Ie 6611 Fundamentals of Lean Six Sigma Objectives



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IE 6611 Fundamentals of Lean Six Sigma
Objectives
Develop conceptual understanding of important aspects of Lean Six Sigma
Develop basic skills in statistical data analysis
Develop basic skills in problem definition and analysis by using six sigma tools
Covers the body of knowledge for certified six sigma black belt
Master the basic skills and knowledge base for greenbelts
How this course is going to be taught?
Book and course lecture notes (in blackboard)
Follows certified six sigma black belt body of knowledge
Weekly quizes (similar to CSSBB exams)
Minitab or other practice problems
Midterm and Final
This is not an easy course, but a very rewarding course
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Certified Six Sigma Black Belt Exam
Six Sigma enterprise wide deployment
6%
9
Business process management
6%
9
Project management
10%
15
Six Sigma - Define
6%
9
Six Sigma – Measure
20%
30
Six Sigma – Analyze
15.3%
23
Six Sigma – Improve
14.7%
22
Six Sigma - Control
10%
15
Lean enterprise
6%
9
Design for Six Sigma
6%
9
Total
100%
150 (a 4 hour test)
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What is Six Sigma?
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Fundamental Beliefs
Everything is a process
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BUSINESS PROCESSES
BUSINESS MANAGEMENT
SUPPLIER MANAGEMENT
INFORMATION TECHNOLOGY
Core Operation
Impet us
Ideat ion Concept development Design Production Sale Service
Products

In business world, there are only products and processes, actually, products
are also processes, because the product usage is also a process
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Do the right thing,
and do things right
Design the right product, the right process
Consistent product, consistent process
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Sigma - the lowercase Greek letter that denotes a statistical unit of measurement used to define the standard deviation of a population. It measures the variability or spread of the data.
What is Sigma?
σ
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Cure Time of Silicone Sealant
Nu
mb
e
r o
f S
a
mp
le
s
Lower Specification Limit
(LSL)
Upper Specification Limit
(USL)
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Upper Specification Limit
(USL)
Lower Specification Limit
(LSL)
x
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In a normal distribution, 99.73% of measurements will fall within +/- 3 sigma
99.99966% will fall within +/- 4.5 sigma
With 1.5 sigma mean shift, we need specification limits=mean+/- 6 sigma to
achieve 99.99966% within spec.
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Six Sigma Process Capability
SIGMA DPM
COPQ
CAPABILITY
6 sigma
3.4 <10% of sales
World Class
5 sigma
230
10 to 15% of sales
4 sigma
6200 15 to 20% of sales Industry average
3 sigma
67,000 20 to 30% of sales
2 sigma
310,000 30 to 40% of sales Noncompetitive
1 sigma
700,000
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What Sigma performance implies
Six Sigma performance implies a level of process
and product performance of no more than 3.4
defects per million opportunities.
2 3
4 5
6 308,537 66,807 6,210 233 Defects per million opportunities
}
Most
Companies
}
Texas
Instruments
& Motorola are here
(DPMO)
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6-Sigma
99.99966% Good

20,000 lost articles of mail per hour
• Unsafe drinking water for almost 15
minutes each day

5,000 incorrect surgical operations
per week

Two short or long landings at most
major airports each day

200,000 wrong drug prescriptions
each year

Seven articles lost per hour
• Unsafe drinking water one minute
every seven months

1.7 incorrect operations per week

One short or long landing every
five years

68 wrong drug prescriptions per
year
3.8-Sigma
99% Good


3.4 defects per million opportunities
Why 6 Sigma quality is needed?
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Cost of Quality
Costs:
Direct/Indirect Labor Sales, Engineering, Mfg, Quality, Operations, Administration
Materials: Raw materials, component parts, processing, supplied products
Activities:
Shift and Lower All Costs
Prevention
Appraisal
Internal
Failure
Planning
Auditing
Warranty
Credits
Complaints/
Rejects
Errors
Rework/
Scrap
Training
Maintenance
Inspect/
Test
External
Failure
Cost of Poor Quality
Product
Liability
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Six Sigma Results
Motorola: Savings of $940,000,000 over 3 years
AlliedSignal: 1.4 billion saving in 1997
GE invested in six sigma for $1 bil and return a $1.75 bil in 1998 and an accumulated
savings of $2.5 bil in 1999.
Average Black belt project will save about $175,000
There should be about 5-6 project per year per black belt
4-5 greenbelts per 100 employees
One black belt per 100 employees, can provide a saving of 6% per year.
For a large company, there is usually one master black belt per 100 black belts.
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Organizational Infrastructure
Leader/
Champion
Master Black Belt
(MBB)
Black Belts (BB)
Green Belts (GB)
Project Team Members
Is responsible for coordinating a business roadmap to achieve
6
σ. Selects projects, executes control, and alleviates roadblocks for the 6
σ projects in his or her area of responsibility.
Is mentor, trainer, and coach of Black Belts and others in the organization. Brings the broad organization up to the required 6
σ competency level.
Is a leader of teams implementing the 6
σ methodology on projects. Introduces the methodology and tools to team members and the broader organization.
Delivers successful small, focused departmental projects using the success strategy.
Participates on the project teams. Supports the goals of the project, typically in the context of his or her existing responsibilities. Is expected to continue to utilize learned methodology and tools as part of his or her normal job.
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Typical Six Sigma Training and Duties
Black Belt Training
4 month training program consisting of one week of instruction each month
Software: Excel and Minitab
Body of knowledge of BB
Black Belt duties
Mentor: have a network of Six Sigma individuals in the company
Teacher: Train local personnel
Coach: provide support to personnel on local projects
Identifier: discover opportunities for improvement
Influencer: Bean advocate of Six Sigma tools and strategy
More black belts stuff will be discussed later
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Project Execution
Project Selection
Project Flowchart
Project Management
Project Evaluation
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Project Selection
Attributes of a good Six Sigma project Strong tie to business deliverables
– Enhances customer satisfaction and loyalty Strategic importance Impact on profit (hard and soft benefits Results are visible or easily link to a key executive metric Meaningful results within 6 months (DMAIC only Goals can be quantified and measured Project is feasible Team can commit time to project Potential solutions are relatively low cost
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Where Do Projects Come From?
Top Down
Bottom Up
Business Unit Analysis
Earned Value Analysis
Legal/Compliance Issues
Strategic Initiatives
Voice of the Customer
Quality Issues/Metrics
Productivity
Operating Personnel
Lean Initiatives
Process Maps
Projects
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Examples of Project Types

Cost Reduction

Yield and Rate Improvement

Downtime Reduction/Equipment Utilization

Raw Material Selection

Utility Optimization (Gas, Steam, Air and Electricity)

Trailer/Railcar Utilization

Accounts Payable Process

Corporate Credit Card Usage

Cost Avoidance

Reducing effects of future cost increases (disposal/environmental)

Inventory Control

Capital Avoidance

Plant Utilization

Rate Improvement

Revenue Enhancement

Plant Capacity Improvement

Tech Service Offerings

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Problem identification
Imp rove Define MeasureAna lyze
Control
6
σ
Measurements
and data
collection
Data interpretation
and root causes
analysis
Generation and
implementation of
corrective actions
Optimized
process
monitoring
Project Roadmap: DMAIC
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The Six Sigma Breakthrough Methodology (DMAIC)
Define
Measure
Analyze
Improve
Control
Purpose
♦ Define Business
Metrics
♦ Identify projects for process improvement Select resources for project improvement Establish baseline performance Validate measurements for each project Set performance objectives Identify sources of variation Prioritize the vital few causes of variation Establish relationships between output and input variables Implement Solutions Ensure Solutions are sustained Document Case
Studies
Primary
Tools
♦ Process Mapping Business Metrics Trend Charts Root Cause analysis Voice of the Customer Trend charts Six Sigma Metrics Process Capability
Analysis
♦ Process Flow
Diagram
♦ Descriptive
Statistics
♦ Basic SPC
♦ Measurement
System Analysis Data Collection forms Control Charts Frequency plots Hypothesis
Testing
♦ Cause and Effect
Diagrams
♦ Affinity Diagrams Data Collection
Forms
♦ FMEA
♦ Root Cause
Verification
♦ Value Stream
Mapping
♦ Design of
Experiments
♦ FMEA
♦ Planning Tools Process Capability
Analysis
♦ SPC Level 2
♦ Measurement
Capability Analysis Principles of Lean
Manufacturing
♦ Mistake Proofing SPC Implementation Control Plans Process Standards Evaluate Process improvement results
Key
Outputs
♦ Project team Project Program plan Management
Commitment
♦ Product performance baseline Measures for evaluating performance of the
Product or Process Defined list of potential sources of variation Cost Benefit
Analysis
♦ Proposed process settings Impact of proposed solutions Process in Control Project
Documentation
♦ Opportunities for transfer of learning
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Project Management
Managing time, deadline
Managing resource
Periodical Reviews
Team work and Leadership
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Project Evaluation
Project benefits have to be certified by accounting
What goes right
What goes wrong
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Training
Executive Overview of Six Sigma Program ( 1 Day)
Six Sigma Leader/Champion ( 1-3 days)
Master Black Belt ( Black belts + projects + additional training)
Black Belt (4 weeks + projects)
Green Belt ( 2 weeks )
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•Black Belts
•Full Time Six Sigma Resource Weeks Initial training, plus followup advanced statistical training
•Work 3-4 projects Simultaneously
•Mentor 5-10 Green Belt project
•Train some Green Belts Green Belts
•Initially trained Manufacturing or Operations GBs
•2 Week training -- equivalent Define and Measure skills
•Expectation is to complete at least 1 project per year, and then work on 2nd project
•Currently 25% of savings is from Green Belts
•Evolved to DFSS, Technical Service, Transactional, Analytical and Reliability GBs
Six Sigma Professional Training and Duties
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Six Sigma Tools Training
Green Belt tools

Benchmarking

Brainstorming

Cause- Effect diagrams

Critical to Customer Tree

Data Collection Plan

FMEA( Failure modes and effect)

Improvement strategy

Interviews/ Precision questioning

Prioritization matrices

Project Charter

Project plan template

SIPOC- Process flow

Surveys

Workflow mapping
Black Belt tools

Activity analysis Affinity diagram

Data Presentation

Histogram

Box Plot

Line Graphs

Run Charts

Control Charts

Pareto Chart

Bar Graphs

Stacked Bar Graphs

Pie Charts

Design of experiments

Full factorial

Reduced fractions

Screening designs

Response surface

Hypothesis tests

t- test

Paired t-test

ANOVA

Chi Square

Process sigma

Kano modeling

Quality Functional Deployment(QFD)

Regression

Rolled throughput and final yield

Sampling

Stratification
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Methods and Tools
Business process
Project management
Team and leadership
Probability and statistics
Simple tools
Advanced statistical tools
Some DFSS
Some Lean Manufacturing
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A Typical Six Sigma Project
D
efine:
Select Critical To Customer (CTx) characteristics and performance standards.
M
easure:
Create/validate measurement system to be used.
A
nalyze:
Identify sources of variation from performance objectives.
I
mprove:
Discover process relations and establish new procedures.
C
ontrol:
Implement process controls.
E
nd:
Verify process controls are institutionalized
R
eplicate:
Incorporate project lessons at other business units
DEFINE
ANALYZE
IMPROVE
CONTROL
MEASURE
REPLICATE
END
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Problem Statement – CTQ (D
MAIC
))
• Customer Satisfaction data shows that our front cab doors have very high TGW scores associated with wind noise. Customers often observe that the offending doors) appear to be poorly fitted, and are often significantly overflush.
• CTQ - Wind noise and door flushness show some basic correlation, and therefore reducing variability in flushness at the top of the A pillar in particular leads to a significant reduction in wind noise.
Copy Right (c) Dr Kai Yang 0.0 0.5 1.0 1.5 2.0 2.5
LSL
USL
Process Capability Analysis for SCH Left in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.80000
*
-0.80000 1.37692 13 0.177305 0.283182 1.50
-1.08 4.09
-1.08
*
0.94
-0.68 2.56
-0.68 0.00 1000000.00 1000000.00 0.00 999430.73 999430.73 0.00 979189.35 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
-1.0
-0.5 0.0 0.5 1.0 1.5 2.0
LSL
USL
Process Capability Analysis for SCH Left in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.80000
*
-0.80000 1.13846 13 0.162530 0.234090 1.64
-0.69 3.98
-0.69
*
1.14
-0.48 2.76
-0.48 0.00 846153.85 846153.85 0.00 981349.83 981349.83 0.00 925892.25 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
-1.0
-0.5 0.0 0.5 1.0
LSL
USL
Process Capability Analysis for SCH Left in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.800000
*
-0.800000 0.107692 13 0.140366 0.299022 1.90 1.64 2.16 1.64
*
0.89 0.77 1.01 0.77 0.00 0.00 0.00 0.00 0.41 0.41 1200.54 10299.91 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
-1 0
1 2
3
LSL
USL
Process Capability Analysis for SCH Right in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.80000
*
-0.80000 1.59231 13 0.310284 0.536180 0.86
-0.85 2.57
-0.85
*
0.50
-0.49 1.49
-0.49 0.00 846153.85 846153.85 0.00 994667.60 994667.60 4.06 930254.71 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
-1 0
1 2
3
LSL
USL
Process Capability Analysis for SCH Right in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.8
*
-0.8 1.4 13 0.339835 0.491439 0.78
-0.59 2.16
-0.59
*
0.54
-0.41 1.49
-0.41 0.00 846153.85 846153.85 0.00 961265.59 961265.59 3.79 888939.09 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
-1.5
-1.0
-0.5 0.0 0.5 1.0
LSL
USL
Process Capability Analysis for SCH Right in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.800000
*
-0.800000
-0.161538 13 0.229019 0.417953 1.16 1.40 0.93 0.93
*
0.64 0.77 0.51 0.51 153846.15 0.00 153846.15 2653.27 13.43 2666.71 63307.17 10707.48 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
-1.0
-0.5 0.0 0.5 1.0
LSL
USL
Process Capability Analysis for SCH Right in March 2001
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total 0.800000
*
-0.800000 0.038462 13 0.169917 0.398808 1.57 1.49 1.64 1.49
*
0.67 0.64 0.70 0.64 0.00 0.00 0.00 0.40 3.70 4.10 17758.24 28096.38 Process Data
Potential (Within) Capability
Overall Capability
Observed Performance
Exp. "Within" Performance
Exp. "Overall" Performance
Within
Overall
SCH1
SCH3
Left Door
Right Door
SCH2
SCH4
Data Collection and Analysis (
D
M
AIC
) )
Copy Right (c) Dr Kai Yang 2.2 3.2 4.2 2.5 3.5 4.5
sch2_Left sch Left sch1_Left = 0.243202 + 1.02741 sch2_Left
S = 0.119456 R-Sq = 98.3 % R-Sq(adj) = 98.0 Regression Plot
-0.6
-0.5
-0.4
-0.3
-0.2
-0.3
-0.2
-0.1
sch4_Left sch Left NO STRONG CORRELATION
sch3_Left = -0.0529412 + 0.411765 sch4_Left
S = 0.0685994 R-Sq = 41.2 % R-Sq(adj) = 29.4 Regression Plot 2.5 3.5 4.5
sch3_Left sch Left NO CORRELATION
sch1_Left = 2.99286 - 2.25 sch3_Left
S = 0.893828 R-Sq = 4.8 % R-Sq(adj) = 0.0 Regression Plot 2.2 3.2 4.2
sch4_Left sch Left NO CORRELATION
sch2_Left = 2.95147 - 0.455882 sch4_Left
S = 0.881860 R-Sq = 0.5 % R-Sq(adj) = 0.0 Regression Plot
SCH1
SCH3
SCH2
SCH4
Statistical Analysis (
DM
A
IC
)
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Load door into welding fixture
Load hinges into welding fixture
Operate automated weld fixture
Move finished door to storage rack
Grind MIG weld splatter
Store doors prior to hanging
CMM check
1 per shift
(Document
GQS-
1D015)
Process End
Improvement (
DMA
I
C
)
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Lean
Waste elimination in process
Pull based production system
One piece flow
Value stream mapping
Set up time reduction
Flexible Work cells
Muda, The Seven Wastes
1.
Overproduction
2.
Waiting
3.
Unnecessary transport
4.
Over processing
5.
Excessive inventory
6.
Unnecessary movement
7.
Defects
Staging
Casting
Transportation
Staging
Setup
Machining
Inspection
Assembly
Staging
Value Added time
Non-Value Added Time
Time
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A Typical Lean Project
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Questions?

Document Outline

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  • Project Roadmap: DMAIC
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