made for training
Upper managers disappear after launching
Program demands people’s time, but no time authorized
Results fall short of promises
Program pushed aside for next get-well-quick program
“Here comes another one”
Cycle of doomed programs
Obstacles to SuccessObstacles to Success
INTRODUCTION TO SIX SIGMA
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 32402-108, Rev. C
GB103-63
Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control
Overcoming the ObstaclesOvercoming the Obstacles
Senior Management visibly involved.
Champion has the responsibility for removing roadblocks & authority to make changes.
Black Belts and Green Belts receive training to lead teams to implement the data-driven improvement changes.
Personal and project accountability and regular reporting to management.
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB104-1
Rev C February 2004© 2003 by Tyco Electronics
LEAN Strategiesfor Operations Improvement
D
M
A
I
C
Rev C January, 2003© 2003 by Tyco Electronics GB104-2
Cycle Time Reductionthrough
Waste Elimination
WHAT IS WHAT IS WASTEWASTE??
Anything that does not add valueReference Guide, Lean, Page 1, ¶ 1 - 2
What Is Lean?What Is Lean?
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-3
NON-VALUE-ADDING ACTIVITYProcess/Operation that take time,resources or space, but do not addvalue to the product or service
VALUE-ADDING ACTIVITY
Changes the information or product
Quote #1
Done right the first time
Customer is willing to pay for it(Customer cares)S
Reference Guide, Lean, Page 2, ¶ 1 - 3
Value and NonValue and Non--ValueValue
Added ActivitiesAdded Activities
Rev C January, 2003© 2003 by Tyco Electronics GB104-4
Over-Production
Waiting
Transportation
Processing
Inventories
Motion
Defective Products
Unused Creativity
Reference Guide, Lean, Page 2, middle
Reference Guide, Lean, Page 2, middle
8 Wastes8 Wastes
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-5
All work is a process of adding value.
The value can be physical (manufacturing) or information (transactional).
If you are the thing or data going through the process, what is your journey like? How often do you wait, take useless trips, or get reworked?
What percent of your total time in the process would be for adding value?
95%
5%Non-Value
Adding
ValueAdding
Reference Guide, Lean, Page 2, BottomReference Guide, Lean, Page 2, Bottom
Rev C January, 2003© 2003 by Tyco Electronics GB104-6
Delivery Performance from cycle time reduction
Labor savings from standard work, cells
Manufacturing space from layout improvements
Increased capacity from quick change-over
Reduced scrap from error-proofing
Reduced inventory from 1-piece flow, kanban
$ or $ ?Reference Guide, Lean, Page 1, ¶ 2Reference Guide, Lean, Page 1, ¶ 2
Why Lean Implementation?Why Lean Implementation?
What are the Opportunities for Six Sigma Projects?What are the Opportunities for Six Sigma Projects?
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-7
Traditional Six Sigma deals with quality and capability: “How can we eliminate defects?”
By adding Lean strategies, we also look at capacity:“How can we do the work faster and more efficiently?”
Lean strives for the best combination of human skill and machines in an elegant process flow.
Not all projects need both. The measure step in DMAIC will lead you to a good balance. To have the data to decide, measure the Lean factors of:
• Cycle time with value-added percent, • Yield compared to theoretical, and • Inventory.
Reference Guide, Lean, Page 1, 1Reference Guide, Lean, Page 1, 1
Rev C January, 2003© 2003 by Tyco Electronics GB104-8
• Total Process Cycle time ( “Throughput time” )
Process step cycle time
• Worker cycle time ( + total for all workers )
• Machine cycle time
• Value added percent ( time )
• TAKT time (customer demand )
• Yield percent of theoretical ( parts )
• Inventory levels before/after each step, + finished goods
• Change-over time
Lean MeasuresLean Measures
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-9
Category
Value Added Operation
Transportation
Inspection
Delay
Process Delay
Lot Delay
Improvement Method
Operations Improvement
Location & Layout
Source Inspection & Error-Proofing
Continuous Flow
Synchronization (JIT)
1-Piece Flow
5 %
10%
5%
80%
Reference Guide, Lean, Page 1, mid - bottom
Reference Guide, Lean, Page 1, mid - bottom
LEAN METHODS:LEAN METHODS:JIT + AutonomationJIT + Autonomation
Rev C January, 2003© 2003 by Tyco Electronics GB104-10
LEANLEANMETHODS:METHODS:
JIT + JIT + AutonomationAutonomation
Error Proofing“Poka Yoke”
Quick Change-Over
Focused Area Improvement Event
Standard Operations
Kanban
WorkplaceOrganization
( 5 - S )
Reference Guide, Lean, Page 1, bottomReference Guide, Lean, Page 1, bottom
Lean Tools: Improvement Step ApplicationsLean Tools: Improvement Step Applications
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-11
As a combined team, implement Lean strategies based on the data and analysis. Include:
TAKT Time p. 8
Standard Operations p. 7
Cycle time p. 3
Pull system, one-piece flow p. 6
Inventory levels p. 5
Cell design and workplace layout p. 9
Plan your improvements, implement the changes, re-run production, measure.
Reference Guide, Lean:
Reference Guide, Lean:
Lean StrategiesLean Strategies
Rev C January, 2003© 2003 by Tyco Electronics GB104-12
TAKT Time =Total Daily Operating Time
Total Daily Requirement
TAKT Time =60 sec x 60 min x 7.5 hrs x 2 shifts
3,600 pieces / day
TAKT Time =54,000 sec.
3,600 / day= 15 sec./ pc.
Reference Guide, Lean, Page 8Reference Guide, Lean, Page 8
TAKT Time=Customer Demand TAKT Time=Customer Demand
in Time Per Piecein Time Per Piece
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-13
The objective of Standard Operations
is to provide balanced production among
all processes, with minimum labor and WIP
inventories, through the best combination
of people and machines.
Reference Guide, Lean, Page 7Reference Guide, Lean, Page 7
Standard OperationsStandard Operations
Rev C January, 2003© 2003 by Tyco Electronics GB104-14
Total of Worker Cycle TimesTAKT Time
5 + 10+ 8 sec.15 sec.
= 1.7 (2) Workers Required
That is machine time without any operations improvement. Is worker time different? Could
the work be done with one person?
Reference Guide, Lean, Page 4 - 5Reference Guide, Lean, Page 4 - 5
Number of Workers RequiredNumber of Workers Required
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-15
Lot size 6 1
Floorspace, equipment _______ _______
Production (min./piece FG) _______ _______
Scrap _______ _______
Inventory WIP _______ _______
FG _______ _______
Operators _______ _______
Utilization of 1, 2, 3 _______ _______
Total cycle time 1 pc (Red block time) _______ _______
TAKT time (set by customer demand) 15 sec. 15 sec.
Improved MeasuresImproved Measures
Rev C January, 2003© 2003 by Tyco Electronics GB104-16
Changeover time is a hidden waste. It is included in standard costs.
Changeovers of < 10 minutes is the Lean goal.
Set in + adjust = Changeover. (Good part to good part) Adjust is typically 90 %.
50% of the savings can be attained with no costs.
Reference Guide, Lean, Page 7Reference Guide, Lean, Page 7
ChangeChange--Over TimeOver Time
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-17
Reference Guide, Lean, Page 10Reference Guide, Lean, Page 10
WPO is sometimes referred to as 5S, from five Japanese words for organizing the workplace:
Japanese 5 S 5 S Translation Tyco Electronics 6 Ts (tools)Seiri Organization Proper arrangementSeiton Orderliness OrderlinessSeiso Cleaning up Clean up Seiketsu Cleanliness CleanlinessShitsuke Discipline Discipline
Safety
Workplace Organization (5S)Workplace Organization (5S)
Rev C January, 2003© 2003 by Tyco Electronics GB104-18
Look first at the business objectives, then use the matrix to determine the most likely Lean tool
to apply to that measure.
Use in combination with Six Sigma statistical tools. The Lean tools will often be the “how to fix” for the problems identified in the statistical
analysis.
Reference Guide, Lean, Page 11Reference Guide, Lean, Page 11
Lean Tools:Lean Tools:Linkage to Business ObjectivesLinkage to Business Objectives
INTRODUCTION TO LEAN STRATEGIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev C January, 2003© 2003 by Tyco Electronics GB104-19
Balance the tools and strategies based on data.
LEAN requires capable processes. One-piece flow
only works with good pieces.
Capable processes are no good if the cycle times
create late shipments and unprofitable processes.
Six Sigma + LEANSix Sigma + LEAN
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-1
Introduction to Introduction to
Minitab Release 14Minitab Release 14
Process Improvement Methodology Process Improvement Methodology
Operations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-2
ObjectivesObjectives
Introduce basic Minitab functions
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-3
BackgroundBackground
Minitab first introduced at Penn State in the late 70’s
Started as a DOS based program and migrated to Windows
Heavily used in the academic world
Reason for its use in our Six Sigma training
The “industry standard” for Six Sigma applications
Used in other Six Sigma companies (GE, AlliedSignal, Motorola)
Ease of use for beginning students
Very strong user base in the former Tyco Electronics companies
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-4
Starting MinitabStarting Minitab
Start MenuStart Menu
Desktop IconDesktop Icon
Windows 2000 Quick Launch ToolbarWindows 2000 Quick Launch Toolbar
Minitab IconMinitab Icon
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-5
Data Window:•A Worksheet, not an Excel Spreadsheet•Column names are above first row•Everything in a column is considered to
be from the same group
Data Window:•A Worksheet, not an Excel Spreadsheet•Column names are above first row•Everything in a column is considered to
be from the same group
Session Window:•The Output
Session Window:•The Output
Main ScreenMain Screen
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-6
Column HeadersT = Text / D = Date
Column HeadersT = Text / D = Date
Column NameColumn Name
Data Direction ArrowData Direction Arrow
Data WindowsData Windows
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-7
Enter Data into Minitab by
Typing it in
Cutting & pasting from other programs
Random number generators in Minitab
Importing it
Excel, Text, ASCII, Dbase files, etc….
Data WindowData Window
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-8
Minitab File TypesMinitab File Types
Minitab Operating Files
Minitab has training files in its data subdirectory
C:\Program Files\MINITAB 14\Data
Minitab File Extensions
MPJ for Data Files (Projects)
Ex: Data.mpj
MTW For Data Files (Worksheets)
Ex: Data.mtw
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-9
Worksheet Files versusWorksheet Files versus
Project FilesProject Files
Worksheets = just data
can be added to existing projects
use “Open Worksheet” to open
multiple worksheets must be saved as a project to keep together
Projects = data, command line history, graphs, and analyses
can store multiple worksheets and all aspects of analysis
retain last settings used in dialog box
hold all previous analyses from last save, as well as any graphs that were created
use “Open Project” or toolbar button to open
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-10
Projects
Worksheets
Opening / Saving WorksheetsOpening / Saving Worksheets
and Projectsand Projects
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-11
Open Project (opens to
your default directory)
Open Project (opens to
your default directory)
Save Project (Saves with same file name. Use
“save as” to save as a different file name.)
Save Project (Saves with same file name. Use
“save as” to save as a different file name.)
Opening / Saving Worksheets and ProjectsOpening / Saving Worksheets and Projects
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-12
Pull Down MenusPull Down Menus
DATA menu CALC menu
GRAPH menuSTAT menu
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-13
Stack / Unstack Columns
Sort Data
Copy Columns
Manipulate Worksheets
Code Data
The Pull Down Menus The Pull Down Menus -- DataData
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-14
Make Patterned Data
Calculator
Create Random Data
The Pull Down Menus The Pull Down Menus -- CalcCalc
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-15
The Pull Down Menus The Pull Down Menus -- StatStat
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-16
The Pull Down MenusThe Pull Down Menus-- GraphGraph
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-17
Moving Between Minitab Windows
The Pull Down Menus The Pull Down Menus -- WindowWindow
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-18
Help
StatGuide
Tutorials
The Pull Down Menus The Pull Down Menus –– HelpHelp
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-19
Editing PreferencesEditing Preferences
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-20
Increase the value of this number(200 is the maximum allowable value)
Increase the value of this number(200 is the maximum allowable value)
Editing PreferencesEditing Preferences
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-21
Control C / Control V
Copies data / Pastes data
Control E
Pulls up previous menu
Alt Tab
Moves you from one Windows application to another
Ex:
Minitab to PowerPoint for making presentations
Excel to Minitab for copying data
Same function as Ctrl E
Short Cut KeysShort Cut Keys
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-22
Paste with Existing Column NamesPaste with Existing Column Names
When pasting a column with a name that already exists in the worksheet, Minitab automatically resolves the conflict by adding an extension (_#)
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-23
Indicates there is a worksheet description
Indicates there is a worksheet description
Indicates there is a column description
Indicates there is a column description
Description IndicatorsDescription Indicators
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-24
Other EasyOther Easy--toto--Use FeaturesUse Features
Project Manager
ReportPad
StatGuide
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-25
Tree-like navigator to view your entire Minitab Project or browse different windows
Better able to associate session output, graphs and worksheets
Project ManagerProject Manager
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-26
Results of all data analyses andgraphs that you have created
Results of all data analyses andgraphs that you have created
Worksheets associated withyour analyses and graphs
Worksheets associated withyour analyses and graphs
Components ofyour project
Components ofyour project
Project Manager ExampleProject Manager Example
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-27
Within Project Manager, Pop-up menus let you access
convenient functions. (Right mouse-click on item to access
pop-up menu)
Project ManagerProject Manager
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-28
A Project Manager toolbar allows easy browsing of several types of windows
Project Manager: Tool BarProject Manager: Tool Bar
INTRODUCTION TO MINITAB
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Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-29
Jump to session output by double-clicking title
See graphs associated with session output
Edit titles
Open StatGuide
Delete specific output
etc…
Browse SessionBrowse Session
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-30
All data analyses and graphscreated during the Minitab session
All data analyses and graphscreated during the Minitab session Results of one specific data
analysis performed during the session
Results of one specific dataanalysis performed during the session
Browse Session ExampleBrowse Session Example
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-31
Replaces Manage Worksheets
Tile worksheets
Rename worksheets
Bring worksheet to front
Save worksheets
Close worksheets
etc…
Browse WorksheetsBrowse Worksheets
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-32
All worksheets associatedwith the project
All worksheets associatedwith the project
The selected worksheetThe selected worksheet
Browse Worksheets ExampleBrowse Worksheets Example
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-33
Replaces Manage Graphs
Tile graphs
Tile graph with associated worksheet
Rename graphs
Save graphs
Close graphs
etc…
Browse GraphsBrowse Graphs
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-34
All graphs associatedwith the project
All graphs associatedwith the project The selected graphThe selected graph
Browse Graphs ExampleBrowse Graphs Example
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-35
Provides information about the current worksheet:
Column names
Column counts
Number of Missing Values
Data type (numerical, text, date, or time)
Column description
etc…
Browse InformationBrowse Information
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-36
Information ExampleInformation Example
Information about a specific worksheetInformation about a specific worksheet
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-37
Provides a history of all the commands you have used - in command language format
Replaces separate History window
Browse HistoryBrowse History
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-38
History ExampleHistory Example
Complete history of your work in Minitab command language formatComplete history of your work in Minitab command language format
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-39
Provides statistical guidance after you run a procedure in Minitab
The StatGuide icon will only be available after acalculation has been performed in Minitab
The tone is informal and practical, not like a textbook
StatGuideStatGuide
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-40
Version 14 Covers
Basic Statistics
Regression
ANOVA
DOE
Control Charts
Quality Tools
SPC & Quality Tools
Reliability/Survival
Multivariate
Time Series
Power and Sample Size
StatGuide TopicsStatGuide Topics
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 21402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-41
ExampleExample
StatGuide LayoutStatGuide Layout
Detailed descriptionDetailed description
Hot-linked definitionsHot-linked definitions
Relevant output highlighted
Relevant output highlighted
Links to in-depth topicsLinks to in-depth topics
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-42
Exercise in the Use of Minitab
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 22402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-43
Under Menu Bar, FILE
OPEN PROJECT
(for opening your previously saved projects)
OPEN WORKSHEET
(for opening just the worksheet with the data)
File >> Open Worksheet >> Wine.mtw >> Open
Note: Multiple worksheets can be open at the same time
Opening the WorksheetOpening the Worksheet
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-44
Transformations are typical requirements in many analyses (both engineering and statistical)
Some typical transformations of data needed include :
natural log, square root, exp, log (base 10), and linear/nonlinear combinations
Transformations are the result of simple mathematical calculations – simple or complex
Transformations and calculations are performed with CALC >> CALCULATOR
Calculations and TransformationsCalculations and Transformations
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-45
Let’s look at the WINE.MTW data
Suppose we want a new variable: Natural Log of Aroma…
CALC > CALCULATOR
“Store result in variable:” (type c8 or a variable name)
“Expression:” (click in this box; then under ‘Functions”, scroll down to find Natural log and double-click on it; then double-click on the variable Aroma)
Click on OK and your transformed data appears in column c8
If you didn’t name the column previously, you can type in a variable name for the new column.
Calculations and TransformationsCalculations and Transformations
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-46
Suppose we want a new variable that is a function of several of the variables.
We’ll make column c9 =
This can be done similar to the previous transformation, only now we can include either typing in the necessary numbers and symbols or using the calculator keyboard with our mouse clicks.
gionRe
Quality3)Aroma(2
Calculations and TransformationsCalculations and Transformations
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 24402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-47
Calculations and TransformationsCalculations and Transformations
Click NoClick No
Click here to close the worksheetClick here to close the worksheet
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-48
• You can easily generatecolumns in the worksheetcontaining patterned orrandom data.
• This will be useful whencreating data matrices forvarious types of analysis.
• Calc >> Make PatternedData >> Simple Set ofNumbers
Creating Patterned DataCreating Patterned Data
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-49
A total of 200 measurements have been taken on 10 samples of product. These measurements have been placed into one column of your worksheet. The first 20 measurements are for the first sample, the second 20 are for the second sample, etc.
To identify these measurements with the correct sample you will need a column of numbers consisting of the value 1, repeated 20 times to identify the first 20 measurements as belonging to sample 1, followed by the value 2, repeated 20 times to identify the next 20 measurements as belonging to sample 2, etc.
Calc >> Make Patterned Data >> Simple Set of Numbers
Enter the values as shown in the dialog box at the right and review the result in the Worksheet.
This tells Minitab to start with thevalue 1, end with the value 10, and listeach value 20 times.
This will result in a column containing the value 1 repeated 20 times, followed by the value 2 repeated 20 times, followed by the value 3 repeated 20 times, etc.
Creating Patterned DataCreating Patterned Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-50
Instead of grouping all of the measurements for each sample together, suppose that your 200 measurements are arranged so that the first measurement in the data column belongs to sample 1, the second measurement to sample 2, the third to sample 3, …, the tenth to sample 10. Then this pattern of data entry repeats 20 times.
To identify these measurements with the correct sample you will need a column of numbers consisting of the values 1 – 10 in sequence, repeated 20 times.
Calc >> Make Patterned Data >> Simple Set of Numbers
Enter the values as shown in the dialog box at the right and review the result in the Worksheet.
This tells Minitab to start with thevalue 1, end with the value 10, and listeach value 1 time. Then repeat the list20 times.
This will create a column of numbersconsisting of the values 1 thru 10,repeated 20 times.
Creating Patterned DataCreating Patterned Data
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 26402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-51
35025015050
100
50
0
simulated data
Frequency
Minitab has the ability to create random data from many statistical distributions including Uniform, Normal, t, Chi-Squared, Lognormal, Weibull, and more.
This is often of interest or need to perform Monte Carlo simulations or to create an “image” of an assumed population from estimated sample parameters.
Creating Random DataCreating Random Data
1000 observations from a simulated Normal distribution with m (mean) = 200 and s (standard deviation) = 35.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-52
Generate 1000 observations froma Normal Distribution with a Meanof 200 and a Standard Deviationof 35. Store the results in column 3.
Generate 1000 observations froma Normal Distribution with a Meanof 200 and a Standard Deviationof 35. Store the results in column 3.
Generate thedata and reviewyour results inthe Worksheet
Generate thedata and reviewyour results inthe Worksheet
Creating Random DataCreating Random Data
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 27402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-53
Column 3 will consist of 1000 random observations.The distributions of these observations will benormal with a mean of 200 and a standarddeviation of 35.
Note: Your results will not be identical to what isshown here. These are random observations, therefore your set of 1000 random observationswill be different, but they will have the same mean(200) and the same standard deviation (35).
Column 3 will consist of 1000 random observations.The distributions of these observations will benormal with a mean of 200 and a standarddeviation of 35.
Note: Your results will not be identical to what isshown here. These are random observations, therefore your set of 1000 random observationswill be different, but they will have the same mean(200) and the same standard deviation (35).
Creating Random DataCreating Random Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-54
Click OKClick OK
Plotting the Data Plotting the Data -- DotplotDotplot
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 28402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-55
Plotting the Data Plotting the Data -- DotplotDotplot
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-56
View descriptive statistics for thedata.
Stat > Basic Statistics > DisplayDescriptive Statistics
Select C3 as the Variable (yourrandom observations)
View descriptive statistics for thedata.
Stat > Basic Statistics > DisplayDescriptive Statistics
Select C3 as the Variable (yourrandom observations)
Descriptive StatisticsDescriptive Statistics
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 29402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-57
Your results will be similar, but not identical, tothese. The mean should be very close to 200and the standard deviation should be very closeto 35.
Your results will be similar, but not identical, tothese. The mean should be very close to 200and the standard deviation should be very closeto 35.
Descriptive StatisticsDescriptive Statistics
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-58
Generate a graphicalsummary of the descriptive statistics
Generate a graphicalsummary of the descriptive statistics
Descriptive StatisticsDescriptive Statistics
INTRODUCTION TO MINITAB
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 30402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-59
Descriptive Statistics Descriptive Statistics –– Graphical SummaryGraphical Summary
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB105-60
Introduction to MinitabIntroduction to Minitab
This concludes the basic introduction to Minitab.
Additional capabilities of the software will be explored as we move through your weeks of Belt training.
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc. GB106-1
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
BasicStatistics
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-2
ObjectivesObjectives
Introduce the concepts of Shape, Center, and Spread of distributions
Learn about the Normal distribution
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-3
Basic StatisticsBasic Statistics
Fundamentals of Improvement Fundamentals of Improvement
Stability
How does the process perform over time?
Stability is represented by a constant mean and predictable variability over time.
Variability
Is the process on target with minimum variability?
We use the mean to determine if process is on target. We use the Standard Deviation ( to determine variability
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-4
VariationVariation
“While every process displays Variation, some processes display controlled variation, while other processes display uncontrolled variation” (Walter Shewhart)
Controlled Variation is characterized by a stable and consistent pattern of variation over time
Associated with Common Causes
Uncontrolled Variation is characterized by variation that changes over time
Associated with Special Causes
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-5
2520151050
75
70
65
Sample Number
Sam
ple
Mea
n
X-Bar Chart for Process A
X=70.91
UCL=77.20
LCL=64.62
2520151050
75
70
65
Sample Number
Sam
ple
Mea
n
X-Bar Chart for Process A
X=70.91
UCL=77.20
LCL=64.62
2520151050
80
70
60
50
Sample Number
Sam
ple
Mea
n
X-Bar Chart for Process B
X=70.98
UCL=77.27
LCL=64.70
Special CausesSpecial Causes
Process A shows controlled variationProcess B shows uncontrolled variation
Variation ExamplesVariation Examples
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-6
Co
st
There will always be variability present in any process
We can tolerate variability if:
The process is on target
The total variability is relatively small compared to the process specifications
The process is stable over time
New ViewNew View
LSLLSL
USLUSL
NomNom
USLUSL
Traditional View
Traditional ViewAcceptableAcceptable
Co
st
LSLLSL USLUSLNomNom
Can We Tolerate Variability?Can We Tolerate Variability?
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-7
Ford’sprocess
Mazda’sprocess
LSL USL
Manufacturing ofAutomobile
Transmissions
Manufacturing ofAutomobile
Transmissions
Cost of OffCost of Off--Target ProductionTarget Production
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-8
Process ImprovementProcess Improvement
Data Analysis TasksData Analysis Tasks
Determine if process is stable
If process is not stable, identify and remove causes of instability
Determine the location of the process mean - Is it on target?
If not, identify the variables which affect the mean and determine optimal settings to achieve target value
Estimate the magnitude of the total variability - Is it acceptable with respect to the customer requirements (spec limits)?
If not, identify the sources of the variability and eliminate or reduce their influence on the process
We will now review statistics that help this process
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-9
Basic StatisticsBasic Statistics
Types of Data
Measures of the Center of the Data
Mean
Median
Measures of the Spread of Data
Range
Variance
Standard Deviation
Properties of a Normal Distribution
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-10
Types of DataTypes of Data
Attribute Data (Qualitative)
Categories
Good / Bad
Machine 1, Machine 2, Machine 3
Shift number
Counted things (# of Errors in a document, # units shipped, etc.)
Variable Data (Quantitative)
Continuous Data (Decimal subdivisions are meaningful)
Time (seconds)
Pressure (psi)
Conveyor Speed (ft/min)
Rate (inches)
etc.
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-11
Attribute Variable
Variable
Attribute
Outputs
Inp
uts
Chi-square Analysis of Variance
Discriminate Analysis
Logistic regression
Correlation
Multiple Regression
There are statistical techniques to cover all combinations of data types
There are statistical techniques to cover all combinations of data types
Selecting Statistical TechniquesSelecting Statistical Techniques
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-12
s = Sample Standard Deviation
x = Sample Average = Population Mean
= Population Standard Deviation
Statistics estimate Parameters
Sample Statistics vs.Sample Statistics vs.
Population ParametersPopulation Parameters
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-13
Measures of Central TendencyMeasures of Central Tendency
Mean: Arithmetic average of a set of values
Reflects the influence of all values
The “balance point” or “center of gravity” for the data
Strongly Influenced by extreme values
Median: Reflects the 50% rank - the center number after a set of numbers has been sorted
Splits the data in half. 50% of the data points will be higher than the median and 50% will be lower than the median.
“Robust” to extreme values. A large change in the data may have very little, if any, effect on the median.
n
x
=x
n
1=i
i
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-14
$10, 20, 30, 40, 50 ($ in thousands)$10, 20, 30, 40, 50 ($ in thousands)
As head of the university’s Communications Dept. you are asked to summarize the average starting salaries of Communications graduates.
What is the average income (or “center of gravity”)?
What is the median income?
What is the average income (or “center of gravity”)?
What is the median income?
$10, 20, 30, 40, 5000 ($ in thousands)$10, 20, 30, 40, 5000 ($ in thousands)
However, under the advice of the Public Relations Dept. you consider to including one of your former Communications majors:
Shaquille O’Neal (a rather wealthy rookie basketball star)
Mean and Median ExampleMean and Median Example
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-15
Measures of VariabilityMeasures of Variability
Range: the distance between the extreme values of a data set (Highest - Lowest)
Variance ( ): the Average Squared Deviation of each data point from the Mean
Standard Deviation ( ): the Square Root of the Variance
The range is more sensitive to outliers than the variance
The most common and useful measure of variation is the standard deviation - why?
The most common and useful measure of variation is the standard deviation - why?
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-16
Population Mean
N
X
= 1
i
N
i
Sample Mean
n
x
=x
n
1=i
i
Population Standard Deviation
N
)(X
=S=
N
1=i
2
i
Sample Standard Deviation
1
2
1
n
xx
s
n
i
i
Computational EquationsComputational Equations
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-17
Calculating SigmaCalculating Sigma
Problem: Using the form above, calculate the standard deviation for the numbers:
2 1 3 5 4
1
2
3
4
5
6
7
8
9
10
Mean
square
1-n
)(XN
1=i
2i X
1-n
)(XN
1=i
2i X
2)X-(XX-XX
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-18
1 2 -1 1
2 1 -2 4
3 3 0 0
4 5 2 4
5 4 1 1
6
7
8
9
10
15 10
Mean 3
square 2.5
1.581139
2)X-(XX-XX
1-n
)(XN
1=i
2i X
1-n
)(XN
1=i
2i X
ExampleExample
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-19
The Normal DistributionThe Normal Distribution
The “Normal” Distribution is a distribution of data which has certain consistent and predictable properties
These properties are very useful in our understanding of the characteristics of the underlying process from which the data were obtained
Most natural phenomena and man-made processes are distributed normally, or can be represented as normally distributed
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-20
The Normal DistributionThe Normal Distribution
Property 1: A normal distribution can be described completely by knowing only the:
mean, and
standard deviation
Distribution OneDistribution One
Distribution Two
Distribution Two
Distribution ThreeDistribution Three
What is the difference among these three normal distributions?
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-21
43210-1-2-3-4
40%
30%
20%
10%
0%
95%
Pro
ba
bil
ity o
f s
am
ple
valu
e
Number of standard deviations from the mean
The Normal Curve and Probability AreasThe Normal Curve and Probability Areas
Associated with the Standard DeviationAssociated with the Standard Deviation
Property 2Property 2: The area under sections of the curve can be used to estimate the cumulative probability of a certain “event” occurring
99.73%
68%Cumulative
probability of obtaining a value
between two values
Cumulative probability of
obtaining a value between two
values
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-22
Number ofStandard
DeviationsTheoretical
NormalEmpiricalNormal
+/- 168% 60-75%
+/- 295% 90-98%
+/- 399.7% 99-100%
The previous rules of cumulative probability apply even when a set of data is not perfectly normally distributed
Let’s compare the values for a theoretical (perfect) normal distributions to empirical (real-world) distributions
Empirical Rules forEmpirical Rules for
the Standard Deviationthe Standard Deviation
BASIC STATISTICS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2001 by Sigma Breakthrough Technologies, Inc.
GB106-23
Introduced the concepts of Shape, Center, and Spread of distributions
Introduced the properties of a Normal distribution
SummarySummary
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-1
Basic Quality Basic Quality
ToolsTools
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-2
ObjectivesObjectives
To introduce you to the 7 Basic Quality Tools
Dotplots / Histograms / Normal Plots
Run charts / Time Series
Pareto Diagrams
Stratification (2nd Level Pareto)
Boxplots
Scatter Plots
Marginal Plots
Show application of these techniques for Data Mining
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-3
Plotting Your DataPlotting Your Data
The first three rules of data analysis
Rule # 1 Plot your data
Rule # 2 Plot your data
Rule # 3 Plot your data
Very little can be learned from looking at a long list of numbers.
Statistics, such as mean and standard deviation, can be deceptive if only presented as numbers.
Your data will be much easier to understand and to explain to others if it is plotted as a graph.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-4
Statistical DistributionsStatistical Distributions
We can describe the behavior of any measurable feature by plotting multiple data points for thevariable:
over time
across products
on different machines, etc.
The accumulation of these data can be viewed as a distribution of values
The data can be represented in:
dot plots
histograms
normal curve or other “smoothed” distribution
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-5
Imagine a painting operation which is engineered to provide a thickness of 21 mils of paint. The actual thickness is measuredon 150 separate samples and plotted above. Each dot represents one “event” of output at a given value. As the dots accumulate,the nature of the painting operation’s actual performance can beseen as a “distribution” of thickness values.
Imagine a painting operation which is engineered to provide a thickness of 21 mils of paint. The actual thickness is measuredon 150 separate samples and plotted above. Each dot represents one “event” of output at a given value. As the dots accumulate,the nature of the painting operation’s actual performance can beseen as a “distribution” of thickness values.
29241914
Thickness
Dotplot DistributionDotplot Distribution
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-6
Let’s take a look at the Thickness of paint applied by a sprayer
Open file DM Basic Tools.MPJ, worksheetPainting.MTW
Use the column Thickness.MTW as the variable
Creating a DotplotCreating a Dotplot
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-7
Now imagine the same data, grouped into “intervals” with the number of times that a thickness data point falls within a giveninterval determining the height of the interval bar.
Now imagine the same data, grouped into “intervals” with the number of times that a thickness data point falls within a giveninterval determining the height of the interval bar.
Histogram DistributionHistogram Distribution
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-8
Once again we will use the column Thickness as the variable
Once again we will use the column Thickness as the variable
Creating a HistogramCreating a Histogram
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-9
Finally, we can view the data as a smoothed distribution (red line), in this example using the “normal distribution” assumption (we’ll discuss this later). It provides an approximation of how the data might look if we were to collect an infinite number of data points.
Finally, we can view the data as a smoothed distribution (red line), in this example using the “normal distribution” assumption (we’ll discuss this later). It provides an approximation of how the data might look if we were to collect an infinite number of data points.
Smoothed (Normal) DistributionSmoothed (Normal) Distribution
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-10
Creating a HistogramCreating a Histogram
with a Normal Curvewith a Normal Curve
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-11
Normal Probability PlotsNormal Probability Plots
We can test whether a given data set can be described as “normal” with a Normal Probability Plot
If a distribution is close to normal, the Normal Probability Plot will be close to a straight line
Minitab makes the normal probability plot easy
Let’s do one for the Painting.MTW data set
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-12
Normal Probability PlotsNormal Probability Plots
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-13
Example Probability PlotExample Probability Plot
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-14
Open the worksheet Distributions.MTWDistributions.MTW
Produce a normal plot of each of the columns of data
Which appear to be normal?
Produce a histogram of each of these columns of data
Notice anything?
You have 5 minutes
ExerciseExercise
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-15
Normal Probability PlotsNormal Probability Plots
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-16
Time SeriesTime Series
So far we have looked at the data ‘lumped together’
Another way to review the data is over time
Let’s review some commonly used time series methods:
Run Charts
Individuals Charts
Let’s switch back to the Painting.MTW worksheet
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-17
Run ChartsRun Charts
A Run Chart applies some initial statistical diagnostic tests to the series
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-18
Observation
Th
ickn
ess
15010050
29
24
19
14
Number of runs about median:
0.83722
70
Expected number of runs: 76.00000
Longest run about median: 9
Approx P-Value for Clustering: 0.16278
Approx P-Value for Mixtures:
Number of runs up or down:
0.32470
102
Expected number of runs: 99.66667
Longest run up or down: 3
Approx P-Value for Trends: 0.67530
Approx P-Value for Oscillation:
Run ChartRun Chart
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-19
IndicationConditionTest
Trends in the dataFewer runs observed than expected
Number of Runs Up or Down
Oscillation (data varies up and down rapidly)
More runs observed than expected
Number of Runs Up or Down
Clustering of DataFewer runs observed than expected
Number of Runs About the Median
Mixture (mixed data from two populations)
More runs observed than expected
Number of Runs About the Median
Run Chart Tests for RandomnessRun Chart Tests for Randomness
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-20
Observation
Th
ickn
ess
15010050
29
24
19
14
Number of runs about median:
0.83722
70
Expected number of runs: 76.00000
Longest run about median: 9
Approx P-Value for Clustering: 0.16278
Approx P-Value for Mixtures:
Number of runs up or down:
0.32470
102
Expected number of runs: 99.66667
Longest run up or down: 3
Approx P-Value for Trends: 0.67530
Approx P-Value for Oscillation:
Run ChartRun Chart
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-21
An Individuals Chart looks like a Run Chart but now they apply some Process Control Limits to the data
Individuals ChartIndividuals Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-22
Observation
Ind
ivid
ua
l V
alu
e
150100501
30
25
20
15
_X=22.03
UCL=30.57
LCL=13.48
I Chart of Thickness
Individuals ChartIndividuals Chart
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-23
Data MiningData Mining
So far we have looked at all data together, giving us an overall look at the shape and trends of the data
However, we still haven’t looked into why the data may be shaped the way it appears
We can use some other graphical techniques to start ‘mining’ the data for the reasons as to WHY…
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-24
There is another method of looking at the data that may be easier to see differences in the distributions
Boxplots show the spread and center of the data
BE CAREFUL!BE CAREFUL!
The center of the Boxplot is the
MEDIANMEDIAN, not the MEANMEAN
BoxplotsBoxplots
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-25
Median (50%)
Q3 (75%)
Q1 (25%)
Maximum value
Minimum value
Outlier (extreme value)
Boxplot of ThicknessBoxplot of Thickness
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-26
We can also generate boxplots by a variable to look at the variation due to that variable
Nozzle
Th
ickn
ess
1051
30.0
27.5
25.0
22.5
20.0
17.5
15.0
Boxplot of Thickness vs Nozzle
BoxplotsBoxplots
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-27
Pareto Diagrams are an essential tool to help prioritize improvement targets
Pareto’s allow us to focus on the 20% of the problems that cause 80% of the poor performance
Let’s open worksheet Quality Control.MTW
Pareto DiagramPareto Diagram
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-28
Defects CountsMissing Screws 274Missing Clips 59Defective Housing 19Leaky Gasket 43Scrap 4Unconnected Wire 8Missing Studs 6Incomplete Part 10
Pareto DiagramPareto Diagram
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-29
We can generate a second level Pareto using the Bystatement
This breaks down the overall Pareto by time of day
Second Level ParetosSecond Level Paretos
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-30
Second Level ParetoSecond Level ParetoFlaws PeriodScratch DayScratch DayPeel DayPeel DaySmudge DayScratch DayOther DayOther EveningPeel EveningPeel EveningPeel EveningPeel EveningScratch EveningScratch EveningPeel NightScratch NightSmudge NightScratch NightPeel NightPeel NightPeel NightPeel NightOther NightOther NightScratch NightScratch NightPeel NightScratch NightSmudge NightScratch NightOther NightScratch NightScratch NightPeel WeekendPeel WeekendPeel WeekendSmudge WeekendSmudge WeekendSmudge WeekendOther Weekend
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-31
Relational PlotsRelational Plots
Many times we wish to graphically review the relationship between two variables
Scatter Plots and Marginal Plots are two effective methods for accomplishing this task
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-32
ScatterplotScatterplot
Scatterplots study the relationship between two variables
Let’s look at an example where the both the Customer and our QC department measure the temperature of ovens set at 350 degrees
Open worksheet Temperature.MTW
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-33
QC
Cu
sto
me
r
400390380370360350340330
390
380
370
360
350
340
Scatterplot of Customer vs QC
ScatterplotScatterplot
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-34
Marginal plots allow you to combine a Scatterplot and one of three other plots
Histograms
Boxplots
Dotplots
Marginal PlotsMarginal Plots
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-35
QC
Cu
sto
me
r
400390380370360350340
390
380
370
360
350
340
Marginal Plot of Customer vs QC
Marginal Plot (ScatterplotMarginal Plot (Scatterplot
with Histogram)with Histogram)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-36
Marginal PlotsMarginal Plots
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-37
Data MiningData Mining
All of the methods introduced have provided insight on how to graphically represent data
Use of the data to answer the question of WHY is the product / process performing in that fashion is Data Mining
Data analysis should follow some basic steps:
Look at the data (raw) to identify any abnormalities (errors, unexpected values)
Analyze the data graphically to get a visual sense of the data
Analyze the data statistically to get a numerical sense of the data
Let’s look into this last point a little deeper
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-38
ReviewReview
When we looked at the three different distributions in the Distributions.mtw worksheet, we saw three totally different distributions (graphically)
Let’s look and see what these three distributions look like numerically
We can use Descriptive Statistics to accomplish this task easily
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-39
Let’s look at the different data sets using some other techniques
We will start with some Descriptive Statistics
This will give us some insight as to the Center, Spread, and Shape of the data
Descriptive StatisticsDescriptive Statistics
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-40
What observations can we make from these numbers?
Look at the Center, Spread, and Shape indicators
The three distributions have the same values for their Mean and Standard Deviation, yet they are not the same
Spread
Center Center
|<--- Shape Indicators --->|(Mean, Median and TrMean)
Descriptive StatisticsDescriptive Statistics
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 21402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-41
Histograms and NormalHistograms and Normal
Probability PlotsProbability Plots
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-42
Open worksheet Mystery.MTW
Generate a Normal Probability Plot for the Mystery variable
What is your conclusion?
Mystery DistributionMystery Distribution
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 22402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-43
Mystery
Pe
rce
nt
200150100500
99.9
99
95
90
80
7060504030
20
10
5
1
0.1
Mean
<0.005
100.0
StDev 32.38
N 500
AD 27.108
P-Value
Probability Plot of MysteryNormal
Normal Probability PlotNormal Probability Plot
for Mystery Datafor Mystery Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-44
My
ste
ry
150
100
50
Boxplot for Mystery DataBoxplot for Mystery Data
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-45
Mystery
Fre
qu
en
cy
160140120100806040
60
50
40
30
20
10
0
Histogram for Mystery DataHistogram for Mystery Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-46
Mystery
15010050
Dotplot for Mystery DataDotplot for Mystery Data
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 24402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-47
Looking at the numbers alone can be confusing
Let’s take a look at the data graphically and add the numbers tothe output
Graphical Descriptive StatisticsGraphical Descriptive Statistics
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-48
160140120100806040
Median
Mean
1201101009080
A nderson-Darling Normality Test
V ariance 1048.78
Skewness 0.00716
Kurtosis -1.63184
N 500
Minimum 41.77
A -Squared
1st Q uartile 68.69
Median 104.20
3rd Q uartile 130.81
Maximum 162.82
95% C onfidence Interv al for Mean
97.15
27.11
102.85
95% C onfidence Interv al for Median
82.78 117.66
95% C onfidence Interv al for StDev
30.49 34.53
P-V alue < 0.005
Mean 100.00
StDev 32.38
95% Confidence Intervals
Summary for Mystery
Graphical Descriptive StatisticsGraphical Descriptive Statistics
BASIC QUALITY TOOLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-49
Index
My
ste
ry
500450400350300250200150100501
175
150
125
100
75
50
Time Series Plot for Mystery DataTime Series Plot for Mystery Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB107-50
Introduced some graphical and analytical tools for Data Mining
Data breakdown (Pareto, Dotplot, Histogram, Boxplot, Descriptive Statistics)
Time Series methods (Individual Charts, Run Charts)
Relational graphical techniques (Scatter Plots, Marginal Plots)
Pareto diagrams
ALWAYS PLOT YOUR DATAALWAYS PLOT YOUR DATA
SummarySummary
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-1
Intro. to Statistical Intro. to Statistical
Process Control Process Control
(SPC)(SPC)
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-2
ObjectivesObjectives
Link Control Chart methods to the Process Improvement
Discuss different types of variation
Introduce a few basic Control Charting methods
Discuss the interpretation of Control Charts
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-3
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-4
* Myth - SPC Is Only Used As A Control Tool* Myth - SPC Is Only Used As A Control Tool
Found Critical X’s
Controlling Critical X’s
All X’s
1st “Hit List”
Screened List
MEASUREMEASURE
ANALYZEANALYZE
IMPROVEIMPROVE
CONTROL*CONTROL*
The Funneling EffectThe Funneling Effect
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-5
Should We Take Action ?
Everyday we are flooded by data and we are forced to make decisions:
Plants Output Decreases By 4%
US Trade Deficit Rises By $40 Billion
Company X’s Earnings Are Off $240 Million From Previous Quarter
We Need Ways of We Need Ways of
Interpreting DataInterpreting Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-6
Leave It Alone It Ain’t Broke
Pain &Suffering
Pain &Suffering
Lower “Customer”Requirement(Lower Spec)
THIS METHOD
•Tells you where you are in regards to customer’s needs
•It will NOT tell you how you got there or what to do next
Pressure To Achieve Customer Requirements Will Cause One To :
1. Actually fix the process2. Sabotage the process3. Sabotage the data (integrity)
Upper “Customer”Requirement(Upper Spec)
How Do We Manage Data How Do We Manage Data ––
HistoricallyHistorically
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-7
2
3
Scra
p L
evel (%
)S
cra
p L
evel (%
)
1
J F M A J F M A
1996 1997
Party TimeParty Time
• The factory scrap level is at a year low of 1.5%
• Manager presents an award to the plant
• Ceremony in the cafeteria: pizza and refreshments for all!
• “Everyone should be proud of what you’ve accomplished”
Derived from Understanding Variation: The Key To Managing Chaos, Donald J. Wheeler, SPC Press. 1993.
Variation Example:Variation Example:Special vs. Common CauseSpecial vs. Common Cause
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-8
2
3
1
J F M A M J J J F M A M J J
1996 1997
Manager wants to take back awardManager wants to take back award
• Three consecutive months of scrap increases
• Manager wishes he could take back the award
• Instead of holding the gains, scrap went right back up
• Manager decides: “Recognition has backfired. This group needs tough management!”
Scra
p L
evel (%
)S
cra
p L
evel (%
)
Derived from Understanding Variation: The Key To Managing Chaos, Donald J. Wheeler, SPC Press. 1993.
Variation Example:Variation Example:Special vs. Common CauseSpecial vs. Common Cause
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-9
2
3
1
J F M A M J J A S O N DJ F M A M J J A S O N D
1996 1997
No more “Soft Management”No more “Soft Management”
• By November, scrap has risen to a value of 2.6% - Year High
• Manager decides to take action
• A “special meeting” is called to solve this problem once and for all
• After a sound lecture on the importance of scrap, the manager leaves. Employees aren’t sure what to do. Besides, they have other metrics which are more important. So they do nothing.
Scra
p L
evel (%
)S
cra
p L
evel (%
)
Derived from Understanding Variation: The Key To Managing Chaos, Donald J. Wheeler, SPC Press. 1993.
Variation Example:Variation Example:Special vs. Common CauseSpecial vs. Common Cause
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-10
• Manager has seen reduced scrap levels since the end of last year. “Things are looking-up!” (REMEMBER: Nothing had been done to change the system)
• His take away: “A tough management style gets results!”
Manager concludes:“Tough Love Makes Things Happen
Manager concludes:“Tough Love Makes Things Happen
2
3
1
J F M A M J J A S O N DJ F M A M J J A S O N D
1996 1997
J F M A M JJ F M A M J
Scra
p L
evel (%
)S
cra
p L
evel (%
)
Derived from Understanding Variation: The Key To Managing Chaos, Donald J. Wheeler, SPC Press. 1993.
Variation Example:Variation Example:Special vs. Common CauseSpecial vs. Common Cause
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-11
What might this data look
like on a control chart?
Variation Example:Variation Example:Special vs. Common CauseSpecial vs. Common Cause
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-12
2
3
1
J F M A M J J A S O N DJ F M A M J J A S O N D
1996 1997
UCL
J F M M J J A S OJ F M M J J A S O
LCL
Scra
p L
evel (%
)S
cra
p L
evel (%
)
Derived from Understanding Variation: The Key To Managing Chaos, Donald J. Wheeler, SPC Press. 1993.
Let the Process do the talking! !
The REAL Story!!The REAL Story!!The Voice of the ProcessThe Voice of the Process
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-13
• Manager “ Hey, I made my decision based on data - How can I be wrong ?”
• Belt“Your decisions were made from observing high and low points as signals, when in reality, it was all noise (Common Cause Variation). Look at the data, there has been no significant change in the process.”
Manager concludes, “Tough Love Makes Things Happen!”
Manager concludes, “Tough Love Makes Things Happen!”
J F M M J J A S OJ F M M J J A S O
2
3
Scra
p L
eve
l (%
)S
cra
p L
eve
l (%
)
1
J F M A M J J A S O N DJ F M A M J J A S O N D
1996 1997
Party TimeParty Time
Manager WantsTo Take Back Award
Manager WantsTo Take Back Award
No more soft managementNo more soft management
UCL
LCL
The Control Chart TellsThe Control Chart Tells
A Different Story A Different Story –– Why?Why?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-14
“Failure to use control charts to analyze data is one of the best ways known to mankind to increase costs,
waste effort, and lower morale.”
- Dr. Donald J. Wheeler
“Failure to use control charts to analyze data is one of the best ways known to mankind to increase costs,
waste effort, and lower morale.”
- Dr. Donald J. Wheeler
Control Chart MethodsControl Chart Methods
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-15
Control Chart MethodsControl Chart Methods
Where Did It Come From?Where Did It Come From?
1920’s - Western Electric / Dr. Walter Shewhart
Used to identify Controlled & Uncontrolled Variation
Controlled: a.k.a. Common Cause or Inherent (Noise)
Uncontrolled: a.k.a Special Cause or Assignable (Signals)
Tries to find the process signals in all of the noise
Uses Control Charts as main tool
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-16
COMMON CAUSE (Noise)
Is present in every process
Is produced by the process itself (the way we do business)
Can be removed and/or lessened but requires a fundamental change in the process
A process is Stable, Predictable, and In-Control when only Common Cause Variation exists in the process
* note - if subgroups are used, the common cause variation is a function of sub-grouping choices
Types of Variation:Types of Variation:Common vs. SpecialCommon vs. Special
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-17
SPECIAL CAUSE (Signals)
Unpredictable
Typically large in comparison to Common Cause variation
Caused by unique disturbances or a series of them
Can be removed/lessened by basic process control and monitoring
A process exhibiting Special Cause variation is said to be Out-of-Control and Unstable
Types of Variation:Types of Variation:Common vs. SpecialCommon vs. Special
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-18
DATA PLOTTED OVER TIME
MO
NIT
OR
ED
C
HA
RA
CT
ER
IST
IC
UCL
Center Line
LCL
UCL = Upper Control Limit / LCL = Lower Control Limit
Plotted Data
The Basic Control Chart:The Basic Control Chart:Key ComponentsKey Components
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-19
•What % of data points should fall between the UCL and LCL ?
•If a point falls beyond the UCL or LCL does this mean we aremaking a defect for the customer ?
UCL
LCL
Control Chart ComponentsControl Chart Components
UCL versus LCLUCL versus LCL
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-20
Upper Control Limits = UCLLower Control Limits = LCL
Upper Specification Limits = USLLower Specification Limits = LSL
Is The Process Below Making Defects ?
UCL
LCL
TIMETIME
USL
LSL
UCL and LCL vs. USL and LSLUCL and LCL vs. USL and LSL
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-21
Upper Control Limits = UCLLower Control Limits = LCL
Upper Specification Limits = USLLower Specification Limits = LSL
Is The Process Below Making Defects ?
UCL
LCL
TIMETIME
USL
LSL
UCL and LCL vs. USL and LSLUCL and LCL vs. USL and LSL
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-22
Process Control Limits are calculated based on data from the process itself
They are based on +/- 3 (99.73% of the process variation is expected to fall between these limits)
Product Specification Limits ARE NOT found on the control chart
Understanding how the process matches up against customer requirements IS important to know
To determine how the process performs to Customer Expectations, a Process Capability Study is required
UCL and LCL vs. USL and LSLUCL and LCL vs. USL and LSL
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-23
# 1) Putting specification limitsspecification limits on a Control Chart
#2 ) Treating UCL and LCL as a specification limit
When you do either of these the control chart becomes just an inspection tool - it’s no longer a control chart
UCL / LCL are not directly tied to customer defects !
TWO BIG CONTROL CHART ERRORS
UCL and LCL vs. USL and LSLUCL and LCL vs. USL and LSL
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-24
Variables Charts
X-Bar / R Chart
X-Bar / s Chart
I-MR Chart
Attribute Charts
I-MR Chart
p-Chart
np-Chart
c-Chart
u-Chart
Major Types of Control ChartsMajor Types of Control Charts
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-25
VARIABLEVARIABLE -
The data are continuous (measured)
Results from the actual measuring of a characteristic such as diameter of a hose, electrical resistance, weight of a unit , etc.
ATTRIBUTEATTRIBUTE -
The data are generally counted
Results from using go/no-go gages, inspection of visual defects, quantity of missing parts, pass/fail or yes/no decisions, etc.
Two General Kinds of DataTwo General Kinds of Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-26
(1) Length of connector housings produced by a molding press
(2) Average production rate of the manufacturing lines in a building
(3) Number of printing defects on a package label
(4) Number of typographical errors per Sales Contract
(5) Number units failing test in monthly production
(6) Percent failed units in Monthly Production
(7) Amount of time taken to close an Account Receivable
(8) Number of parts with defects per 100 parts produced
Exercise: What Type of Data?Exercise: What Type of Data?
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-27
DiscreteVariable
What Type Of Data ?
Data Collected In Groups or Individuals ?
CountingSpecific Defects orDefective Items ?
GROUPS(Averages)
(n>1)
INDIVIDUALVALUES
(n=1)
X-Bar RX-Bar S
IndividualsMoving Range
SpecificTypes Of “Defects”
DefectiveItems
Is The Probability OfA Defect Low ?
If You Know How ManyAre Bad, Do You Know How Many Are Good?
Poisson Distribution Binomial Distribution
IndividualsMoving Range
NO
YES YES
Area ofOpportunity Constant
In Each SampleSize ?
YESNO
C ChartU Chart
ConstantSample Size ?
NP Chart
NO YES
P Chart
Choosing the Correct Control Chart Choosing the Correct Control Chart
NOTE: X-Bar S is appropriate
for subgroup sizes (n) of > 10
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-28
Minitab Minitab –– Control ChartsControl Charts
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-29
The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself (#cycles x #points in pattern >15)
The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself (#cycles x #points in pattern >15)
A set of standard tests have been created to help identify SPECIAL CAUSE events present in our process
We use the phrase “Out of Control” when the points on the chart have failed a test.
When any of the points on the chart fail a test, it means something “unusual” has happened –
Go Check It Out !!Go Check It Out !!
Control Charts TestsControl Charts Tests
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-30
““Rules Of The Road”Rules Of The Road”(1) Start with Test #1 and Pattern Detection test(1) Start with Test #1 and Pattern Detection test
(2) If higher sensitivity is needed, also use tests (2) If higher sensitivity is needed, also use tests
#2, 3, & 4#2, 3, & 4
The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself
The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself
Detecting Lack of ControlDetecting Lack of Control
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-31
1 Sigma
2 Sigma
3 Sigma
1 Sigma
2 Sigma
3 Sigma
60-75%
90-98%
99-99.9%
% of Data PointsUCL
LCL
TIMETIME
The ItemWe Are
Measuring
Rules of Standard Deviation:Rules of Standard Deviation:Where Does the Data Lie?Where Does the Data Lie?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-32
Test #1
Test #2
Test #3
Test #4
Minitab Tests for Lack of ControlMinitab Tests for Lack of Control
Trend
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-33
Control Charts:Control Charts:Individual / Moving RangeIndividual / Moving Range
Use:
When its inconvenient or impossible to obtain more than one measurement per sample
When technology allows for easy measurement of every unit at minimal cost
Data availability is sparse
Variation
Short Term: Represented by the variation from one unit to the next (Moving Range chart)
Long Term: Represented by a long sequence of such events (Individuals chart)
Charts are Based on a Subgroup Size of 1
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-34
Open worksheet: Individual MR.MTWin DM SPC.MPJ
Stat > Control Charts > Variables Charts for Individuals > I-MR
Variable = Errors
Minitab Exercise:Minitab Exercise:Individuals / Moving RangeIndividuals / Moving Range
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-35
What are the charts telling you?
Are the process data stable?
Observation
In
div
idu
al
Va
lue
24222018161412108642
10
5
0
_X=4.04
UC L=10.13
LC L=-2.05
Observation
Mo
vin
g R
an
ge
24222018161412108642
8
6
4
2
0
__MR=2.292
UC L=7.488
LC L=0
I-MR Chart of Errors
Minitab Exercise:Minitab Exercise:Individuals / Moving Range OutputIndividuals / Moving Range Output
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-36
Requirements for EffectiveRequirements for Effective
Use of Control ChartsUse of Control Charts
Management MUST establish and support an environment that promotes proper action and support to the information collected on the control charts
Control Charts are implemented ONLY on Key Processes on which improvement will bring benefit to the organization and/or the customer
Data collected from the process is validated through the use of a CAPABLE measurement system
Biggest Failure Of Control Chart Programs
Flood the system with control charts, then don’t take action on the data
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-37
Control Charts Control Charts –– SamplingSampling
vs. 100% Inspectionvs. 100% Inspection
Control Chart sampling is a simple and effective method for process understanding
If actions are taken to eliminate special causes (stabilize the process) and capability is proven, 100% Inspection can be eliminated (but beware of customer specified inspection plans)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-38
ExercisesExercises
Open the Minitab Project File DM SPC.MPJ
Practice Creating Control Charts using theWorksheets contained in this project file
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-39
Tests forTests for
Statistical ControlStatistical Control
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-40
0 5 10 15 200
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Rang
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Normal VariationNormal Variation
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 21402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-41
0 5 10 15 200
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Zone Test 1: Zone Test 1: Range ChartRange Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-42
0 5 10 15 200
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Any Point Outside the Control LimitAny Point Outside the Control Limit
Zone Test 1: Zone Test 1: Average ChartAverage Chart
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 22402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-43
Two of Three Points in Zone ATwo of Three Points in Zone A
0 5 10 15 200
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Zone Test 2: Zone Test 2: Range ChartRange Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-44
Two of three successive points in Zone ATwo of three successive points in Zone A
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Zone Test 2: Zone Test 2: Average ChartAverage Chart
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-45
Four of Five successive points in Zone B and BeyondFour of Five successive points in Zone B and Beyond
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Zone Test 3: Zone Test 3: Range ChartRange Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-46
Four of Five successive points in Zone B and BeyondFour of Five successive points in Zone B and Beyond
0 5 10 15 20
0
1
2
3
4
5
R-bar
X-dbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Zone Test 3: Zone Test 3: Average ChartAverage Chart
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 24402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-47
Eight successive points in Zone C and Beyond, on the same Side of the Center Line
Eight successive points in Zone C and Beyond, on the same Side of the Center Line
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Range
8
9
10
11
12
13
14
ABCCB
A
XB
AR
Zone Test 4: Zone Test 4: Range ChartRange Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-48
Eight successive points in Zone C and Beyond, on the same Side of the Center Line
Eight successive points in Zone C and Beyond, on the same Side of the Center Line
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Range
8
9
10
11
12
13
14
ABCCB
A
XB
AR
Zone Test 4: Zone Test 4: Average ChartAverage Chart
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-49
Seven Consecutive Points Without a Major Change in Direction
Seven Consecutive Points Without a Major Change in Direction
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Range
8
9
10
11
12
13
14
ABCCB
A
XB
AR
Trend Test: Trend Test: Range ChartRange Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-50
Seven Consecutive Points Without a Major Change in Direction
Seven Consecutive Points Without a Major Change in Direction
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Range
8
9
10
11
12
13
14
ABCCB
A
XB
AR
Trend Test: Trend Test: Average ChartAverage Chart
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 26402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-51
Eight or more points fall on both sides of centerline in Zones A & B, with none in Zone C.
Eight or more points fall on both sides of centerline in Zones A & B, with none in Zone C.
0 5 10 15 20
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Range
8
9
10
11
12
13
14
ABCCBA
XB
AR
Mixture Test: Mixture Test: Average ChartAverage Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-52
Fifteen or more consecutive points in either C Zone; few or no points beyond Zone C
Fifteen or more consecutive points in either C Zone; few or no points beyond Zone C
0 5 10 15 200
2
4
6
8
10
12
14
Ave
LCL
UCL
Range
4
6
8
10
12
14
16
ABCCBA
Ave
LCL
UCL
XB
AR
Stratification Test: Stratification Test: Average ChartAverage Chart
INTRODUCTION TO SPC
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 27402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-53
A repeating pattern of change.A repeating pattern of change.
0 5 10 15 20 25
0
1
2
3
4
5
Rbar
Xdbar
LCL
UCL
UCL
LCL
Ra
ng
e
8
9
10
11
12
13
14
ABCCBA
XB
AR
Cycle Test: Cycle Test: Range & Average ChartsRange & Average Charts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB108-54
Linked Control Chart Methods to the process improvement and the DMAIC roadmap
Discussed different types of variation
Introduced a few basic Control Charting methods
Discussed the interpretation of Control Charts
SPCSPC -- SummarySummary
TEAM BUILDING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB109-1
Team Building and Interpersonal
Skills for Black Belts and Green
Belts
D
M
A
I
C
TeamTeamBuildingBuilding
GB109-2
What is the team in this picture?
Why two horses?
Roles?
Team boundaries?
“Form follows function. . . .” Teams provide:• Process knowledge• Process ownership• Time• Creativity
Reference Guide:Reference Guide:Page 2Page 2
TEAM BUILDING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
GB109-3
CRITICAL FACTORS FOR TEAM SUCCESS
PURPOSE: Why do we have a team? What’s our output?
PROCESS: What process will we follow?
COMMUNICATION: Is our communication complete and appropriate?
COMMITMENT: How is commitment nourished?
INVOLVEMENT: Are the right people on the team?
TRUST: How is trust built and maintained?
Reference Guide:Reference Guide:Page 3Page 3
GB109-4
Team Size: 5 – 7
Why not more?
Reference Guide:Reference Guide:Page 3Page 3
TEAM BUILDING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
GB109-5
Team BuildingStart-up “to do” list:
• Purpose / mission• Form team• Six Sigma training• Plan kick-off meeting• Black / Green Belt as coach / leader
Reference Guide:Reference Guide:
Page 4Page 4
GB109-6
First Team Meeting Agenda
• Management Challenge / Commitment
• Team Charter / Goals
• Team Member Roles And Responsibilities
• Empowerment ( Team’s Budget And Decision-
Making Authority)
• Measures (Team Milestones)
• Ground Rules And Meeting Schedule
• Team Training Needs
Reference Guide:Reference Guide:Page 4Page 4
TEAM BUILDING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
GB109-7
GOALPROCESS
ENVIRONMENT
- Physical
- Emotional
- Business / Political
GROUP
Team Dynamics – Group Behavior
Reference Guide:Reference Guide:Page 5Page 5
GB109-8
NORMING:• Expression of opinions and data flow• Development of cohesion
PERFORMING:• Collaboration and interdependence• Problem-solving
Four phases of team development
B.W. Tuckman, “Developmental Sequence in Small Groups.”
Psychological Bulletin, 63, 1965, pp. 384-399
FORMING:• Orientation• Testing and dependency
ISTORMING:• Emotional response to task demands• Intragroup conflict
II
III
IV
Reference Guide:Reference Guide:Page 5Page 5--77
TEAM BUILDING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
GB109-9
Achieving Team Synergy
(Lost at Sea exercise)
Reference Guide:Reference Guide:Page 8Page 8
GB109-10
1P
eop
le9
1 Task 9
S-4 S-1
S-2S-3
9 Maturity 1
1P
eop
le9
1 Task 9
S-4 S-1
S-2S-3
9 Maturity 1
Situational Leadership Styles
Reference Guide:Reference Guide:Page 11Page 11
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB110-1Rev. C February 2004© Tyco Electronics
ProcessProcess
MappingMapping
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004 © Tyco Electronics GB110-2
Process Mapping Process Mapping
Provide an overview of Process Mapping
To show the step-by-step example of Process Mapping
To demonstrate where Process Mapping fits into the DMAIC Process Improvement Model
To provide examples of Process Maps
To perform a Process Mapping exercise
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-3
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
Rev. C January 2004 © Tyco Electronics GB110-4
Critical Input VariablesCritical Input Variables
30+ Inputs
8 - 10
4 - 8
3 - 6
Found Critical X’s
Controlling Critical X’s
10 - 15
All X’s
1st “Hit List”
Screened List
• “0” Process & Value Stream Maps
• Failure Modes and Effects Analysis
• Multi-Vari Studies
• Design of Experiments (DOE)
• Control Plans
• C&E Matrix
The Funneling EffectThe Funneling Effect
MEASUREMEASURE
ANALYZEANALYZE
IMPROVEIMPROVE
CONTROL*CONTROL*
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-5
Types of Process MapsTypes of Process Maps
‘Level 0’ Process Map ( I-P-O or SIPOC )
Used to define the macro level for Suppliers and Customers. (Only 3 or 5 blocks in map)
Value Stream Map
Process and information flow (macro)
Key metrics (e.g. cycle time, yield, inventory) for key process steps and for the overall project. Shows constraints.
Detailed Process Map shows all steps in the process, including non-value adding steps.
Rev. C January 2004 © Tyco Electronics GB110-6
Detailed Process MappingDetailed Process Mapping
What is the tool?
Graphical illustration of the work process
What will the tool identify?
All value-added and non-value-added process steps
Process Inputs (X’s)
Process Outputs (Y’s)
Data Collection Points
First Input Variables to put in FMEA
When do you apply process mapping?
Always
What are results of Process Mapping?
Identify systems needing Measurement Studies
Identify Output Variables for Capability Studies
Identify missing elements in the Control Plan
Identify possible non-value-added process steps
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-7
Process MappingProcess Mapping
Should describe:
Major activities / tasks
Sub-processes
Process Boundaries
Input Variables (X’s)
Output Variables (Y’s)
Should be reviewed frequently and updated
),...,,( 21 kxxxfy
Rev. C January 2004 © Tyco Electronics GB110-8
Increasing Process Description Detail: Increasing Process Description Detail: Top Down Mapping MethodTop Down Mapping Method
Map – Start with P from SIPOC
Detail – I/O Analysis
Prioritize
C&E Matrix
Common Sense
Map – drill into sub processes
Flow Chart
Cross Functional Map
Cross Resource Map
Detail
Prioritize
Map - drill into sub-sub processes
Flow Chart
Cross Functional Map
Cross Resource Map
Detail
Prioritize
Ro
un
d 1
Ro
un
d 2
Ro
un
d 3
Stop
StartStart
StopStop
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-9
Increasing Process Description Detail:Increasing Process Description Detail:Types of MapsTypes of Maps
Flow Chart (aka Block Diagram)
Shows process steps, without worrying about details
Useful as a first pass to understand process steps
Cross Functional Map (aka “Swim lane”)
Rearranges flow chart process steps into lanes of functions
Clearly shows hand-offs
Process steps move left to right as time progresses
Cross Resource Map
Further drill down into functions, showing actual resource to resource hand offs
customer
No
Yes
No
Yes
Function 1
Function 2
Function 3
Function 4
Function 1
Function 2
Function 3
Function 4
Resource 1
Resource
2
Rev. C January 2004 © Tyco Electronics GB110-10
Transactional Non-Manufacturing
Example Manufacturing Example
MACRO Organization Customer
Service
Plant East Berlin
LOCAL Process Customer Orders Process Area Z-Pack Assembly
MICRO Subprocess Order Entry Process Step Work Center
5052
• Start at Macro level and work towards the Micro level, as required
• Focus Local and Micro level work in areas of largest potential gain
• Don‘t go more detailed than you need to – avoid analysis paralysis.
Different Levels of Process MapsDifferent Levels of Process Maps
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-11
Preparing the Process MapPreparing the Process Map
Team Effort – Manufacturing Example:
Manufacturing engineers
Line operators
Line supervisors
Maintenance technicians
Suppliers
Inputs to Mapping
Brainstorming
Manuals, work instructions
Engineering specifications
Operator experience
Fishbone diagrams (6 Ms)
Man, Machine (Equipment), Method (Procedures), Measurement, Materials, Mother Nature (Environment)
• Transactional Example:
Sales force
Engineers
Operators
Supervisors
Rev. C January 2004 © Tyco Electronics GB110-12
Setting up the InterviewSetting up the Interview
Contact the people in advance and schedule the interview. Make sure not to conflict with other schedules
Keep in mind that people may be concerned about why you want to talk to them
Keep the interview short
no more than 30-45 minutes
Ask them to collect any forms, documents, lists, or procedures that are normally used
Develop a set of interview questions
Let your team members conduct the interviews too!Let your team members conduct the interviews too!
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-13
Setting up the InterviewSetting up the Interview
Whenever possible hold the interview at the individual’s actual place of work and not a conference room.
At the workplace, make note of its condition.
Is this a quiet, orderly, neat, and efficient work area or is it in chaos
Are there manuals or other documents in the office?
Look around the area and at other work areas:
Make note of the above characteristics for other desks in the area.
Rev. C January 2004 © Tyco Electronics GB110-14
Conducting the InterviewConducting the Interview
Begin with questions that are aimed at an understanding of the process as a system
When an entity (work) arrives, what is the first thing that is done?
Then what? Loop until sequence is complete.
When is it done?
As you move through the “Then what?” loop,begin to create a straw-man flow chart of the process.
Loop through all activities in sequence until the end of the process is identified.
Describetask
or activity
Thenwhat?
End
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-15
Conducting the InterviewConducting the Interview
The SOLAR ModelThe SOLAR Model
S - Enter at the Social level.
Put them at ease. Discuss the weather, something from the news. Go easy.
O - Explain the Objective of the meeting, discussion, etc.
Why are you here, what are you doing, why are you doing it?
L - Listen to what they say.
Ensure you’re non-verbal cues show you are interested.
A – Advise or Ask
Influence by recommendations, coaching, persuading, confronting, or asking probing questions for more understanding
R - Record.
Identify and record specific steps or outcomes.
The SOLAR Model is a ‘checklist’ for you to remember as you are in the middle of the interview
The SOLAR Model is a ‘checklist’ for you to remember as you are in the middle of the interview
Rev. C January 2004 © Tyco Electronics GB110-16
Interviewing:Interviewing:Notes & Things to RememberNotes & Things to Remember
Be sure to thank the people you interview.
Be sure to thank their manager/supervisor for letting them meet with you.
Remember they can’t be doing their job if they are talking to you.
Be aware of how much time you have spent conducting an interview.
Don’t ask for 30 minutes and stay 60.
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-17
Level 0 Process and Inputs/OutputsLevel 0 Process and Inputs/Outputs
Identify the process in basic terms
Identify External Supplier (Inputs)
Raw materials
Incoming information
Personnel
Identify the Customer Requirements (Outputs)
Process or Product customer specifications or critical attributes.
Business/Organization Performance metrics.
(We need to get a “high level” view of the process first)(We need to get a “high level” view of the process first)
Rev. C January 2004 © Tyco Electronics GB110-18
Traditional SIPOC LayoutTraditional SIPOC Layout
Supplier
Supplier
S
ProcessInput
ProcessInput
ProcessInput
“Big X’s”
I
ProcessStep 1
ProcessStep 2
ProcessStep 3
ProcessStep 4
ProcessStep 5
ProcessStep 6
P
ProcessOutput
ProcessOutput
ProcessOutput
“Big Y’s”
O
Customer
Customer
C
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-19
Methods for Making a IMethods for Making a I--PP--O or SIPOCO or SIPOC
Start with the Customers and work to the left
Always keeps the customer as the focus
Can be confusing if Process is unclear
Or
Start with the Process and work outward
More intuitive for team members
Risk less customer focus – you must facilitate the focus!
Try either method … see which works better for you. Try either method … see which works better for you.
Rev. C January 2004 © Tyco Electronics GB110-20
Level 0 Examples:Level 0 Examples:
Order Entry
PhoneFAXEmailEDI
PriceAvailabilityConfirmation of
OrderPromise DateOrder number
Inputs Outputs
NonNon-- ManufacturingManufacturing
ManufacturingManufacturing
Stamping
SOPDiesRaw StockEquipmentDie MaintenanceOperator
FlatnessUndamaged PartsDimensionalityTensile StrengthBurr freeCycle Time
Inputs Outputs
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-21
• Do a first draft of your “Level 0” process map.
• Discuss at your table.
Time: 15 min.
ExerciseExercise
Rev. C January 2004 © Tyco Electronics GB110-22
METALFORMING
CT: 2 days--------------------------------
Yield: 0.8 %-------------------------------
WIP: 3,500
Value Stream MapValue Stream Map
A Value Stream Map is a special type of process map that includes:
Major process steps and info flow
Lean symbols
Before-and-after map at the macro level
Project measures, activity update
Team membership
The map includes key Lean indicators for
the major process steps, including:
Cycle Time
Inventory
Yield at each major step and
Rolled throughput yield: RTY
(your key project metrics)
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-23
Value Stream Map IconsValue Stream Map Icons
Rev. C January 2004 © Tyco Electronics GB110-24
Date:
xx-xx-
Project:
Lodenbel
Black Belt: R. Lee
Team Members: AB, CD, EF, IF, KL
Push Prod. No takt time Pull Production
TAKT
time
from 8'
to 5.6 '
Std.
Work /
Cell
design
Ded. Depts No std. Work
Current State Future State
Metrics Improvement Proposals
Status
by
Lead time Cycle Time WIP / Inventory Scrap Tape Detailed process map, QFD
Base Proposed Base Proposed Base Proposed Base Proposed Base Proposed Measures, validation
30
days2 days 30' 8' 200 19 10% 0.9 9 - 10" 2"
Kaizan 1
Kaizan 2, DOE on cutting opns.
Die cut analysis for 1 and 2
Productivity focus **
Started
Define Measure Analyze Improve Control Completed
Past due
D
M
A
I
C
Precision
Card Co.
RED, Inc
Prod.
Control
Sub Assy
# 1
Sub Assy
# 2
Finishing
Dept
Pink
Blue
White
Precision
Card Co.
RED, Inc
MRP
S
S 2000
pull
B
B 500
-1- -2- -3-
CT: 2 2 4
Yld: 87% 85% 91%
WIP 10 9 0
RTY = 69%
Prod Goal = 1pp/12 min **
current is 1pp/16.8 min
dailydaily
Ship
800 / week
3 people
2 shifts
1
2
3
Kaizan
Cell
Kaizan
Supplier
Certif ied
ValueStreamMap
Project Data
Before/After Maps
Key Metrics:Base /
Current
Project Status
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-25
• Do a first draft of your Value Stream process map. (Current state, measures, team, etc.)
• Use blank form provided. Do in pencil
• Discuss at your table.
Time: 20 min.
ExerciseExercise
Rev. C January 2004 © Tyco Electronics GB110-26
Detail Process Mapping StepsDetail Process Mapping Steps
Start with Level 0 and Value Stream Map: the process, its external inputs and customer outputs
Identify all steps in the process graphically, including non-value adding steps like delay.
List key output variables at each step
List key input variables and classify process inputs as controlled or uncontrolled
Add process specifications for Input Variables
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-27
Detail Process MapDetail Process Map
Include all value-added and non value-added steps(Process steps, inspection/test, rework, scrap, delay.)
ManufacturingManufacturing
Answer Phone
Greet customer
Determine parts needed
Identify need date
Identify ship to address
Identify price
Identify shipment method
Internal Information
Get internal P/N
Determine terms
Identify lead time
Complete order worksheet
Order Entry
Input information
Print order confirmation
Determine ship date
Review order
File / Queue
File for end-of-day processing, queue for batch download 10:00
EST next work day
NonNon--ManufacturingManufacturing
Move
Move material from
warehouse
Wait
Store in staging area
Set-up
Set up and adjust die, reeler, etc.
Stamp
4-out die, 6 stations in die
Move
Move to inspection operation,
wait
Inventory
Return to stores to wait
further processing
Rev. C January 2004 © Tyco Electronics GB110-28
List Output VariablesList Output VariablesInclude both process and product output variables
ManufacturingManufacturingOutputs
• Cycle Time
• Material properties
• NVA
Move
Move material from
warehouse
Stamping
Wait
Store in staging area
Set up
Set up and adjust die,
reelers, etc.
Move
Move to inspection operation,
wait
Inventory
Return to stores to wait
further processing
• Cycle Time
• NVA
• Cycle Time
• NVA
Outputs
• Cycle Time
• Correct part
• Correct dimensions
• Burr free
• Cycle Time
• NVA
• Cycle Time
• NVA
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-29
List Output VariablesList Output VariablesInclude both process and product output variables
• Prompt answer
• Live body
• Part number
• Availability
• Need date
• Customer number
• Order worksheet
• Pricing
NonNon--ManufacturingManufacturing
Outputs
Answer Phone
• Greet customer
• Determine P/N
• Identify need date
• Identify ship to address
• Identify ship method
Internal Information
•Get internal P/N
•Determine terms
•Identify lead time
•Complete order worksheet
Outputs
• Order in computer
• All line items complete
• Correct info
• Promise date
• Ordernumber
• Printed confirmation
• Cycle time
• NVA
Order Entry
• Input information
• Print order confirmation
• Determine ship date
• Review order
File / Queue
• File/store for end-of-day processing, queue for batch download 10:00 EST next work day.
Rev. C January 2004 © Tyco Electronics GB110-30
List and Classify Input VariablesList and Classify Input Variables
List all key input variables and classify them asControlled inputsUncontrolled inputs
C = Controlled Inputs: Input Variables that can be changed to see the effect on Output Variables. Sometimes called “Knob” variables
U = Uncontrolled Inputs: Input Variables that impact the Output Variables but are difficult or impossible to control (may also be controllable, just not under control currently)
Example: Environmental variables such as humidity
KIPV = Critical Inputs: Input Variables that have been statistically shown to have a major impact on the variability of the Output Variables
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-31
Is it Controlled or Uncontrolled?Is it Controlled or Uncontrolled?
Is it Controllable or Uncontrollable?Is it Controllable or Uncontrollable?
Current System
Controlled UncontrolledP
ossib
le Un
co
ntr
olla
ble
C
on
tro
llable
No short term solution
Long term –possibly implement
technology
Validate with Measurement
System Analysis
Why?
Validate it’s truly uncontrollable with
DOE
OPPORTUNITY!
Put in control system
Label as U*
Label C or U Using Current System
Rev. C January 2004 © Tyco Electronics GB110-32
Process Mapping Manufacturing Process Mapping Manufacturing
Example (VExample (V--A only)A only)
Outputs
• Cycle Time
• Material properties
Feed
Feedmaterial from roll
Cutting
Cut material to length
• Cycle Time
• Correct length
• Burr free
Stamping
Stamp piece to size
• Cycle Time
• Correct part
• Burr free
• Correct dimensions
• Tensilestrength
Drawing
Draw features
Outputs
• Cycle Time
• Correct part
• Correct dimensions
• Tensilestrength
Punching
Punch out features
• Cycle Time
• Correct part
• Burr free
• Correct dimensions
• Tensilestrength
Cleaning
Clean metal surface
• Cycle Time
• Clean surface
• Residue free
• Gear speed
• Gear wear
• Material lot
• Material properties
Inputs Type
C
U
U
U
• Shear speed
• Shear wear
• Material properties
• Clampingforce
• Shear force
C
U
U
C
U
• Die wear
• Material properties
• Ram force
• Ram speed
• Die number
• Die hardness
U
U
C
C
C
C
• Drawing speed
• Material properties
• Die number
Inputs Type
C
U
C
• Punch speed
• Punch wear
• Material properties
• Clampingforce
• Ram force
C
U
U
C
U
• Solvent purity
• Solvent age
• Surface roughness
• Contaminantlevel
• Humidity
• Temperature
C
U
U
U
U
U
U
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-33
Process Mapping Transactional Process Mapping Transactional
Example (VExample (V--A only)A only)
(Non(Non--Manufacturing)Manufacturing)
Outputs
Answer Phone
• Greet customer
• Determine P/N
• Identify need date
• Identify ship to address
• Identify ship method
Internal Information
• Get internal P/N
• Determine terms
• Identify lead time
• Complete order worksheet
Order Entry
• Input information
• Print order confirmation
• Determine ship date
• Review order
Order Confirmation
• FAX confirmation to customer
• Verify manufacturing receipt of order
Inputs Type
• Orderworksheet
• Computerentry screens
• Lead time information from mfg
• Shipment method
• Printed confirmationsheet
• Productionschedule
• Customer contact info
• Productioncontact info
• Confirmationprocedure
U
C
U
C
C
U
C
C
C
Inputs Type
• Information fromcustomer
• Greeting script
• Answering procedure
• Telephonesystem
• Cross reference for P/N’s
• Orderinformation
• Plant loading information
• Orderworksheet form
• Pricingalgorithm
C
C
C
U
C
U
U
C
C
• Promptanswer
• Live body
• Part number
• Availability
• Need date
• Customer number
• Orderworksheet
• Pricing
Outputs
• Order in computer
• All line items complete
• Promise date
• Ordernumber
• Printed confirmation
• Orderconfirmationto customer
• Order to production
Rev. C January 2004 © Tyco Electronics GB110-34
Controlled Input
VariableTarget
Upper
Spec
Lower
Spec
Die Wear ? 2.56 2.33
Solvent Type ? ? ?
Ram Speed ? 800 750
Clamping Force ? ? ?
Add Specifications for InputsAdd Specifications for Inputs
For Input variables identified as Controlled and for Critical Inputs, we can add the operational specifications and Targets for these variables
This information is the beginnings of the Control Plan!
Use spec numbers if available, not just input category
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-35
Example:Example:Coffee ShopCoffee Shop
When the customer arrives at the shop, they have a need-- a need for a cup of coffee.
That need is translated into a customer order. It is this orderthat actually flows through the coffee process and carries the need.
This customer order may change from information to physical material and equipment as it moves through the process.
As the order moves through the process, each activity adds a degree of fulfillment to that order.
At the end of the process, the fulfilled order is delivered.
To the customer, the output of this process is a cup of coffee.
To the process manager, the output of the process is a successfully completed order.
What is the entity that moves through a coffee shop?What is the entity that moves through a coffee shop?
Rev. C January 2004 © Tyco Electronics GB110-36
Coffee Making ExerciseCoffee Making Exercise
You have been identified as a team for instructing future Six Sigma Leaders in the activity of making coffee.
There have been complaints about the coffee, and your mission is to improve the process of making coffee with our existing equipment (Mr. Coffee type maker).
Your first task is to document the process with a Level 0 and Detailed Process Map.
Good luck: The last coffee team was awarded the Bilge-Water Coffee Award and had to drink their own brew. Everyone else is buying from the machine. (The previous team did not use any Six Sigma tools.)
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-37
Uses for Process MapUses for Process Map
Provides inputs to Cause and Effects Matrix
Provides inputs to FMEA
Provides inputs to Control Plan Summary
Provides inputs to Capability Summary
Provides inputs to Multi-Vari Studies
Evaluate Experimental Designs
Tracks variables studied
Allows evaluation of design’s robustness to noise variables
Can also be used to track team activitiesCan also be used to track team activities
Rev. C January 2004 © Tyco Electronics GB110-38
Process Map Analysis:
Overview of Opportunity and Strategy
?Capability Issues:
Statistical and Quality Tools
Capacity Issues:Lean Tools
Does the nature of the gap suggest one or the other, or both?
Does the nature of the gap suggest one or the other, or both?
Process Map AnalysisProcess Map Analysis
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-39
Next StepsNext Steps
Process MapProcess Map
1 2 3 4 5 6 7 8 9 10 11
Process Step Process Inputs He
avi
es
in
Pro
du
ct
Lig
hts
in
Pro
du
ct
Mo
istu
re in
P
rod
uc
t
Ac
idity
in
Pro
du
ct
Lo
w C
ap
ac
ity
Fro
m U
nit
Ex
ces
sive
D
ow
ntim
e
Ma
teri
al
Lo
sse
s
Co
rro
sio
n o
f E
qu
ipm
en
t
Po
or
Re
act
or
Pe
rfo
rma
nce
Total
139 Day Tanks Analysis 10 10 9 9 335
9 Reactor Cat./HF Ratio 5 8 7 157
7 Reactor Rxr Temperature 6 5 4 7 149
73 Lights Removal Condenser Leak 4 8 2 4 1 148
74 Lights Removal Reboiler Leak 4 8 2 4 1 148
131 Purification Low Stages 8 8 144
144 Final Storage Containers 3 2 6 6 140
100 Neutralization pH Value 6 6 3 138
16 Catalyst Stripper Pluggage 3 6 5 3 137
111 Drying Decomposition 2 6 3 2 2 134
39 Drier Water Carryover 4 6 5 1 132
34 Drier Molecular Sieve 3 3 2 7 2 125
1 2 3 4 5 6 7 8 9 10 11
Process Step Process Inputs He
avi
es
in
Pro
du
ct
Lig
hts
in
Pro
du
ct
Mo
istu
re in
P
rod
uc
t
Ac
idity
in
Pro
du
ct
Lo
w C
ap
ac
ity
Fro
m U
nit
Ex
ces
sive
D
ow
ntim
e
Ma
teri
al
Lo
sse
s
Co
rro
sio
n o
f E
qu
ipm
en
t
Po
or
Re
act
or
Pe
rfo
rma
nce
Total
139 Day Tanks Analysis 10 10 9 9 335
9 Reactor Cat./HF Ratio 5 8 7 157
7 Reactor Rxr Temperature 6 5 4 7 149
73 Lights Removal Condenser Leak 4 8 2 4 1 148
74 Lights Removal Reboiler Leak 4 8 2 4 1 148
131 Purification Low Stages 8 8 144
144 Final Storage Containers 3 2 6 6 140
100 Neutralization pH Value 6 6 3 138
16 Catalyst Stripper Pluggage 3 6 5 3 137
111 Drying Decomposition 2 6 3 2 2 134
39 Drier Water Carryover 4 6 5 1 132
34 Drier Molecular Sieve 3 3 2 7 2 125
Cause and Effects Matrix
Cause and Effects Matrix
Process
Step/Part
Number
Potential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
RPNActions
RecommendedResp.
COATING &
IMAGING
DIRTY PHOTOMASK MICROCRACKING,
DELAMINATION, STREAKS 8
LOW FREQUENCY OF CLEANING
8
SOP, VISUAL INSPECTION
7 448
INCREASE FREQUENCY
TO ONCE EVERY 20
PANELS
MG
IMPROVE CLEANING
METHOD
PF
PURCHASE OFF-LINE
CLEANING SYSTEM
MG
TEST ON-LINE MASK
REPLACEMENT
PF
FMEAFMEA
Process Step Input Output
Process
Specification
(Target, LSL, USL)
Cpk
Mean - Sigma
Measurement
Technique
%R&R
P/T
Sample
Size
Sample
Frequency
Control
MethodReaction Plan
Coating Dosage 22.5, 22, 23 1.22 UIL-1700 25% 1/hr Auto-timer Cross check
Coating Height
24,23,25 1.54 Micrometer 31%/0.47 35 pts per panel
1/hr Coating & pump
speed
Adjust previous
Coating width
14,12,16 1.78 Laser Measuring Device
1/hr None in place
Coating
length
36,34,38 1.43 Laser Measuring
Device
1/hr None in place
Vacuum 35" Hg Vacuum Gauge 1/hr Monitor Compare
guages, look for blockage
Initial Assessment of Capabilities and Control Plans
Initial Assessment of Capabilities and Control Plans
OUTPUTS
INP
UT
S
Customer Requirement
(Output Variable)
Measurement
Technique
%R&R or P/T
Ratio
Upper
Spec
Limit
Target
Lower
Spec
Limit
Cp CpkSample
SizeDate Actions
Gel Time
ViscosityCleanliness
Color
Homogeneity
ConsistencyDigets Time
Temperature
Solids
Key Process Output Variable
Capability Status Sheet
Input Type Output
Agit speed Contr Stab timeTemperature Contr Stabilize Acid numberPressure Contr Color
Air flow Contr Put in setpoints Viscosity
% O2 in air Noise Slowly reduce press Reactor temp
Air temp Contr Monitor temp Temp profileOffgas flowOffgas compHT coeff
Belt speed Contr Finish timeBelt temp Contr Finish Acid numberFlow rate Contr Color
Nozzle type SOP Check nozzle type ViscosityHole size Contr Put in setpoints Drop PointRoom temp Noise Monitor appearance HardnessAgit speed Contr Pellet appear
Tank temp Contr
Input Type Output
Wax grade SOP Prep timeAmt wax Contr Prepare Reactor Acid numberCharge rate Contr ViscosityAgit speed Contr Charge melted wax Reactor tempRxn temp Contr Bring to reaction temp Temp profilePressure Contr HT coeffAir flow Contr
% O2 in air Noise
Viscosity NoiseWax temp Noise
AN target SOP Oxid timeAgit speed * Contr Oxidize Acid numberTemperature * Contr ColorPressure * Contr Put in setpoints ViscosityAir flow * Contr Sample hourly Reactor temp
% O2 in air Noise Monitor acid number Temp profile
Air temp Contr Offgas flowAir humidity Noise Offgas comp
HT coeffResp time
Rev. C January 2004 © Tyco Electronics GB110-40
Initial Assessment of Control PlanInitial Assessment of Control Plan
Start an initial assessment of the control plan after the process map is complete
Add the measurement technique, operating specifications, and targets for the controlled and critical inputs as well as the outputs
Excel template: Control Plan.XLS
Process Step Input Output
Process Spec
(LSL, USL,
Target)
Cpk / Date
(Sample
Size)
Measurement
System
%R&R
or P/T
Current
Control
Method
(from FMEA)
Who Where WhenReaction
Plan
Current Control Plan
PROCESS MAPPING
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 21402-108, Rev. C
Rev. C January 2004 © Tyco Electronics GB110-41
Process Mapping SummaryProcess Mapping Summary
Provided overview of process mapping
Showed the step-by-step example of process mapping
Demonstrated where process mapping fits in the DMAIC Process Improvement Method
Provided examples of process maps
Created draft and sample process maps
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-1
MeasurementSystemsAnalysis
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-2
ObjectivesObjectives
Introduce Measurement Systems Analysis
Define basic measurement terms
Outline procedure for performing a Measurement Systems Analysis
Perform an exercise to practice methodology
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-3
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-4
Measurement ProcessMeasurement Process
The ideal measurement system produces “true” measurements every time
Quality of the measurement system is characterized by statisticalproperties
The measurement process should include:
Design and certification
Capability assessment over Time
Control
Repair and re-certification
Properties
Must be in Statistical Control
Variability must be small compared to product specifications
Variability must be small compared to process variation
Discrimination should be about one-tenth of product specification or process variation
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-5
Possible Sources of Process VariationPossible Sources of Process Variation
We will look at “repeatability” and “reproducibility” as primarycontributors to measurement error
We will look at “repeatability” and “reproducibility” as primarycontributors to measurement error
To address actual process variability, the variation due to the measurement system must first be identified and separated from that
of the process
To address actual process variability, the variation due to the measurement system must first be identified and separated from that
of the process
Stability Linearity
Long-term
Process Variation
Short-term
Process Variation
Variation
w/in sample
Actual Process Variation
Repeatability Calibration
Variation due
to gage
Variation due
to operators
Measurement Variation
Observed Process Variation
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-6
Basic ModelBasic Model
2
SystemtMeasuremen
2
Product
2
Total
The Total Variation is equal to the real product variation plus the variation due to the
measurement system
The Total Variation is equal to the real product variation plus the variation due to the
measurement system
2
ilityReproducib
2
ityRepeatabil
2
SystemtMeasuremen
The Measurement System Variation is equal to the variation due to repeatability plus the variation
due to reproducibility.
The Measurement System Variation is equal to the variation due to repeatability plus the variation
due to reproducibility.
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-7
Measurement Process ComponentsMeasurement Process Components
Measurement tools:
Hardware
Software
All procedures for using the tools:
Which operators
Set-up and handling procedures
Off-line calculations and data entry
Calibration frequency and technique
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-8
Sources of Measurement VariationSources of Measurement Variation
Environme
Mechanical instability
Tool
Environment
Work Methods
Wear
Electrical instability
Algorithm instability
Ease of Data Entry
Operator Training
Calibration Frequency
Maintenance Standard
Sufficient Work Time
Standard Procedures
Operator Technique
‘Measurement Variation’
Humidity
Cleanliness
Vibration
Line Voltage Variation
Temperature Fluctuation
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-9
Information to be ObtainedInformation to be Obtained
How big is the measurement error?
What are the sources of measurement error?
Is the tool stable over time?
Is the tool capable for this study?
How do we improve the measurement system?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-10
11010090807060504030
15
10
5
0
Observed
Fre
quen
cy
LSL USL
Actual process variation -No measurement error
Observed processvariation -With measurement error
11010090807060504030
15
10
5
0
Process
Fre
quen
cy
LSL USL
How does Measurement Error Appear?How does Measurement Error Appear?
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-11
Sources of VariationSources of Variation
Product Variability
(Actual variability)
Product Variability
(Actual variability)
Measurement
Variability
Measurement
Variability
Total Variability(Observed variability)
Total Variability(Observed variability)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-12
TerminologyTerminology
Discrimination
Accuracy related terms
True value
Bias
Linearity
Precision related terms
Repeatability
Reproducibility
Linearity
Stability
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-13
DiscriminationDiscrimination
The number of decimal places that can be measured by the system. Increments of measure should be about one-tenth of the width of the product specification or process variation.
1 2 3 4 5
1 2 3 4 5
Poor DiscriminationRange ofproductvariationcan only
be writtenas 0,1,or 2
Good DiscriminationRange ofproductvariation
can be writtenas 0, 0.1, 0.2,0.3, …1.5, for
a total of16 values.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-14
Precision and AccuracyPrecision and Accuracy
Accurate Precise not Neither Preciseand Precise Accurate or Accurate
Accurate Precise not Neither Preciseand Precise Accurate or Accurate
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-15
Accuracy TermsAccuracy Terms
Accuracy - Does the average of the measurements deviate from the true value?
True value
Theoretically correct value
NIST standards
Bias
Distance between average value of all measurements and true value
Amount the measuring instrument is consistently off target
Systematic error or offset
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-16
AccuracyAccuracy
Instrument accuracy is related to the difference between the observed average value of measurements and the master or “true” value
The master value is an accepted, traceable reference standard (e.g., NIST)
MasterValue
MasterValue
Average Value of MeasurementsAverage Value of Measurements
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-17
BiasBiasAverage of measurements differ from the Master Value by a fixed amount. Bias effects include:
Operator bias - different operators get detectable different averages for the same thing
Instrument bias - different instruments get detectable different averages for the same thing, etc.
Master Value
Master Value
Average 1
Instrument 1 Instrument 2
Average 2
Instrument 2 Bias
Instrument 1 Bias
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-18
Precision TermsPrecision Terms
Total variation in the measurement system
Measure of natural variation of repeated measurements
Repeatability and Reproducibility
222
rpdrptMS
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-19
RepeatabilityRepeatability
The inherent variability of the measurement device
Variation that occurs when repeated measurements are made of the same variable under absolutely identical conditions
Same operator
Same set-up
Same units
Same environmental conditions
Short-term
Estimated by the pooled standard deviation of the distribution of repeated measurements
Repeatability is always less than the total variation of the system
rpt
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-20
RepeatabilityRepeatability
The variation between successive measurements of the same
part, same characteristic, by the same person using the same
instrument. Also known as test - retest error; used as an
estimate of short-term variation.
Master Value
Master Value
Average
GoodRepeatability
Average
PoorRepeatability
MEASUREMENT SYSTEMS ANALYSIS
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Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-21
ReproducibilityReproducibility
The variation that results when different conditions areused to make the measurements
Different operators
Different set-ups
Different test units
Different environmental conditions
Long-term
Estimated by the standard deviation of the averages of measurements from different measurement conditions
rpd
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-22
ReproducibilityReproducibility
The difference in the average of the measurements made by different persons using the same or different instrument when measuring the identical characteristic
Master Value
Master Value
GoodReproducibility
Operator 1
PoorReproducibility
Operator 2 Operator 3Operator 1 Operator 2 Operator 3
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-23
Accuracy vs. PrecisionAccuracy vs. Precision
Suppose we have a reference material with a ‘true’ hardness of 5.0
Method 1 gives the following readings:3.8, 4.4, 4.2, 4.0
Method 2 gives the following readings:6.5, 4.0, 3.2, 6.3
Which method is more accurate?
Which method is more precise?
Which method do you prefer? Why?
Method 2 Method 2 -- average is equal to ‘true’ hardness valueaverage is equal to ‘true’ hardness value
Method 1 Method 1 -- variability is very lowvariability is very low
Method 1 Method 1 -- we can compensate easier for mean shift we can compensate easier for mean shift
than variabilitythan variability
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-24
Accuracy vs. PrecisionAccuracy vs. Precision
Related to Difference between Average Measurements and the
True Value
Related to Difference between Average Measurements and the
True Value
Related to the Variance and Standard Deviation of the
Measurements
Related to the Variance and Standard Deviation of the
Measurements
Measurement System Bias -Determined through “Calibration Study”
Measurement System Variability - Determined through “R&R Study”
AccuracyAccuracy
PrecisionPrecision
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-25
Measurement Capability Index Measurement Capability Index -- P/TP/T
Precision to Tolerance RatioPrecision to Tolerance Ratio
Addresses what percent of the tolerance is taken up by measurement error
Includes both repeatability and reproducibility
Ideal: 8% or less Acceptable: 30% or less
Usually expressed as percent
Usually expressed as percent
ToleranceTP MS*15.5
/
Note: 5.15 standard deviations accounts for 99% of MS variation. The use of 5.15 is an industry standard
Note: 5.15 standard deviations accounts for 99% of MS variation. The use of 5.15 is an industry standard
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-26
Precision to Tolerance RatioPrecision to Tolerance Ratio
Product Tolerance
LSL USL
measurement system variation
P/T = 20%
P/T = 100%
P/T = 200%
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-27
Uses of P/T RatioUses of P/T Ratio
The P/T ratio is the most common estimate of Measurement System precision
This estimate may be appropriate for evaluating how well the Measurement System can perform with respect to specifications
Specifications, however, may be too tight or too loose
Generally, the P/T ratio is a good estimate when the measurement system is only used to classify production samples, for process control use, etc.
Even then, if process capability (Cpk) is not adequate, the P/T ratio may give you a false sense of security
Note: In MINITAB, the P/T Ratio is called % Tolerance
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-28
Measurement Capability Index Measurement Capability Index -- %R&R%R&R
Addresses what percent of the Total Variation istaken up by measurement error
Includes both repeatability and reproducibility
As a target, look for % R&R less than 28%
Less than 14% is ideal
Usually expressed as percent
Usually expressed as percent
100&%Total
MSRR
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-29
% Repeatability & Reproducibility% Repeatability & Reproducibility
(% R&R)(% R&R)
Observed Process Variation
measurement system variation
%R&R = 20%
%R&R = 75%
%R&R = 100%
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-30
Uses of %R&RUses of %R&R
The %R&R is the best measure for the Belt
This estimates how well the Measurement System performs with respect to the overall process variation
%R&R is the best estimate when performing process improvement studies
Care must be taken to use samples representing full, but typical, process
variation
Care must be taken to use samples representing full, but typical, process
variation
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-31
%R&R = 20%
%R&R = 50%
Observed Process Variation
%R&R = 100%
Product Tolerance
LSL USL
Measurement System Variation
P/T = 20%
P/T = 50%
P/T = 100%
3 Sigma Process:3 Sigma Process:P/T Ratio and % R&RP/T Ratio and % R&R
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-32
%R&R = 25%
%R&R = 50%
Observed Process Variation
%R&R = 100%
Product Tolerance
LSL USL
P/T = 50%
P/T = 100%
P/T = 200%
2 Sigma Process:2 Sigma Process:P/T Ratio and % R&RP/T Ratio and % R&R
Measurement System Variation
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-33
%R&R = 20%
%R&R = 40%
%R&R = 100%
Product Tolerance
LSL USL
P/T = 10%
P/T = 20%
P/T = 50%
ObservedProcess Variation
6 Sigma Process:6 Sigma Process:P/T Ratio and % R&RP/T Ratio and % R&R
Measurement System Variation
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-34
Measurement Error EffectMeasurement Error Effect
on Capability Indiceson Capability Indices
The higher the Measurement Error the more dramatic the impact on your
ability to understand the true process capability!
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-35
Gage R&R Study SetGage R&R Study Set--upup
Generally two to three operators
Generally 10 units to measure
Each unit is measured 2-3 times by each operator
Units are selected to cover the normal full range of the process
Don’t select consecutive units
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-36
Sample Size IssuesSample Size Issues
Number of operators
If process uses multiple operators, choose 2-4 at random
If process uses only one operator, or no operators, perform study without operator effects (reproducibility effects ignored)
Number of samples
Select enough samples so that
(number of samples) X (number of operators) > 15
If not practical or possible, choose number of trials so that:
if S x O < 15, trials = 3
if S x O < 8, trials = 3 to 4
if S x O < 5, trials = 4 to 5
if S x O < 4, trials = 5 to 6
MEASUREMENT SYSTEMS ANALYSIS
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Page 19402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-37
Study Sample SelectionStudy Sample Selection
Samples should be pulled from the process that span the normal variation of the process
Example: If you produce a material with a mean thickness of 0.020” and a s of 0.001”, get samples that have thickness from 0.017 - 0.023” (99% of the range)
BE CAREFUL!
If you produce the same base material with different thickness with the same process, sub-group them and perform the R&R study
Example:
A process produces a material with thickness 0.070”, 0.085”, and 0.090” all with a +/- 0.005” tolerance, all measured with the same system
Perform 3 R&R studies - one for each thickness target
If you lumped the above samples together, the GR&R value would be artificially low
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-38
Procedure for Performing an R&R StudyProcedure for Performing an R&R Study
Calibrate the gage, or assure that it has been calibrated
Have operator 1 measure all samples once in random order
Have operator 2 measure all samples once in random order
Continue until all operators have measured samples once (this isTrial 1)
Repeat steps 2-4 for the required number of trials
Calculate the statistics of the R&R study
Repeatability
Reproducibility
Standard deviations of each of the above
%R&R
P/T Ratio
Analyze results and determine follow-up action, if any
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-39
Minitab Gage R&R Data MatrixMinitab Gage R&R Data Matrix
Let’s set up a table for a 3 Operators, 10 Sample per trial, 2 Trials gage study
We want to create a Minitab file with the following columns
Part or Sample
Operator
Trial
Measurement
Let’s set up the table to have the correct Trial / Operator / Sample combinations using the MakePatterned Data procedure
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-40
Minitab Gage R&R Data MatrixMinitab Gage R&R Data Matrix
Setting up the Gage R&R Data Matrix in the Minitab worksheetis similar to setting up a matrix for a designed experiment.
TRIAL 1 TRIAL 2
OPERATOR 1 OPERATOR 2 OPERATOR 3 OPERATOR 1 OPERATOR 2 OPERATOR 3
SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10
We have 2 trials to run.During each trial we have 3 operators.
Each operator will measure 10 samples.
MEASUREMENT SYSTEMS ANALYSIS
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Page 21402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-41
Minitab Gage R&R Data MatrixMinitab Gage R&R Data Matrix
TRIAL 1 TRIAL 2
OPERATOR 1 OPERATOR 2 OPERATOR 3 OPERATOR 1 OPERATOR 2 OPERATOR 3
SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10
We must set up 3 columns in Minitab to fit the matrixColumn 1 (C1) will be named Trials
Column 2 (C2) will be named OperatorsColumn 3 (C3) will be named Samples
Entries in the 3 columns will be usedto identify the factors in the study
Open a new worksheet in Minitab and name the 3 columns
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-42
Minitab Gage R&R Data MatrixMinitab Gage R&R Data Matrix
TRIAL 1 TRIAL 2
OPERATOR 1 OPERATOR 2 OPERATOR 3 OPERATOR 1 OPERATOR 2 OPERATOR 3
SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10
The matrix above will provide you withinformation on the length of the three columns
The total number of measurements to be made is 60(2 trials X 3 operators X 10 measurements = 60)
This tells us that each of the 3 columnswill have 60 entries (60 rows per column)
MEASUREMENT SYSTEMS ANALYSIS
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Page 22402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-43
Minitab Gage R&R Data MatrixMinitab Gage R&R Data Matrix
TRIAL 1 TRIAL 2
OPERATOR 1 OPERATOR 2 OPERATOR 3 OPERATOR 1 OPERATOR 2 OPERATOR 3
SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10
Column 3 (Samples) will consist of the numbers1-10, repeated 6 times to identify the 10 samples
measured by each of the 3 operators during each trial
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-44
Making Patterned DataMaking Patterned Data
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-45
Making Patterned DataMaking Patterned Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-46
Making Patterned DataMaking Patterned Data
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 24402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-47
Making Patterned DataMaking Patterned Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-48
Making Patterned DataMaking Patterned Data
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-49
Making Patterned DataMaking Patterned Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-50
Minitab Gage R&R FileMinitab Gage R&R File
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 26402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-51
Minitab Gage R&R FileMinitab Gage R&R File
Measurement taken during Trial 1by Operator 1 on Sample 6.
Measurement taken during Trial 1by Operator 2 on Sample 8.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-52
Coding VariablesCoding Variables
You can change the Operator values to the actual person’s name and still perform the analysis using a Coding procedure
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 27402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-53
Coding VariablesCoding Variables
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-54
Coding VariablesCoding Variables
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 28402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-55
Last Checks....Last Checks....
Before running your study, make sure that you have the right number of cells for each operator and sample
To do this we use Cross Tabulation
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-56
Cross TabulationCross Tabulation
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 29402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-57
We look for equal cell counts in all
combinations
Table StatisticsTable Statistics
The table should contain equal counts for all operators and all parts before continuing
Rows: Trials Columns: Operators
Dillon Fred Tom All
1 10 10 10 30
2 10 10 10 30
All 20 20 20 60
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-58
Gage R&R Example Gage R&R Example
Let’s use Minitab to analyze some data
Open worksheet AIAG MSA Example.MTW in file DMANOVA MSA.MPJ
We will be using Minitab’s Gage R&R Study (Crossed) function
MEASUREMENT SYSTEMS ANALYSIS
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Page 30402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-59
Minitab Gage R&R Study (Crossed)Minitab Gage R&R Study (Crossed)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-60
Options / Info ScreensOptions / Info Screens
Enter the specific gage information here for records
Important when doing a number of iterations
Enter the specific gage information here for records
Important when doing a number of iterations
Enter the Process Tolerance in the Options screen
REMEMBER – the tolerance is the entire range of the specifications!
You can add a descriptive Title as well
Enter the Process Tolerance in the Options screen
REMEMBER – the tolerance is the entire range of the specifications!
You can add a descriptive Title as well
Accepted value for 99% of the distribution
Accepted value for 99% of the distribution
MEASUREMENT SYSTEMS ANALYSIS
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Page 31402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-61
Minitab OutputMinitab Output
Minitab produces both analytical and graphical analysis information
Analytical Results
ANOVA table(s)
Components of Variation
Percent contribution table
Graphical Results
X-Bar / R chart
Components of Variation
Operator*Part Interaction Plot
By Operator and By Part plots
Let’s look at the analytical first and then return to the graphical
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-62
Gage R&R ReportGage R&R Report
Total Gage R&RIdeal: 2% or lessAcceptable: 7.7% or less
MEASUREMENT SYSTEMS ANALYSIS
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Page 32402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-63
StdDev Study Var %Study Var %Tolerance
Source (SD) (5.15*SD) (%SV) (SV/Toler)
Total Gage R&R 0.066615 0.34306 32.66 68.61
Repeatability 0.035940 0.18509 17.62 37.02
Reproducibility 0.056088 0.28885 27.50 57.77
Operator 0.030200 0.15553 14.81 31.11
Operator*Sample 0.047263 0.24340 23.17 48.68
Part-To-Part 0.192781 0.99282 94.52 198.56
Total Variation 0.203965 1.05042 100.00 210.08
Number of Distinct Categories = 4
Gage R&R ReportGage R&R Report
• % R&R (for process improvement efforts)
• %P/T (for acceptance to spec efforts)
• BE CAREFUL - watch for low Distinct Categories! Must be at least 5 for Process Improvement use!
• % R&R (for process improvement efforts)
• %P/T (for acceptance to spec efforts)
• BE CAREFUL - watch for low Distinct Categories! Must be at least 5 for Process Improvement use!
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-64
Number of Distinct CategoriesNumber of Distinct Categories
The number of distinct categories of data within the process that the measurement system can detect.
Represents the number of non-overlapping confidence intervals within the range of the product variation and is an indication of the Discrimination of the measurement system.
If the number of categories is less than 2, the measurement system is of minimal value since it will be difficult to distinguish one part from another.
If the number of categories is 2, the measurement system can only divide the data into 2 groups – low and high.
If the number of categories is 3, the measurement system can divide the data into 3 groups – low, medium and high.
A measurement system that is acceptable and useful for process improvement activities must have 5 or more distinct categories. 10 or more is ideal.
MEASUREMENT SYSTEMS ANALYSIS
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Page 33402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-65
Number of Distinct CategoriesNumber of Distinct Categories
Control charts, process parameters, capability indices, process improvement
5 or more
Insensitive control charts
Coarse estimates of process parameters and capability indices
2 – 4
Generally of no use, but may provide minimal information on conformance versus nonconformance
0 – 1
Use of Measurement SystemNumber of Categories
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-66
Graphical OutputGraphical Output
MEASUREMENT SYSTEMS ANALYSIS
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Page 34402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-67
Gage R&R X / R ChartGage R&R X / R Chart
• We want to see variability in the X-bar chart outside the Control Limits
• This indicates Part-to-Part variability
• If there was none, you probably did not get samples that cover the normal range in production
• We want to see variability in the X-bar chart outside the Control Limits
• This indicates Part-to-Part variability
• If there was none, you probably did not get samples that cover the normal range in production
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-68
XX--Chart IndicatorsChart Indicators
If the averages for each operator is different, the reproducibility is suspect
We want more averages to fall outside the control limits but consistently for all operators
This indicates more part-to-part variability which is what we want
We want to see the majority of the points on the chart outside the control limits
If this is the case and the R-Chart is in control, then we will be able to determine the percent of the process variability that is consumed by the measurement system
MEASUREMENT SYSTEMS ANALYSIS
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Page 35402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-69
Gage R&R X / R ChartGage R&R X / R Chart
• The Range chart should show a process that is in control.
• If a point is above the UCL, the operator is having a problem making consistent measurements.
• The Range chart can also help identify inadequate discrimination
• We want at least 5 possible values within the control limits
• The Range chart should show a process that is in control.
• If a point is above the UCL, the operator is having a problem making consistent measurements.
• The Range chart can also help identify inadequate discrimination
• We want at least 5 possible values within the control limits
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-70
RR--Chart IndicatorsChart Indicators
Suspect inadequate Discrimination if:
the range chart has less than 5 distinct levels within the Control Limits
5 or more levels for the range but more than 1/4 of the values are zero
Repeatability is questionable if the range chart shows out-of-control conditions
If the range for an operator is out-of-control and the others are not, the method is suspect
If all operators have ranges out-of-control, the system is sensitive to operator technique
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 36402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-71
• We desire lines that follow the same pattern and are reasonably parallel to each other
• Significant interactions are indicated by crossing lines betweenoperators
• We also want to see that the part averages vary enough that the differences between parts are clear.
OperatorOperator--Part Interaction PlotPart Interaction Plot
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-72
• A graphical representation of the data discussed before
• We want the Gage R&R bars to be as small as possible, driving the Part-to-Part bars to be larger
• A graphical representation of the data discussed before
• We want the Gage R&R bars to be as small as possible, driving the Part-to-Part bars to be larger
Components of VariationComponents of Variation
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 37402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-73
By OperatorBy Operator
• The By Operator graph shows the average value (Circle) and the spread of the data for each operator
• We want the grouping to be similar across all operators and a flat line across the means for the operators
• The By Operator graph shows the average value (Circle) and the spread of the data for each operator
• We want the grouping to be similar across all operators and a flat line across the means for the operators
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-74
By PartBy Part
• The graph shows the average (circles) and spread of the values for each sample
• We want to see minimal spread for each part, but variability between samples (means shifting)
• The graph shows the average (circles) and spread of the values for each sample
• We want to see minimal spread for each part, but variability between samples (means shifting)
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 38402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-75
Gage Run ChartGage Run Chart
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-76
Gage Run ChartGage Run Chart
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 39402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-77
Gage Run ChartGage Run Chart
• The chart shows the two measurements made by each operator on the 10 parts.
• We want to see consistency of measurement values within each operators results and between the operators.
• The chart shows the two measurements made by each operator on the 10 parts.
• We want to see consistency of measurement values within each operators results and between the operators.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-78
8%8% 2%2% 1010
30%30% 28%28% 557.7%7.7%
P/T
Ratio
P/T
Ratio
R&R %Contribution(Ratio of Variances)
R&R %Contribution(Ratio of Variances)
Number of Distinct
Categories(Discrimination
Index)
Number of Distinct
Categories(Discrimination
Index)
%Study Variation
%Study Variation
Red
Yello
wG
reen
14%14%
%R&R Contribution is more statistically correct than %Study Variation
The latter is based on the standard deviations, which is not accurate
If you follow this chart you will be OK No Matter Which Metrics You Use
GR&R MetricsGR&R Metrics -- Additional MetricsAdditional Metrics
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 40402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-79
Measurement System EvaluationMeasurement System Evaluation
Written inspection/ measurement procedure?
Detailed process map developed?
Specific measuring system and set-up defined?
Trained or Certified Operators?
Instrument calibration performed in a timely manner?
Accuracy?
R&R?
Bias?
Linearity?
Discrimination?
Correlation with supplier or customer where appropriate?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-80
SummarySummary
Measurement systems are important to analyze BEFORE embarking on Process Improvement activities
Be careful when picking samples - watch for correct sub-grouping and sample size requirements
Analyze the measurement system for Operator, Part, and Trial effects
Make sure that the gage system has enough discrimination to be useful in determining different levels in the measured attribute
Always generate a GR&R Report to document findings, methods, and improvement opportunities
Total Variation includes Measurement Error - try to minimize the controllable error in the gage system
MEASUREMENT SYSTEMS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 41402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-81
GR&R ExerciseGR&R Exercise
The Department of Health has decided to let you check your own waste water cleanliness provided you can measure the water volume with acceptable repeatability and reproducibility
Due to the nature of the materials being measured, if you touch the container while measuring you will be exposed to potentiallyhigh levels of biological and chemical toxins.
If you touch the container while measuring, you will not be permitted to perform your own measurements, costing the company thousands of $$ in labor and testing by outside laboratories
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB111-82
GR&R ExerciseGR&R Exercise
Equipment needed:
10 plastic containers
1 measurement device
Specifications:
See container
Procedure:
Fill 10 containers with varying amounts of water between the marked lines on each container (ask instructor)
Have each of three operators independently measure the height of the water three times (use proper MSA techniques), WITHOUT touching the container
REMEMBER - Maintain one operator per trial!
Randomize the measurement order of the containers (i.e., 1,3,7,5,2,...) for each operator
Analyze the results and present to class
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-1
CapabilityCapability
StudiesStudies
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-2
ObjectivesObjectives
“Traditional” process capability indexes
Attribute and Variable Capability Studies
Short Term and Long Term Process Capability
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-3
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-4
We continually gather data on our process and ask:“Is it capable of producing defect free products ?”
Gathering and analyzing the data is called....
A Capability Study
USLUSLLSLLSL
BackgroundBackground -- Capability StudiesCapability Studies
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-5
What Type of Data
Do You Have ?
What Type of Data
Do You Have ?
Variables DataVariables DataAttribute DataAttribute Data
Collect Data
On Process
Collect Data
On ProcessCollect Data
On Process
Collect Data
On Process
Analyze Data
In Minitab
Analyze Data
In MinitabAnalyze Data
In Excel
Analyze Data
In Excel
State Capability
DPU, PPM
Cp, Cpk, Pp, Ppk
State Capability
DPU, PPM
Cp, Cpk, Pp, Ppk
State Capability
DPU, PPM
State Capability
DPU, PPM
Capability Roadmap Capability Roadmap -- 30,000 Ft. View30,000 Ft. View
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-6
“Defects”( )
1 2 3 4 5 6 7
Number of Mistakes
“Defects”(x > 130 Min)“Non-Defects”
(x < 130 Min)
15 110 115 120 125 130 135 140
Assembly Time (Minutes)
“Defect Free”( )
CustomerRequirement
CustomerRequirement
ATTRIBUTESATTRIBUTES VARIABLESVARIABLES
Classifying Data Classifying DataClassifying Data
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-7
Capability StudiesCapability Studies
withwith
Variables DataVariables Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-8
Standard Normal DistributionStandard Normal Distribution
One Standard Deviation (
This is a standard Normal distribution where:Mean = 0
Standard Deviation = 1
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-9
Transformation to the Standard Transformation to the Standard
Normal DistributionNormal Distribution
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-10
ZZ--TransformTransform
General form:
This transform produces a value Z on the Standard Normal Distribution where the mean µ = 0 and the standard deviation = 1
The value indicates how far the number is from the mean in units of standard deviations
For example, if Z = 2, that would say that the value in question is 2 standard deviations greater than the Mean
By using this method, we can calculate the proportion of product that is out-of-spec based on the process mean and standard deviation.
)-(x=Z
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-11
ZZ--TransformTransform
You may see the equation for the Z-Transform writtenin a number of different ways.
You may see the equation for the Z-Transform writtenin a number of different ways.
)-(x=Z
)x-(x=Z
s
)x-(x=Z
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-12
Estimating % NonEstimating % Non--conformingconforming
For estimating percent nonconforming for a process, we will substitute the Lower Spec Limit (LSL) and the Upper Spec Limit (USL) for x
)x-(SL=Z
)x-(x=Z
The result will tell us how far the process mean is from the specification limit in units of sigma
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-13
USLUSL
Z
Z ScoreZ Score
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-14
What’s the Area Under the Tail ?What’s the Area Under the Tail ?
USLUSL
2.52.5 We get the area under the tail from a Z table ......…or we can use Minitab!
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-15
Z TableZ Table
2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970
2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539
2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191
2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914
2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695
2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523
2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391
2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289
2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212
2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154
3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111
3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079
2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970
2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539
2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191
2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914
2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695
2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523
2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391
2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289
2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212
2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154
3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111
3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079
.00621or .621%or 6210 ppm
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-16
MinitabMinitab
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-17
MinitabMinitab
Cumulative Distribution Function
Normal with mean = 0 and standard deviation = 1.00000
x P( X < = x )
2.5000 0.9938
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-18
MinitabMinitab
1 - .9938 = .0062or .62%or 6200 ppm
Normal with mean = 0 and standard deviation = 1.00000
x P( X <= x)2.5000 0.9938
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-19
ExampleExample
Assume that a transmitter contains a chip that must operate at afrequency no higher than 1.030 GHz. (USL = 1.030 GHz)
Our process is producing chips having an average operating frequency of 0.995 GHz, with a standard deviation of 0.010 GHz.
What is the percent nonconforming for the chips produced by the process?
The mean of the process is 0.995 GHz
The standard deviation is .010 GHz
USL – X(bar)Z = —————
s
1.030 - .995= —————— = 3.5
.010
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-20
Z TableZ Table
3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111
3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079
3.2 0.00069 0.00066 0.00064 0.00062 0.00060 0.00058 0.00056
3.3 0.00048 0.00047 0.00045 0.00043 0.00042 0.00040 0.00039
3.4 0.00034 0.00032 0.00031 0.00030 0.00029 0.00028 0.00027
3.5 0.00023 0.00022 0.00022 0.00021 0.00020 0.00019 0.00019
3.6 0.00016 0.00015 0.00015 0.00014 0.00014 0.00013 0.00013
3.7 0.00011 0.00010 9.964E-05 9.577E-05 9.204E-05 8.844E-05 8.498E-05
3.8 7.237E-05 6.951E-05 6.675E-05 6.409E-05 6.154E-05 5.908E-05 5.671E-05
3.9 4.812E-05 4.617E-05 4.429E-05 4.249E-05 4.076E-05 3.909E-05 3.749E-05
4.0 3.169E-05 3.037E-05 2.911E-05 2.790E-05 2.674E-05 2.562E-05 2.455E-05
4.1 2.067E-05 1.979E-05 1.895E-05 1.815E-05 1.738E-05 1.663E-05 1.592E-05
2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970
2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539
2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191
2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914
2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695
2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523
2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391
2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289
2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212
2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154
3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111
3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079
.00023or .023%or 230 ppm
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-21
MinitabMinitab
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-22
MinitabMinitab
Cumulative Distribution Function
Normal with mean = 0 and standard deviation = 1.00000
x P( X <= x )
3.5000 0.9998
Remember – Minitab gives the area under the curve to the left of thespecified value (in this case, the proportion conforming). To obtainthe proportion nonconforming we must subtract the result from 1.0.
1.0 – 0.9998 = 0.0002 or .02% or 200 ppm
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-23
MinitabMinitab
x P( X <= x)3.5000 0.9998
Tail Probability = 1 Tail Probability = 1 -- .9998.9998
Tail Probability = .0002 or Tail Probability = .0002 or
200 ppm200 ppm
.9998 .0002
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-24
ZZ--Transform ExampleTransform Example
If we look at the following, we see that, for 150 Melt Index measurements:
Mean = 10
= 0.5
Let’s say:
LSL = 9
USL = 11
LSL
12111098
4 0 0
3 0 0
2 0 0
1 0 0
0
Population
Freq
uency
USL
Question: What is the estimated percentage of the distribution that will be out of spec?
Question: What is the estimated percentage of the distribution that will be out of spec?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-25
ZZ--TransformTransform
The task is to determine estimates of the proportion of the normal curve that is outside both the upper and lower specification limits. We do that by calculating Z-score for each spec limit.
We can now calculate the areas below the lower spec and above the upper spec using the normal probability function.
Z =(USL- x)
=0.5
= 2.00
UZ =(LSL- x)
=9 - 10
0.5
= -2.00
L
11 - 10
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-26
Minitab for Estimating Proportion Minitab for Estimating Proportion
outside Lower Specoutside Lower Spec
Go to Calc > Probability Distributions > NormalCalc > Probability Distributions > Normal
Mean and Sigma
Lower Spec Limit
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-27
MinitabMinitab
Cumulative Distribution Function
Normal with mean = 10.0000 and standard deviation = 0.500000
x P( X <= x )
9.0000 0.0228
Since this is the Lower Spec Limit and Minitab gives us the area to theleft of the specified value, this will be the proportion that is below thelower spec limit. It is not necessary to subtract this result from 1.0.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-28
LSL
12111098
4 0 0
3 0 0
2 0 0
1 0 0
0
Population
Freq
uency
USL
Minitab OutputMinitab Output
Cumulative Distribution Function
Normal with mean = 10.0000 and standard deviation = 0.500000
x P( X <= x)9.0000 0.0228
LSL
CAPABILITY STUDIES
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Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-29
Mean and Sigma
Upper Spec Limit
Minitab for Estimating Proportion Minitab for Estimating Proportion
outside Upper Specoutside Upper Spec
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-30
MinitabMinitab
Cumulative Distribution Function
Normal with mean = 10.0000 and standard deviation = 0.500000
x P( X <= x )
11.0000 0.9772
This is the Upper Spec Limit. Minitab provides us with the area underthe curve to the left of this value. We need the area to the right (abovethe spec limit), so we must subtract from 1.0.
1.0 – 0.9772 = 0.0228 or 2.28% or 22,800 ppm
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-31
MinitabMinitab
Cumulative Distribution Function
Normal with mean = 10.0000 and standard deviation = 0.500000
x P( X <= x)11.0000 0.9772
USL
LSL
12111098
4 0 0
3 0 0
2 0 0
1 0 0
0
Population
Freq
uency
USL
1- .9772 = .0228
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-32
MinitabMinitab
.0228
LSL USL
.0228
.0228 + .0228 = .0456 or 4.56% or 45,600 ppm
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-33
ProblemProblem
Suppose we have a process with:
Mean = 37.5
Standard Deviation = 7.83
Specification: 30 +/- 15
Determine the total estimated percentage outside specification for this process
How many standard deviations is the mean from the upper spec limit?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-34
Process CapabilityProcess Capability
How much of the process output is out of spec?
In the short term?
In the long term?
USLUSLLSLLSL
CAPABILITY STUDIES
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Page 18402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-35
Voice of The Customer
Voice of The Process
USLUSLLSLLSL
Process Capability RatiosProcess Capability Ratios
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-36
Centering – Put The Process On Target
Spread – Reduce The Variation
LSL USL
DefectsDefects
Process Capability Process Capability -- The StrategyThe Strategy
CAPABILITY STUDIES
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Page 19402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-37
Statisticians Developed 2 key MetricsFor Measuring Capability
Statisticians Developed 2 key MetricsFor Measuring Capability
USLC
- LSL
6p
s
C Min(X-LSL
3
USL- X
3pk
s s, )
Process Capability RatiosProcess Capability Ratios
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-38
USLC
- LSL
6p
s
Total ToleranceC
Process Spreadp
Process Capability RatiosProcess Capability Ratios
CAPABILITY STUDIES
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Page 20402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-39
Measurement Error Effect on Measurement Error Effect on
Capability IndicesCapability Indices
Obs
pObs
s
LSLUSLC
6
22
MSActObs sss
226 MSAct
pObs
ss
LSLUSLC
We know that
Therefore:
where
What happens to Cp when the Measurement System Error increases?
CCpp
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-40
%R&R Guidelines%R&R Guidelines
Once %R&R has been calculated, you must determine which to target first:
Process variation, or
Measurement systems variation
Ask the question: is your measurement system capable for the process improvement work for which you’ll be using it?
You can assume that the measurement system is capable for use in process improvement activities when the following conditions exist for Observed Process Capability (CpObs) and %R&R:
If CpObs < 1.0 and %R&R < 50%
If 1.0 < CpObs < 1.5 and %R&R < 40%
If CpObs > 1.5 and %R&R < 30%
Note: See detailed graph to follow
CAPABILITY STUDIES
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Page 21402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-41
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
0.5
0
0.6
0
0.7
0
0.8
0
0.9
0
1.0
0
1.1
0
1.2
0
1.3
0
1.4
0
CpObs
Cp
Act
0%
10%
20%
30%
40%
50%
60%
70%
%R&R Effect on Capability%R&R Effect on Capability
Source: Larry B. Barrentine: Concepts for R&R Studies
Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-42
Which might need the most attention -
Measurement System or Process Improvement?
Process %R&R CpObs Which do we work on?
1 10% 0.5
2 40% 1.0
3 60% 1.5
4 70% 5.5
Process
Measurement system
*Process 4: Would improving %R&R really be worth the effort ?
Measurement system*
Either one
Process 2: May Benefit more by addressing Measurement system than Process improvements at this point.
%R&R vs. Capability%R&R vs. Capability
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 22402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-43
p3C=igmaS
LSL USL
xbar = 30 MHzs = 1
27 MHz 33 MHz
CUSL - LSL
6p
s
Cp = __________ Sigma = __________1.01.0 3.03.0
Cp vs. Sigma LevelCp vs. Sigma Level
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-44
CUSL - LSL
6p
s
Cp = __________
LSL USL
xbar = 30 MHzs = 1
29 MHz 31 MHz
0.330.33 1.01.0
p3C=igmaS
Sigma = __________
Cp vs. Sigma LevelCp vs. Sigma Level
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-45
Cp = __________
LSL USL
xbar = 30 MHzs = .333
28 MHz 32 MHz
2.02.0 6.06.0
p3C=igmaS
Sigma = __________
CUSL - LSL
6p
s
Cp vs. Sigma LevelCp vs. Sigma Level
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-46
LSL USL
28 MHz 32 MHz
xbar = 33 MHzs = .333
Cp = __________2.02.0 6.06.0
p3C=igmaS
Sigma = __________
CUSL - LSL
6p
s
Cp vs. Sigma LevelCp vs. Sigma Level
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 24402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-47
C Min(X-LSL
3
USL- X
3pk
s s, )
CX-LSL
3pL
s
USL- X
3sCpU
A Metric - To Take Into Account Process ShiftA Metric - To Take Into Account Process Shift
Process Capability RatiosProcess Capability Ratios
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-48
To include the effects of process centering, we know
Where and
Therefore,
Measurement Error Effect Measurement Error Effect
on Capability Indices on Capability Indices
Obs
Obs
Obs
Obs
pkObs
s
LSLXor
s
XUSLMINC
33
22
MSActObs sss MSActObs XXX
2222 33 MSAct
MSAct
MSAct
MSAct
pkObs
ss
LSLXXor
ss
XXUSLMinC
Be careful of the direction of the bias (the
sign of the XMS)
Be careful of the direction of the bias (the
sign of the XMS)
What happens to Cpk when the Measurement System Error increases?
Cpk
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-49
CpU = ___________CpL = ___________
Cpk = ____________
5.05.0 --1.01.0
--1.01.0
LSL USL
28 MHz 32 MHz
xbar = 33 MHzs = .333
CpL, Cpk, CpUCpL, Cpk, CpU
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-50
Can Cp and Cpk be equal in value ?
Can CpL and CpU be equal to each other ?
Can Cpk take on a negative value ?
What’s a controversial, but easy way of increasing Cp ?
Questions …Questions …
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 26402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-51
The challenge in business is obtaining accurate estimates of true Process Capability
Let’s take a look at an example - Minutes of Production Downtime
Is this process Capable? Is it Stable?
Spec Man went out and gatheredproduction downtime data for the last 30 days
The customer’s (Plt. manager) specification limits :No more than 40 minutes / day
Spec Man went out and gatheredproduction downtime data for the last 30 days
The customer’s (Plt. manager) specification limits :No more than 40 minutes / day
YOU MAKE THE CALL !
ShortShort--Term CapabilityTerm Capability
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-52
A Short-term Capability study covers a relatively short period of time (Days, Weeks) generally consisting of 30 to 50 data points. The actual number depends on the subject under study.
A Short-term Capability study covers a relatively short period of time (Days, Weeks) generally consisting of 30 to 50 data points. The actual number depends on the subject under study.
Is The ProcessIn Control ?
Is It Meeting theRequirements?
Is The ProcessIn Control ?
Is It Meeting theRequirements?
35302520151050
39
34
29
24
Observation Number
Indiv
idua
l Va
lue
X=30.60
3.0SL=37.36
-3.0SL=23.84
Short Term CapabilityShort Term Capability
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 27402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-53
Is this process Capable? Is it Stable?
Spec Man is excited, he declares “The process is meeting the downtime requirements!”
However, the ever-wise Belt isn’t ready to declare victory. She suggests Long-Term Validation of the data.
Dejected, Spec Man goes off to perform the long-term study.
Spec Man is excited, he declares “The process is meeting the downtime requirements!”
However, the ever-wise Belt isn’t ready to declare victory. She suggests Long-Term Validation of the data.
Dejected, Spec Man goes off to perform the long-term study.
YOU MAKE THE CALL !
LongLong--Term CapabilityTerm Capability
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-54
A Long-term capability study covers a relatively long period of time (Weeks, Months) generally consisting of 100-200 data points. Again, the actual amount depends
on the subject under study.
A Long-term capability study covers a relatively long period of time (Weeks, Months) generally consisting of 100-200 data points. Again, the actual amount depends
on the subject under study.
Is The ProcessIn Control ?
Is It Meeting theRequirements?
Is The ProcessIn Control ?
Is It Meeting theRequirements?
100500
50
40
30
20
Observation Number
Indiv
idua
l Va
lue
X=33.80
3.0SL=47.12
-3.0SL=20.49
Long Term CapabilityLong Term Capability
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 28402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-55
What Does This Tell Us About The Way We Look At Data ?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-56
LSLLSL
Short-Term Capability
Long-TermCapability
Over time, a process tends to shift by approximately 1.5Over time, a process tends to shift by approximately 1.5
Short-Term Capability
USLUSL
The Dynamic ProcessThe Dynamic Process
Short-Term Capability
Short-Term Capability
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 29402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-57
Back to the Process Improvement PlanBack to the Process Improvement Plan
The 6 Sigma Methodology calls for performing a Short-term Capability study during the Measurement Phase to establish the process baseline
Minitab has many tools that will help you in this areaCapability Sixpack, Capability Analysis, Capability Analysis (attribute data)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-58
Capability Analysis Capability 6 Pack
Subgrouped data
Individual Samples
Capability ToolsCapability Tools
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 30402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-59
Pooled Standard DeviationPooled Standard Deviation
This is the default option for Minitab when subgroups are specified
This is a weighted average of the subgroup standard deviations
Caution: This option is used for best case short-term capability when rational subgroups are used
The results using this option can lead to misleading information about capability
This option is a good method for measuring “instantaneous” capability and may be used to establish process entitlement (Cp & Cpk)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-60
Average Moving RangeAverage Moving Range
This option for estimating the Standard Deviation should be used when you have independent data points
This is typical of a process where:
Each item is measured only once, and
Items produced ‘near’ each other are not reflective of each other
Example:
• Each refrigerator is measured for cool down rate
• Two refrigerators manufactured next to each other can not predict the other’s cool down rate
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 31402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-61
Capability vs. PerformanceCapability vs. Performance
5040302010
14
13
12
11
10
Index
CO
2-S
hrt
CO2 Levels for 55 Time Points
Process Performance: Total Variation including shifts and
drifts (Pp & Ppk)
Process Performance: Total Variation including shifts and
drifts (Pp & Ppk)
Capability: Only random or short term variability
(Cp & Cpk)
Capability: Only random or short term variability
(Cp & Cpk)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-62
Performance vs. CapabilityPerformance vs. Capability
These data show that the process, if well controlled can perform much better than it currently is.
5040302010
14
13
12
11
10
Index
CO
2-S
hrt
CO2 Levels for 55 Time Points
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 32402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-63
Process IndicesProcess Indices
We will discuss indexes about process performance and process capability
Process capability is the “Potential” of the process (Cpand Cpk)
Process performance is the processes “Real” performance (Pp and Ppk)
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-64
Is it Normal ?
Is ItNormal ?
How does theprocess variationcompare to thespec limits ?
Is it in control ?
What do the last 25 groups look like ?
Is it in control ?
Capability SixpackCapability Sixpack
What are the resultsfor Cpk and Ppk ?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 33402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-65
Capability Sixpack Capability Sixpack
What’s happening here ? ___________________
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-66
WARNING!!!WARNING!!!
Statistical Assumptions Made In Capability Studies
1. Data Comes From A Stable ProcessData Comes From A Stable Process
If Not, work towards getting the process in control
Don’t despair, you can still make some assumptions about your process in the mean time
2. Has A Normal DistributionHas A Normal Distribution
If Not, transform it (ask the instructor)
If Items #1 and #2 aren’t met, results will be misleading
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 34402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-67
Capability / Performance IndicesCapability / Performance Indices
Minitab can give both Short-term and Long-term process capability data for the same data set
Use the file DM CAPABILITY.MPJDM CAPABILITY.MPJ, worksheet
CARBAT2.MTWCARBAT2.MTW
Fill in the dialog boxes as follows
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-68
2
Minitab Capability StudyMinitab Capability Study
2.
3.
4.
1.
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 35402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-69
Capability Results for CO2Capability Results for CO2--LongLong
15.614.413.212.010.89.68.4
LSL USL
Process Data
Sample N 155
StDev (Within) 0.51407
StDev (O v erall) 0.84298
LSL 8.00000
Target *
USL 16.00000
Sample Mean 12.64258
Potential (Within) C apability
C C pk 3.02
O v erall C apability
Pp 1.84
PPL 2.14
PPU 1.55
Ppk
C p
1.55
C pm *
3.02
C PL 3.51
C PU 2.54
C pk 2.54
O bserv ed Performance
PPM < LSL 0.00
PPM > USL 0.00
PPM Total 0.00
Exp. Within Performance
PPM < LSL 0.00
PPM > USL 0.00
PPM Total 0.00
Exp. O v erall Performance
PPM < LSL 0.02
PPM > USL 34.05
PPM Total 34.07
Within
Overall
Process Capability of CO2-Long
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-70
Computing the Standard DeviationComputing the Standard Deviation
)1(
2
1
n
xx
RM
n
i
ii
1
2
1
n
xx
s
n
i
i
overall
Cp and Cpk
Pp and Ppk
128.1
* RMswithin
* For subgroup size = 1
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 36402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-71
Capability Study ResultsCapability Study Results
Notice that both Cp / Cpk and Pp / Ppk values are given
The data shows that the Capability of the process (Cpk) is 2.18 and the Entitlement (Cp) is 2.59
This shows that if the process were under good control it could be a world class performer >6 !!
The data also shows the Performance of the process (Ppk)is reasonably capable with a value of 1.33
But, does this tell the entire story?
Perform the analysis on the same data using the Capability Sixpack (Normal) option in Minitab. Do you see any
problems that may need resolution?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-72
Process Performance and Capability Process Performance and Capability
The Ppk can closely approach the Cp when
The Customer specifications truly reflect customer requirements
The process in under statistical control
The data approximate the normal distribution
The Cp is like a benchmark or entitlement
The sigma for capability is driven primarily by random error
We would like Ppk to be very close to Cp
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 37402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-73
The Idea of Rational SubgroupsThe Idea of Rational Subgroups
There is another way of establishing the sigma for capability and that is through rational subgroups
Goal:
To establish a sampling window small enough to exclude systematic non-random influences (special causes)
Intended Result:
Data exhibiting only common cause variation withingroups of n items and special cause (if it exists) variation between groups
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-74
Rational Subgroups Rational Subgroups -- ImplicationsImplications
If done correctly, averaged (Pooled) sigma’s from the subgroups estimate “best case” process capability of the current process
If there is a big difference between the pooled standard deviation and the total
standard deviation, then either the process mean, or the process sigma is changing
over time
If there is a big difference between the pooled standard deviation and the total
standard deviation, then either the process mean, or the process sigma is changing
over time
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 38402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-75
An Example of Rational SubgroupsAn Example of Rational Subgroups
Open the worksheet RATIONAL SUBGROUPS.MTWRATIONAL SUBGROUPS.MTW
Plot the data using the Time Series plot
Click on Graph > Time Series Plot and fill in the information asshown below.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-76
An Example of Rational SubgroupsAn Example of Rational Subgroups
How does the ‘Within Groups’ Sigma relate to the Total Sigma?
Hint: You can run the Descriptive Stat procedure twice – once for the total data set and then use the BY function with Shift as the BY variable.
How does the ‘Within Groups’ Sigma relate to the Total Sigma?
Hint: You can run the Descriptive Stat procedure twice – once for the total data set and then use the BY function with Shift as the BY variable.
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 39402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-77
Descriptive StatisticsDescriptive Statistics
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-78
Results of Descriptive StatisticsResults of Descriptive Statistics
What implication does this have in studying process capability? What Sigma represents the “real” process capability? What
Sigma represents the potential process capability?
What implication does this have in studying process capability? What Sigma represents the “real” process capability? What
Sigma represents the potential process capability?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 40402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-79
Components of Variation andComponents of Variation and
Rational SubgroupsRational Subgroups
2g
1j
n
1=i
jij
g
1j=
2
j
2g
1j=
n
1=i
ij )X(X)X-X(n)XX(
Total Sum-of Squares = Between Grp SS + Within Grp SS
7654321023222120191817161514131211109876543210
3.5
2.5
1.5
Hour
Outp
ut
Demonstation of Rational Subgroups
Shift is the Grouping Variable
Mean Shift Variability
Pooled Within Grps Variability
Total Variability
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-80
Components of Variation andComponents of Variation and
Rational SubgroupsRational Subgroups
CapabilityCapability RandomRandomSpecial CauseSpecial Cause
7654321023222120191817161514131211109876543210
3.5
2.5
1.5
Hour
Outp
ut
Demonstation of Rational Subgroups
Shift is the Grouping Variable
2g
1j
n
1=i
jij
g
1j=
2
j
2g
1j=
n
1=i
ij )X(X)X-X(n)XX(
Total Sum-of Squares = Between Grp SS + Within Grp SS
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 41402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-81
Visualizing the ComponentsVisualizing the Components
LSLLSL
Over time, a process tends to shift by approximately 1.5Over time, a process tends to shift by approximately 1.5
USLUSL
2g
1j
n
1=i
jij )X(X
2g
1j=
n
1=i
ij )XX(
g
1j=
2
j )X-X(n
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-82
Exercises in DiagnosticsExercises in DiagnosticsNow let’s look at 4 long-term capability studies to practice what we’ve learned
Use worksheets Diag 1.MTWDiag 1.MTW, Diag 2.MTWDiag 2.MTW, Diag 3.MTWDiag 3.MTW,
Diag 4.MTWDiag 4.MTW in DM CAPABILITY.MPJDM CAPABILITY.MPJ
Process Target: 70
Process USL: 100
Process LSL: 40
Use the data sets and complete the exercise sheets on the following pages for each
Each data set represents a long term capability study using 150 data points
Use the diagnostics from the Capability Analysis (Normal) and the Capability Six Pack (Normal) to set a course of action
Use the diagnostics from the Capability Analysis (Normal) and the Capability Six Pack (Normal) to set a course of action
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 42402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-83
Diag 1 WorksheetDiag 1 Worksheet
Cp Cpk
Pp Ppk
Sigma: Capability
Sigma: Performance
Normal ?
In Statistical Control ?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-84
Diag 2 WorksheetDiag 2 Worksheet
Cp Cpk
Pp Ppk
Sigma: Capability
Sigma: Performance
Normal ?
In Statistical Control ?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 43402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-85
Diag 3 WorksheetDiag 3 Worksheet
Cp Cpk
Pp Ppk
Sigma: Capability
Sigma: Performance
Normal ?
In Statistical Control ?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-86
Diag 4 WorksheetDiag 4 Worksheet
Cp Cpk
Pp Ppk
Sigma: Capability
Sigma: Performance
Normal ?
In Statistical Control ?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 44402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-87
Capability Summary SheetCapability Summary SheetMany times you will be interested in the capability of more than one Key Output or Key Input Variable. Use the Capability Summary Sheet to track the status on the variables of interest. This is found in the Excel spreadsheet: Capability Summary Capability Summary
Sheet.xlsSheet.xls.
Customer
Requirement
(Output Variable)
Measurement
Technique
%R&R or P/T
Ratio
Upper
Spec
Limit
Target
Lower
Spec
Limit
Cp CpkSample
SizeDate Actions
Key Process Output Variable
Capability Status Sheet
Customer
Requirement
(Output Variable)
Measurement
Technique
%R&R or
P/T Ratio
Upper
Spec
Limit
Target
Lower
Spec
Limit
Cp CpkSample
SizeDate Status
Fire RetardencyUL 700 25% 3 1.5 na
No Data
Available
Selvage Edge
Consistency
On-line Physical
Measurement15% 4.5 4 3.5 1.15 0.85 50 Sep-95
Improvement
Plan in Place
Membrane
Stability
Oven Test None 0.75 0.5 na 1.1 0.65 25 Aug-95
Measurement
Study
Scheduled
Exam
ple
This example shows lack of capability data for the first KPOV, lack of good capability for the second KPOV and lack of measurement
systems evaluation for the third KPOV.
This example shows lack of capability data for the first KPOV, lack of good capability for the second KPOV and lack of measurement
systems evaluation for the third KPOV.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-88
Types of Capability IndexesTypes of Capability Indexes
Instantaneous Capability:
Process Capability over an extremely short period of time
This should represent the very best performance a process is capable of over a short time
This should be a close estimate of process entitlement
Can be estimated using the “best run” of a short-term or long-term study
Short-Term Capability:
Capability study based on 30-50 data points
Usually equal to or greater than long-term capability
Long-Term Capability:
Capability study based on a large number of data points
Best estimate of true process capability
Diagnostics can be made from this data
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 45402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-89
Goal SettingGoal Setting
This is a generic sequence of process improvement:
Near Term Goal: Move the Ppk to Pp (Center the process)
Mid-Term Goal: Move the Pp to Cpk (Reduce Variation)
Long-Term Goal: Move Cpk to Cp (Random Variation)
Six Sigma Process
Cp = 2.00
Ppk = 1.5
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-90
Questions to Ask About Output VariablesQuestions to Ask About Output Variables
Is the measurement system adequate?
For a Key Process Output Variable is:
Nominal Best,
Larger Better
Smaller Better?
Is the concern for
Process Centering
Process Variation
or both?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 46402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-91
Questions to Ask About Output VariablesQuestions to Ask About Output Variables
What is the baseline for the process variable?
Mean
Sigma
Is the Output currently in statistical control?
Is the Output affected by time?
Are there multiple responses you’re concerned with?
What’s the priority?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-92
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 47402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-93
What Type of Data
Do You Have ?
What Type of Data
Do You Have ?
Variables DataVariables DataAttribute DataAttribute Data
Collect Data
On Process
Collect Data
On ProcessCollect Data
On Process
Collect Data
On Process
Analyze Data
In Minitab
Analyze Data
In MinitabAnalyze Data
In Excel
Analyze Data
In Excel
State Capability
DPU, PPM
Cp, Cpk, Pp, Ppk
State Capability
DPU, PPM
Cp, Cpk, Pp, Ppk
State Capability
DPU, PPM
State Capability
DPU, PPM
Capability Roadmap Capability Roadmap -- 30,000 Ft. View30,000 Ft. View
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-94
SummarySummary
You should be able to set short, intermediate and long-term goals based on capability data
These tools will be used extensively in the Measurement, Improvement, and Control Phases of process improvement !!
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 48402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-95
Capability StudiesCapability Studies
withwith
Attribute DataAttribute Data
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-96
Attribute Capability StudiesAttribute Capability Studies
Use the Attribute Sigma Calculator (Excel spreadsheet Attribute Sigma Calculator.xlsAttribute Sigma Calculator.xls)
Use the Minitab functions –
Capability Analysis (Binomial)Capability Analysis (Binomial)
Capability Analysis (Poisson)Capability Analysis (Poisson)
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 49402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-97
Calculate Defects per Unit (DPU):
ProducedUnitsof#Total
Defectsof#Total=DPU
Go to a Sigma Chart and Estimate the Sigma Levelor
Use the Attribute Sigma Calculator
Calculate Defects per Million Opportunities (DPMO):
000,000,1Unit / Oppty
UnitPerDefects=DPMO x
Calculating the Product SigmaCalculating the Product Sigma--LevelLevel
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-98
Attribute Sigma Calculator.XLSAttribute Sigma Calculator.XLS
Capability Tool Capability Tool -- Attribute DataAttribute Data
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 50402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-99
Attribute Exercises: Attribute Exercises: ManufacturingManufacturing
#1 Defective Bolts
The plant has just completed a run of 40,000 bolts. 100 of the bolts were found to be defective.
#2 Defective Lots
During March, 12,412 Type A motors were assembled into dryers. Each motor has 3 opportunities to be produced correctly(1 opportunity each for Power, Vibration, and Overall Weight). During the month, 200 defective occurences were observed.
#3 Defective Batches
The plant has just completed a run of 400 refrigerators (134 parts per refrigerator). During the build, 12,312 defective (misassembled or damaged) parts were observed.
DPU = 0.0025 DPMO = 2500 Sigma = 2.81
DPU = 0.0161 DPMO = 5371 Sigma = 2.55
DPU = 30.78 DPMO = 229,701 Sigma = 0.74
What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-100
#1 Defective CoilsThe plant has just completed a build of 1291 coils. 104 of the coils were found to be defective.
#2 Defective Sheets of SteelDuring March, 15,514 Type A sheets were manufactured. Each sheet has 5 opportunities to be manufactured in a defective manner (1 opportunity at each station). During the build, 203 defects were observed.
Attribute Exercises: Attribute Exercises: ManufacturingManufacturing
What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 51402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-101
#1 Defective Purchase OrdersDuring March, 764 purchase orders were placed. 321 of them had defects.
#2 Late ShipmentsFor 1997, 42,100 shipments of spare parts were made. 4,100 arrived late.
#3 Defective Purchase OrdersDuring March, 764 purchase orders were placed. On the forms, there are 8 locations in which to place information. 1234 locations had defective information.
Attribute Exercises: Attribute Exercises:
NonNon--ManufacturingManufacturing
What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-102
MinitabMinitab
Minitab will provide the same calculations asthe Attribute Sigma Calculator and it will provideadditional information, as well.
Minitab will provide –
A control chart of the inspection data, which will allowyou to determine if your process is in control.
A plot of the defect distribution or a binomial plot,which will allow you to test assumptions about the distribution of the defectives.
A plot of the cumulative percent defective, which willtell you if you have taken enough samples and if yourprocess has stabilized.
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 52402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-103
MinitabMinitab
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-104
MinitabMinitab
Open the Minitab project file Attribute Capability.mpjAttribute Capability.mpj
The active worksheet should be Binomial1.MTWBinomial1.MTW. If this is not the active worksheet click on Window > Window >
Binomial1Binomial1.
Perform an attribute capability study on the data in the worksheet using the Capability Analysis (Binomial)Capability Analysis (Binomial)function of Minitab.
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 53402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-105
MinitabMinitab
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-106
MinitabMinitab
Is the process
stable and
in control?
Is the process
stable and
in control?
Have you collected
enough samples to
allow the %Defective
to stabilize?
Have you collected
enough samples to
allow the %Defective
to stabilize?
What are the
capability estimates?
What are the
capability estimates?
Was the sampling
from a binomial
distribution?
Was the sampling
from a binomial
distribution?
What is the
distribution of
the defectives
in the samples?
What is the
distribution of
the defectives
in the samples?
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 54402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-107
MinitabMinitab
Change the active worksheet to Binomial2.MTWand perform the same analysis on this data.
Change the active worksheet to Binomial2.MTWand perform the same analysis on this data.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-108
MinitabMinitab
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 55402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-109
MinitabMinitab
Change the active worksheet to Poisson.MTW and performa capability analysis on the data.
Change the active worksheet to Poisson.MTW and performa capability analysis on the data.
The Poisson distribution is associated with the number ofdefects observed in an item where the item occupies aspecified amount of time or space. It is not necessary forthe size of the item inspected to be constant. The size ofthe item may vary.
Examples –
The number of breaks in a piece of wire. The pieces of wire
inspected may be of different lengths.The number of scratches on the surface an appliance.
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-110
MinitabMinitab
Change the active worksheet to Poisson.MTW and performa capability analysis on the data.
Change the active worksheet to Poisson.MTW and performa capability analysis on the data.
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 56402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-111
MinitabMinitab
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-112
QuestionsQuestions
Does the performance meet capability?
If performance meets capability there is no reason for looking for process control techniques
If performance is not meeting capability, then there may be opportunities for SPC and APC
Does capability meet the customers needs?
If capability meets customer requirements, then there is no need to work on the process
If capability is not meeting customer requirements, then a process change is required.
CAPABILITY STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 57402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB112-113
Does Performance meet Capability?Does Performance meet Capability?
No (Ppk < Cp) Yes (Ppk = Cp)D
oe
s C
ap
ab
ilit
y m
ee
t C
us
tom
er
Need
s?
Do
es
Ca
pa
bil
ity
me
et
Cu
sto
me
r N
eed
s?
No (Cp < Goal)
Yes (Cp >> Goal)
ChangeProcess
(reduce variability)
Improve Control
ChangeProcess
(reduce variability)
Improve Control
LittleOpportunity
forImprovement
Performance and Capability MatrixPerformance and Capability Matrix
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-1
Cause and Cause and
Effects MatrixEffects Matrix
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-2
ObjectivesObjectives
To link the Cause and Effects (C&E) Matrix to the Process Map
To review the steps to create the C&E Matrix
To link the C&E Matrix to further steps in the DMAIC Process Improvement
To create a C&E Matrix
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-3
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-4
Cause and Effects MatrixCause and Effects Matrix
This is a simplified QFD (Quality Function Deployment) matrix to emphasize the importance of understanding the customer requirements
Relates the Key Inputs to the Key Outputs (Customer Requirements) using the process map as the primary information source
Key Outputs are scored as to importance to the customer
Key Inputs are scored as to relationship to key outputs
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-5
Step 1: List Key OutputsStep 1: List Key Outputs
Translate Into… Requirements
VOC
VOB
Vendor
Customer
Shipper
S
Inventory
Information
Pickers
Packers
Shipping supplies
I
Customer places order
Process Order
Fill Order
Pack Order
Ship Order
P
Shipment
Sales $
Inventory Reduction
O
Customer placing order
C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-6
Cause and Effects Matrix OutputsCause and Effects Matrix Outputs
Pareto of Key Inputs to evaluate in the FMEA and control plans
Input into the Capability Study in the Measurement Phase
Input into the initial evaluation of the Process Control Plan
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-7
Cause & Effect Matrix StepsCause & Effect Matrix Steps
Identify key customer requirements (Outputs) from process map
Rank order and assign priority factor to each Output (usually on a 1 to 10 scale)
Identify all process steps and materials (Inputs) from the Process Map
Evaluate correlation of each input to each output
low score: changes in the input variable (amount, quality, etc.) have small effect on output variable
high score: changes in the input variable can greatly affect the output variable
Cross multiply correlation values with priority factors and sum for each Input
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-8
ExamplesExamples
Order Entry
PhoneFAXEmail
PriceAvailabilityConfirmation of
OrderPromise DateOrder number
Inputs Outputs
NonNon-- ManufacturingManufacturing
Manufacturing
Stamping
SOPDiesSteel MaterialEquipmentDie MaintenanceShiftOperator
FlatnessUndamaged PartsDimensionalityTensile StrengthBurr freeCycle Time
Inputs Outputs
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-9
Process Mapping ExamplesProcess Mapping Examples
ManufacturingOutputs
• Cycle Time
• Material properties
Feed
Feed material from roll
Cutting
Cut material to length
• Cycle Time
• Correct length
• Burr free
Stamping
Stamp piece to size
• Cycle Time
• Correct part
• Burr free
• Correct dimensions
• Tensilestrength
Drawing
Draw features
Outputs
• Cycle Time
• Correct part
• Correct dimensions
• Tensilestrength
Punching
Punch out features
• Cycle Time
• Correct part
• Burr free
• Correct dimensions
• Tensilestrength
Cleaning
Clean metal surface
• Cycle Time
• Clean surface
• Residue free
• Gear speed
• Gear wear
• Material lot
• Material properties
Inputs Type
C
U
U
U
• Shear speed
• Shear wear
• Material properties
• Clamping force
• Shear force
C
U
U
C
U
• Die wear
• Material properties
• Ram force
• Ram speed
• Die number
• Die hardness
U
U
C
C
C
C
• Drawing speed
• Material properties
• Die number
Inputs Type
C
U
C
• Punch speed
• Punch wear
• Material properties
• Clamping force
• Ram force
C
U
U
C
U
• Solvent type
• Solvent purity
• Solvent age
• Surface roughness
• Contaminant level
• Humidity
• Temperature
C
U
U
U
U
U
U
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-10
Rating of
Importance to
Customer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Total
Process Step Process Input
1 02 0
3 0
4 05 0
6 0
7 08 0
Cause & Effect Matrix FormCause & Effect Matrix Form
Cause and Effect Matrix.XLSCause and Effect Matrix.XLS
1. List Key Outputs
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-11
Rating of Importance to
Customer
1 2 3 4 5 6
Fla
tness
Un
dam
ag
ed
Part
s
Dim
en
sio
nality
Ten
sile
Str
en
gth
Bu
rr F
ree
Cycle
Tim
e
Total
Process
StepProcess Input
ExampleExample
1. List Key Outputs
1. List Key Outputs
The Outputs are defined in Step 1 of Process MappingThe Outputs are defined in Step 1 of Process Mapping
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-12
Rating of
Importance to
Customer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Total
Process Step Process Input
1 02 0
3 0
4 05 0
6 0
7 08 0
Cause & Effect Matrix FormCause & Effect Matrix Form
2. Rank Outputs as to
Customer importance
2. Rank Outputs as to
Customer importance
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-13
Rating of Importance to
Customer7 9 9 4 6 8
1 2 3 4 5 6
Fla
tness
Un
dam
ag
ed
Part
s
Dim
en
sio
nality
Ten
sile
Str
en
gth
Bu
rr F
ree
Cycle
Tim
e
Total
Process
StepProcess Input
ExampleExample
This step should include Marketing, Product Development and Manufacturing. If possible, include customer representatives.
This step should include Marketing, Product Development and Manufacturing. If possible, include customer representatives.
2. Rank Outputs as to
Customer importance
2. Rank Outputs as to
Customer importance
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-14
Rating of
Importance to
Customer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Total
Process Step Process Input
1 02 0
3 0
4 05 0
6 0
7 08 0
Cause & Effect Matrix FormCause & Effect Matrix Form
3. List Key Inputs by
Process Step
3. List Key Inputs by
Process Step
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-15
ExampleExampleRating of Importance to
Customer7 9 9 4 6 8
1 2 3 4 5 6
Fla
tne
ss
Un
da
ma
ge
d
Pa
rts
Dim
en
sio
na
lity
Te
ns
ile
S
tre
ng
th
Bu
rr F
ree
Cy
cle
Tim
e
Total
Process
StepProcess Input
Feed Gear SpeedFeed Gear WearFeed Material lotFeed Material properties
Cutting Shear speedCutting Shear wearCutting Material propertiesCutting Clamping force
This step uses the Process Map inputs directly. Notice the Process Inputs follow the Process map step-by-step.
This step uses the Process Map inputs directly. Notice the Process Inputs follow the Process map step-by-step.
3. List Key Inputs by
Process Step
3. List Key Inputs by
Process Step
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-16
Rating of
Importance to
Customer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Total
Process Step Process Input
1 02 0
3 0
4 05 0
6 0
7 08 0
Cause & Effect Matrix FormCause & Effect Matrix Form
4. Relate Inputs to Outputs
4. Relate Inputs to Outputs
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-17
Relating Inputs to Customer RequirementsRelating Inputs to Customer Requirements
You are ready to relate the customer requirements to the processinput variables
Correlational scores: No more than 3 levels
1, 3 and 5
1, 3 and 9
Assignment of the scoring takes the most time
To avoid this, spell out the criteria for each score:
0 = No Correlation
1 = The process input only remotely affects the customer requirement
3 = The Process input has a moderate effect on the customer requirement
9 = The Process input has a direct and strong effect on the customer requirement
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-18
Rating of Importance to
Customer7 9 9 4 6 8
1 2 3 4 5 6
Fla
tne
ss
Un
da
ma
ge
d
Pa
rts
Dim
en
sio
na
lity
Te
ns
ile
Str
en
gth
Bu
rr F
ree
Cy
cle
Tim
e
Total
Process
StepProcess Input
Feed Gear Speed 3 9 9 9 1 9Feed Gear Wear 0 3 9 1 9 9Feed Material lot 9 1 9 9 0 0Feed Material properties 9 1 9 9 0 0
Cutting Shear speed 9 9 9 1 9 9Cutting Shear wear 3 9 3 0 9 3Cutting Material properties 9 1 1 9 9 9Cutting Clamping force 9 3 9 0 9 1Cutting Shear force 9 1 9 0 9 3
Stamping Die wear 9 3 9 0 9 0
ExampleExample
This is a subjective estimate of how influential the Input Variables are on the Output Variables
This is a subjective estimate of how influential the Input Variables are on the Output Variables
4. Relate Inputs to Outputs
4. Relate Inputs to Outputs
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-19
Rating of
Importance to
Customer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Re
qu
ire
me
nt
Total
Process Step Process Input
1 02 0
3 0
4 05 0
6 0
7 08 0
Cause & Effect Matrix FormCause & Effect Matrix Form
Sum of Rating x Correlational Score values for all Requirements
Sum of Rating x Correlational Score values for all Requirements
5. Cross-multiply and
prioritize
5. Cross-multiply and
prioritize
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-20
Rating of Importance to
Customer7 9 9 4 6 8
1 2 3 4 5 6
Fla
tne
ss
Un
da
ma
ge
d
Pa
rts
Dim
en
sio
na
lity
Te
ns
ile
Str
en
gth
Bu
rr F
ree
Cy
cle
Tim
e
Total
Process
StepProcess Input
Feed Gear Speed 3 9 9 9 1 9 297Feed Gear Wear 0 3 9 1 9 9 238Feed Material lot 9 1 9 9 0 0 189Feed Material properties 9 1 9 9 0 0 189
Cutting Shear speed 9 9 9 1 9 9 355Cutting Shear wear 3 9 3 0 9 3 207Cutting Material properties 9 1 1 9 9 9 243Cutting Clamping force 9 3 9 0 9 1 233Cutting Shear force 9 1 9 0 9 3 231
Stamping Die wear 9 3 9 0 9 0 225
ExampleExample
We now start getting a feel for which variables are most important to explaining variation in the outputs
We now start getting a feel for which variables are most important to explaining variation in the outputs
5. Cross-multiply and
prioritize
5. Cross-multiply and
prioritize
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-21
Process
StepProcess Input
Cutting Shear speed 9 9 9 1 9 9 355Punching Punch speed 9 9 9 0 9 9 351Drawing Drawing speed 9 9 9 9 0 9 333
Stamping Ram speed 9 9 9 3 1 9 315Feed Gear Speed 3 9 9 9 1 9 297
Punching Punch wear 9 3 9 0 9 3 249Cutting Material properties 9 1 1 9 9 9 243
We have sorted on the cross-multiplied numbers and find that the Input variables in the box above are the most
important
We can now evaluate the control plans for these Input Variables
We have sorted on the cross-multiplied numbers and find that the Input variables in the box above are the most
important
We can now evaluate the control plans for these Input Variables
ExampleExample
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-22
NonNon--Manufacturing ExampleManufacturing Example
Rating of Importance
to Customer
9 7 10 3 3
1 2 3 4 5
Process Step Process Inputs Ava
ilib
ility
Pri
cin
g
Pro
mis
e D
ate
Ord
er
co
nfir
ma
tion
Ord
er
Nu
mb
er
Total
11 Order Entry
Computer entry
screens 9 9 9 9 3 270
8Internal Info Order worksheet form 9 9 9 1 9 264
3 Answer Phone Answering procedure 9 9 9 9 0 261
10 Order Entry Order worksheet 9 9 9 0 9 261
6 Internal Info Order info 9 9 9 1 3 246
17 Order ConfirmationProduction contact info 9 0 9 9 0 198
1 Answer Phone Info from customer 9 3 9 1 0 195
7 Internal Info Plant loading info 9 3 9 0 0 1925 Internal Info Cross ref for P/N 3 9 9 1 0 183
12Order Entry
Lead time information from mfg 9 1 9 0 1 181
15 Order Confirmation Production schedule 9 0 9 1 0 174
18Order Confirmation
Confirmation procedure 0 0 9 9 9 144
14Order Confirmation
Printed confirmation sheet 3 3 3 9 9 132
13 Order Entry Shipment method 1 3 9 0 0 120
16Order Confirmation Customer contact info 0 0 9 9 1 120
9 Internal Info Pricing algorithm 0 9 0 0 0 63
2 Answer Phone Greeting script 1 1 1 3 0 35
4 Answer Phone Telephone system 0 0 0 1 0 3
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-23
Focused ApproachFocused Approach
Phase I
Place the Outputs across the top of the matrix and rank
Place the process steps down the side of the matrix
Correlate process step to Outputs
Pareto the process steps
Phase II
Start a new C&E Matrix with the Inputs from the top three or four process steps
Recommended when first starting a project
Focuses the efforts and gives the team a feeling that they’re working on the important process steps first
Gives you a running start at the FMEA and preliminary Control Plan Analysis
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-24
Focused Approach, cont.Focused Approach, cont.
Place the Outputs across the top of the matrix and rank
Place Inputs down the side of the matrix starting with the first process step and moving to the last
This approach is okay for small process with relatively few steps
Should only be used for processes with a relatively small number of steps and Inputs
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-25
Rating of
Importance to Customer
9 9 7 10 10 9 3 2 6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Process Inputs Ge
l Tim
e
Vis
co
sity
Cle
an
line
ss
Co
lor
Ho
mo
ge
ne
ity
Co
ns
iste
ncy
Dig
ets
Tim
e
Te
mp
era
ture
So
lids
Total
1Scales Accuracy
9 8 2 1 1 9 1 1 8 321
2Preheating DICY TK
1 1 1 1 1 1 1 1 1 65
3DMF Load Accuracy
3 8 1 1 1 8 1 3 8 255
4DMF
Cleanliness1 1 4 2 1 2 1 1 1 105
5DMF Raw Materials
1 1 1 1 1 2 1 1 1 74
6DICY Load Accuracy
9 7 1 1 1 9 1 1 2 269
7DICY Envir.
Factors8 5 3 1 1 8 1 1 2 247
8DICY Raw Materials
8 5 1 1 1 9 1 1 2 242
9DICY Mixer Speecd
1 1 1 1 7 1 1 1 1 125
C&E Matrix
FMEA
Process or Product Name:
Prepared by:
Responsible: FMEA Date (Orig) ______________ (Rev) ___
Process
Step/Part
Number Potential Failure Mode Potential Failure Effects
S
E
V Potential Causes
O
C
C Current Controls
D
E
T
R
P
N
Spin Draw Process
Fiber Breakouts Undersized package, High SD panel-hours lost 2
Dirty Spinneret8
Visual Detection of Wraps and broken Filaments 9 144
5Filament motion
2Visual Sight-glass
8 80
8Polymer defects
2Fuzzball Light
9 144
0
Process/Product
Failure Modes and Effects Analysis
(FMEA)
Key Inputs are explored
Capability Summary
Customer Requirement
(Output Variable)
Measurement
Technique
%R&R or P/T
Ratio
Upper
Spec
Limit
Target
Lower
Spec
Limit
Cp CpkSample
SizeDate Actions
Gel Time
Viscosity
Cleanliness
Color
Homogeneity
Consistency
Digets Time
Temperature
Solids
Key Process Output Variable
Capability Status Sheet
The Key Outputs are listed and evaluated
Outp
uts
Control Plan SummaryProduct: Core Team: Date (Orig):
Key Contact:Phone: Date (Rev):
Process Process Step Input Output
Process
Specification (LSL,
USL, Target)
Cpk /Date Measurement
Technique
%R&R
P/T
Sample
Size
Sample
Frequency
Control
MethodReaction Plan
DICY Turn Steam on Scales
Accuracy
DMF Load DMF DMF Load Accuracy
DMF Load DMF DMF Cleanliness
DICY Load DICY DICY Envir. Factors
DICY Load DICY DICY Load Accuracy
DICY Load DICY DICY Raw Materials
DICY Load DICY DICY Mixer Speecd
DMF Load DMF DMF Raw Materials
DICY Turn Steam on Preheating DICY TK
Control Plan
The Key Inputs are evaluated
Inputs
Linking the C&E Matrix to Other ToolsLinking the C&E Matrix to Other Tools
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-26
Next StepsNext Steps
Control Plan Review
Perform an initial assessment of the control plan for high ranked Inputs in the C&E matrix Pareto
Perform the same assessment for high ranked Outputs (customer requirements)
This helps identify “low hanging fruit” at the front end of a Process Improvement project
Capability Review
Review the Capability Summary for those Inputs ranked high in the C&E matrix Pareto
If there are blanks (unknown capability), review measurement systems and collect baseline data
FMEA
High ranked Inputs will be evaluated using the FMEA
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-27
Control Plan EvaluationControl Plan Evaluation
Standard Operating Procedures
Do they exist?
Are they understood?
Are they being followed?
Are they current?
Is operator certification performed?
Is there a process audit schedule?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-28
Control Plan EvaluationControl Plan Evaluation
Controllable Input Variables:
How are they monitored?
How often are they verified?
Are optimum target values and specifications known?
How much variation is there around the target value?
How consistent are they?
Other Questions:
How often is the Input Variable out of control?
Which Input Variables should have control charts?
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-29
Is it Controlled or Uncontrolled?Is it Controlled or Uncontrolled?
Is it Controllable or Uncontrollable? Is it Controllable or Uncontrollable?
Current System
Controlled UncontrolledP
ossib
le
Un
co
ntr
olla
ble
C
on
tro
llable
No short term solution
Long term –possibly implement
technology / advanced design
Validate with Measurement
System Analysis
Why?
Could validate it’s truly uncontrollable with DOE/Response
Surface Methods
OPPORTUNITY!
Put in control system
Label as U*
Label C or U Using Current System
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-30
SummarySummary
Linked the Cause and Effects (C&E) Matrix to the Process Map
Reviewed the steps to create the C&E Matrix
Linked the C&E Matrix to further steps in the DMAIC Process Improvement
Performed an exercise to create a C&E Matrix
CAUSE AND EFFECTS MATRIX
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB113-31
Coffee Making C&E Matrix ExerciseCoffee Making C&E Matrix Exercise
Objective:
Take 20 minutes to generate a C&E Matrix using your previous “Coffee Making” process map
Assumptions:
Customer requirements and associated Importance Ranking
Great taste, not bitter - 10
Hot, but not scalding – 8
Condiments - 8
Procedure:
Include all Input Variables
Controlled and Uncontrolled
Deliverables:
Determine ranking of Input Variables
Choose top 3 Input Variables for the next step
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB114-1
Failure Modes and Failure Modes and
Effects Analysis Effects Analysis
(FMEA)(FMEA)
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-2
ObjectivesObjectives
Provide insight to the uses of FMEA
Identification of risk sources
Define the different types of FMEA
To learn the steps in developing a Process FMEA
Create an FMEA
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-3
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-4
DefinitionDefinition -- FMEAFMEA
A structured approach to:
identifying the ways in which a product or process can fail
estimating the risk associated with specific modes of failure
prioritizing the actions that should be taken to reduce the risk
evaluating the design validation plan (Product) or the current control plan (Process)
Primary Directive: Identify ways the product or process can fail and eliminate or reduce the risk of failure
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-5
Where do Risks Come From?Where do Risks Come From?
VagueWorkmanship
Standards
Poor control plans & SOP’s
Raw Material Variation
Poorly developed Specification
LimitsMeasurement
Variation (Online and QC)
Machine Reliability
Potential Safety
HazardsUnclear Customer Expectations
D. H. Stamatis, FMEA:FMEA from Theory to Practice, Quality Press, 1995
Poor Process Capability
Cumulative Risk
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-6
Prevent causePrevent cause
Prevent cause from Prevent cause from
leading to modeleading to mode
Prevent mode from Prevent mode from
leading to effectleading to effect
Detect & remove Detect & remove
defective partsdefective parts
Concept Concept firmfirm
ProductionProductionprints completeprints complete
Initial Initial tooling builttooling built ProductionProduction
TIMETIME
Sa
vin
gs
Sa
vin
gs
The Opportunities to Reduce or Eliminate The Opportunities to Reduce or Eliminate
Failures Decrease with TimeFailures Decrease with Time
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-7
HistoryHistory
First used in the 1960’s in the Aerospace industry during the Apollo missions
In 1974 the Navy developed MIL-STD-1629 regarding the use of FMEA
In the late 1970’s, automotive applications driven by liability costs
Tyco Electronics Manual 402-29 describes the FMEA process in detail
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-8
Types of FMEATypes of FMEA
System - used to analyze complete systems and sub-systems in the early concept and design stages
Focuses on potential failure modes, caused by the design, associated with the functions of the system
Design - used to analyze product designs before they are released to production
Focuses on Product Function
Assumes that the product is properly manufactured to the specifications
Process - used to analyze manufacturing and assembly processes
Focuses on Process Inputs (what can go wrong in the manufacturing process)
Assumes that all components meet the design requirements
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-9
Role of Process FMEARole of Process FMEA
Key tool of process team to improve the process in a preemptive manner (before failures occur)
Used to prioritize resources to insure process improvement efforts are beneficial to customer
Used to document completion of projects
Should be a dynamic document, continually reviewed, amended, updated
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-10
Purposes of Process FMEAPurposes of Process FMEA
Analyzes new processes
Identifies deficiencies in the process control plan
Establishes the priority of actions
Evaluates the risk of process changes
Identifies potential variables to consider in Multi-vari and DOE studies
Guides the development of new processes
Helps set the stage for breakthrough
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-11
FMEA Inputs and OutputsFMEA Inputs and Outputs
Inputs
Process map
C&E matrix
Process history
Process technical procedures
Team experience and knowledge of the process
Outputs
List of actions to prevent causes or to detect failure modes
History of actions taken
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-12
FMEA TeamFMEA Team
Team approach is necessary
Responsible Belt leads the team
Recommended representatives:
Design
Practitioners / Operators / Supervisors
Quality
Reliability
Maintenance
Materials
Testing
Suppliers
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-13
Definition of TermsDefinition of Terms
Failure Mode
Effect
Cause
Current Controls
Severity, Occurrence, Detection
Risk Priority Number (RPN)
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-14
Failure ModeFailure Mode
Failure Mode - the way in which a specific process input fails. If the mode is not detected and either corrected or removed it willcause Effect to occur
Can be associated with a defect (in discrete manufacturing) or aprocess input variable that goes outside of specification
Anything that can be wrong with a process input or output is considered a Failure Mode
Examples
Temperature too high
Incorrect PO number
Surface contamination
Dropped call (customer service)
Stubbing with contact receptacle during mating
Paint too thin
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-15
EffectEffect
Failure Effect - impact on customer requirements -generally external customer focus, but can also include internal customers / downstream processes
Examples
Difficult to mate. High mating force.
Stubbing with contact receptacle during mating
Poor coveragePaint too thin
Customer dissatisfactionDropped call
Poor adhesionSurface contamination
Accounts Receivable traceability errors
Incorrect PO number
Paint cracksTemperature too high
EffectMode
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-16
CauseCauseCause
Sources of variation that allow the Failure Mode to become active
Identification of Causes should start with Failure Modes associated with the highest severity ratings
Examples
High solvent contentPaint too thin
Incorrect dimensions specifiedStubbing with contact receptacle during mating
Insufficient number of Customer Service representatives
Dropped call
Lubrication residueSurface contamination
Typographical errorIncorrect PO number
Thermocouple out of calibrationTemperature too high
CauseMode
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-17
Current ControlsCurrent Controls
Current Controls (Sometimes referred to as Methods of Verification)
Systematized methods/devices in place to prevent or detect Failure Modes
Three types of control methods
Type 1: Prevents the Cause or prevents the Cause from leading to the Failure Mode.
Type 2: Detects the Failure Mode in time to lead to corrective action before the Effect takes place.
Type 3: Detects the Effect of the Failure Mode after it has occurred.
Prevention consists of mistake proofing (Poka Yoke), automated control and set-up verifications
Detection consists of audits, checklists, inspection, laboratory testing, training, SOP’s, preventive maintenance, etc.
Which is more important to process improvement -- prevention or detection?
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-18
ControlsControls
FMEA ModelFMEA Model
EffectEffect
External customer or downstream process step
CauseCause
Failure Mode(Defect)
Failure Mode(Defect)
Process Step
Material or process input
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-19
The Dynamic Nature of the FMEAThe Dynamic Nature of the FMEA
Lossof
Nail
Lossof
Shoe
Lossof
Horse
Lossof
Rider
Lossof
Battle
Why Armies of Knights Lose Battles
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-20
FMEA ModelFMEA Model
ControlsControls
EffectEffect
External customer or downstream process step
CauseCause
Failure Mode(Defect)
Failure Mode(Defect)
Process Step
Material or process input
Prevention
Detection
Detection
Detection
Which is best case?Which is worst case?
Which is best case?Which is worst case?
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-21
Linking Failure Modes to EffectsLinking Failure Modes to Effects
Note that the relationship between the Failure Mode and the Effect is not always 1-to-1
Effect 1Effect 1
Effect 2Effect 2Failure Mode 1Failure Mode 1
Failure Mode 1Failure Mode 1
Effect 1Effect 1
Failure Mode 2Failure Mode 2and
Failure Mode 1Failure Mode 1
Failure Mode 2Failure Mode 2Effect 1Effect 1or
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-22
Risk Priority Number (RPN)Risk Priority Number (RPN)
The output of an FMEA is the Risk Priority Number
The RPN is a calculated number based on information you provide regarding
the SEVERITY of the EFFECT,
the probability of OCCURRENCE of the CAUSE
the ability of the CURRENT CONTROLS to DETECT the failures modes
It is calculated as the product of three quantitative ratings, each one related to the effects, causes, and controls:
EffectsEffects CausesCauses ControlsControls
RPN = Severity X Occurrence X Detection
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-23
Risk Priority NumberRisk Priority Number
Risk Priority Number is not sacred
Scaling for Severity, Occurrence and Detection can be locally developed
Other categories can be added
For example, one engineer added an Impact score to the RPN calculation to estimate the overall impact of the Failure Mode on the process
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-24
Definition of RPN TermsDefinition of RPN Terms
Severity (of Effect)- importance of effect on customer requirements - could also be concerned with safety and other risks if failure occurs (1=Not Severe, 10=Very Severe)
Occurrence (of Cause)- frequency with which a given Cause occurs and creates Failure Mode. Can sometimes refer to the frequency of a Failure Mode (1=Not Likely, 10=Very Likely)
Detection (capability of Current Controls) - ability of current control scheme to detect or prevent:
the causes before creating failure mode
the failure modes before causing effect
1=Likely to Detect, 10=Not Likely at all to Detect
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-25
Example Rating ScaleExample Rating Scale
Rating Severity of Effect Probability of Occurrence Ability to Detect
10 Hazardous without warningVery high:
Can not detect
9 Hazardous with warningFailure is almost inevitable
Very remote chance of detection
8 Loss of primary functionHigh:
Remote chance of detection
7Reduced primary function
performance
Repeated failuresVery low chance of detection
6 Loss of secondary functionModerate:
Low chance of detection
5Reduced secondary function
performance
Occasional failuresModerate chance of detection
4Minor defect noticed by most
customers
Moderately high chance of
detection
3Minor defect noticed by some
customers Low:High chance of detection
2Minor defect noticed by
discriminating customers
Relatively few failuresVery high chance of detection
1 No effect Remote: Failure is unlikely Almost certain detection
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-26
FMEA ModelFMEA ModelBecause the Detection score is a little difficult to understand, we will do a quick exercise. Give a Detection score for each of the
boxes below:
Det = 10
Det = 7
Det = 1Det = 3
ControlsControls
EffectEffect
External customer or downstream process step
CauseCause
Failure Mode(Defect)
Failure Mode(Defect)
Process Step
Material or process
input
Prevention
Detection
Detection
Detection
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-27
FMEA ScoringFMEA Scoring
There are a wide variety of scoring “anchors”, both quantitative or qualitative
Two types of scales are 1-5 or 1-10
The 1-5 scale makes it easier for the teams to decide on scores
The 1-10 scale allows for better precision in estimates and a wide variation in scores
The 1-10 scale is generally considered to be the best option
The 1-10 scale is generally considered to be the best option
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-28
FMEA Form FMEA Form -- Initial AssessmentInitial Assessment
Process
Step/InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
Actions
Recommended
What is the
process step/
Input under
investigation?
In what ways does the
Input Variable go wrong?
What is the impact on the
Output Variables (Customer
Requirements) or internal
requirements?
Ho
w S
eve
re i
sth
e
effe
ct
to t
he
cus
tom
er? What causes the Input
Variable to go wrong?
Ho
w o
ften d
oes c
au
se
or
FM
occu
r? What are the existing controls
and procedures (inspection and
test) that prevent either the cause
or the Failure Mode? Should
include an SOP number.
Ho
w w
ell
ca
n y
ou
dete
ct
cau
se
or
FM
? What are the actions
for reducing the
occurrence of the
Cause, or improving
detection? Should
have actions only on
high RPN's or easy
fixes.
0
0
0
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-29
Philosophical ApproachPhilosophical Approach
Focus on Safety Issues
Assume incoming material is perfect and process is not good
Assume process is perfect and incoming material is not good
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-30
MethodologyMethodology
Two major approaches:
Starting with QFD/Cause & Effect Matrix
Starting with FMEA directly from the Process Map
We will explain the approach using the C&E matrix, though both approaches are very similar
Spreadsheet tools have been prepared to assist you in the preparation of the FMEA
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-31
FMEA Methodology FMEA Methodology –– StartingStarting
with C&E Matrixwith C&E Matrix
Advantage: The Cause & Effect Matrix assists the team in defining the important issues that the FMEA should address by helping to prioritize
Important customer requirements
Process inputs that could potentially impact these requirements
Prioritizing the Key Process Inputs according to their impact on the Output variables (We want to focus on Inputs that highly impact a large number of Outputs first)
The C&E Matrix also provides quantitative output that can be used in the determination of the specific severity ratings for the next stage of the FMEA process
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-32
FMEA FMEA -- Step by StepStep by Step
1. For each Process Input, determine the ways in which the input can go wrong (Failure Modes)
2. For each Failure Mode associated with the inputs, determine Effects of the Failures on the customer
Remember the internal customers!3. Identify potential Causes of each Failure Mode
4. List the Current Controls for each Cause or Failure Mode
5. Create Severity, Occurrence, and Detection rating scales
6. Assign Severity, Occurrence and Detection ratings to each Cause
7. Calculate RPN’s for each Cause
8. Determine Recommended Actions to reduce high RPN’s
9. Take appropriate actions and recalculate RPN’s
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-33
Process Mapping ExamplesProcess Mapping ExamplesManufacturing
Outputs
• Cycle Time
• Material properties
Feed
Feed material from roll
Cutting
Cut material to length
• Cycle Time
• Correct length
• Burr free
Stamping
Stamp piece to size
• Cycle Time
• Correct part
• Burr free
• Correct dimensions
• Tensilestrength
Drawing
Draw features
Outputs
• Cycle Time
• Correct part
• Correct dimensions
• Tensilestrength
Punching
Punch out features
• Cycle Time
• Correct part
• Burr free
• Correct dimensions
• Tensilestrength
Cleaning
Clean metal surface
• Cycle Time
• Clean surface
• Residue free
• Gear speed
• Gear wear
• Material lot
• Material properties
Inputs Type
C
U
U
U
• Shear speed
• Shear wear
• Material properties
• Clamping force
• Shear force
C
U
U
C
U
• Die wear
• Material properties
• Ram force
• Ram speed
• Die number
• Die hardness
U
U
C
C
C
C
• Drawing speed
• Material properties
• Die number
Inputs Type
C
U
C
• Punch speed
• Punch wear
• Material properties
• Clamping force
• Ram force
C
U
U
C
U
• Solvent type
• Solvent purity
• Solvent age
• Surface roughness
• Contaminant level
• Humidity
• Temperature
C
U
U
U
U
U
U
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-34
Process
StepProcess Input
Cutting Shear speed 9 9 9 1 9 9 355Punching Punch speed 9 9 9 0 9 9 351
Drawing Drawing speed 9 9 9 9 0 9 333Stamping Ram speed 9 9 9 3 1 9 315
Feed Gear Speed 3 9 9 9 1 9 297
Punching Punch wear 9 3 9 0 9 3 249Cutting Material properties 9 1 1 9 9 9 243
Feed Gear Wear 0 3 9 1 9 9 238Drawing Material properties 9 1 9 3 0 9 237
Punching Clamping force 9 9 3 1 9 1 237Cleaning Solvent type 0 9 3 0 9 9 234
Cleaning Solvent purity 0 9 3 0 9 9 234Cutting Clamping force 9 3 9 0 9 1 233
C&E Matrix ExampleC&E Matrix Example
This is the C&E Matrix for the previous Manufacturing Process Map with the Key Inputs sorted by the Total Score
We will address ONLY the top 5 items in the initial FMEA activity
FAILURE MODES AND EFFECTS ANALYSIS
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Page 18402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-35
FMEA WorksheetFMEA WorksheetBefore we move to the example let’s look at an FMEA worksheet
The information on this sheet is transferred directly to the FMEA form
The purpose of this worksheet is to focus the team on the FMEA inputs and not on scoring
The scoring should be done after the basic inputs have been made
PFMEAPFMEA--FRMFRM--WRKSHT.XLSWRKSHT.XLS
Process
Step
Key Process
Input
Failure Modes - What can go
wrong? Effects Causes
Current
Controls
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-36
Determine Failure ModesDetermine Failure Modes
We will only deal with the first two Key Process Input Variables on the C&E Matrix which are Shear Speed and Punch Speed
First we’ll list Shear Speed for the Cutting process step
Process
Step
Key Process
Input
Failure Modes - What can go
wrong? Effects Causes
Current
Controls
Cutting Shear Speed Speed too high
Speed too low
1. For each Process Input, determine the ways in which the input can go wrong (Failure Modes)
1. For each Process Input, determine the ways in which the input can go wrong (Failure Modes)
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-37
Determine EffectsDetermine Effects
These effects are internal requirements for the next process and/or to the final customer
Process
Step
Key Process
Input
Failure Modes - What can go
wrong? Effects Causes
Current
Controls
Cutting Shear Speed Speed too high Burrs
Damage to blade
and material
Speed too low Insufficient cut
Rounded edge
2. For Each Failure Mode Associated with the Inputs, Determine Effects
2. For Each Failure Mode Associated with the Inputs, Determine Effects
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-38
Identify Potential CausesIdentify Potential Causes
In most cases, there will be more than one Cause for a Failure Mode but we’ll keep it simple for this exercise
Process
Step
Key Process
Input
Failure Modes - What can go
wrong? Effects Causes
Current
Controls
Cutting Shear Speed Speed too high Burrs Incorrect set point
Damage to blade
and materialPoor calibration
Speed too low Insufficient cut Incorrect set point
Rounded edge Poor calibration
Galling of blade
3. Identify Potential Causes of Each Failure Mode3. Identify Potential Causes of Each Failure Mode
FAILURE MODES AND EFFECTS ANALYSIS
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Page 20402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-39
Process
Step
Key Process
Input
Failure Modes - What can go
wrong? Effects Causes
Current
Controls
Cutting Shear Speed Speed too high Burrs Incorrect set point Operator verification
Damage to blade
and materialPoor calibration Monthly maintenance
Speed too low Insufficient cut Incorrect set point Operator verification
Rounded edge Poor calibration Monthly maintenance
Galling of blade None
List Current ControlsList Current Controls
For each Failure Mode/Cause we list how we are either preventingthe Cause or detecting the Failure Mode
We will list the procedure number where we have a SOP
This is how the FMEA identifies initial holes in the Current Control Plan - process teams can start working on these holes right away
This is how the FMEA identifies initial holes in the Current Control Plan - process teams can start working on these holes right away
4. List the Current Controls for Each Cause4. List the Current Controls for Each Cause
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-40
Create ScalesCreate Scales
Example Rating ScaleRating Severity of Effect Likelihood of Occurrence Ability to Detect
10 Hazardous without warningVery high:
Can not detect
9 Hazardous with warningFailure is almost inevitable
Very remote chance of detection
8 Loss of primary functionHigh:
Remote chance of detection
7Reduced primary function
performance
Repeated failuresVery low chance of detection
6 Loss of secondary functionModerate:
Low chance of detection
5Reduced secondary function
performance
Occasional failuresModerate chance of detection
4Minor defect noticed by most
customers
Moderately high chance of
detection
3Minor defect noticed by some
customers Low:High chance of detection
2Minor defect noticed by
discriminating customers
Relatively few failuresVery high chance of detection
1 No effect Remote: Failure is unlikely Almost certain detection
5. Create Severity, Occurrence, and Detection Rating Scales
5. Create Severity, Occurrence, and Detection Rating Scales
FAILURE MODES AND EFFECTS ANALYSIS
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Page 21402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-41
Assign RatingsAssign Ratings
We are now ready to transfer the worksheet input to the FMEA form
Copy and paste the worksheet columns into the appropriate FMEA form columns
The team then starts scoring each row to compute the RPN values
Notes:
You will only use one Severity value
Determine which Effect has the highest associated Severity and use that SEV value for ALL Effects for the related Failure Mode
Example: If a Failure Mode could lead to either a scratched surface (SEV 3) or an incorrect dimension (SEV 9), then use a SEV of 9 for all of the Effect related to that Failure Mode
6. Assign Severity, Occurrence and Detection Ratings to Each Cause
6. Assign Severity, Occurrence and Detection Ratings to Each Cause
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-42
6. RPN Component Value Assignment6. RPN Component Value Assignment
Process
Step/InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
What is the
process step/
Input under
investigation?
In what ways does the
Input Variable go wrong?
What is the impact on the
Output Variables (Customer
Requirements) or internal
requirements?
How
Seve
re is t
he
effe
ct
to t
he
cusotm
er? What causes the Input
Variable to go wrong?
How
often d
oe
s c
ause
or
FM
occur? What are the existing controls
and procedures (inspection and
test) that prevent either the cause
or the Failure Mode? Should
include an SOP number.
How
well
can y
ou
dete
ct
cause o
r F
M?
Cutting / Shear
Speed
Speed too high Burrs; Damage to blade and
material 9Incorrect set point
7Operator verification
6 378
Cutting / Shear
Speed
Speed too high Burrs; Damage to blade and
material 9Poor calibration
2Monthly maintenance
3 54
Cutting / Shear
Speed
Speed too low Insufficient cut; Rounded
edge 6Incorrect set point
4Operator verification
6 144
Cutting / Shear
Speed
Speed too low Insufficient cut; Rounded
edge 6Poor calibration
2Monthly maintenance
3 36
Cutting / Shear
Speed
Speed too low Insufficient cut; Rounded
edge 6Galling of blade
5None
10 300
Punching /
Punch Speed
Speed to high Damage to punch5
Incorrect set point9
None10 450
Punching /
Punch Speed
Speed to high Damage to punch5
Poor calibration4
Quarterly maintenance5 100
Punching /
Punch Speed
Speed to low Deformation of material;
Incorrect feature size 10Incorrect set point
3None
10 300
Punching /
Punch Speed
Speed to low Deformation of material;
Incorrect feature size 10Poor calibration
4Quarterly maintenance
5 200
Punching /
Punch Speed
Variable Speed Variable feature sizes9
Current variation6
None10 540
FAILURE MODES AND EFFECTS ANALYSIS
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Page 22402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-43
7. Calculate RPNs7. Calculate RPNs
Multiply the Severity, Occurrence, and Detection values for each line in the FMEA
Then sort all lines by RPN
Notice that you have to identify all cells so you can carry the Failure Modes, Effects, Causes and Current Controls along with the sort
Process
Step/InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
Punching /
Punch Speed
Variable Speed Variable feature sizes9
Current variation6
None10 540
Punching /
Punch Speed
Speed to high Damage to punch5
Incorrect set point9
None10 450
Cutting / Shear
Speed
Speed too high Burrs; Damage to blade and
material 9Incorrect set point
7Operator verification
6 378
Cutting / Shear
Speed
Speed too low Insufficient cut; Rounded
edge 6Galling of blade
5None
10 300
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-44
RPN ReviewRPN Review
Once you calculate the RPN for each Failure Mode / Cause / Controls combination, review the results and look for insights
Do the gut check - does the Pareto of items make sense?
If not, maybe the ratings given are varying
Determine potential next steps:
Data collection
Experiments
Process improvements
Process control implementations
FAILURE MODES AND EFFECTS ANALYSIS
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Page 23402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-45
Determine Recommended ActionsDetermine Recommended Actions
Now fill in recommended actions for top RPNs
Actions are recommended for only the high RPN’s
The key is FOCUS!
Process
Step/InputPotential Failure Mode Potential Failure Effects Potential Causes Current Controls
R
P
N
Actions
RecommendedResp.
Punching /
Punch Speed
Variable Speed Variable feature sizes Current variation None540
Investigate line
condition systems
XYZ by 4/15
Punching /
Punch Speed
Speed to high Damage to punch Incorrect set point None450
Determine set point
and put in SOP 1234
CA by 3/31
Cutting / Shear
Speed
Speed too high Burrs; Damage to blade and
material
Incorrect set point Operator verification378
Determine max set
point; instruct workers
CA by 3/31
Cutting / Shear
Speed
Speed too low Insufficient cut; Rounded
edge
Galling of blade None300
Include on
maintenance schedule
RBM by 4/09
8. Determine Recommended Actions to Reduce High RPN’s
8. Determine Recommended Actions to Reduce High RPN’s
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-46
Act and RecalculateAct and Recalculate
We have recorded the action taken and the impact on the RPN
Notice that this is a nice way to track past activities
The FMEA should be re-evaluated by the group as new recommended actions are identified, completed and recorded
R
P
N
Actions
RecommendedResp. Actions Taken
S
E
V
O
C
C
D
E
T
R
P
N
540Investigate line
condition systems
XYZ by 4/15 Installed line conditioner
5/1 9 3 2 54
450Determine set point
and put in SOP 1234
CA by 3/31 SOP updated, operators
being audited 4/12 5 4 6 120
378Determine max set
point; instruct workers
CA by 3/31 SOP updated, operators
being audited 4/3 9 5 5 225
300Include on
maintenance schedule
RBM by 4/09 Maintenance schedule
updated 4/01 6 5 5 150
9. Take Appropriate Actions and Recalculate RPNs9. Take Appropriate Actions and Recalculate RPNs
FAILURE MODES AND EFFECTS ANALYSIS
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Page 24402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-47
NonNon--Manufacturing ExampleManufacturing Example
Process
Step/InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
Actions
Recommended
What is the
process step/
Input under
investigation?
In what ways does the Key
Input go wrong?
What is the impact on the
Key Output Variables
(Customer Requirements) or
internal requirements?
How
Seve
re is t
he e
ffect
to t
he c
usotm
er? What causes the Key Input to
go wrong?
How
often d
oes c
ause o
r
FM
occur? What are the existing controls
and procedures (inspection and
test) that prevent eith the cause
or the Failure Mode? Should
include an SOP number.
How
well
can y
ou d
ete
ct
cause o
r F
M? What are the actions
for reducing the
occurrance of the
Cause, or improving
detection? Should
have actions only on
high RPN's or easy
fixes.
Order Entry Computer entry screens
missing information
Incomplete order, incorrect
information, missing
information
9
Critical data not required to
move to next entry field 7
None
10 630
Make certain fields
mandatory in software
Order Entry Computer entry screens
with incorrect information
Incomplete order, incorrect
information, missing
information
9
Typographical error
5
None
10 450
Training / auditting /
metrics for typos
Order Entry Computer entry screens
missing information
Incomplete order, incorrect
information, missing
information
9
Definition of information needed
not clear 4
None
10 360
Improve Help in
computer software
Answering
Procedure
Do not follow procedure Missing / incorrect
information for order9
Method too complicated7
Audit to procedure XXXXXXX5 315
Answering
Procedure
Do not follow procedure Missing / incorrect
information for order9
Poor training6
Audit training with 'blind' callers5 270
Order Entry Computer entry screens
missing information
Incomplete order, incorrect
information, missing
information
9
Too many screens
3
None
10 270
Order Entry Computer entry screens
with incorrect information
Incomplete order, incorrect
information, missing
information
9
Incorrect information from
customer 3
Confirmation rejection by
customer 9 243
Answering
Procedure
Do not follow procedure Missing / incorrect
information for order9
Documented method out of
date3
Revision audit every 6 months8 216
Order Entry Computer entry screens
with incorrect information
Incomplete order, incorrect
information, missing
information
9
Information wrong on order
worksheet 4
Review worksheet for
completeness before entry 5 180
Answering
Procedure
Do not follow procedure Missing / incorrect
information for order9
New person answering phones3
Apprentice system / training5 135
Order Entry Computer entry screens
missing information
Incomplete order, incorrect
information, missing
information
9
System overrides
2
Overrides only permitted to
Supervisor / password protected 3 54
Order Entry Computer entry screens
with incorrect information
Incomplete order, incorrect
information, missing
information
9
Automatic computer cross
reference data incorrect 2
System audits monthly
3 54
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-48
Process
Step
Key Process
Input
Failure Modes - What can go
wrong? Effects Causes
Current
Controls
Pour into
glassBeer volume Overflow
Wasted Beer/
Wet LapDrunk None
Glass too small Visual
Not paying
attentionNone
Too much foam
Bad Taste /
Don't get as
drunk
No tilt Visual
Pouring too highVisual and operator
training
Pouring too fastVisual and operator
training
No Foam
No beer
mustache/ Poor
taste
Flat beer Expiration date
Tilted glass Visual
Slow Pour Operator training
Empty glass No drink Too drunk None
Broken Glass Visual
No Money
Job / Process
Excellence - big
bonus
No Friends Personality
A Well Loved Process FMEA!!A Well Loved Process FMEA!!
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 25402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-49
Approaches to FMEAApproaches to FMEA
Approach One (C&E Matrix Focus)
Start with Key Inputs with the highest scores from the C&E Matrix analysis
Fill out the FMEA worksheet for those Inputs
Calculate RPN’s and develop recommended actions for the highest RPN’s
Complete the Process FMEA for other Inputs over time
Approach Two (Customer Focused)
Fill out the Failure Mode and Effects columns of the worksheet. Copy to FMEA form and rate Severity.
For High Severity Ratings, List Causes and rate Occurrence for each Cause
For the highest Severity * Occurrence Ratings, evaluate Current Controls
For Highest RPN’s develop recommended actions
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-50
Approaches to FMEAApproaches to FMEA
Approach Three (Comprehensive)
Good approach for small processes
Fill out the FMEA worksheet beginning with the first process step and ending with the last
Score SEV, OCC and DET for all causes
Develop recommended actions for highest RPN’s
Approach Four (Super Focused)
Pick the top Pareto defect item (Damaged Components) or Failure Mode (Variability in Temperature)
Focus the FMEA process on only that defect or Failure Mode
Purpose: To “Kill” that Failure Mode
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 26402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-51
OverviewOverview
Process
Step/InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
Actions
Recommended
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
What is the Input
What What
can go can go
wrong wrong
with the with the
Input?Input?
What can What can
be done?be done?
What is What is
the Effect the Effect
on the on the
Outputs?Outputs?
What are What are
thethe
Causes?Causes?
How can How can
these be these be
found or found or
prevented?prevented?
How How
Bad?Bad?How How
Often?Often?How How
well?well?
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-52
SummarySummary
Provided insight to the uses of FMEA
Identified sources of risk
Defined the different types of FMEA
Introduced the steps in developing a Process FMEA
Practiced creating a FMEA
FAILURE MODES AND EFFECTS ANALYSIS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 27402-108, Rev. C
Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.
GB114-53
Coffee Making FMEA ExerciseCoffee Making FMEA Exercise
Get back into your teams and perform a FMEA on your three top ranked input variables from your Coffee Making C&E Matrix
Identify all potential causes for each failure mode and the current controls
For the top 3 items in the FMEA, identify recommended actions
Report results on a flip chart or on the Excel spreadsheet using the computer display
List lessons learned as part of the team presentation
You have 20 minutes!
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 1402-108, Rev. C
Rev. C February 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-1
Attribute MSAAttribute MSA
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-2
Kappa TechniquesKappa Techniques
For classifications of attribute/categorical data
Used when a quality criteria is difficult or impossible to define
Several units must be classified more than once and by more than one “rater”
If there is substantial agreement, there is the possibilitythat the ratings are accurate
If agreement is poor, the usefulness of the ratings is EXTREMELY LIMITED
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 2402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-3
Kappa TechniquesKappa Techniques
Non-Parametric approaches provide some tools to deal with subjective attribute data
Applies to all types of distributions, so it does not matter if the distribution is normal, binomial, etc.
Requires only that the observations be independent
Are less efficient than parametric approaches, and require larger sample sizes to achieve the same results
Hypotheses are less precise, and yield less information in conclusions
Example: we may know that two distributions are not the same, but we do not know if they are different in terms of central tendency or in terms of variability
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-4
Kappa TechniquesKappa Techniques
Used when a measurement system is needed to classify items in a nonquantitative manner
Kappa techniques treat al l misclassifications equally
Kappa techniques do not assume ratings are equally distributed across the possible range
Requirements for use:
Units to be measured are independent from one another
Raters inspect and classify independently
The rating categories are mutually exclusive and exhaustive
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 3402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-5
Studying Measurement SystemsStudying Measurement Systems
Once we have the right data we must determine our ability to measure it
Information to be attained
How big is the measurement error?
What are the sources of measurement error?
Is the measurement system stable over time?
Is it capable for this study?
How do we improve the measurement system?
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-6
Attribute Data MSAAttribute Data MSA
If many people are evaluating the same thing they need to agree:
With each other
With themselves
Attribute data contains less information than variables data, but often it is all that’s available
Therefore, we must be diligent about the integrity of attribute measurement systems
The issue is
Can I rely on the data coming from my measurement system?
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 4402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-7
Attribute MSA GoalsAttribute MSA Goals
Determine:
% overall agreement
% agreement within appraisers (Repeatability)
% agreement between appraisers (Reproducibility)
% agreement with known standard (Accuracy)
Kappa (how much better the measurement system is than random chance)
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-8
Attribute Measurement Studies:Attribute Measurement Studies:SamplesSamples
Need 30-50 samples which span the normal extremes of the process
The majority should be from the ‘gray’ areas
Remainder should be clearly good or clearly bad
Example: Invoice Errors
We collect a sample of 30 invoices
5 units will be clearly defective (a single large defect or enough smaller defects to reject it)
5 units will be clearly acceptable (everything correct)
The remaining samples vary in the quantity and type of defects
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 5402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-9
Attribute Measurement Studies:Attribute Measurement Studies:MethodsMethods
Select 2-3 people who normally conduct the assessment
Randomly provide the samples to one person (without indicating which sample is which) and have the person rate each of the items
Once the first person has reviewed all items, repeat with the remaining people
Once everybody has rated each item, repeat the steps above for asecond trial
Note: All possible combinations of appraisers, items, and “trials” should be represented
Each appraiser must examine all of the items
Each appraiser must examine those items the same number of times (trials)
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-10
Attribute MSA Example: Attribute MSA Example: Spots on FilmSpots on Film
A film plant was having problems with spots on one of its products. Operators made the decision as to whether to reject rolls for spots. There had been considerable operator turnover, and engineers questioned how well the operators were trained in recognizing rejectable spots.
50 samples were selected – some clearly acceptable, some clearly unacceptable, and some on both sides of “borderline” conditions
Four operators and an “expert” (the tech service representative) recorded Reject or Accept decision
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 6402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-11
Sample A B C D
1 R R R A
2 A A R A
3 A A A A
4 A A A A
5 A A A A
6 A A A A
7 A A A A
8 A A A A
9 A A A A
10 A R R R
11 R A A R
12 A A R A
13 R A A R
14 A R A A
15 A A A A
16 A A A A
17 A A A A
18 A A A A
19 A A A A
20 A A A A
Agreement Among Inspectors?
Agreement Among Inspectors?
Attribute MSA Example: Attribute MSA Example: Spots on FilmSpots on Film
• Results indicate definite problems(Results for 20 of the 50 samples are shown)
• Of the 50 samples, the inspectors actually agreed unanimously on only one “Rejected” sample
• They agreed unanimously on 29 “Accepted” samples
• %Parts with disagreement =
20/50 = 40%
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-12
Sample A B C D Expert
1 R R R A A
2 A A R A A
3 A A A A A
4 A A A A A
6 A A A A A
7 A A A A A
8 A A A A A
9 A A A A A
11 R A A R A
14 A R A A A
15 A A A A A
16 A A A A A
17 A A A A A
18 A A A A A
19 A A A A A
20 A A A A A
5 A A A A R
10 A R R R R
12 A A R A R
13 R A A R R
% Correct Decisions Overall = 150/200 = 75%
Inspector vs. Expert Results
%Bad Accepted %Good Rejected
A 9/13 = 69% 4/37 = 11%
B 9/13 = 69% 4/37 = 11%
C 9/13 = 69% 4/37 = 11%
D 8/13 = 62% 3/37 = 8%
Agreement With the Expert?
Agreement With the Expert?
Attribute MSA Example: Attribute MSA Example: Spots on FilmSpots on Film
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 7402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-13
Kappa TechniquesKappa Techniques
In an Attribute MSA study, Kappa is used to summarize the level of agreement between raters after agreement by chance has been removed
If there is substantial agreement, there is the possibility thatthe ratings are accurate
If agreement is poor, the usefulness of the ratings is extremely limited
Requirements for use:
Units to be measured are independent from one another
Raters’ inspect and classify independently
The rating categories are mutually exclusive and exhaustive
Minitab computes the value of Kappa as part of the output for anAttribute Agreement Analysis
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-14
Kappa (K) is defined as the proportion of agreement between raters after agreement by chance has been removed:
where:
Pobserved = Proportion of units classified in which the
raters agreed
Pchance = Proportion of units for which one would
expect agreement by chance
KappaKappa
chance
chanceobserved
P
PPK
1
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 8402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-15
Kappa Value Suggested Interpretation
-1 to 0.0 Random agreement
> 0.60 Marginal - Significant effort required
> 0.70 Good - Improvement warranted
> 0.90 Excellent
Kappa will be between -1 and +1
A Kappa value of +1 means perfect agreement
General RuleGeneral Rule: If K<0.70, the measurement system needs attention!
Kappa TechniquesKappa Techniques
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-16
Kappa TechniquesKappa Techniques
Kappa can also be applied in cases of multiple assessors and multiple classification categories
Customer Returns example:
A Green Belt had a project to reduce customer returns. A reason code is assigned to every customer return by a Customer Service Representative (CSR). There was concern that CSRs weren’t consistent in their assignment of codes.
Three assessors were asked to assign reason codes to the same 30 returns. Each assessor made the assignments twice (once on each of two different days).
The Green Belt also asked a division expert to evaluate the returns
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 9402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-17
Minitab Attribute Minitab Attribute
Agreement AnalysisAgreement Analysis
Project: BB MSA.MPJBB MSA.MPJ
Worksheet: Customer Customer
Returns.MTWReturns.MTW
Design:
3 CSRs (Appraisers)
2 Days
30 Returns(Samples)
Codes are in column CategoryCategory
Expert values are in column ExpertExpert
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-18
Minitab Attribute Minitab Attribute
Agreement AnalysisAgreement Analysis
Use the Attribute Agreement Analysis to analyze attribute measurement system results
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 10402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-19
Minitab Attribute Minitab Attribute
Agreement AnalysisAgreement Analysis
Fill in the appropriate column information first
Fill in the appropriate column information first
Make sure that the Kappa option is checked in the Results window
Make sure that the Kappa option is checked in the Results window
Include expert’s ratingsInclude expert’s ratings
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-20
Graphical AnalysisGraphical Analysis
Note: These two graphs have different scales!
Note: These two graphs have different scales!
Agreement Within Appraiser is like repeatability
Here CSR 1 agreed with herself 90% of the time. CSR 2 was internally consistent 86.7% of the time, and CSR 3 matched himself on 96.7% of the returns
Graph shows the 95% CI for the % agreement
This graph shows % agreement between the CSRs and the expert opinion
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 11402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-21
Numerical Output: Numerical Output: Within AssessorWithin Assessor
Within Appraiser
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 27 90.0 ( 73.5, 97.9)
B 30 26 86.7 ( 69.3, 96.2)
C 30 29 96.7 ( 82.8, 99.9)
# Matched: Appraiser agrees with him/herself across
trials.
Attribute Gage R&R Study
Attribute Gage R&R Study for Category
Within Appraiser
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 27 90.0 ( 73.5, 97.9)
B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)
# Matched: Appraiser agrees with him/herself across trials.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.8693 0.1826 4.7612 0.000
Damaged 1.0000 0.1826 5.4772 0.000
Incorrect Pr -0.0169 0.1826 -0.0928 0.537
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.7917 0.1826 4.3361 0.000 Pricing 0.7917 0.1826 4.3361 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * *
Overall 0.8768 0.0855 10.2533 0.000
B Administrati 0.6078 0.1826 3.3293 0.000
Damaged 1.0000 0.1826 5.4772 0.000 Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.6296 0.1826 3.4486 0.000
Pricing 0.7689 0.1826 4.2116 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * * Overall 0.8346 0.0845 9.8785 0.000
C Administrati 1.0000 0.1826 5.4772 0.000
Damaged 0.8383 0.1826 4.5914 0.000
Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 1.0000 0.1826 5.4772 0.000
Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000
Quantity 1.0000 0.1826 5.4772 0.000
Overall 0.9597 0.0803 11.9524 0.000
* When no or all responses across trials equal the value,
kappa cannot be computed.
Each Appraiser vs Standard
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)
C 30 22 73.3 ( 54.1, 87.7)
# Matched: Appraiser's assessment across trials agrees with standard.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.3847 0.1291 2.9802 0.001
Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 0.4915 0.1291 3.8073 0.000
Overstock 0.6491 0.1291 5.0281 0.000 Planned Adju 0.8958 0.1291 6.9391 0.000
Pricing 0.8952 0.1291 6.9345 0.000
Quality 0.8295 0.1291 6.4256 0.000
Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.7561 0.0583 12.9782 0.000
B Administrati 0.2357 0.1291 1.8256 0.034 Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.4643 0.1291 3.5963 0.000
Planned Adju 0.5673 0.1291 4.3943 0.000
Pricing 0.7016 0.1291 5.4348 0.000
Quality 0.8137 0.1291 6.3026 0.000 Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.6352 0.0574 11.0724 0.000
C Administrati 0.3750 0.1291 2.9047 0.002
Damaged 0.9191 0.1291 7.1196 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.6491 0.1291 5.0281 0.000
Planned Adju 0.8887 0.1291 6.8837 0.000 Pricing 0.6839 0.1291 5.2976 0.000
Quality 0.7054 0.1291 5.4640 0.000
Quantity 0.6491 0.1291 5.0281 0.000 Overall 0.7011 0.0558 12.5715 0.000
Between Appraisers
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 17 56.7 ( 37.4, 74.5)
# Matched: All appraisers' assessments agree with each other.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.4778 0.0471 10.1365 0.000 Damaged 0.9412 0.0471 19.9650 0.000
Incorrect Pr 0.7943 0.0471 16.8493 0.000
Overstock 0.8761 0.0471 18.5844 0.000
Planned Adju 0.6800 0.0471 14.4250 0.000
Pricing 0.6397 0.0471 13.5708 0.000
Quality 0.8399 0.0471 17.8166 0.000
Quantity 0.1910 0.0471 4.0520 0.000 Overall 0.7106 0.0210 33.8869 0.000
All Appraisers vs Standard
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI 30 15 50.0 ( 31.3, 68.7)
# Matched: All appraisers' assessments agree with standard.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.3318 0.0745 4.4517 0.000 Damaged 0.9730 0.0745 13.0548 0.000
Incorrect Pr 0.8305 0.0745 11.1424 0.000
Overstock 0.5875 0.0745 7.8823 0.000 Planned Adju 0.7839 0.0745 10.5177 0.000
Pricing 0.7603 0.0745 10.2000 0.000
Quality 0.7829 0.0745 10.5033 0.000
Quantity 0.1934 0.0745 2.5945 0.005
Overall 0.6975 0.0330 21.1414 0.000
• Similar information as left graph (see previous slide)
• Adds specific numbers of matched and inspected
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-22
Numerical Output:Numerical Output:Appraiser vs. ExpertAppraiser vs. Expert
Each Appraiser vs Standard
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 23 76.7 ( 57.7, 90.1)
B 30 20 66.7 ( 47.2, 82.7)
C 30 22 73.3 ( 54.1, 87.7)
# Matched: Appraiser's assessment across trials agrees
with standard.
This indicates how often the assessor is giving the “correct” answer
We want 90% or better agreementWe want 90% or better agreement
Attribute Gage R&R Study
Attribute Gage R&R Study for Category
Within Appraiser
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 27 90.0 ( 73.5, 97.9)
B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)
# Matched: Appraiser agrees with him/herself across trials.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.8693 0.1826 4.7612 0.000
Damaged 1.0000 0.1826 5.4772 0.000
Incorrect Pr -0.0169 0.1826 -0.0928 0.537
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.7917 0.1826 4.3361 0.000 Pricing 0.7917 0.1826 4.3361 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * *
Overall 0.8768 0.0855 10.2533 0.000
B Administrati 0.6078 0.1826 3.3293 0.000
Damaged 1.0000 0.1826 5.4772 0.000 Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.6296 0.1826 3.4486 0.000
Pricing 0.7689 0.1826 4.2116 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * * Overall 0.8346 0.0845 9.8785 0.000
C Administrati 1.0000 0.1826 5.4772 0.000
Damaged 0.8383 0.1826 4.5914 0.000
Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 1.0000 0.1826 5.4772 0.000
Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000
Quantity 1.0000 0.1826 5.4772 0.000
Overall 0.9597 0.0803 11.9524 0.000
* When no or all responses across trials equal the value,
kappa cannot be computed.
Each Appraiser vs Standard
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)
C 30 22 73.3 ( 54.1, 87.7)
# Matched: Appraiser's assessment across trials agrees with standard.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.3847 0.1291 2.9802 0.001
Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 0.4915 0.1291 3.8073 0.000
Overstock 0.6491 0.1291 5.0281 0.000 Planned Adju 0.8958 0.1291 6.9391 0.000
Pricing 0.8952 0.1291 6.9345 0.000
Quality 0.8295 0.1291 6.4256 0.000
Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.7561 0.0583 12.9782 0.000
B Administrati 0.2357 0.1291 1.8256 0.034 Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.4643 0.1291 3.5963 0.000
Planned Adju 0.5673 0.1291 4.3943 0.000
Pricing 0.7016 0.1291 5.4348 0.000
Quality 0.8137 0.1291 6.3026 0.000 Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.6352 0.0574 11.0724 0.000
C Administrati 0.3750 0.1291 2.9047 0.002
Damaged 0.9191 0.1291 7.1196 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.6491 0.1291 5.0281 0.000
Planned Adju 0.8887 0.1291 6.8837 0.000 Pricing 0.6839 0.1291 5.2976 0.000
Quality 0.7054 0.1291 5.4640 0.000
Quantity 0.6491 0.1291 5.0281 0.000 Overall 0.7011 0.0558 12.5715 0.000
Between Appraisers
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 17 56.7 ( 37.4, 74.5)
# Matched: All appraisers' assessments agree with each other.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.4778 0.0471 10.1365 0.000 Damaged 0.9412 0.0471 19.9650 0.000
Incorrect Pr 0.7943 0.0471 16.8493 0.000
Overstock 0.8761 0.0471 18.5844 0.000
Planned Adju 0.6800 0.0471 14.4250 0.000
Pricing 0.6397 0.0471 13.5708 0.000
Quality 0.8399 0.0471 17.8166 0.000
Quantity 0.1910 0.0471 4.0520 0.000 Overall 0.7106 0.0210 33.8869 0.000
All Appraisers vs Standard
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI 30 15 50.0 ( 31.3, 68.7)
# Matched: All appraisers' assessments agree with standard.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.3318 0.0745 4.4517 0.000 Damaged 0.9730 0.0745 13.0548 0.000
Incorrect Pr 0.8305 0.0745 11.1424 0.000
Overstock 0.5875 0.0745 7.8823 0.000 Planned Adju 0.7839 0.0745 10.5177 0.000
Pricing 0.7603 0.0745 10.2000 0.000
Quality 0.7829 0.0745 10.5033 0.000
Quantity 0.1934 0.0745 2.5945 0.005
Overall 0.6975 0.0330 21.1414 0.000
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 12402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-23
Numerical OutputNumerical Output
All Appraisers vs. Standard gives an indication of the effectiveness of the inspection standards:
Between AppraisersAssessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 17 56.7 ( 37.4, 74.5)
# Matched: All appraisers' assessments agree with each other.
All Appraisers vs Standard
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 15 50.0 ( 31.3, 68.7)
# Matched: All appraisers' assessments agree with
standard.
Between-Appraisers agreement is like Reproducibility:
Attribute Gage R&R Study
Attribute Gage R&R Study for Category
Within Appraiser
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 27 90.0 ( 73.5, 97.9)
B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)
# Matched: Appraiser agrees with him/herself across trials.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.8693 0.1826 4.7612 0.000
Damaged 1.0000 0.1826 5.4772 0.000
Incorrect Pr -0.0169 0.1826 -0.0928 0.537
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.7917 0.1826 4.3361 0.000 Pricing 0.7917 0.1826 4.3361 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * *
Overall 0.8768 0.0855 10.2533 0.000
B Administrati 0.6078 0.1826 3.3293 0.000
Damaged 1.0000 0.1826 5.4772 0.000 Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.6296 0.1826 3.4486 0.000
Pricing 0.7689 0.1826 4.2116 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * * Overall 0.8346 0.0845 9.8785 0.000
C Administrati 1.0000 0.1826 5.4772 0.000
Damaged 0.8383 0.1826 4.5914 0.000
Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 1.0000 0.1826 5.4772 0.000
Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000
Quantity 1.0000 0.1826 5.4772 0.000
Overall 0.9597 0.0803 11.9524 0.000
* When no or all responses across trials equal the value,
kappa cannot be computed.
Each Appraiser vs Standard
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)
C 30 22 73.3 ( 54.1, 87.7)
# Matched: Appraiser's assessment across trials agrees with standard.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.3847 0.1291 2.9802 0.001
Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 0.4915 0.1291 3.8073 0.000
Overstock 0.6491 0.1291 5.0281 0.000 Planned Adju 0.8958 0.1291 6.9391 0.000
Pricing 0.8952 0.1291 6.9345 0.000
Quality 0.8295 0.1291 6.4256 0.000
Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.7561 0.0583 12.9782 0.000
B Administrati 0.2357 0.1291 1.8256 0.034 Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.4643 0.1291 3.5963 0.000
Planned Adju 0.5673 0.1291 4.3943 0.000
Pricing 0.7016 0.1291 5.4348 0.000
Quality 0.8137 0.1291 6.3026 0.000 Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.6352 0.0574 11.0724 0.000
C Administrati 0.3750 0.1291 2.9047 0.002
Damaged 0.9191 0.1291 7.1196 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.6491 0.1291 5.0281 0.000
Planned Adju 0.8887 0.1291 6.8837 0.000 Pricing 0.6839 0.1291 5.2976 0.000
Quality 0.7054 0.1291 5.4640 0.000
Quantity 0.6491 0.1291 5.0281 0.000 Overall 0.7011 0.0558 12.5715 0.000
Between Appraisers
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 17 56.7 ( 37.4, 74.5)
# Matched: All appraisers' assessments agree with each other.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.4778 0.0471 10.1365 0.000 Damaged 0.9412 0.0471 19.9650 0.000
Incorrect Pr 0.7943 0.0471 16.8493 0.000
Overstock 0.8761 0.0471 18.5844 0.000
Planned Adju 0.6800 0.0471 14.4250 0.000
Pricing 0.6397 0.0471 13.5708 0.000
Quality 0.8399 0.0471 17.8166 0.000
Quantity 0.1910 0.0471 4.0520 0.000 Overall 0.7106 0.0210 33.8869 0.000
All Appraisers vs Standard
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI 30 15 50.0 ( 31.3, 68.7)
# Matched: All appraisers' assessments agree with standard.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.3318 0.0745 4.4517 0.000 Damaged 0.9730 0.0745 13.0548 0.000
Incorrect Pr 0.8305 0.0745 11.1424 0.000
Overstock 0.5875 0.0745 7.8823 0.000 Planned Adju 0.7839 0.0745 10.5177 0.000
Pricing 0.7603 0.0745 10.2000 0.000
Quality 0.7829 0.0745 10.5033 0.000
Quantity 0.1934 0.0745 2.5945 0.005
Overall 0.6975 0.0330 21.1414 0.000
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-24
Numerical Output:Numerical Output:
KappaKappa
Kappa and Kappa values for each category of defect are provided for Between-Appraisers (and other sections)
How well can we identify the defect types?
Between AppraisersAssessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 17 56.7 ( 37.4, 74.5)
# Matched: All appraisers' assessments agree with each other.
Fleiss’ Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.4778 0.0471 10.1365 0.000
Damaged 0.9412 0.0471 19.9650 0.000
Incorrect Pr 0.7943 0.0471 16.8493 0.000
Overstock 0.8761 0.0471 18.5844 0.000
Planned Adju 0.6800 0.0471 14.4250 0.000
Pricing 0.6397 0.0471 13.5708 0.000
Quality 0.8399 0.0471 17.8166 0.000
Quantity 0.1910 0.0471 4.0520 0.000
Overall 0.7106 0.0210 33.8869 0.000
The overall Kappa (between appraisers) represents the study-wide agreement (across appraisers and categories)
Measures how much better than chance agreement is the matching you see on a sample by sample basis
Attribute Gage R&R Study
Attribute Gage R&R Study for Category
Within Appraiser
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 27 90.0 ( 73.5, 97.9)
B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)
# Matched: Appraiser agrees with him/herself across trials.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.8693 0.1826 4.7612 0.000
Damaged 1.0000 0.1826 5.4772 0.000
Incorrect Pr -0.0169 0.1826 -0.0928 0.537
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.7917 0.1826 4.3361 0.000 Pricing 0.7917 0.1826 4.3361 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * *
Overall 0.8768 0.0855 10.2533 0.000
B Administrati 0.6078 0.1826 3.3293 0.000
Damaged 1.0000 0.1826 5.4772 0.000 Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 0.6296 0.1826 3.4486 0.000
Pricing 0.7689 0.1826 4.2116 0.000
Quality 1.0000 0.1826 5.4772 0.000
Quantity * * * * Overall 0.8346 0.0845 9.8785 0.000
C Administrati 1.0000 0.1826 5.4772 0.000
Damaged 0.8383 0.1826 4.5914 0.000
Incorrect Pr 1.0000 0.1826 5.4772 0.000
Overstock 1.0000 0.1826 5.4772 0.000
Planned Adju 1.0000 0.1826 5.4772 0.000
Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000
Quantity 1.0000 0.1826 5.4772 0.000
Overall 0.9597 0.0803 11.9524 0.000
* When no or all responses across trials equal the value,
kappa cannot be computed.
Each Appraiser vs Standard
Assessment Agreement
Appraiser # Inspected # Matched Percent (%) 95.0% CI
A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)
C 30 22 73.3 ( 54.1, 87.7)
# Matched: Appraiser's assessment across trials agrees with standard.
Kappa Statistics
Appraiser Response Kappa SE Kappa Z P(vs > 0)
A Administrati 0.3847 0.1291 2.9802 0.001
Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 0.4915 0.1291 3.8073 0.000
Overstock 0.6491 0.1291 5.0281 0.000 Planned Adju 0.8958 0.1291 6.9391 0.000
Pricing 0.8952 0.1291 6.9345 0.000
Quality 0.8295 0.1291 6.4256 0.000
Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.7561 0.0583 12.9782 0.000
B Administrati 0.2357 0.1291 1.8256 0.034 Damaged 1.0000 0.1291 7.7460 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.4643 0.1291 3.5963 0.000
Planned Adju 0.5673 0.1291 4.3943 0.000
Pricing 0.7016 0.1291 5.4348 0.000
Quality 0.8137 0.1291 6.3026 0.000 Quantity -0.0345 0.1291 -0.2671 0.605
Overall 0.6352 0.0574 11.0724 0.000
C Administrati 0.3750 0.1291 2.9047 0.002
Damaged 0.9191 0.1291 7.1196 0.000
Incorrect Pr 1.0000 0.1291 7.7460 0.000
Overstock 0.6491 0.1291 5.0281 0.000
Planned Adju 0.8887 0.1291 6.8837 0.000 Pricing 0.6839 0.1291 5.2976 0.000
Quality 0.7054 0.1291 5.4640 0.000
Quantity 0.6491 0.1291 5.0281 0.000 Overall 0.7011 0.0558 12.5715 0.000
Between Appraisers
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI
30 17 56.7 ( 37.4, 74.5)
# Matched: All appraisers' assessments agree with each other.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.4778 0.0471 10.1365 0.000 Damaged 0.9412 0.0471 19.9650 0.000
Incorrect Pr 0.7943 0.0471 16.8493 0.000
Overstock 0.8761 0.0471 18.5844 0.000
Planned Adju 0.6800 0.0471 14.4250 0.000
Pricing 0.6397 0.0471 13.5708 0.000
Quality 0.8399 0.0471 17.8166 0.000
Quantity 0.1910 0.0471 4.0520 0.000 Overall 0.7106 0.0210 33.8869 0.000
All Appraisers vs Standard
Assessment Agreement
# Inspected # Matched Percent (%) 95.0% CI 30 15 50.0 ( 31.3, 68.7)
# Matched: All appraisers' assessments agree with standard.
Kappa Statistics
Response Kappa SE Kappa Z P(vs > 0)
Administrati 0.3318 0.0745 4.4517 0.000 Damaged 0.9730 0.0745 13.0548 0.000
Incorrect Pr 0.8305 0.0745 11.1424 0.000
Overstock 0.5875 0.0745 7.8823 0.000 Planned Adju 0.7839 0.0745 10.5177 0.000
Pricing 0.7603 0.0745 10.2000 0.000
Quality 0.7829 0.0745 10.5033 0.000
Quantity 0.1934 0.0745 2.5945 0.005
Overall 0.6975 0.0330 21.1414 0.000
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 13402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-25
Kappa TechniquesKappa Techniques
For this example, the following Kappa values are calculated
Within appraiser
The individual category Kappa represents how consistently each appraiser rates the same samples over multiple trials
The overall Kappa for each appraiser represents that appraiser’s consistency across all categories
Between appraisers
The individual category Kappa represents how consistently all the appraisers categorized the samples in that category
The overall Kappa represents the study-wide agreement (across appraisers and categories)
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-26
Improving this measurement system would likely requirechanging operational definitions for each defect type,
retraining the CSRs, or both
Improving this measurement system would likely requirechanging operational definitions for each defect type,
retraining the CSRs, or both
Kappa TechniquesKappa Techniques
Interpreting the Results
The individual Kappa values range from 0.19 to 0.94, meaning that agreement among raters is:
EXCELLENT for Damaged;
UNACCEPTABLY LOW for Administrative and Quantity;
MARGINAL for Planned adjustments and Pricing
GOOD for Incorrect product, Overstock, and Quality
The overall Kappa was 0.7106, indicating that the system could use some improvement
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 14402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-27
Improving Attribute Improving Attribute
Measurement SystemsMeasurement Systems
Sense multipliers (devices to improve human senses)
Masks / templates (block out unimportant information)
Checklists
Automation
Reorganization of work area
Visual aids
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-28
Measurement Systems:Measurement Systems:Questions to AskQuestions to Ask
Have you picked the right measurement system? Is this measurement system associated with either critical inputs or outputs?
What do the precision, accuracy, and stability look like?
What are the sources of variation and what is the measurement error?
What needs to be done to improve this system?
Have we informed the right people of our results?
Who owns this measurement system?
Who owns trouble shooting?
Does this system have a control plan in place?
What’s the training frequency? Is that frequent enough?
Do identical systems match?
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
KAPPA STUDIES
Page 15402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-29
SummarySummary
Measurement systems are important to analyze BEFORE embarking on Process Improvement activities
Be careful when picking samples – watch for correct sub-grouping and sample size requirements
Analyze the measurement system for Operator / Assessor, Part / Item, and Trial effects
Make sure that the measurement system has enough resolution to distinguish between samples
Always generate a Gage R&R report to document findings, methods, and improvement opportunities
Total observed variation includes measurement error –try to minimize the controllable error in the measurement system
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB115-30
Exercise:Exercise:Candy InspectionCandy Inspection
Objective:Evaluate the M&M inspection system
Tools:
3 inspectors
Bag of M&Ms
Procedure:
BEFORE opening the bag, operationally define the defects you will be concerned with
Have your ‘inspectors’ evaluate the materials in the samples of candies
Evaluate the measurement system
Report any recommended improvement actions
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB116-1Rev C February 2004
Team Meetings andTeam Meetings andPresentation SkillsPresentation Skills
DMAIC Process ImprovementDMAIC Process Improvement
For Operations Green BeltsFor Operations Green Belts
GB116-2
ObjectivesObjectives
Discuss techniques for conducting effective team meetings
Introduce technical presentation Do’s and Don’ts
Content
Format and Structure
Slide Design
Review basic presentation skills Do’s and Don’ts
Verbal
Visual
Energy
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
GB116-3
Conducting Effective Meetings Conducting Effective Meetings
(Refer to the Team Building and Interpersonal Skills Reference Guide page 20)
PREPARE
Determine the objective of the meeting
Gather background information and data
Decide who needs to be invited
Prepare an Agenda (Item, its outcome, and who will present or lead discussion)
Assign the amount of time to be dedicated to each topic
Distribute the agenda and background information to participants before the meeting. This will give them a chance to prepare for their participation
GB116-4
Begin The MeetingBegin The Meeting
Begin the meeting on time (even if people are late).
Review the Objective, the Agenda and the Time assigned to each item
Assign Roles (optional, as needed)
Leader - Focuses on the business objective. Encourages participation.
Timekeeper - Keeps the team on the agreed upon time track (critical)
Scribe - Records ideas, suggestions, and action items (critical)
Facilitator - Assists the leader.
Process Guide - If using 8D, DMAIC, or other process, keeps the team on track
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
GB116-5
Conduct The MeetingConduct The Meeting
Review background information and data
Ask participants for new data or ideas, discuss
If meeting is for problem solving, identify root causes, then brainstorm possible solutions
If meeting is for continuous improvement of a process, Brainstorm suggestions/ideas
Strive for quantity
No criticizing/evaluating ideas during brainstorming.
Build on others’ ideas.
Clarify to make sure everyone understands the suggestions/ideas
Combine similar ideas
GB116-6
Conduct The Meeting (Cont.)Conduct The Meeting (Cont.)
Determine best solutions
If the team cannot come to consensus, you could have everyone vote--3 points for their first choice, 2 points for their second choice, 1 point for their third choice
If issues arise that are separate from the meeting or cannot be resolved in the meeting, record them on a parking lot for later resolution
Determine an action or implementation plan
Assign action items and completion dates (Who will do What by When)
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
GB116-7
Close The MeetingClose The Meeting
End the meeting on time, even if you are not complete; or re-negotiate ending time.
Summarize and review action plans, setting dates and times for follow-up or future meetings
Thank all participants
Issue minutes with action items, times, and dates documented and send to all participants and others who need to know the results of the meeting
GB116-8
Why Should You Care About Why Should You Care About
Making Presentations?Making Presentations?
Each of you will have to make a presentation about your project.
Part of your project’s success will be dependent on the support you get based on the your technical results…
… and part will be based on your ability to present your findings in order to gain support from:
Management
Process Owners
Peers
Team Members
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
GB116-9
Successful Presentations Depend On …Successful Presentations Depend On …
Good communications skills
Ability to present technical material to an audience with differing backgrounds and levels of technical ability
Verbal and non-verbal communications
Advance preparation
Presentation material must carry the message to your audience
Delivery must be enthusiastic and convey knowledge of the subject
Team Reference Guide, page 27
GB116-10
Two Types Of MeetingsTwo Types Of Meetings
Information meetingAdviseUpdateSell
Decision Making MeetingGoal settingProblem Solving
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
GB116-11
Key Differences In Types Of MeetingKey Differences In Types Of Meeting
ELEMENTS INFORMATION
MEETING
DECISION MAKING
MEETING
Number of
attendees
Any number Small size
Who should
attend ?
Those who need to know Those responsible and those who
can contribute
Communication
process
One way from leader to
participants with opportunities
for questions
Interactive discussion among all
attending
Meeting room
setup Participants facing front of
room-classroom style
Participants facing each other-
conference style
Most effective
style of leadership Authoritative Participative
Emphasis should
be on :
Content Interaction and problem solving
Key to success Planning and preparation of
information to be presented
Meeting climate that support open,
free expression
GB116-12
Selecting ParticipantsSelecting Participants
FOR INFORMATION MEETINGS :- Those who need to know the information to
be presented
FOR DECISION MAKING MEETING :- Those who have knowledge to contribute- Those who have authority over the area
affected by the decision- Those you need the commitment to carry out
the decision- Those who have diversity of view-point
(People with expressiveness and open-mindedness)
Danielle MUYL .AMP.GEP
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
GB116-13
ContentContent
Most presentations fail due to their lack of focus
Content is essential – know what your audience wants and needs!
Do’s
Use simple graphical representations
Use bullet statements
Highlight meaningful information
Don’ts
Include information that is too complex
Write and read paragraphs of information
Clutter the presentation with busy graphics
GB116-14
Organizing Your PresentationOrganizing Your Presentation
It is a good idea to start by developing objectives. Once this is done you need to thoroughly analyze the audience. You must complete these steps before you separately brainstorm the main points and the sub points of your presentation. If it’s a persuasive presentation, then you must state the benefits. You then gather factual information and prepare a blueprint of your presentation. Also prepare any visual aids, handouts and notes that you will need. And don’t forget to practice.
What is your reaction to this slide?
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
GB116-15
Organizing Your PresentationOrganizing Your Presentation
Develop objectives
Analyze the audience
Brainstorm
Introduction
Main ideas and benefits
Sub ideas
Conclusions
Develop visuals, handouts and notes
Practice
GB116-16
ContentContent
So how do you know what to include?
Some guidelines:
Project description and targeted value
Key learning's and the tools used to accomplish them
Next steps / action plan / timing
Roadblocks to success needing resolution by the audience
Recognition of contributors
Results to date (financial and learning's)
Team Reference Guide, page 28
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
GB116-17
Format And StructureFormat And Structure
One of the best ways to structure a presentation is to:
Tell them what you’re going to tell them
Tell them
Tell them what you told them
Identify the 3 key concepts, actions, or issues that you want the audience to address
Work from your conclusion and build the supporting materials around these key points
GB116-18
Presentation SequencePresentation Sequence
Suggested flow for a presentation:
Introduction
Main Ideas and Sub Ideas
Benefits
Conclusion
Questions and Answers
Review Sentence
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
GB116-19
The Last SlideThe Last Slide
Make sure that your 3 key points are summarized in the Review Sentence
This can be viewed as your Elevator Speech!
Characteristic of a great presentation:
If your presentation time is cut short and you can only present one slide, you can convey your entire presentation in the last slide
GB116-20
Using Visuals / GraphicsUsing Visuals / Graphics
Use visuals and graphics to:
Focus the attention of the audience
Reinforce your verbal message
Stimulate interest
Illustrate factors that are hard to visualize
Graphically represent data
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
GB116-21
Using Visuals / GraphicsUsing Visuals / Graphics
Don’t use visuals or graphics to:
Try and impress the audience with overly detailed tables or graphs
Avoid interaction with the audience
Make more than one point
Present simple ideas that can be presented verbally
GB116-22
Slide Design Do’s And Don’tsSlide Design Do’s And Don’ts
Do’s
KISS (Keep It Simple and Short)
Present one key point per visual
Use bullets, not full sentences
Statements on the slide should only be a reference to you and drive a key point
Team Reference Guide, page 29
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
GB116-23
Slide Design Do’s And Don’tsSlide Design Do’s And Don’ts
Do’s
Highlight key items in a graphical item
Show only part of the C&E Matrix or top items in the FMEA, not the entire item
State the conclusions that you reach based on the graphic
Use simple animation – too much can degrade the validity of the message
GB116-24
Slide Design Do’s And Don’tsSlide Design Do’s And Don’ts
Don’ts
Use Yellow text (e.g. Yellow)
It is not easily read except on a very dark background
Use more than three colors per graph
Use more than 3 curves per graph
Present rows and columns of data
Provide summary information only
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
GB116-25
Slide Design Do’s And Don’tsSlide Design Do’s And Don’ts
Don’ts
Make text and graphs too small
Rule of Thumb: If you can read everything on your computer screen from 8 feet away, it is good
Have too much text per slide
Rule of Thumb: 50% white space on the slide
Add clipart and pictures that don’t clearly link to the idea presented
GB116-26
Presentation Skill ReviewPresentation Skill Review
Some of you have never had to speak in public before and many feel uncomfortable speaking in front of a group
Regardless, each of you will have to present in front of many different groups throughout your project and your career
The following material contains some reminders on techniques that will ease your anxiety
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
GB116-27
PreparationsPreparations
Organize
Your thoughts, your materials, your presentation
Memorize
Key points, key messages, your elevator speech
Visualize
The success of your presentation and the expected outcome
Positive Affirmations
Remind yourself that you CAN do this!
Release Tensions Exercises
Stretching, breathing, meditation prior to speaking
Practice
GB116-28
AppearanceAppearance
Posture
Standing upright = authoritative
Leaning / sitting = personal
Movement
Purposeful - not anxious
Shoulder Orientation
Squared up - not slouching
Gestures
Purposeful, natural - not erratic
Attire
Appropriate for audience
Team Reference Guide, page 30
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
GB116-29
During The PresentationDuring The Presentation
Remember to Breathe
If you find yourself stressing or speaking too fast, simply stop and take a breath
Move
Use the space that you have effectively
Eye Contact
Make the presentation personal by connecting visually with the audience
Do NOT have any caffeine before or during the presentation
Remember - You are surrounded by friends!
GB116-30
DeliveryDelivery
Enthusiasm is contagious
If you are not enthusiastic about your presentation, no one else will be!
Relate with your audience
Make a common bond through examples
Voice projection
Be clear and loud
Use inflection in your voice
Use conversational speed and tone
Don’t read every word on every slide
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
GB116-31
Eye ContactEye Contact
Good eye contact –
Opens the channel of communication with the audience
Establishes and builds rapport
Involves the audience
Talk to the audience – not to the projected material
GB116-32
DeliveryDelivery
Place yourself at “center stage”
Orient your shoulders toward the audience
Leave some light on in the room so you can be seen
Use pointers sparingly to draw attention to a specific item or to trace the relationship on a visual / graphic
Nervousness is amplified when using a pointer
Especially evident when using laser pointers
Don’t “play” with the pointer – when not using the pointer, lay it down
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
GB116-33
Keeping Their AttentionKeeping Their Attention
The first 120 seconds is “free”
The words “in conclusion” draws them back
Use “energizers” to hold them in the middle
People speak at 120 - 200 words per minute, and comprehend at 600 words per minute
GB116-34
Examples Of EnergizersExamples Of Energizers
Enthusiasm
Movement
Visual Aids
Samples, something that they can touch or see
Animation
Pictures / clipart
Humor
Interactive Questions
Voice and Body Language
Team Reference Guide, page 31
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
GB116-35
Questions And AnswersQuestions And Answers
Prepare for them
Welcome them
Listen to the question and clarify if necessary
Restate for the whole audience
Respond to the whole audience
Be honest
GB116-36
Benefits Of Good Benefits Of Good
Presentation MaterialsPresentation Materials
Audience is 43% more likely to be persuaded
You can cover the material in 25-40% less time
Learning is improved 200%
Retention is improved 38%
You are perceived as professional, persuasive, credible, interesting, and prepared
TEAM MEETINGS AND PRESENTATION SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
GB116-37
Practice, Practice, PracticePractice, Practice, Practice
Key words on notes pages
Mental run through for flow of ideas
Stand up rehearsal using the presentation material
Video tape or rehearse with a friend
GB116-38
Final ThoughtsFinal Thoughts
Smile; enjoy sharing your information
x Tell jokes unless you are good at it
x Waste your first 120 seconds thanking them or making apologies.
x Read your presentation
Remember the audience wants you to succeed
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-1
Introduction to Multi-Vari Studies
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-2
The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter
Measure• Level 0 and Value Stream Map• Determine Baseline Performance
• Initial Capability Studies
• Process Capacity• Initial Control Plan
• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA
Analyze• Root Cause Analysis
• Multi – level Pareto Diagrams
• 5 Why Diagrams• Identification of Waste
• Multi – Vari Studies• ANOVA• Correlation and Regression
Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map
Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-3
TopicsTopics
Overview of Multi-Vari Studies
Noise Variables and Analysis Introduction
Planning Multi-Vari’s
Data Collection
Note: The name “Multi-Vari” was given to this methodology by L. A. Seder in the paper titled, “Diagnosis with Diagrams” which appeared in Industrial Quality Control in January and March, 1950.
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-4
MultiMulti--Vari StudiesVari Studies
Method of characterizing the baseline capability of a process while in production mode
Data collection is passive in that the process is monitored in its natural state
Data is collected for a short period of time and analyzed to determine capability, stability and relationships between Input Variables and Output Variables
Multi-Vari study should continue until the full range of the output variable is observed
Span from Low to High as observed in the short-term capability or historic data
Identifies inherent process capabilities and limitations
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-5
Why MultiWhy Multi--Vari?Vari?
To determine with high statistical confidence the capability of the Output Variables of a process
To identify assignable causes of variability
To obtain initial components of variability (Shift-to-Shift, Run-to-Run, Operator-to-Operator)
To get a first-look at process stability over time
To provide direction and input for Design of Experiments (DOE) activities
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-6
PhasesPhases
Phase I:
Perform Short-term Capability Study
Derive a plan for more in-depth study of the process based on data and notes from the Short-term Capability Study
Phase II:
Initial study of the effects of controlled, uncontrolled (noise), and material input variables on the output variable
Focus on characterizing the effect of major uncontrolled (noise) variables in the system
Initial relationships between controlled inputs and outputs
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 4402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-7
MAJOR FOCUSMAJOR FOCUS
To study the uncontrolled Noise Variables first!To study the uncontrolled Noise Variables first!
Variation in the Noise variables produces chronic and acute mean shifts and changes in variability that lead to process instability
We must remove, if possible, these sources of variation first before we can leverage the important controlled input variables in a systematic way
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-8
Noise VariablesNoise Variables
We can classify Noise Variables into three main families of variation:
Positional - Differences in variation due to similar processes across a production line
Reactor-to-reactor differences
Line-to-line differences
Press to Press differences
Production location to location differences
Operator-to-Operator differences
Temporal - Differences in variation of a process over time
Shift-to-Shift
Day-to-Day
Week-to-Week
Sequential - Differences across a series of processes
If the output variable is affected by several different processes, we test Process-to-Process variability
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-9
Analyzing Noise Variables Analyzing Noise Variables
For Continuous Processes
Test for Variability within a time span
Example: Four Measurements per Shift
Test for Variability across short time spans
Example: Variability across shifts
Test for Variability across longer time times
Example: Variability across days, weeks and/or months
For Discrete Processes
Test for Variability within a piece
Example: Four measurements per oven cavity
Test for Variability within a batch
Example: Variability across cavities within a batch
Test for Variability across batches
Example: Variability across batches within a month
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-10
Positional Noise TreePositional Noise Tree
… and any other plants making this product
Plant 1
Mold 2Mold 1 Mold 3
… and other cavities
Cavity 1 Cavity 2 Cavity 3
…and other molds
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-11
Material Noise TreeMaterial Noise Tree
Lot/Batch 1 Lot/Batch 3Lot/Batch 2
Part/Spool 2 Part/Spool 3Part/Spool 1
Supplier A … and other suppliers
Location 1 Location 2 Location 3 … and other locations within a part
Measurement 1 Measurement 2
… and other measurements within a location
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-12
Temporal Noise TreeTemporal Noise Tree
Month 1 Month 3Month 2
Year 1
Day 1 Day 2 Day 3 … and other days within weeks
Shift 1 Shift 2
Week 2 Week 3Week 1 Week 4
Hour 1 Hour 4 … and other hours within Shift
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-13
Studying Noise Variables Studying Noise Variables
Sampling
We would sample several points within a shift, several shifts over several days or weeks
Let’s look at a sample data collection sheet on the next page for a process with:
Two controlled Inputs (Temperature, Pressure)
One Output (% Impurities)
Three Noise variables (Day, Shift and Time within Shift)
We’ll sample over two days
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-14
Data SheetData SheetDay Shift Time Input: Temperature Input: Pressure Output: % Impurities
1 1 1 91 48 0.020000
1 1 2 97 52 0.020000
1 1 3 88 44 0.020000
1 1 4 87 43 0.010000
1 2 1 109 50 0.060000
1 2 2 98 45 0.040000
1 2 3 103 55 0.030000
1 2 4 99 49 0.050000
2 1 1 111 55 0.010000
2 1 2 103 53 0.010000
2 1 3 106 54 0.040000
2 1 4 93 55 0.000000
2 2 1 101 46 0.050000
2 2 2 93 48 0.040000
2 2 3 97 54 0.010000
2 2 4 99 49 0.030000
Notice that this data sheet format is the same format as a Minitab data file
With this sheet we can easily investigate differences in % Impurities due to time-to-time, shift-to-shift and day-to-day Variations
We can also correlate Temperature and Pressure with Impurities
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-15
MultiMulti--Vari Phase I:Vari Phase I:Capability StudyCapability Study
1. Set up the process to your “best guess”“best guess” setup and record the values of your process Input Variables
2. Identify a reasonable way to create rational subgroups; i.e, how will you select the samples?
3. Run the product over a short period of time to remove as much external variation as possible
Approximately 30 time points30 time points is the target for data collection
4. Have your team carefully observe the process and take plenty of notes
5. Measure and record values for the process Output Variable(s)
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-16
MultiMulti--Vari Phase I:Vari Phase I:Capability StudyCapability Study
6. Run Capability SixCapability Six--packpack and Review:
Normal Plot, Histogram
SPC Charts (Check for Stability, Accuracy)
7. Run the Test for NormalityTest for Normality on the data
8. Run the Capability StudyCapability Study and identify both the Short Term and Long Term capability indices
9. Diagnose for Mean Shift and Variance Inflation
10. Develop action plan based on diagnostics
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-17
MultiMulti--Vari Phase II:Vari Phase II:PlanningPlanning
1. Determine Objective
2. Identify Input and Output Variables to be studied
3. Identify Measurement Systems for each variable
Which should be studied to assure capability?
4. Determine sampling plan
5. Determine data collection, formatting and storage procedure
6. Description of procedure and settings used to run process
7. Team assigned and trained
8. Clear responsibilities assigned
9. Outline of data analysis to be performed
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-18
MultiMulti--Vari Phase II:Vari Phase II:Execution and AnalysisExecution and Analysis
1. Run process and collect data
2. Analyze data:
Is the process stable, in control?
Which are the key noise variables affecting the output variable?
Which are the key controlled variables that influence the output variable?
3. Validate results with follow-up DOE
4. Conclusions
5. Reporting results, recommendations
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-19
PlanningPlanningCritical Step
Provides for:
Statement of Objective
List of Input Variables and Output Variables to be studied
Ensure Measurement Systems are capable
Description of sampling plan
Method of data collection, formatting and storage
Description of procedure and settings used to run process
Team assigned and trained
Clear responsibilities assigned
Outline of data analysis to be performed
Teams are crucial !
Up-front communication of Objectives essential !
Teams are crucial !
Up-front communication of Objectives essential !
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-20
ObjectiveObjective
Objective is usually stated in terms of the Input’s effects on the Outputs
Objective should tie to the Team Charter
Examples:
1 Study the effects of temperature and pressure on Percent Impurities
2 Study the stability and capability of the lead wire drawing process
3 The Objectives of the study are to:
Study the effects of furnace operating variables on fractures
Exercise: Each team write each objective on a flip chart
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-21
Identify Inputs and OutputsIdentify Inputs and Outputs
ProcessProcessProcess Key Process Outputs
Key Process Key Process
OutputsOutputs
Noise Inputs
(Discrete)
Noise InputsNoise Inputs
(Discrete)(Discrete)
Examples
• Different Operators
• Different Machines
• Different Shifts
Examples
• Different Operators
• Different Machines
• Different Shifts
Noise Inputs
(Continuous)
Noise InputsNoise Inputs
(Continuous)(Continuous)
Examples
• Room Temperature
• Barometric Pressure
• Relative Humidity
• Raw Material Characteristics
Examples
• Room Temperature
• Barometric Pressure
• Relative Humidity
• Raw Material Characteristics
Examples
• Temperature
• Pressure
• Time
Examples
• Temperature
• Pressure
• Time
Controlled Inputs
Controlled Controlled
InputsInputs
Tools
• Scatterplots
• Correlation
• Regression
Tools
• Scatterplots
• Correlation
• Regression
Tools
• Boxplots
• Main Effects and Interaction Plots
• ANOVAs, T-test
Tools
• Boxplots
• Main Effects and Interaction Plots
• ANOVAs, T-test
Tools
• C&E Matrix
• FMEA
• Fishbone
• Short-term Capability
Tools
• C&E Matrix
• FMEA
• Fishbone
• Short-term Capability
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-22
Controllable in real life
Controllable in real life
ControlFactors &
“HeldConstant”
Factors
Noise Factors that we can
afford to manipulate
for the experiment
only
Influential Noises that
are not controlled
Factors with no
impact on the
response
Factors that are controlled
in the experiment
Factors that are controlled
in the experiment
(Entire right circle)
Factors that are
influential on the
response
Factors that are
influential on the
response
(Entire left circle)
Remember, Noise is in the Eyes of the BeholderRemember, Noise is in the Eyes of the BeholderRemember, Noise is in the Eyes of the Beholder
Controlled vs. UncontrolledControlled vs. Uncontrolled
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-23
Measurement System CapabilityMeasurement System Capability
Perform Gage R&R on important Inputs and, especially, critical Outputs
Use standard Gage R&R procedures
Remember, a noisy gage distorts your view of the trueprocess variation
2
tmeasuremen
2
actual
2
observed
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-24
Sampling PlanSampling Plan
A good sampling plan will capture all relevant sources of noise variability
Lot-to-lot, batch-to-batch
Different shifts, operators, machines or processes
Sample Size rule of thumb: 30
Input Variables do not always have to be measured for each sample
Example:
Samples are drawn for an output variable measurement every hour
Ambient humidity (input variable) is assumed to be constant over a 4 hour period
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-25
Sampling DesignsSampling Designs
You can use one or more of the following sampling designs
Sampling designs help insure a representative sample of the process without having to collect extreme amounts of data
Nielson Rating are based on about 1500 viewers
Those viewers are chosen to best represent all viewers in the country
Sampling designs:
Simple Random Sample
Stratified Sampling
Cluster Sampling
Systematic Sampling
Subgroup Sampling
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-26
Sampling Plan WorksheetSampling Plan Worksheet
Key Outputs: Variable How Measured When Measured
1
2
3
Noise Variables: Variable How Measured When Measured
1
2
3
4
5
Controllable Inputs Variable How Measured When Measured
1
2
3
4
5
Overall Sampling Plan:
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-27
Discrete ExampleDiscrete Example
Key Outputs: Variable How Measured When Measured
1. Surface Quality Pits Visual Sample
Dents Visual Sample
Noise Variables: Variable How Measured When Measured
Section to Section Section id. Count
Sheet to Sheet Sheet number Count
Pack to Pack Pack number Count
Controllable Inputs Variable How Measured When Measured
Copper Exposure Exposure time Timer Each Pack
Infeed Optimization Infeed ratio Meter Each Pack
Equipment cleaning Cleanliness rating Visual Each Pack
Overall Sampling Plan:
Pits and Dents will be measured in each Section of Sheets A, B, C on Packs 1, 7, 13.
Controllable Inputs will be recorded for each Pack 1, 7, 13.
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-28
Fibers ExampleFibers Example
Key Outputs: Variable How Measured When Measured
1. Uptime / Quality Breakouts Operator On Occurrance
Breaking Strength Lab Breakout Sample
Broken Filaments Lab/Visual Breakout Sample
Noise Variables: Variable How Measured When Measured
Fiber Batch Batch number Recorded
Polymer Batch Batch number Recorded
Panel Panel number Recorded
Day Day Recorded
Shift Shift number Recorded
Controllable Inputs Variable How Measured When Measured
1
2
3
4
5
Overall Sampling Plan:
For each Breakout occurance, Breaking Strength and Broken filaments will be
measured.
Fiber and Polymer Batch will be recorded along with panel number, day, shift and time.
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-29
Data FormatsData Formats
Data quickly logged into a database (e.g. Minitab)
Data should include a column for each of:
ID Information (Time, Lot#, Shift, Site, etc.)
Uncontrolled (Noise) inputs
Process Inputs
Process outputs
Comments
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-30
Fiber Breakouts
Objective To identify the key variables affecting fiber breakouts
Metrology At each breakout occurrence:
1) Take sample of material for lab testing of breaking strength and fiber breakouts
2) Record all other (known) information as indicated below
Occurrence data Lab Test Results Cause (if known)
Me
as
ure
Data
Point Fiber Batch
Polymer
Batch Panel Day Shift Time
Time
between
failures
Breaking
strength
Broken
filaments
Dirty
Spinneret
Slow/fast
Holes
Oil on
L.E.D
Rough/Dirty
Roll
Surface
Threadpath
Alignment
Filament
Motion
Process
Variability
Fo
rma
t
control
number batch # batch # i.d.# MM/DD # HH:MM (calc.) lbs. % broken
(Description of
variable, if known)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
X’s Y’s Comments
Looking For X’s That Cause Machine ShutdownLooking For X’s That Cause Machine Shutdown
Manufacturing Quality Data SheetManufacturing Quality Data Sheet
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-31
X’s
Looking For X’s That Cause Dirty/Unsafe TrailersAttributes or Variables Present Here?
Looking For X’s That Cause Dirty/Unsafe TrailersAttributes or Variables Present Here?
Transactional Quality Data SheetTransactional Quality Data Sheet
Date:
Branch:
Observer:
Process Inputs Perform Inspection
Inspection Other
Insp. Area Weather Interrupted Activity
C)ollection
D)evon
# trailers P)hone C)ustomer
D)irt to be C)ust V)endor
Unit Arr. P)aved P)uddle R)ain insp. V)end B)reakdown
Unit # Lease # Time G)ravel N)o Pdl. Insp. Name Temp. D)ry In & Out # M)ech C)omputer
Du
ratio
n (
min
.)
Dri
ver
part
ic.
inin
sp. (Y
/N)
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-32
ExerciseExercise
Objective: Establish a sampling plan for your project’s first Multi-Vari study
Procedure:
Identify key output variable(s) to be studied
Identify key input variables to be studied
Controlled
Uncontrolled (Noise)
• Continuous / Variable
• Discrete / Attribute
Identify measurement systems for each output and input variable; identify those which need to be evaluated
Specify your initial sampling plan using the worksheet provided (see next slide)
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-33
Sampling Plan WorksheetSampling Plan Worksheet
Purpose of the Experiment: ___________________________________________________
Output : _________________________ How will you measure it ? ___________________
Inputs:
Controlled How / When will you measure it ?1) ______________________ _______________________2) ______________________ _______________________3) ______________________ _______________________4) ______________________ _______________________
Uncontrolled How / When will you measure it ?1) ______________________ _______________________2) ______________________ _______________________3) ______________________ _______________________4) ______________________ _______________________
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-34
Rules for Effective Data CollectionRules for Effective Data Collection
Team must follow sampling plan consistently
Do a short Pilot Run to test your procedures
Note changes in operating conditions that are not part of the normal or initial operating conditions
Maintain monitors on gauges for key process inputs
Record any events that are out of the ordinary
Log data into database quickly
Keep a log book
Run your MV study until Y varies over full range!Run your MV study until Y varies over full range!
INTRODUCTION TO MULTI-VARI STUDIES
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-35
1) Passive observation provides too narrow a range of “X” behavior
2) Interactions are present, but we only investigate one “X” at a time
3) Multi-colinearity (Confounding) is present
.9 .95 1 1.05 1.1
X=Car Weight (tons)
Y=Gas Mileag
e(mpg)
30
25
20
0 .5 1 1.5 2X=Car Weight (tons)
Y=Gas Mileage(mpg)
30
20
10
You SeeYou SeeYou See
13 13.5 14 14.5 15
X=Age of Car (Yrs)
Y=Selling
Price(Thousa
nds)
6
4
2
1 6 14 22 30X=Age of Car (Yrs)
Y=Selling
Price(Thousa
nds)
35
25
5
You SeeYou SeeYou See
We See...X (Heat)Looks Unimportant
Reality...X (Heat) Is
Important and Influenced by Shift
7654321
37
32
27
22
Heat
Yie
ld
7654321
37
32
27
22
Heat
Yie
ld
SHIFT
12
MultiMulti--Vari PitfallsVari Pitfalls
Counter to 1:
Run your MV study until Y varies over full range!
Counter to 1:
Run your MV study until Y varies over full range!
Counter to 2 & 3:
Run DOE’s to verify Key X’s & interactions
Counter to 2 & 3:
Run DOE’s to verify Key X’s & interactions
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB117-36
SummarySummary
Overviewed Multi-Vari studies
Reviewed noise variables and analysis introduction
Planning Multi-Vari studies
Identified methods for data collection
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 1402-108, Rev. C
Rev. C February 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-1
SamplingSampling
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-2
TopicsTopics
Overview Sampling
Introduce Sampling guidelines
Discuss Sampling techniques and sample sizes
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 2402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-3
It is not always feasible or possible to analyze 100% of the population...
But, we can draw conclusions about the population through statistically relevant samples
Why Sample?Why Sample?
Population
Sample
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-4
A good sample is a miniature version of the population - just like it, only smaller
A good sample is a miniature version of the population - just like it, only smaller
What is a Sample?What is a Sample?
Collecting only a portion of the data that is available or could be available
Using the data from the sample to draw statistical inferences
A faster, less costly way to gain insight into a process or large population
Remember: Even if we collect 100% of the data points for our process or population during a certain period of time, it is still only a sample of the whole population
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 3402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-5
When Does Sampling Work?When Does Sampling Work?
Each member of the population has an equal chance of being selected
Selecting one member doesn’t influence likelihood of another member being selected or not
There aren’t any significant differences between those selected and those that weren’t
You have a large enough sample to find what you’re looking for. If it’s a rare event or you want to be very precise, you’ll need a large sample.
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-6
Process vs. Population Determines Sampling Approach, Sample Size Calculations, and Other Considerations
Process or Population?Process or Population?
A Process . .A Process . . ..
Back guard assembly
Porcelain spraying
Quoting activity
Inventory counts maintenance
A Population . . .A Population . . .
All the back guards produced
Product made using one supplier lot
Deals that exceed margin targets
Items with discrepancies
What is a Process and what is a PopulationWhat is a Process and what is a Population
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 4402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-7
Sampling: How, What, WhenSampling: How, What, When
Purpose Considerations Sample Size Approach
Process Take action or predict future
- In Control?
- Capable?
- Improve?
Where to sample
Frequency
Grouping
Representative
Cost
Use Sample Size by Tool or Statistic guidelines
Subgroup Sampling
Systematic Sampling
Large Population
Describe or Quantify Characteristics of the Population
Precision (+/- )
Amount of characterisitic’s variation: or P
- Confidence level
- Representative
- Cost
Use Minitab procedures
Random Sampling
Stratified Random Sampling
Systematic Sampling
Cluster Sampling
Sampling Reference MatrixSampling Reference Matrix
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-8
Simple Random Sample (SRS)Simple Random Sample (SRS)
If all possible samples of n experimental units are equally likely to be selected, then the procedure is a simple random sample
Example: We generate 15 random numbers from 1 to the number of items produced in a shift for the enamel baking process
These items identified are then evaluated with respect to the output measures
Characteristics of Simple Random Sample
Unbiased: Every experimental unit has the same chance of being chosen
Independence: The selection of an experimental unit is not dependent on the selection of another
In continuous processes, using a SRS is very difficult because there is no clear experimental unit. Generally we sample from astream of material.
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 5402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-9
Stratified SampleStratified Sample
Divide the population into homogeneous groups and randomly sample from within each group
Example: Stamping Batches
There are a large number of batches (batched by steel lot) going through the stamping press with each press batch having many items created
Randomly sample 2 items from each batch
This sample will effectively represent each batch’s effect on the variability of the output variables
Another example
If you have 2 paint lines, randomly sample product from each line
This sample allows you to represent the effect of each line on the variability of the output
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-10
Cluster SampleCluster Sample
Divides the sample into smaller homogeneous groups, then the groups are randomly sampled
Example: Paint Lines
Assume there are a number of positions for product within each paint line
Number each paint line and randomly sample a subset of the paint lines
Then randomly sample positions within the selected paint lines (within the subset)
This represents line effects without having to sample from all lines
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 6402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-11
Systematic SampleSystematic Sample
Start with a randomly chosen unit and sample every kth
unit thereafter
Example:
For a assembly shift, there are N number of product produced
Pick a number at random to start and then select every 15th unit to evaluate
This method is good because its simple
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-12
Subgroup SampleSubgroup Sample
Sample output of step or activity with some frequency - usually a time increment
Remember the Sources of Variation -
Will a subgroup show the variation you’re interested in?
Frequency - predetermined, corresponds to control chart sampling
Example:
Pull 5 ice machines at 10 a.m., 12 p.m., 2 p.m., and 4 p.m.
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 7402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-13
To Sample or Not to Sample?To Sample or Not to Sample?
For some populations, sampling may not be the best approach
Example: If you have only 25 sales over $10 million per year, would you sample?
You would probably look at ALL those sales (or units)
This is NOT sampling
It’s known as a census and has its own set of rules
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-14
When In Doubt, Ask For Help!When In Doubt, Ask For Help!
Standard Sampling StrategiesStandard Sampling Strategies
Consider what main factors or Sources of Variation are on your suspect list
Will your strategy allow you to see what you want to see?
The more complex the strategy, the harder it is to plan, execute, and control
Only use the degree of sophistication you really need. Don’t use a complex sampling scheme if you don’t need one.
Sampling can be a complicated question
Review your approach with a mentor or Master Black Belt before you start data collection
Which Strategy Is Right For My Project?Which Strategy Is Right For My Project?
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 8402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-15
Considerations To Keep In MindConsiderations To Keep In Mind
Sampling from ProcessesSampling from Processes
Where you Sample
Location in the process where your process variable affects the output variable
Sample as far upstream as practically and logically possible
Frequency of Sampling
Often enough to catch it going from good to bad
It is better to have several small samples over time than one large sample at single point in time
Sample more frequently if process stability is questionable
Subgrouping:
Minimize opportunity for special cause within subgroup
Want special causes to change between subgroups
Keep track of what, where, when, who, etc.
For subgroups or Sources of Variation, you’ll need at least 5 data points for each category of the variable
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-16
Tool or Statistic Minimum Sample Size
Average 5 - 10
Standard Deviation 25 - 30
Proportion Defective (P) 100 and nP >= 5
Histogram or Pareto 50
Scatter Diagram 25
Control Chart 20
Remember these are MINIMUMS for sampling from Processes. More data points means higher confidence in conclusions you can draw from your data
Sample Size By Tool or StatisticSample Size By Tool or Statistic
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 9402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-17
Common Issues With Sample QualityCommon Issues With Sample Quality
Selection Bias
No structure to determine which items you sample
Convenience sampling; Systematic sampling matches some structure
Changes in the Environment
Environmental changes make the sample no longer representative
Non-Response Bias
Especially in surveys, characteristics of those who don’t respond are different in a significant way from those who do (vocal minority)
Measurement Bias
Often related to unequal batch sizes - tendency to select larger batches to sample from
Non-representative batches . . . long overdue bills without samplingquickly paid accounts
Sampling Plan Executed Improperly
Try to be aware of biases and other quality problems that can creep in and affect your data!
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-18
Sampling from PopulationsSampling from Populations
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 10402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-19
Team ExerciseTeam Exercise
Divide into teams
Evaluate the sampling examples on the next page
Identify strengths and weaknesses for each example
Think of at least one suggestion to improve the approach for each example
You have 15 minutes
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-20
Sampling ExamplesSampling Examples
1. A team is studying “First Call Resolution”. Approximately 15% of callers are sampled to complete a customer survey. Only 10% of those sampled were willing to complete the survey.
2. A team is working on SAP information input accuracy. They have decided to collect a sample of transactions processed from 4:00 -5:00 p.m. every day for the next four days.
3. A team studying cycle time for product changeovers is going to sample data from the one production line that has data readily available.
4. A team is looking at packaging defects. They plan to sample by pulling every 200th unit processed over the next 30 days.
5. A team is working on material shortages. They’ve developed a subgroup sampling approach for all items in the database.
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
SAMPLING
Page 11402-108, Rev. C
Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.
GB118-21
SummarySummary
Provided an overview to sampling
Introduced some sampling guidelines
Discussed sampling techniques and sample sizes
INTERPERSONAL MANAGING SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
GB119-1
Rev C February 2004©Tyco
Interpersonal Managing Skills
& Dealing with Resistance
D
M
A
I
C
DMAIC Process ImprovementDMAIC Process Improvement
Green BeltsGreen Belts
GB119-2
Rev C January, 2003©Tyco
Team Reference Guide, Page 17Team Reference Guide, Page 17
When:• You’ll make a decision or take action based on the
information, opinion, or suggestion offered, or• Your immediate impulse is to reject, ignore, or disagree
with what you are hearing.
How:
Clarify by seeking additional information about:
What has been said, and/or
Why
Confirm by stating your understanding of:
What has been said, and/or
Why
Clarifying and ConfirmingClarifying and Confirming
INTERPERSONAL MANAGING SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 2402-108, Rev. C
GB119-3
Rev C January, 2003©Tyco
Team Reference Guide, Page 17Team Reference Guide, Page 17
When:• You want someone to change his or her performance or
suggestion, and • You have confirmed your understanding.
How:• Give balanced feedback:
• Specify the merits you want to see retained• Specify the concerns you want to see eliminated
• Explore ideas for retaining merits and eliminating concerns• Invite suggestions• Make suggestions
Constructive CriticismConstructive Criticism
GB119-4
Rev C January, 2003©Tyco
Team Reference Guide, Page 18
Team Reference Guide, Page 18
When• You think a difference exists
How:• Define the difference
• State what’s important to you, and why• Clarify / Confirm what’s important to the other
person, and why
Managing DifferencesManaging Differences
INTERPERSONAL MANAGING SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 3402-108, Rev. C
GB119-5
Rev C January, 2003©Tyco
Team Reference Guide, Page 18Team Reference Guide, Page 18
WhenYou are willing and able
to consider alternatives
HowDiscuss the difference:
Explore ideas to find alternatives
WhenYou are unwilling or unable
to consider alternatives, orYou’re unable to reach a
mutually acceptable decision
How:Terminate the discussion:
Acknowledge the other person’s right to differ
Explain what you’ve decided and why
No longer a difference
Managing Differences ( Cont. )Managing Differences ( Cont. )
GB119-6
Rev C January, 2003©Tyco
WhenThe work of someone whose performance matters
to you: • Exceeds expectations • Consistently meets expectations • Meets expectations not usually met by that person.
How• Give specific examples of performance• Mention personal qualities that contributed to performance• Mention resulting benefits to you, the department, and/or the organization.
Team Reference Guide, Page 18Team Reference Guide, Page 18
CreditingCrediting
INTERPERSONAL MANAGING SKILLS
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Page 4402-108, Rev. C
GB119-7
Rev C January, 2003©Tyco
Write Your Own Role Play…a “real” Write Your Own Role Play…a “real”
situation you may be insituation you may be in
Describe the situation and enough information so your partner can play your role and practice the skills.
You will play the other person.
The third person in the triad will be the coach for the skill practice. Create
Triads
CreateTriads
GB119-8
Rev C January, 2003©Tyco
VISIONDRIVERS
LEADERSHIPPARTICIPATION
COMMUNICATIONTRAINING AND EDUCATION
REINFORCEMENT
WestinghouseResearch:
Change video
Team Reference Guide, Page 41Team Reference Guide, Page 41
Managing Change: Managing Change:
Dealing With Resistance to ChangeDealing With Resistance to Change
INTERPERSONAL MANAGING SKILLS
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 5402-108, Rev. C
GB119-9
Rev C January, 2003©Tyco
• Wants more detail• Flood with detail• Time• Attack• Impracticality• I’m not surprised• Confusion• Silence• Intellectualizing• Moralizing• Compliance• Methodology
Team Reference Guide, Page 42-43
Team Reference Guide, Page 42-43
Faces of Resistance Faces of Resistance
GB119-10
Rev C January, 2003©Tyco
Team Reference Guide, Page 44
Team Reference Guide, Page 44
Write Your Own Role Play… a “resistance” Write Your Own Role Play… a “resistance”
situation you may be insituation you may be in
COST SAVINGS GUIDELINES
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Page 1402-108, Rev. C
GB121(Appendix)-1
Tyco InternationalTyco International
Cost Savings GuidelinesCost Savings Guidelines
DMAIC Process ImprovementDMAIC Process Improvement
For Operations Green BeltsFor Operations Green Belts
GB121-2
Calculating Six Sigma Project
Financial Impact
COST SAVINGS GUIDELINES
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Page 2402-108, Rev. C
GB121-3
Why is the Financial Impact Important ?Why is the Financial Impact Important ?
Whether we are trying to improve a transactional process, the quality of a product or plant’s yields or efficiency, there is always a financial impact.
Measurement is important for Tyco, as a Company to determine how we are continually improving. (Note that we have to measure consistently as “One Company.”)
Measurement is critical for tracking individual performance and division performance as well.
We must track and measure each quarter and report the financial impact as a company.
- A tracking mechanism is being established using a PowerSteering tool. Training on this tool may be separate from this training.
GB121-4
Where does Financial Impact trackingWhere does Financial Impact tracking
Fit in to a Project ?Fit in to a Project ?Process Map (See Next Page)
Defines and schedules expected involvement from project leaders and finance community
Defines project timeline and maps it to DMAIC structure
Critical points include:
Initial Measurement of the Project Estimate
Updating estimates during steady-state or at end of Control State (Committed)
At end of each month, implemented savings need to be updated
At end of Each Quarter, Finance approves implemented savings
The Period of Measurement should be 12 months. The actual start date of the measurement should be defined by the Project Leader and validated by Finance
COST SAVINGS GUIDELINES
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Page 3402-108, Rev. C
GB121-5
1. 2. 3. 4. 5.
Define Measure Analyze Improve Control
BB enters project into tracking tool, project costs are estimated and entered as they occur
Projected Savings are estimated (Finance consulted and briefed on project costs and benefits)
Enter steady-state of savings during or at end of Control and estimate Committed Savings
At the end of each month, BB enters Implemented Savings based on actual results.
At the end of each quarter, Finance approvesImplemented Savings and enables Roll-up!!
BBEngaged
Project LifecycleProject Lifecycle
Projected Savings – Net savings estimated to be achieved over 12 month period
Committed Savings – Net savings identified as a result of a signed contract, pricing agreement, or proposed Budget Reduction
Implemented Savings – Net Savings realized through buying off contracts, release of POs, or reductions in budget
GB121-6
Keys Points
Six Sigma Financial Impact SummaryAs part of measuring the financial impact of Six Sigma projects, Tyco International and its subsidiaries have adopted common categorization and measurement standards to determine savings related to each project.
The financial impact of every project is versus the prior year or current year established baseline. However, all financial impacts need to be reviewed and approved by the financial individual responsible for the department and/or division Operating Plan (Budget).
All project specific costs must be netted against the project savings (cost reductions or margin associated with revenue increase). General costs (e.g. Six Sigma resources and training, external consultants, license fees, etc…) will be netted against the aggregate net savings for reporting purposes.
COST SAVINGS GUIDELINES
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Page 4402-108, Rev. C
GB121-7
Six Sigma Financial Impact Classification Matrix
Prior Year or Current Year Baseline Impact
Hard Soft
Category (Impact or change vs. (No Impact or change
Baseline) vs. Baseline)
Revenue Growth * Increase Revenue Avoid Loss of Revenue
Cost Productivity Reduce Costs Avoid Additional Cost
Working Capital/
Cash Flow Increase Cash (Sustainable) Avoid Additional Investment
* The Hard Budget impact of revenue growth will be the margin associated with
increase in revenue
GB121-8
Detailed Project Classification Criteria
Revenue Growth *
* The Hard Budget impact of revenue growth will be the margin associated with the increase in revenue
Category Area Discussion Comments How to Measure
Revenue
Growth -
Hard *
Increased Mfg. Flexibility * -Translates into lead time
reduction which can be tied
to revenue growth and fall
through.
-“Hard” savings is revenue
growth due to additive business
without additional capacity.
-Look for revenue growth due
to ability to meet customer
requested dates.
-Delivery performance
improvement does not equate
to increasing inventory.
Revenue
Growth -
Soft*
Customer Returns / Customer
Complaints
-Project the potential loss of
business.
-Savings associated with lost
business due to poor quality
Revenue
Growth -
Hard *
Revenue
Growth -
Hard *
Delivery Performance
Administrative Errors -Reduction in customer
returns and complaints.
-Increases revenue due to
elimination of errors.
-“Hard” savings is revenue
growth due to additive business
without additional capacity.
NOTE: The information above are only guidelines and should serve as a starting point only
COST SAVINGS GUIDELINES
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Page 5402-108, Rev. C
GB121-9
Detailed Project Classification Criteria
Cost Productivity - HardCategory Area Discussion Comments How to Measure
-Purchased price reduction is
“hard” savings.
-Reduction in receiving
inspection is a “hard” savings –
only if the person / position is
eliminated.
-Monitor the people
component.
-Redeployment of people may
result in “soft” savings.
-Report the equivalent
people’s work that we don’t
need to pay.
-Reduction in overtime results in
“hard” savings.
-Reduction in use of agency
results in “hard” savings.
-Possible savings from: -“Hard” savings if less raw
material is purchased.
-Not needing to buy a certain
amount of raw material.
-Calculate the savings using the
standard manufacturing costs.
-Calculate the direct costs
associated with the
replenishment process
-Must be cautious of the use
of variable vs. fixed costs.
Productivity Performance
(Direct costs)
Cost of Poor Quality
Supplier Cost, Delivery,
Service, Quality
-May result in a reduction in
WIP inventory.
Cost
Productivity
- Hard
Cost
Productivity
- Hard
Cost
Productivity
- Hard
NOTE: The information above are only guidelines and should serve as a starting point only
GB121-10
Detailed Project Classification Criteria
Cost Productivity - Soft
Category Area Discussion Comments How to Measure-To generate savings, the
floor space that is “freed up”
must be reallocated for
production.
-If the production space is
reallocated, the savings will be
“hard”.
-Utilize purchased cost
avoidance.
-The cost savings will be “soft”
until the production space is
utilized.
-Cost per square foot is
considered overhead.
-Creates the ability to run
more product, but the savings
is “soft” until there is
demand.
-Determine the increase in
billings / revenue or reduction in
backlog.
-Provides opportunities for
insourcing.
-Reduction in overtime.
-There is no savings when
you create downtime more
quickly.
-Reduction in variance over time.
-Opportunity to reduce the
equipment base.
-A cost must be eliminated.
“Hard” savings if there are
purchasing savings or if the
useful life is extended resulting
in reduced depreciation
“Soft” savings if internal
capacity is freed up – becomes a
“hard” savings if there is a
resource reduction or additive
work.
Equipment Utilization *
Cost
Productivity
-Soft
Floor Space
Tooling -Initiatives to improve /
increase the life of the tooling.
Cost
Productivity
- Soft
Cost
Productivity
- Soft
NOTE: The information above are only guidelines and should serve as a starting point only
COST SAVINGS GUIDELINES
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Page 6402-108, Rev. C
GB121-11
Detailed Project Classification Criteria
Working Capital / Cash Flow
Category Area Discussion Comments How to Measure
-Projects will need to be able
to determine the WIP.
-Hard savings are the result of a
decrease in Work-in-Process
Inventory
-This could have a negative
impact on EBIT – Finance to
stay close to the project to
ensure the correct targets are
established.
- The carrying cost of inventory
should not be included as this is
a financing activity and it does
not impact EBIT or Free Cash
Flow.
-Inventory reduction frees up
cash.
-Essentially the same
discussion as WIP inventory.
-Soft savings could include
savings due to reduction in
obsolete inventory.
-Generally we have data at the
product line level.
-Savings can be determined from
the accrual process or from
actual write off data.
Working
Capital /
Cash Flow -
Hard
Reduction in Receivables or
Increase in Payables terms
- Frees up cash - Hard savings are recognizable
when benefits can be sustained
over time
Finished Goods and
Component Inventory
Work-in-Process InventoryWorking
Capital /
Cash Flow -
Hard
Working
Capital /
Cash Flow -
Hard / Soft
NOTE: The information above are only guidelines and should serve as a starting point only
GB121-12
Six Sigma Project
Financial Impact
Examples
COST SAVINGS GUIDELINES
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Page 7402-108, Rev. C
GB121-13
Financial Impact Examples:
Revenue Growth
Cost Productivity
Working Capital (Cash)
GB121-14
Example 1: Growth Project
A production step in a one of Tyco’s plants is currently running at full capacity (24 hours x 7 days per week) and limiting the total potential sales because the demand for our product is greater than what we can produce. The next capacity limitation for our plant is over 1300 pounds per hour. There are more customers that want to buy this product but our plant is unable to meet their demand due to the capacity constraint. The prior year financials were:
Sales 12.0 MM$
Factory Cost 6.0 MM$ (2.0 Fixed and 4.0 Variable)
O-heads & Chgs 3.0 MM$ (1.5 Fixed and 1.5 Variable)
Operating Income 3.0 MM$
The project Y for the Six Sigma project is to increase the output of this limiting production step from 1200 pounds per hour to 1300 pounds per hour. If this is achieved, the revenue is expected to increase from 12.0 MM$ to 13.0 MM$ / year. In addition, the variable factory cost per unit is expected to then drop from 33.33% of sales to 32.00% of sales.
COST SAVINGS GUIDELINES
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GB121-15
Example 1: Growth Project
The financial benefit of this project is two-fold:
First, the variable manufacturing cost is cut from 33.33% to sales to 32.00% of sales. At last years sales volume, this will result in an operating income impact of $159,600 (12.0 MM$ x (.3333 - .3200)).
Second, since the sales were constrained by the plant capacity, there is a financial benefit of the added sales volume that occurred as the capacity increased. In this example, the sales increased by 1 MM$. The operating income/EBIT impact of this added sales is $555,000 (1.0 MM$ x (1 - .32 - .125)). The .32 is the variable factory cost % and the .125 is the variable other.
The total project benefits were:
$159,600 from reduced variable manufacturing cost
+ $555,000 from income from added sales
$714,600 Total Operating Income impact
If the plant were not operating at full capacity, the project savings would only have been the $159,600 from the reduced variable manufacturing cost.
GB121-16
Example 2: Cost / Productivity ProjectThe final manufacturing step for a product has had poor yields. For the last year, the yield has
been 80%. The Production Reporting information for the last 4 quarters is as follows:
Prior production information
Q1 Q2 Q3 Q4
Output (MM yds) 1.000 1.000 1.000 1.000
Labor (hrs) 1000 1000 1000 1000
Input (MM yds) 1.250 1.250 1.250 1.250
The Input material has a fully burdened cost of $2.00 / sq yd. The variable cost is $1.50 / sq yd. The fully burdened labor cost is $60 / hour. The variable labor cost is $35 / hour.
After the Six Sigma project is complete, the yield improved to 90% and the production reporting information for the next 4 quarters is as follows:
New production information
Q1 Q2 Q3 Q4
Output (MM yds) 1.000 1.000 1.000 1.000
Labor (hrs) 900 900 900 900
Input (MM yds) 1.111 1.111 1.111 1.111
The Operating Income impact is $208,500 / quarter.
(1,250,000 – 1,111,000) x $1.50 per sq yard + (100 x $35) = $212,000
The savings is based on the reduced input material required multiplied by the variable cost of the input material and the variable labor savings (Note that the fully burdened amount is not used since benefit costs for labor are not reduced due to the project).
COST SAVINGS GUIDELINES
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Page 9402-108, Rev. C
GB121-17
Example 3: Cash ProjectA business currently has sales of 45 MM$ per quarter and a DSO (Days Sales Outstanding of 40 days. Several of these days are the result of collection delays due to discrepancies. The Six Sigma Project is
to try to reduce the days of DSO due to discrepancies from 5 days to 1 day. If everything else stays equal, this will mean that the DSO will drop by 4 days.
Sales & A/R in MM$.
Last 4 quarters 4 Quarters after Project
Q1 Q2 Q3 Q4 | Q1 Q2 Q3 Q4
Sales 45 45 45 45 | 47.25 47.25 47.25 47.25
A/R 20 20 20 20 | 18.9 18.9 18.9 18.9
DSO 40 40 40 40 | 36 36 36 36
If DSO had stayed at 40, the AR would have been: 21.0 21.0 21.0 21.0
The Cash Benefit from this project is 2.1 MM$ (21.0 – 18.9). We would record the cash flow benefit in the first quarter of the project as 2.1 MM$. If the DSO stays at 36 days for the remaining 3 quarters and the sales remain at 47.25 MM$ / quarter the benefit in Q2, Q3, and Q4 would be 0. If either the sales grow
or the DSO drops further, then there would be additional benefit recorded in Q2, Q3, or Q4.
Some notes on this example:
- It is recommended to use the average DSO figures from the prior 4 quarters in establishing the baseline for comparison. If that will distort the real benefit, another method is to use the DSO figure from the prior quarter (or 3 months). It is not considered appropriate to use a projected DSO number as the baseline for comparison.
- Sometimes a DSO project will result in operating income impacts as well. For example, if by reducing discrepancies the business were able to reduce the employee count handling discrepancies from 3 people to 1 person, a cost savings equivalent to two people should be included as an operating income benefit.
GB121-18
There is a Monthly and Quarterly roll up of savings for your entire segment that is reported for all of Tyco. Therefore, youshould update estimates (costs and savings) regularly.
The Appendix shows the timeline of this reporting.
The examples used in this module are very basic. You will be involved in projects that have a much greater complexity. Some more scenarios are enclosed in the appendix.
Involve Finance from the beginning. You must be aligned with them in measuring costs and savings.
Tracking and Roll Up of Savings
As noted before, the tracking tool to be used for the entire company for measurement will be part of a separate training, however, some important things to be aware of are as follows:
COST SAVINGS GUIDELINES
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Page 10402-108, Rev. C
GB121(Appendix)-19
APPENDIXAPPENDIX
(Refer to Excel file)(Refer to Excel file)
GB121-20
All MBBs/BBs/GBs enter their Actual
impacts for the completed period.
Financial Leaders review Actual numbers for each
project and set Ready for Rollup to ‘Yes’.
Financial System Manager resets all
audit flags (Ready for Rollup) to No for Financial Benefit
metric.
MBB/BB/GB
Financial Leaders
Executives
Financial Manager
Audited Reporting Period
Executives and Financial Leaders can
run Enterprise Financial Benefit rollup reports for audited and un-audited projects
Casual Reporting Period
Financial Benefit reports can be run,
but numbers reported will be un-audited or
may be rough estimates.
5th 10th 11th 15th 15th – 5th
Monthly / Quarterly Update CycleMonthly / Quarterly Update Cycle(To be covered in specific Tracking training separately)(To be covered in specific Tracking training separately)
Quarterly
Quarterly
Quarterly
COST SAVINGS GUIDELINES APPENDIX
tyco
SUMMARY
1 As part of measuring the financial impact of Six Sigma projects, Tyco International and its
subsidiaries has adopted common categorization and measurement standards to determine
savings related to each project.
2 The financial impact of every project is versus the prior year or current year established baseline.
Also, the financial impact needs to be reviewed and approved by the financial individual responsible
for the department and/or division Operating Plan (Budget).
3 All project specific costs must be netted against the increased revenue or reduction of costs for each project.
General costs (e.g. Six Sigma resources and training, external consultants, license fees, etc…) will be
netted against the aggregate net savings for reporting purposes.
Prior Year or Current Year Baseline Impact
Hard Soft
Category (Impact or change vs. (No Impact or change
Baseline) vs. Baseline)
Revenue Growth * Increase Revenue Avoid Loss of Revenue
Cost Productivity Reduce Costs Avoid Additional Cost
Working Capital/ Cash
FlowIncrease Cash (Sustainable) Avoid Additional Investment
* The Hard Budget impact of revenue growth will be the margin associated with the
increase in revenue
Six Sigma Financial Impact Categories
Six Sigma Training for Green Belts - Week 1Tyco, Six Sigma Operational Excellence 402-108, Rev. C
COST SAVINGS GUIDELINES APPENDIX
tyco
Six Sigma Financial Impact Examples
Category Area Discussion Comments How to Measure
RevenueGrowth - Hard
Increased Mfg. Flexibility * -Translates into lead time reduction which can be tied to revenue growth and fall through.
-“Hard” savings is revenue growth due to additive business without additional capacity.
-Look for revenue growth due to ability to meet customer requested dates.-Delivery performance improvement does not equate to increasing inventory.
RevenueGrowth - Soft
Customer Returns / Customer Complaints
-Project the potential loss of business.
-Savings associated with lost business due to poor quality
-Purchased price reduction is “hard” savings.-Reduction in receiving inspection is a “hard” savings – only if the person / position is eliminated.
-Monitor the people component. -Redeployment of people may result in “soft” savings.
-Report the equivalent people’s work that we don’t need to pay.
-Reduction in overtime results in “hard” savings.-Reduction in use of agency results in “hard” savings.-Elimination of outside trucking costs.
-Reduction in premium freight is a “hard” savings.-Elimination of direct labor, e.g. material handler.
-Possible savings from: -“Hard” savings if less raw material is purchased.
-Not needing to buy a certain amount of raw material.
-Calculate the savings using the standard manufacturing costs.
-Calculate the direct costs associated with the replenishment process
- There could be a Revenue impact if the scrap is being sold.
-Must be cautious of the use of variable vs. fixed costs.-To generate savings, the floor space that is “freed up” must be reallocated for production.
-If the production space is reallocated, the savings will be “hard”.
-Utilize purchased cost avoidance.
-The cost savings will be “soft” until the production space is utilized.
-Cost per square foot is considered overhead.
-Creates the ability to run more product, but the savings is “soft” until there is demand.
-Determine the increase in billings / revenue or reduction in backlog.
-Provides opportunities for insourcing.
-Reduction in overtime.
-There is no savings when you create downtime more quickly.
-Reduction in variance over time.
-Opportunity to reduce the equipment base.-A cost must be eliminated.
Equipment Utilization *
Floor Space
Administrative Errors -Reduction in customer returns and complaints.
-Increases revenue due to elimination of errors.
Productivity Performance (Direct costs)
-“Hard” savings is revenue growth due to additive business without additional capacity.
Transportation, parts movement
Cost of Poor Quality
Delivery Performance
Supplier Cost, Delivery, Service, Quality
-May result in a reduction in WIP inventory.
RevenueGrowth - Hard
CostProductivity - Hard
RevenueGrowth - Hard
CostProductivity - Hard
CostProductivity - Hard
CostProductivity - Hard
CostProductivity - Soft
CostProductivity - Soft
Six Sigma Training for Green Belts - Week 1Tyco, Six Sigma Operational Excellence 402-108, Rev. C
COST SAVINGS GUIDELINES APPENDIX
tyco
Six Sigma Financial Impact Examples
Category Area Discussion Comments How to Measure
-Savings associated with decreased rework, inspection, production orders to replenish inventory.
“Soft” savings if internal capacity is freed up – becomes a “hard” savings if there is a resource reduction or additive work.
“Hard” savings if there are purchasing savings or if the useful life is extended resulting in reduced depreciation“Soft” savings if internal capacity is freed up – becomes a “hard” savings if there is a resource reduction or additive work.
-CAUTION: this is an area that frequently results in “double dipping”.-Reduction in overhead costs usually won’t occur during the short life of a Six Sigma project.-Projects will need to be able to determine the WIP.
-Hard savings are the result of a decrease in Work-in-Process Inventory
-This could have a negative impact on EBIT – Finance to stay close to the project to ensure the correct targets are established.
- The carrying cost of inventory should not be included as this is a financing activity and it does not impact EBIT or Free Cash Flow.
-Inventory reduction frees up cash.-Essentially the same discussion as WIP inventory.
-Soft savings could include savings due to reduction in obsolete inventory.
-Generally we have data at the product line level.
-Savings can be determined from the accrual process or from actual write off data.
WorkingCapital / Cash Flow - Hard
Reduction in Receivables or Increase in Payables terms
- Frees up cash - Hard savings are recognizable when benefits can be sustained over time
-Some inventory write off is tied to the business environment.
-Avoiding the write off provides “hard” savings by making a contribution to cash flow.
-Can be characterized as “per unit of demand”.
-Reduction in provision balance at the PL or PC level.
Finished Goods and Component Inventory
CostProductivity - Soft
Customer Returns / Customer Complaints
Overhead Costs
CostProductivity -Soft
Work-in-Process Inventory
-Costs associated with processing CRM.
Inventory Write-off
Indirect Manufacturing Costs -All these savings are “soft”.
Tooling -Initiatives to improve / increase the life of the tooling.
WorkingCapital / Cash Flow
CostProductivity - Soft
CostProductivity - Soft
WorkingCapital / Cash Flow - Hard
WorkingCapital / Cash Flow - Hard / Soft
Six Sigma Training for Green Belts - Week 1Tyco, Six Sigma Operational Excellence 402-108, Rev. C
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 1402-108, Rev. C
Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-1
SummaryWeek 1
Process Improvement Methodology Process Improvement Methodology
Operations Green BeltsOperations Green Belts
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-2
PurposePurpose
You have been exposed to a number of tools and techniques this week
You now must plan to put those tools to use on your project
This module will provide some insight as to what the expectations are for your project for next week’s presentation
Take the next few hours to develop your action plan for your project efforts over the next few weeks
SUMMARY
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Page 2402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-3
Getting Ready for “Week” 2 TrainingGetting Ready for “Week” 2 Training
A project team is in place and functioning with the Project Leader effectively facilitating
The team understands the business impact of the project
Definition of the business and project metrics are complete and documented in the Charter
A detailed level 1 process map is developed and reflects key customer requirements and all process variables
The initial Value Stream Map is developed
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-4
Getting Ready for “Week” 2 TrainingGetting Ready for “Week” 2 Training
The relationship between process inputs and customer requirements has been investigated to provide direction for variable investigation (C&E Matrix)
Risk assessment and reduction efforts are documented and implemented as a result of the FMEA
The measurement system is in place and the measurement system capability studies are completed for both variables and attributes
Initial Capability Studies have been completed
SUMMARY
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Page 3402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-5
MeasureMeasure
AnalyzeAnalyze
ImproveImprove
ControlControl
ProjectDescription
ProcessMap
C & EMatrix
PreliminaryFMEA
MSA 1st Capability Study & SPC
Multi-Vari
DOE (or other improvement activities)
ControlPlan
Hand OffTraining
Final Capability
Study
OwnerSign-Off
Final ProjectReport
These are typically doneBefore the second class.
These are typically doneBefore the second class.
Project TrackingProject Tracking
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-6
Level 0 Process Map / Initial Value Stream MapLevel 0 Process Map / Initial Value Stream Map
Importance to Improvement Process
ALL processes need to be mapped
Identifies ALL potential Key Process Input Variables (Input Variables) affecting the process
If not done correctly and in detail, further actions may not be effective(don’t miss the noise !)
All other tools in the DMAIC process rely on a well-defined, well-documented process map
The results of this effort will be the input for Input Variable selection later on
IT MUST BE DONE THOROUGHLY
SUMMARY
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Page 4402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-7
Level 0 Process Map / Initial Value Stream MapLevel 0 Process Map / Initial Value Stream Map
Application Methods
A cross functional team is utilized to document the process steps and Input Variables through a series of focused meetings facilitated by the Project Leader
All pertinent process experts need to be involved
Outputs
A documented process
A complete list of Input Variables by step
Visual representation of the process
Each Input Variable classified as Controlled or Uncontrolled
Initial Control Plans for Output Variables
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-8
Level 0 Process Map / Initial Value Stream MapLevel 0 Process Map / Initial Value Stream Map
Questions to Ask
Who was on your team?
How were the Customer Requirements identified?
What does the team feel are the most important Uncontrolled Input Variables in the process?
Does this map reflect the current process or the one we want?
Where are the rework loops identified?
What are the non-value added processes?
Where are the measurement points in the process?
What does your Control Plan include at this time?
How many Input Variables did you identify per step (on average)?
What is the team going to do next?
SUMMARY
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Page 5402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-9
C&E MatrixC&E Matrix
Importance to Process
Aids in the prioritization of process steps and Input Variables to evaluate further
Provides a method to reduce the efforts in FMEA by only addressing Input Variables correlated highly to Customer Requirements
Application Methods
The Project Leader will develop the matrix and use the Team members to determine the correlation scores
In processes with many steps (over 10), process steps are first prioritized using a C&E Matrix
Then, all Input Variables from the key process steps are prioritized for further action
Marketing, Sales, QA/QC, and others should be utilized to determine customer ranking scores for Customer Requirements
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-10
C&E MatrixC&E Matrix
Outputs
A prioritized list of Input Variables to investigate further using FMEA, Multi-vari, etc.
Updated Control Plan focused on variables related to Customer Requirements
Prioritization of potential Measurement Systems to be analyzed
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 6402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-11
C&E MatrixC&E Matrix
Questions to Ask
Who was on your team?
Who determined the Customer Requirements weighting?
Did you use the C&E Matrix to prioritize Process Steps to address first?
How did the team determine the correlation values?
Does the prioritization make sense?
How many Uncontrolled Variables rated highly in the sorted matrix?
How did the team determine the cut-off for variables to investigate further?
Did your team include internal customer requirements?
What are your next steps?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-12
C&E Matrix C&E Matrix -- ExampleExampleRating of
Importance to
Customer
5 10 7 8 10 8 5
1 2 3 1 2 3 4
On
-tim
e
De
live
ry
Qu
alit
y to
me
et
Exp
ecta
tion
s
Ca
pa
city
Total Up
tim
e
Sa
fety
1st P
ass
Effic
ien
cy
Sp
ee
d
Total
Process Step Process Input
14 Pre-Heat Plan Limits 9 9 9 198 9 3 9 9 219
16 Pre-Heat
Roll Cleaning
(frequency and
procedure)
3 9 3 126 3 9 9 1 191
7 Pre-HeatRoll Surface
Temperature3 9 3 126 9 3 9 3 189
9 Pre-Heat Valve Positions 3 9 3 126 9 3 9 3 189
17 Pre-HeatAir Pressure to
Control Valves3 9 3 126 9 3 9 3 189
19 Pre-HeatSteam Pressure
from Boiler3 9 3 126 9 3 9 3 189
2 Pre-HeatSheet Gauge
(Variation)3 9 3 126 9 1 9 3 169
8 Pre-HeatRoll Surface
Quality9 9 3 156 3 3 9 1 131
5 Pre-Heat Sheet Dryness 3 9 1 112 3 3 9 1 131
26 Pre-Heat
Airborne
Contaminant
Level
1 9 1 102 3 3 9 1 131
10 Pre-Heat Roll Speeds 3 9 3 126 3 1 9 3 121
28 Pre-Heat Resin Properties 3 9 3 126 1 0 9 3 95
6 Pre-HeatInlet Sheet
Temperature3 3 3 66 3 0 3 9 93
11 Pre-HeatDancer Load Cell
Calibration3 1 1 32 3 3 1 3 77
21 Pre-HeatBearing
Lubrication1 1 1 22 3 3 1 1 67
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 7402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-13
FMEAFMEA
Importance to Process
Identifies potential risks to the process performance as relatedto the Customer Requirements
Identifies prioritized actions to reduce risk
Provides a history of actions taken to improve the process
Provides a basis for a Troubleshooting Guide
Application Methods
The Project Leader will develop the initial FMEA inputs and use the Team members to complete the inputs and rate Severity, Occurrence, and Detection values
Each prioritized Input Variable from the C&E Matrix should be evaluated with the FMEA
Members of the Team determine top ranked Input Variables (highest RPNs) and assign actions to reduce the risk
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-14
FMEAFMEA
Outputs
A prioritized list of actions to take to reduce risk
Actions identify Who, What, and When
Re-evaluated impact of actions taken (new RPN value)
Identification of variables to be investigated further using:
Measurement Systems Analysis
Multi-vari studies
DOE methods
Control methods (Poka-yoke, SOPs, etc.)
Updated Control Plan
Initial Troubleshooting Guide / Reaction Plan
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 8402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-15
FMEAFMEA
Questions to Ask
Who was on your team?
How were the Severity, Occurrence and Detection values determined by the team?
What was the source of the rating scales for Severity, Occurrence and Detection?
What are the top Input Variables?
What actions are being taken and by whom?
What barriers exist to completion of the top ranked items?
Are there any ‘quick fixes’ to be completed?
Were Safety items included?
How did the team decide what items to investigate further?
How did the actions affect the process performance?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-16
FMEA FMEA -- ExampleExample
Process StepKey Process
InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
What is the
process step
What is the Key
Process Input?
In what ways does the Key
Input go wrong?
What is the impact on the Key
Output Variables (Customer
Requirements) or internal
requirements?
Ho
w S
eve
re is th
e e
ffe
ct
to th
e c
uso
tme
r? What causes the Key Input to
go wrong?
Ho
w o
fte
n d
oe
s c
au
se
or
FM
occu
r? What are the existing controls and
procedures (inspection and test)
that prevent eith the cause or the
Failure Mode? Should include an
SOP number.
Ho
w w
ell
ca
n y
ou
de
tect
ca
use
or
FM
?
MDO Preheat Roll Surface
Temperature
Too high Pick-off, scuffs, increased
cleaning, increased scrap,
decreased uptime
10
Control valve sticking
3
Valve positioner and valve PM
3 90
10Control valve sticking
3Alarm / operator response
3 90
10Failed pressure transducer
4Calibration PM
5 200
10Incorrect set-point
6Plan limits
9 540
10Control hardware failure
3Unknown
10 300
10Control power failure
2None
10 200
10Control software error
2Unknown
10 200
10Temperature sensor incorrect
4Calibration PM
5 200
10Alarm setting incorrect
3None
10 300
10Alarm annuciator failure
1None
10 100
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 9402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-17
Studying Measurement SystemsStudying Measurement Systems
Once we have the right data we must determine our ability to measure it
Information to be attained
How big is the measurement error?
What are the sources of measurement error?
Is the measurement system stable over time?
Is it capable for this study?
How do we improve the measurement system?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-18
Measurement Systems AnalysisMeasurement Systems AnalysisImportance to Process
Measurement System error must be understood to determine the impact to ANY measurement made
Measurement error can have a negative impact on a variety of future actions:
Capability assessment
Multi-vari studies
Statistical Process Control methods
Testing for improvements (any statistical evaluation)
Design of Experiments
Tolerancing of designs
Supplied material assessment
Final product assessment
etc.
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 10402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-19
Measurement Systems AnalysisMeasurement Systems Analysis
Application Methods
The Project Leader will conduct Measurement Systems Analysis (MSA) for appropriate process measures
Team members and support personnel will be involved in MSA activities and improvements
Initially, each measurement system used for measuring Output Variables and project metrics must be evaluated
Eventually, measurement systems for Critical Input Variables must be evaluated and tracked
This is completed after the verification that the Input Variable has a need to be controlled through DOE
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-20
Measurement Systems AnalysisMeasurement Systems Analysis
Outputs
Assessment of measurement system capability to measure:
Process changes (%R&R)
Compliance to customer requirements (% P/T)
Actions required to make the measurement system effective in measuring process change and compliance to customer requirements
Updated Control Plans and FMEA
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 11402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-21
Measurement Systems AnalysisMeasurement Systems Analysis
Questions to Ask
Who performed the MSA?
What are the %P/T and %R&R values?
What was the main source of measurement error?
What actions are necessary for improvement, and what impact willthey have?
How were the samples selected for the MSA?
What is the measurement system acceptable for use in assessing -Process Shift or Acceptance to Spec?
What variable(s) have you completed MSA on - Output Variables or Input Variables?
What effect will this measurement system have on Capability assessments, Multi-vari studies, and DOEs?
How is the measurement capability going to be monitored into thefuture?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-22
MSAMSA -- ExampleExample
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 12402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-23
Attribute Data MSAAttribute Data MSA
If many people are evaluating the same thing they need to agree:
With each other
With themselves
Attribute data contains less information than variables data, but often it is all that’s available
Therefore, we must be diligent about the integrity of attribute measurement systems
The issue is
Can I rely on the data coming from my measurement system?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-24
Attribute MSA GoalsAttribute MSA Goals
Determine:
% overall agreement
% agreement within appraisers (Repeatability)
% agreement between appraisers (Reproducibility)
% agreement with known standard (Accuracy)
Kappa (how much better the measurement system is than random chance)
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 13402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-25
Kappa Value Suggested Interpretation
-1 to 0.0 Random agreement
> 0.60 Marginal - Significant effort required
> 0.70 Good - Improvement warranted
> 0.90 Excellent
Kappa will be between -1 and +1Kappa will be between -1 and +1
A Kappa value of +1 means perfect agreement
General Rule: If K<0.70, the measurement system needs attention!
Kappa TechniquesKappa Techniques
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-26
Measurement Systems Measurement Systems -- Questions to AskQuestions to Ask
Have you picked the right measurement system? Is this measurement system associated with either critical inputs or outputs?
What do the precision, accuracy, and stability look like?
What are the sources of variation and what is the measurement error?
What needs to be done to improve this system?
Have we informed the right people of our results?
Who owns this measurement system?
Who owns trouble shooting?
Does this system have a control plan in place?
What’s the training frequency? Is that frequent enough?
Do identical systems match?
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 14402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-27
Capability StudiesCapability Studies
Importance to Process
Initial capability (baseline) of the process to perform to customer requirements (Output Variables) needs to be determined before changes are made
Assessment after improvements are made are used to establish the level of improvement and long-term performance to specification
This is the basis for Statistical Tolerancing (used in designing new products) to provide more designs aligned better with manufacturability
Provides an estimate of the percentage of product that will be non-conforming
Provides first look into the sources of variation in the process through data-mining techniques
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-28
Capability StudiesCapability Studies
Application Methods
The Project Leader will conduct Capability Studies on appropriate project measures
Output Variables
Business Metrics
Team members and support personnel will be involved in data collection
Baseline performance, project goals and process entitlement values are established based on these studies
Outputs
Baseline, Goal and Entitlement values for Business Metrics and
Updated Charter and FMEA reflecting new data
Initial data analysis to identify potential sources of variation
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 15402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-29
Capability StudiesCapability Studies
Questions to Ask
What variables have you conducted capability studies on?
What is the expected Short Term and Long Term performance of that variable?
What are the suspected contributing factors to the variation in the process?
How does the measurement system performance impact the process capability?
What should the goals for the process capability (Cp / Cpk) be based on the baseline data?
Over what time frame were the data taken?
How many data points / sub-groups were used to determine the process capability?
Was the data normally distributed?
How do we handle non-normal data?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-30
Capability Study Capability Study -- ExampleExample
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 16402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-31
Project PresentationProject Presentation
<Project Name>
<Belt>
<Project Review Date>
<Business Unit>
<Champion>
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-32
Project PresentationProject Presentation
<Brief Statement of Project Scope>
Belt Candidate: <name> <email>Team Members: <name> <email>…
<Projected Financial Results>
<Name of Project><Name of Project>
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 17402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-33
Project Accomplishments To DateProject Accomplishments To Date
Key Learning:
Tools Used:
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-34
Project Accomplishments To DateProject Accomplishments To Date
Obstacles Overcome:
Benefits to Date:
Financial Results to Date:
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 18402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-35
Tools OverviewTools Overview
<List tools utilized since the last project review>
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-36
Specific Tools ReviewSpecific Tools Review
<What tool was used, where, how>
<Output from Minitab>
<Impact>
<Benefit gained>
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 19402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-37
Project PlansProject Plans
<Upcoming planned activities and tools to be used to achieve the project deliverables>
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-38
Project IssuesProject Issues
<Obstacles that need to be overcome>
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 20402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-39
Example Process MapExample Process MapQuestions
Who helped develop the map and what organizations do they represent?
Does it reflect current state or the desired process?
Are all non value-added steps included?
What quick-hits did you find from this effort?
What process steps does the team feel can be eliminated or combined to reduce opportunities for scrap and increase rate?
What characterizes an Uncontrolled and a Controlled variable?
How do we measure the product for defects within the process?
PAPERWORK TURN STEAM ON
TO DICY TANKLOAD DMF LOAD DICY LOAD 2MI
BILL OF MATERIALS
ISO PROCEDURES
REWORK
SCALE ACCURACY
PREHEATING
LOAD ACCURACY
CLEANLINESS
RAW MATERIAL
LOAD ACCURACY
ENVIRONMENT
(HUMIDITY)
RAW MATERIAL
MIXER SPEED
LOAD ACCURACY
ENVIRONMENT
(HUMIDITY)
RAW MATERIAL
MIXER SPEED
GET PORTABLE CLEAN IF
NECESSARY
LOAD SOLVENT
TO PORTABLE
LOAD DRUM
STOCK TO
PORTABLE
2
PROPER SIZE
REWORK
CLEANLINESS
CROSS
CONTAMINATION
LOAD ACCURACY
ENVIRONMENT
RAW MATERIAL
LOAD ACCURACY
ENVIRONMENT
RAW MATERIAL
MIXER SPEED
LOAD BULK
MATERIAL TO
PORTABLE
2PUMP DICY
SOLUTION TO
PORTABLE
DIGEST STAGE BATCH TO
TREATER
LOAD ACCURACY
ENVIRONMENT
RAW MATERIAL
MIXER SPEED
PUMP RATE
MIXER SPEED
FILTERING
TEMPERATURE
COMPLETE
SOLUTION
TIME
MIX SPEED
VENTING
ENVIRONMENT
TIME
MIX SPEED
VENTING
ENVIRONMENT
COMPOUNDINGINPUTS
Techniques
Procedures
Raw Materials
Rework
Equipment
Environment
Cleanliness
Dicy Solution Temp
OUTPUTS
Reactivity (Gel)
Viscosity
Cleanliness
Color
Homogeneity
Consistency Batch to Batch
Digestion Time
Temperature
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-40
Example Cause and Effects MatrixExample Cause and Effects Matrix
Rating of Importance to
Customer7 9 4 10 8 10
1 2 3 4 5 6
Pain
t
thic
kness
Pain
t
hard
ness
Rate
Covera
ge
Surf
ace
quality
Pro
per
schem
e
Process Step Process InputTotal
1 Paint Nozzle type 9 0 9 9 9 9 351
2 Paint Paint viscosity 9 9 9 9 3 3 324
3 Paint Air pressure 9 3 9 9 9 3 318
4 Prime Primer age 9 9 9 3 9 0 282
5 Paint Paint age 9 9 9 3 9 0 282
6 Paint Surface contamination 1 9 3 9 9 1 272
7 Prime Air pressure 9 1 9 9 9 0 270
8 Prime Nozzle type 9 0 3 9 9 0 237
9 Prime Surface contamination 1 3 3 9 9 0 208
10 Prime Relative humidity 1 9 9 1 9 0 206
11 Paint Ambient temp 1 9 9 0 9 0 196
12 Paint Surface roughness 3 1 3 3 9 3 174
13 Paint Lot number 0 1 0 3 1 9 137
14 Prime Surface roughness 0 1 3 3 9 0 123
15 Prime Lot number 1 3 0 3 1 3 102
16 Prime Ambient temp 1 3 9 0 3 0 94
Total 504
630
384
820
928
310
Lower Spec
Target
Upper Spec
Cause and Effect
Matrix
This table provides the initial input to the FMEA. When each of the
output variables (requirements) are not correct, that represents
potential "EFFECTS". When each input variable is not correct, that
represents "Failure Modes".
1. List the Key Process Output Variables
2. Rate each variable on a 1-to-10 scale to importantance to the
customer
3. List Key Process Input Variables
4. Rate each variables relationship to each output variable on a 1-
to-10 scale
Questions
Who provided inputs to the Customer Requirements for this matrix?
What groups determined the relationship ratings for the Inputs and Outputs?
What method was used to determine the final relationship score?
What actions are being taken on the top ranked Input Variables?
Are there any quick hits that can be assigned to lower ranking Input Variables?
Do the current Control Plans reflect the need to monitor these top Input Variables?
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 21402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-41
Process
Step/InputPotential Failure Mode Potential Failure Effects
S
E
V
Potential Causes
O
C
C
Current Controls
D
E
T
R
P
N
Load DMF/DMF
Load Accuracy Mischarge of DMF Viscosity out of spec 7 SOP not Followed 5Operator Certification/ Process
Audit5 175
Steam to
DICY/Scale
Accuracy
Scale Not Zeroed Mischarge DMF 3 Faulty Scale 2 None 9 54
Load DMF/DMF
Load Accuracy Mischarge of DMF Viscosity out of spec 7 Equipment Failure 2Maintenance Procedure (SOP
5821)/Visual Check3 42
Steam to
DICY/Scale
Accuracy
Scale > 0 Low DMF Charge 3 Water in Jacket 2Visual Check of Jacket (SOP
5681)4 24
Steam to
DICY/Scale
Accuracy
Scale Inaccurate High DMF Charge 3 Tank Hanging Up 2 Visual Check (SOP 5681) 4 24
Example FMEAExample FMEAQuestions
Who helped develop the FMEA and what organizations do they represent?
Does the tool reflect current state or the desired process?
What quick-hits did you find from this effort?
What type of ranking system did you use ?
Did you complete the actions recommended section of the tool ?
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-42
Example Baseline Capability DataExample Baseline Capability DataQuestions
What variables were evaluated?
Are they stable?
Are there explanations for out of control points?
How long was the process monitored to determine stability?
What are the largest types of variation over time - shift to shift, week to week, etc.?
Registration ScrapRegistration Scrap
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
Fiscal Week
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 22402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-43
Example MSA ResultsExample MSA ResultsQuestions
What are the major sources of measurement error?
How much measurement error exists in comparison to the process variation? In comparison to the customer requirements?
Is the measurement system acceptable for the process improvement efforts?
If not, what actions do you suggest?
How were the samples chosen?
Was the team sensitive to sub-grouping for sample selection?
Gage R&R
%Contribution
Source VarComp (of VarComp)
Total Gage R&R 0.0044375 10.67
Repeatability 0.0012917 3.10
Reproducibility 0.0031458 7.56
Operator 0.0009120 2.19
Operator*Sample 0.0022338 5.37
Part-To-Part 0.0371644 89.33
Total Variation 0.0416019 100.00
Study Var %Study Var %Tolerance
Source StdDev (SD) (5.15 * SD) (%SV) (SV/Toler)
Total Gage R&R 0.066615 0.34306 32.66 68.61
Repeatability 0.035940 0.18509 17.62 37.02
Reproducibility 0.056088 0.28885 27.50 57.77
Operator 0.030200 0.15553 14.81 31.11
Operator*Sample 0.047263 0.24340 23.17 48.68
Part-To-Part 0.192781 0.99282 94.52 198.56
Total Variation 0.203965 1.05042 100.00 210.08
Number of Distinct Categories = 4
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-44
Example Capability SummaryExample Capability SummaryQuestions
What are the long and short term process capability values?
How does this compare to the customer’s perspective of performance?
Is there a significant opportunity to improve beyond current levels (i.e., how large is the gap between Cp and Ppk)?
What are the definitions of defects and opportunities?
Are the capabilities established for Input Variables and Output Variables from the C&E?
Customer
Requirement
(Output Variable)
Measurement
Technique
%R&R or
P/T Ratio
Upper
Spec
Limit
Target
Lower
Spec
Limit
Cp CpkSample
SizeDate Status
Fire RetardencyUL 700 25% 3 1.5 na
No Data
Available
Selvage Edge
Consistency
On-line Physical
Measurement15% 4.5 4 3.5 1.15 0.85 50 Sep-95
Improvement
Plan in Place
Membrane
Stability
Oven Test None 0.75 0.5 na 1.1 0.65 25 Aug-95
Measurement
Study
Scheduled
SUMMARY
Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence
Page 23402-108, Rev. C
Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.
GB121-45
Summary of “Week” 1 TrainingSummary of “Week” 1 Training
Effective team in place with clear goals and plans to achieve success
Good understanding of the current process and it’s capabilities
Measurement systems established and their capabilities understood
Initial Control Plans in place and gaps identified
Linkage between project goals and business impact is internalized by all
Visible project plans at all levels
Recommended