Transcript Introduction to Six Sigma

Six Sigma
a short preview
Statistics
Decision Science
Philosophy
What is Six Sigma?
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B.
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D.
A really small number
Managerial flavor of the month
Current incarnation of TQM
None of the above
Foundations of Modern
Management Philosophies
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Edwards Deming’s 14 Points
Create constancy of purpose
Reject defects
Reject inspection
Use quality criteria to award business
Constantly improve
Modernize training
Modernize management
Drive out fear
Break down functional barriers
Eliminate targets & slogans
Eliminate numerical quotas
Remove barriers from hourly workers
Train vigorously
Create a supportive management
structure
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Joseph Juran’s 10 Steps
Build awareness of the need and
opportunity for improvement
Set goals for improvement
Organize to reach the goals (have a
plan and an organizational structure)
Provide training
Carry out projects to solve problems
Report progress
Give recognition
Communicate results
Keep score
Maintain momentum by making
annual improvement part of the
regular systems and processes of the
organization
Six Sigma: A Modern Definition
 Six Sigma is a philosophy that underlies
efforts to improve business performance
and customer satisfaction
– Using facts and data to eliminate waste and
variation
– Eliminating activities that don’t add value
Doing the math
6 Sigma = 3.4 defects per million
5 Sigma = 230 defects per million
4 Sigma = 6,210 defects per million
3 Sigma = 66,800 defects per million
2 Sigma = 308,000 defects per million
1 Sigma = 690,000 defects per million
It’s more than simply statistics
 Asking the right question
– Questions managers should ask
– Questions managers should answer
 Using data
 Using the right tools
 Using the right process
Steps
Inputs
(C,N,X)
Activities
Questions
Tools
Outputs
Questions Managers Should Ask
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What activities are you responsible for? Who is the owner of these processes? Who are the team
members? How well does the team work together
Which processes have the highest priority for improvement? How did you come to this conclusion?
Where is the data that led to this conclusion?
How is the process performed?
What are your process performance measures? How accurate and precise is your measurement
system?
What are the customer-driven specifications for all of your performance measures? How good or bad
is the current performance? Show me the data. What are the improvement goals for the process?
What are all the sources of variability in the process?
Which sources of variability do you control?
Are any of the sources of variability supplier-dependent? If so, what are they, who is the supplier,
and what are we doing about it?
What are the key variables that affect the average and variation of the measures of performance?
What are the relationships between the measures of performance and the key variables?
Do any key variables interact?
What setting for the key variables will optimize the measures of performance?
For the optimal settings of the key variables, what kind of variability exists in the performance
measures?
How much improvement has the process shown in the past 6 months?
How much time and money have your efforts saved or generated for the company?
Questions Managers Should Answer
(Source: Knowledge Based Management (KBM), Air Academy Press)
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What is your product or service and who are your customers?
What perception do your customers have of your product or service? How do you know?
Do you believe quality issues are important to your company? Why? Which ones?
What is the company’s current share of the total market? Can quality improvement efforts assist you
in increasing the market share and/or increasing profits? How?
How many hours per week do you currently have scheduled that are devoted strictly to quality
issues?
How often per week do you solicit feedback from the people you mange? What kind of feedback do
you solicit?
What are the right quality-oriented questions managers need to ask their people? What methods pr
tools can be used to answer them?
Are your people trained to successfully use the best quality improvement tools? What is your ROI
for the training?
Do you have a SOP for documenting quality improvement efforts?
What barriers do your people face when trying to do quality improvement? What are you doing to
remove these barriers?
What metrics are you evaluated on that relate to quality issues? Are you held accountable for these
metrics? What are the specific improvement goals for these metrics?
How much waste does your company have? What is the cost of poor quality?
One year from now, what evidence will you have to show that you made a difference?
Questions form a Stepped
Strategy
Strategy
Analyze
Characterize
Measure
Prioritize
Phase
Optimize
Control
Realize
Use Data
 Measure of central tendency
– mean
– median
 Measures of dispersion
– range
– sample variance
– sample deviation
 Measures of Relative Standing
– z - score
Use Tools…..the right tools
 Quality Function Deployment (QFD)
 Pareto
 Histograms
 Run Charts
 Control Charts
 Design of Experiments (DOE)
 Scatter Diagrams
 Process Flow Diagrams
 Nominal Group Techniques
 Teamwork
House of Quality
What
How
How
How
Component
What
Product
What
System
What
How
Process
Run Chart
Range Chart 3 with Standard Deviations Control Limits
12
10
Range
8
Series1
Center = 3.2308
UCL = 11.2763
LCL = 0
Zone A Above
Zone B Above
Zone A Below
6
4
2
0
1
3
5
7
9
Run Number
11
13
15
Control Chart
Symptoms of an out-of-control process
Upper Control Limit (UCL)
=
x
 One or more points outside
3
Zone A
2
Zone B
1
Zone C
Zone C
Zone B
Zone A
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2
3
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Lower Control Limit (LCL)
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the control limits
7 consecutive points on one
side of the centerline
7 consecutive increasing or
decreasing intervals
2 out of 3 consecutive
points in a specific zone A
or beyond
4 out of 5 consecutive
points in a specific zone B
or beyond
14 consecutive points that
alternate up or down
14 consecutive points in
either zone C
Design of Experiments
(there are many)
Factor
A
B
C
Row # Mold Temperature Pour Time Vendor
1
300
1
1
2
300
1
2
3
300
3
1
4
300
3
2
5
350
1
1
6
350
1
2
7
350
3
1
8
350
3
2
Y1
2.49
2.39
2.41
2.27
4.11
4.33
4.15
4.33
Y2
2.31
2.3
2.32
2.23
4.2
4.26
4.22
4.19
Y bar
2.4
2.345
2.365
2.25
4.155
4.295
4.185
4.26
S
0.127279
0.06364
0.06364
0.028284
0.06364
0.049497
0.049497
0.098995
Y-hat Par e to of Coe ffs
1
0.9
0.8
0.7
Absolute Coefficient
Design of Experiments: A way of shifting
or reducing variation in a process by
carrying out a methodical sequence of
experiments based on coded matrices. Each
combination of adjustments becomes an
equation that can either be solved as a matrix
or entered into a computer for solution. DOE
allows users to efficiently test a large number
of variables.
0.6
0.5
Series1
0.4
0.3
0.2
0.1
0
Mold
Temperature
AC
Pour Time
AB
Effe ct Nam e
BC
V endor
A BC
Statistics in the Design of
Experiments: Decision
 Where to get Data
 Measuring Data
 Mean
 Variance
 Standard Deviation
 Hypothesis Testing
 Chi Square
 Confidence Intervals
 Decision
Deployment
 Roles
– Champions
– Blackbelts
– Greenbelts
 Change Management
 Customer
 Teamwork