Six Sigma and
Statistical Quality Control
Outline
• Quality and Six Sigma: Basic ideas and history
• Juran Trilogy
– Control
– Improvement
– Planning
• Quality Strategy
• Focus on Statistical Methods
– Process Capability ideas and metrics
– Control charts for attributes and variables
A Brief History
• The Craft System
• Taylorism (Scientific Management)
• Statistical Quality Control
– Pearson, Shewhart, Dodge
• Human Relations School
– Mayo, Maslow, Simon, Herzberg, Likert
• The Japanese Revolution (1950)
– Ishikawa, Taguchi, Deming, Juran, Feigenbaum
• The USA Wakes Up (1980)
– Crosby
• 1990s: Six Sigma
• The Need for Organizational Change
JIT and TQM
Walter Shewhart
1891 - 1967
Operations -- Prof. Juran
W. Edwards Deming
1900 - 1993
4
Joseph M. Juran
1904 - 2008
What is Quality?
• Freedom from Defects
– Quality Costs Less
– Affects Costs
• Presence of Features
– Quality Costs More
– Affects Revenue
Juran Trilogy
Planning, Control, Improvement
Juran Trilogy
40
35
30
Defect Rate
25
20
15
10
5
Time
40
38
36
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
0
Juran Trilogy
Planning, Control, Improvement
Juran Trilogy
Planning
Control
Improvement
Control
40
Sporadic Spike
35
30
Defect Rate
25
20
15
Chronic Waste
10
5
40
38
36
34
32
30
28
24
22
20
18
16
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
14
Time
26
Chronic Waste
0
Quality Control
• Aimed at preventing unwanted changes
• Works best if deployed at the point of
production or service delivery
(Empowerment)
• Tools:
–
–
–
–
Established, measurable standards
Measurement and feedback
Control charts
Statistical inference
Quality Control
Establish Standard
Operate
Measure Performance
Yes
OK?
Corrective Action
No
Compare to Standard
Quality Improvement
• Aimed at creating a desirable change
• Two distinct “journeys”
– Diagnosis
– Remedy
• Project team approach
• Tools
–
–
–
–
Process flow diagram
Pareto analysis
Cause-effect (Ishikawa, fishbone) diagram
Statistical tools
Quality Improvement
• Identify problem
• Analyze symptoms
• Formulate theories
• Test theories - Identify root
cause
• Identify remedy
• Address cultural resistance
• Establish control
Quality Planning
• Aimed at creating or redesigning (reengineering) a process to satisfy a need
• Project team approach
• Tools
–
–
–
–
–
Market research
Failure analysis
Simulation
Quality function deployment
Benchmarking
Quality Planning
• Verify goal
• Identify customers
• Determine customer needs
• Develop product
• Develop process
• Transfer to operations
• Establish control
Strategic Quality Planning
•
•
•
•
•
•
•
•
Mission
Vision
Long-term objectives
Annual goals
Deployment of goals
Assignment of resources
Systematic measurement
Connection to rewards and
recognition
Strategic Quality Planning
• Aimed at establishing long-range quality
objectives and creating an approach to
meeting those objectives
• Top management’s job
• Integrated with other objectives
–
–
–
–
Operations
Finance
Marketing
Human Resources
Six Sigma Defined (High-Level)
• The application of rigorous quantitative
tools in the planning, control, and
improvement processes in an effort to
meet customer requirements 100% of
the time.
• Old wine in new bottles?
Process Capability
• The Relationship between a Process and
the Requirements of its Customer
• How Well Does the Process Meet
Customer Needs?
Process Capability
• Specification Limits reflect what the
customer needs
• Natural Tolerance Limits (a.k.a. Control
Limits) reflect what the process is
capable of actually delivering
• These look similar, but are not the same
Specification Limits
• Determined by the Customer
• A Specific Quantitative Definition of
“Fitness for Use”
• Not Necessarily Related to a Particular
Production Process
• Not Represented on Control Charts
Tolerance (Control) Limits
• Determined by the inherent central tendency
and dispersion of the production process
• Represented on Control Charts to help
determine whether the process is “under
control”
• A process under control may not deliver
products that meet specifications
• A process may deliver acceptable products
but still be out of control
Measures of Process Capability
• Cp
• Cpk
• Percent Defective
• Sigma Level
Example: Cappuccino
• Imagine that a franchise food service organization
has determined that a critical quality feature of their
world-famous cappuccino is the proportion of milk
in the beverage, for which they have established
specification limits of 54% and 64%.
• The corporate headquarters has procured a customdesigned, fully-automated cappuccino machine
which has been installed in all the franchise locations.
• A sample of one hundred drinks prepared at the
company’s Stamford store has a mean milk
proportion of 61% and a standard deviation of 3%.
Example: Cappuccino
• Assuming that the process is in control and normally
distributed, what proportion of cappuccino drinks at the
Stamford store will be nonconforming with respect to milk
content?
• Try to calculate the Cp, Cpk, and Parts per Million for this
process.
• If you were the quality manager for this company, what would
you say to the store manager and/or to the big boss back at
headquarters? What possible actions can be taken at the store
level, without changing the inherent variability of this process,
to reduce the proportion of non-conforming drinks?
Lower Control Limit
LNTL    3
 .61  3.03
 .52
Upper Control Limit
UNTL    3
 .61  3.03 
 .70
Nonconformance
Z1  .54  .61 / .03  2.33
Z2  .64  .61 / .03  1.00
Nonconformance
-4
-3
-2
-1
0
1
values of z
2
3
4
Nonconformance
• 0.00990 of the drinks will fall below the
lower specification limit.
• 0.84134 of the drinks will fall below the
upper limit.
• 0.84134 - 0.00990 = 0.83144 of the drinks
will conform.
• Nonconforming:
1.0 - 0.83144 = 0.16856 (16.856%)
Cp Ratio
Cp
USL  LSL

6
0.64  0.54

60.03
0.10

0.18
 0.555
Cpk Ratio
C pk
USL     LSL 

 min 
,

3 
 3
 0.64  0.61 0.61  0.54 
 min 
,

30.03  
 30.03 
 min 0.333 ,0.777 
 0.333
Parts per Million
PPM  1 ,000 ,000  0.169
 169 ,000
(about 1.38 Sigma)
A
1
2 Defects per million
3
4 Sigma level
B
169000
1.375424
C
D
E
=1000000*2*(1-NORMSDIST(B4))
F
Quality Improvement
• Two Approaches:
– Center the Process between the
Specification Limits
– Reduce Variability
Approach 1: Center the Process
Approach 1: Center the Process
Approach 1: Center the Process
ZLSL  .54  .59  / .03  1.67
ZUSL  .64  .59  / .03  1.67
Approach 1: Center the Process
• 0.04746 of the drinks will fall below the
lower specification limit.
• 0.95254 of the drinks will fall below the
upper limit.
• 0.95254 - 0.04746 = 0.90508 of the drinks
will conform.
• Nonconforming:
1.0 - 0.90508 = 0.09492 (9.492%)
Approach 1: Center the Process
• Nonconformance decreased from 16.9%
to 9.5%.
• The inherent variability of the process
did not change.
• Likely to be within operator’s ability.
Approach 2: Reduce Variability
• The only way to reduce
nonconformance below 9.5%.
• Requires managerial intervention.
Quality Control
Establish Standard
Operate
Measure Performance
Yes
OK?
Corrective Action
No
Compare to Standard
Quality Control
• Aimed at preventing and detecting unwanted
changes
• An important consideration is to distinguish between
Assignable Variation and Common Variation
• Assignable Variation is caused by factors that can
clearly be identified and possibly managed
• Common Variation is inherent in the production
process
• We need tools to help tell the difference
When is Corrective
Action Required?
• Operator Must Know How They Are
Doing
• Operator Must Be Able to Compare
against the Standard
• Operator Must Know What to Do if the
Standard Is Not Met
When is Corrective
Action Required?
• Use a Chart with the Mean and 3-sigma
Limits (Control Limits) Representing
the Process Under Control
• Train the Operator to Maintain the
Chart
• Train the Operator to Interpret the
Chart
Example: Run Chart
0.285
0.280
0.275
0.270
0.265
0.260
0.255
0.250
0.245
0.240
0.235
0.230
0.225
0.220
0.215
0.210
0.205
0.200
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
When is Corrective
Action Required?
Here are four indications that a process is “out of
control”. If any one of these things happens, you
should stop the machine and call a quality engineer:
•One point falls outside the control limits.
•Seven points in a row all on one side of the center
line.
•A run of seven points in a row going up, or a run of
seven points in a row going down.
•Cycles or other non-random patterns.
Example: Run Chart
15.500
15.400
15.300
15.200
15.100
15.000
14.900
14.800
14.700
14.600
14.500
14.400
14.300
14.200
14.100
14.000
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Type I and Type II Errors
In Control
Out of Control
Assume Process is OK
Correct Decision
ERROR (Type II)
Take Corrective Action
ERROR (Type I)
Correct Decision
When is Corrective Action Required?
One point falls outside the control limits.
•0.27% chance of Type I Error
Seven points in a row all on one side of the center line.
•0.78% chance of Type I Error
A run of seven points in a row going up, or a run of
seven points in a row going down.
•0.78% chance of Type I Error
Basic Types of Control Charts
Attributes (“Go – No Go” data)
•A simple yes-or-no issue, such as “defective or not”
•Data typically are “proportion defective”
•p-chart
Variables (Continuous data)
•Physical measurements such as dimensions, weight, electrical
properties, etc.
•Data are typically sample means and standard deviations
•X-bar and R chart
Statistical Symbols (Attributes)
n
Number of observations in each sample
p
Proportion defective (an unknown population parameter)
p
Sample proportion defective (estimate of p from one sample)
p
Sample proportion defective (estimate of p from all samples)
sp
Standard deviation of sample proportions
p-chart Example
Number of defectives in sample
p
Sample size
p
Number of defectives in all samples
Sample size * Number of samples
p-chart Example
sp 
 
p 1 p
n
UCL  p  zsp
LCL  p  zsp
Sample
Sample Size
Number of Defects
p
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
4
2
5
3
6
4
3
7
1
2
3
2
2
8
3
0.04
0.02
0.05
0.03
0.06
0.04
0.03
0.07
0.01
0.02
0.03
0.02
0.02
0.08
0.03
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
A
B
C
D
Sample Sample Size Number of Defects p-bar
1
100
4
0.04
2
100
2
0.02
3
100
5
0.05
4
100
3
0.03
5
100
6
0.06
6
100
4
0.04
7
100
3
0.03
8
100
7
0.07
9
100
1
0.01
10
100
2
0.02
11
100
3
0.03
12
100
2
0.02
13
100
2
0.02
14
100
8
0.08
15
100
3
0.03
p-bar-bar
0.0367
stdev(p-bar)
0.0188
E
F
G
=AVERAGE(D2:D16)
=SQRT(((D17)*(1-D17))/(B16))
H
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
A
B
C
D
Sample Sample Size Number of Defects p-bar
1
100
4
0.04
2
100
2
0.02
3
100
5
0.05
4
100
3
0.03
5
100
6
0.06
6
100
4
0.04
7
100
3
0.03
8
100
7
0.07
9
100
1
0.01
10
100
2
0.02
11
100
3
0.03
12
100
2
0.02
13
100
2
0.02
14
100
8
0.08
15
100
3
0.03
p-bar-bar
0.0367
stdev(p-bar)
0.0188
UCL
LCL
0.0930
-0.0197
E
LCL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
F
UCL
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
0.0930
G
=$D$20
=D17+3*D18
=D17-3*D18
Note: If the LCL is negative, we round it up to zero.
0.150
0.125
0.100
0.075
0.050
0.025
0.000
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Statistical Symbols (Variables)
X
A Random Variable
x
One Observation of X
i
n
The Number of Observations of X

Mean (A Population Parameter)
X
Mean (An Estimate of  from Sample Data)

Standard Deviation (A Population Parameter)
s
Standard Deviation (An Estimate of  from Sample Data)
X-bar, R chart Example
For X-bar chart
LCL  X  A2 R
UCL  X  A2 R
For R chart
LCL  D3 R
UCL  D4 R
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Obs 1
10.682
10.787
10.780
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.660
Obs 2
10.689
10.860
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
Obs 3
10.776
10.601
10.838
10.812
10.790
10.738
10.689
10.110
10.641
10.708
10.764
10.818
10.893
10.859
10.644
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.850
10.658
10.872
10.544
10.801
10.747
Obs 5
10.714
10.779
10.723
10.730
10.671
10.606
10.603
10.750
10.725
10.712
10.708
10.727
10.750
10.701
10.728
n
2
3
4
5
6
7
8
9
10
11
A2
1.88
1.02
0.73
0.58
0.48
0.42
0.37
0.34
0.31
0.29
From Exhibit TN7.7
D3
0.00
0.00
0.00
0.00
0.00
0.08
0.14
0.18
0.22
0.26
D4
3.27
2.57
2.28
2.11
2.00
1.92
1.86
1.82
1.78
1.74
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
A
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
B
Obs 1
10.682
10.787
10.780
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.660
C
Obs 2
10.689
10.860
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
D
Obs 3
10.776
10.601
10.838
10.812
10.790
10.738
10.689
10.110
10.641
10.708
10.764
10.818
10.893
10.859
10.644
E
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.850
10.658
10.872
10.544
10.801
10.747
F
Obs 5
10.714
10.779
10.723
10.730
10.671
10.606
10.603
10.750
10.725
10.712
10.708
10.727
10.750
10.701
10.728
Averages
G
Avg
10.732
10.755
10.759
10.727
10.724
10.705
10.735
10.624
10.710
10.732
10.748
10.768
10.733
10.783
10.692
10.728
H
I
J
K
Range
0.116
0.259
0.171
0.221
0.119
0.143
0.274
0.669
=AVERAGE(B10:F10)
0.132
0.179
=MAX(B12:F12)-MIN(B12:F12)
0.163
0.250
0.349
0.158
0.103
=AVERAGE(H2:H16)
0.220
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
A
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
B
Obs 1
10.682
10.787
10.780
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.660
C
Obs 2
10.689
10.860
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
D
Obs 3
10.776
10.601
10.838
10.812
10.790
10.738
10.689
10.110
10.641
10.708
10.764
10.818
10.893
10.859
10.644
E
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.850
10.658
10.872
10.544
10.801
10.747
F
Obs 5
10.714
10.779
10.723
10.730
10.671
10.606
10.603
10.750
10.725
10.712
10.708
10.727
10.750
10.701
10.728
Averages
G
Avg
10.732
10.755
10.759
10.727
10.724
10.705
10.735
10.624
10.710
10.732
10.748
10.768
10.733
10.783
10.692
10.728
H
Range
0.116
0.259
0.171
0.221
0.119
0.143
0.274
0.669
0.132
0.179
0.163
0.250
0.349
0.158
0.103
0.220
I
J
n
2
3
4
5
6
7
8
9
10
11
K
A2
1.88
1.02
0.73
0.58
0.48
0.42
0.37
0.34
0.31
0.29
A2
D3
D4
Mean
Range
0.58
0
2.11
10.728
0.220
L
D3
0.00
0.00
0.00
0.00
0.00
0.08
0.14
0.18
0.22
0.26
=K5
=G17
=H17
M
D4
3.27
2.57
2.28
2.11
2.00
1.92
1.86
1.82
1.78
1.74
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
A
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
B
Obs 1
10.682
10.787
10.780
10.591
10.693
10.749
10.791
10.744
10.769
10.718
10.787
10.622
10.657
10.806
10.660
C
Obs 2
10.689
10.860
10.667
10.727
10.708
10.714
10.713
10.779
10.773
10.671
10.821
10.802
10.822
10.749
10.681
D
Obs 3
10.776
10.601
10.838
10.812
10.790
10.738
10.689
10.110
10.641
10.708
10.764
10.818
10.893
10.859
10.644
E
Obs 4
10.798
10.746
10.785
10.775
10.758
10.719
10.877
10.737
10.644
10.850
10.658
10.872
10.544
10.801
10.747
X-Bar
UCL
LCL
10.85625
10.60058
R-Bar
UCL
LCL
0.465044
0
F
Obs 5
10.714
10.779
10.723
10.730
10.671
10.606
10.603
10.750
10.725
10.712
10.708
10.727
10.750
10.701
10.728
Averages
G
Avg
10.732
10.755
10.759
10.727
10.724
10.705
10.735
10.624
10.710
10.732
10.748
10.768
10.733
10.783
10.692
10.728
=K16+K13*K17
=K16-K13*K17
=K15*K17
=K14*K17
H
Range
0.116
0.259
0.171
0.221
0.119
0.143
0.274
0.669
0.132
0.179
0.163
0.250
0.349
0.158
0.103
0.220
I
J
K
A2
D3
D4
Mean
Range
0.58
0
2.11
10.728
0.220
X-bar Chart
10.90
10.85
10.80
10.75
10.70
10.65
10.60
10.55
10.50
10.45
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
R chart
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Interpretation
• Does any point fall outside the control limits?
• Are there seven points in a row all on one
side of the center line?
• Is there a run of seven points in a row going
up, or a run of seven points in a row going
down?
• Are there cycles or other non-random
patterns?
Six Sigma Defined (Low-Level)
A Process in which the Specification
Limits are Six Standard Deviations above
and below the Process Mean
Two Approaches:
•Move the Specification Limits Farther Apart
•Reduce the Standard Deviation
Approach #1
Ask the Customer to Move the
Specification Limits Farther Apart.
2-sigma Process
Cp = 0.667
Cpk = 0.667
45,500 ppm
Spec Limits
3-sigma Process
Cp = 1.0
Cpk = 1.0
2,700 ppm
Spec Limits
4-sigma Process
Cp = 1.333
Cpk = 1.333
63 ppm
Spec Limits
5-sigma Process
Cp = 1.667
Cpk = 1.667
0.57 ppm
Spec Limits
6-sigma Process
Cp = 2.0
Cpk = 2.0
0.002 ppm
Spec Limits
Approach #2
Reduce the Standard Deviation.
2-sigma Process
Cp = 0.667
Cpk = 0.667
45,500 ppm
Spec Limits
3-sigma Process
Cp = 1.0
Cpk = 1.0
2,700 ppm
Spec Limits
4-sigma Process
Cp = 1.333
Cpk = 1.333
63 ppm
Spec Limits
5-sigma Process
Cp = 1.667
Cpk = 1.667
0.57 ppm
Spec Limits
6-sigma Process
Cp = 2.0
Cpk = 2.0
0.002 ppm
Spec Limits
Process Drift
What Happens when the Process
Mean Is Not Centered between
the Specification Limits?
3-sigma Process
(centered)
Cp = 1.0
Cpk = 1.0
2,700 ppm
Spec Limits
3-sigma Process
(shifted 0.5 std. dev.)
Cp = 1.0
Cpk = 0.833
ppm = 6,442
(about 2.72-sigma)
Spec Limits
3-sigma Process
(shifted 1.0 std. dev.)
Cp = 1.0
Cpk = 0.667
ppm = 22,782
(about 2.28-sigma)
Spec Limits
3-sigma Process
(shifted 1.5 std. dev.)
Cp = 1.0
Cpk = 0.5
ppm = 66,811
(about 1.83-sigma)
Spec Limits
Six Sigma: Many Meanings
• A Symbol
• A Measure
• A Benchmark or Goal
• A Philosophy
• A Method
Six Sigma: A Symbol
•  is a Statistical Symbol for
Standard Deviation
• Standard Deviation is a Measure
of Dispersion, Volatility, or
Variability
Six Sigma: A Measure
• The “Sigma Level” of a process
can be used to express its
capability — how well it
performs with respect to
customer requirements.
• Percent Defects, Cp, Cpk, PPM
Six Sigma: A Benchmark or Goal
• The specific value of 6 Sigma (as
opposed to 5 or 4 Sigma) is a
benchmark for process excellence.
• Adopted by leading organizations as a
goal for process capability.
Six Sigma: A Philosophy
• A vision of process performance
• Tantamount to “zero defects”
• A “Management Mantra”
Six Sigma: A Method
• Really a Collection of Methods:
–
–
–
–
Product/Service Design
Quality Control
Quality Improvement
Strategic Planning
Where Does “3.4 PPM” Come From?
• Six Sigma is commonly defined to be
equivalent to 3.4 defective parts per
million.
• Juran says that a Six Sigma process will
produce only 0.002 defective parts per
million.
• What gives?
Normal Curve Probabilities
±1 Sigma
±2 Sigmas
±3 Sigmas
±4 Sigmas
±5 Sigmas
±6 Sigmas
68.3% of Data
95.4%
99.73%
99.994%
99.99994%
99.9999998%
1
2
3
4
5
6
7
8
A
Area under the curve z > 6
0.00000000099
Total area outside of 6 sigma limits
0.00000000198
Parts per Million
0.00198
B
C
=1-NORMSDIST(6)
=2*A2
=A5*1000000
Process Centered
between Spec Limits
Sigma Level
1
2
3
4
5
6
Cp
0.333
0.667
1.000
1.333
1.667
2.000
Cpk
PPM
0.333 317,310
0.667 45,500
1.000
2,700
1.333
63.4
1.667
0.57
2.000
0.002
Process Shifted by 1.5
Standard Deviations
Sigma Level
1
2
3
4
5
6
Cp
Cpk
PPM
0.333 -0.167 697,672
0.667 0.167 308,770
1.000 0.500 66,810
1.333 0.833
6,210
1.667 1.167
232.7
2.000 1.500
3.4
Where Does “3.4 PPM” Come From?
• The 3.4 defective parts per million
definition of Six Sigma includes a
“worst case” scenario of a 1.5 standard
deviation shift in the process.
• It is assumed that there is a very high
probability that such a shift would be
detected by SPC methods (low
probability of Type II error).
Six Sigma in Context
• Is Six Sigma dramatically different from
old-fashioned quality control?
• Is Six Sigma a departure from 1980’sstyle TQM?
Six Sigma in Context
• What Is New?
–
–
–
–
–
–
Focus on Quantitative Methods
Focus On Control
A Higher Standard
A New Metric for Defects (PPM)
Lots of training
Linkage between quality goals and
employee incentives?
Using Six Sigma
• A New Standard; Not Adopted
Uniformly across Industries
• Beyond Generalities, Need to Develop
Organization-Specific Methods
• Hard Work, Not Magic
• “A Direction Not a Place”
Summary
• Quality and Six Sigma: Basic ideas and history
• Juran Trilogy
– Control
– Improvement
– Planning
• Quality Strategy
• Focus on Statistical Methods
– Process Capability ideas and metrics
– Control charts for attributes and variables