Quality Management
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What and Why?
Tools for Continuous Improvement
Statistical Process Control
Process Capability
6 Sigma Quality
Taguchi’s View
What is Quality?
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8 Dimensions of Quality
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Performance - primary operating
characteristics
Features - little extras
Reliability - no failure in a given time span
Conformance - meeting standards
Durability - length of usefulness
Serviceability - speed, ease of repair
Aesthetics - pleasing to the senses
Perceived quality - reputation
How is Quality Defined in
Service?
What is Total Quality
Management?
TQM: Philosophical Elements
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Top management involvement
Customer driven quality standards
Quality at the source
Supplier-customer links
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everyone has a customer
Prevention orientation
Continuous improvement
What are the Costs Associated with
Managing Quality?
Kaizen: Continuous Improvement
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Standard-Maintaining System
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Performance standards are fixed unless
major breakthrough in technology occurs
Continuous-Improvement System
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Performance level should be continuously
challenged and incrementally upgraded
Typically requires
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multifunctional work teams
participative management
decentralized decision making
The PDSA Cycle (Deming Wheel)
Act -Adopt the change,
or abandon it, or run
through the cycle again
A
S
Study the
results. What did
we learn? What went wrong?
Source: The New Economics, Deming
P
D
Plan a change or
a test, aimed at
improvement
Do - Carry out the
change or the test
Tools for Continuous
Improvement
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Used extensively to improve quality
Deceptively simple - workers at all levels
can use
Yet, very powerful
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Flowcharts, Check sheets, Pareto
diagrams, Histograms, Cause-and-effect
diagrams, Scatter Diagrams, Run Charts
Control charts
Flow Chart
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A picture of a process that shows the
sequence of steps for a process
Makes the process explicit
Facilitates group understanding
Identifies unknown or misunderstood
steps
Can quickly illustrate problems and
solutions
Medicare Inpatient Billing Process at Massachusetts
General Hospital – High Level Flow Diagram
(Source: Berwick, Godfrey, Roessner, 1990)
Physician’s
Office
Admitting
Financial
Planning
Emergency
Dept
Inpatient
Accounting
Unit
Utilization
Management
Clinical
Admin
Medical
Records
Medicare
Selected Flow Diagram Symbols
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Operation/activity
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Decision
Storage
Information flow
Flow
Check Sheets
Monday
Billing Errors
Wrong Account
Wrong Amount
A/R Errors
Wrong Account
Wrong Amount

Histograms
Graphical representation of the variation in
a set of data
 Provide clues about the characteristics of
the population
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e.g. bimodal pattern suggests two individual processes
Pareto Diagram
To separate "vital few" from the "trivial many"
 80/20 rule
 Histogram of data from the largest frequency
to the smallest
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Pareto Chart-Occurrences of Errors
in
Providing a Product to a Customer
60
50
40
30
20
10
0
Delivery
Raw
materials
Fabrication Final
Subassembly
assembly
Cause-and-Effect Diagram
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Helps in understanding the possible causes of the
problem
Problem is listed at one end of a horizontal line
Branches are drawn to represent possible cause
Machine
People
Effect
Environment
Method
Material
Fishbone Chart-Delivery of Goods by
Truck
Shipping
Documents
Trucking
Packing
list
Invoice
Latest traffic
& road conditions
Truck
maintenance
Container
labeling
Driver knows
route
Delivery of
goods by
truck
Protective
packing
Right
information
Label stuck
on well
Leave at
right time
Right
container
Label
location
Packing
Quantity
in container
Scatter Diagram
Shows correlation between variables
 Variables can be from the cause-and-effect
diagram
Defects
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Hours of Training
Control Charts
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Why bother?
Understanding Variability:
An Experiment
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Write small letter ‘a’ ten times with your
“good” hand.
Now write it ten times with your other
hand.
Understanding Variability
Common
Causes
Process
Variation
Special
Causes
Common Vs. Special Causes
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Common Causes: Random,
unidentifiable sources of variation
Special Causes: Variation causing factors
that can be identified and eliminated
Statistical Process Control:
Conceptual Framework
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Every process has variation
Must differential between acceptable
and unacceptable variations
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Acceptable - - random or COMMON
Unacceptable - non-random or SPECIAL
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- assignable cause exists
A process is in control when all SPECIAL causes
are removed.
The Normal
Distribution
 = Standard deviation
Figure 7.5
Mean
-3 -2 -1
+1 +2 +3
68.26%
95.44%
99.74%
Control Charts
UCL
Nominal
LCL
1
Figure 7.6
Assignable
causes likely
2
Samples
3
Using Control Charts for Process
Improvement
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Measure the process
When problems are indicated, find the
assignable cause
Eliminate problems, incorporate
improvements
Repeat the cycle
Types of Control Charts
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Measure variables - continuous scale
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x-chart; R Chart
Measure attributes -for yes/no decisions
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proportion defective -- p-chart
number of defects per unit -- c-chart
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number of paint defects/sq yard
Control charts for attributes
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p-chart, for the population proportion
defective
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take a random sample, inspect
plot the sample proportion defective
compare with UCL and LCL to see
whether the process is in control
Control Charts
for Variables
West Allis Industries
Example 7.1
Control Charts
for Variables
Special Metal Screw
Sample
Number
1
2
3
4
5
Example 7.1
1
0.5014
0.5021
0.5018
0.5008
0.5041
Sample
2
3
0.5022 0.5009
0.5041 0.5024
0.5026 0.5035
0.5034 0.5024
0.5056 0.5034
4
0.5027
0.5020
0.5023
0.5015
0.5047
R=
R
0.0018
0.0021
0.0017
0.0026
0.0022
0.0021
=
x=
_
x
0.5018
0.5027
0.5026
0.5020
0.5045
0.5027
Control Charts
for Variables
Control Charts – Special Metal Screw
R-Charts
UCLR = D4R
LCLR = D3R
Example 7.1
R = 0.0021
Control Charts
for
Variables
Control Chart
Factors
Factor for UCL Factor for
Factor
Control
- Special
Metal
Screw UCL for
Size of Charts
and LCL
for
LCL for
Sample
R-Charts
R-Charts
R = 0.0020
D4 = 2.2080
R - Charts x-Charts
(n)
(A2)
(D3)
(D4)
2
3
4
5
6
7
Example 7.1
1.880
1.023
0.729
0.577
0.483
0.419
0
0
0
0
0
0.076
3.267
2.575
2.282
2.115
2.004
1.924
Control Charts
for Variables
Control Charts—Special Metal Screw
R-Charts
R = 0.0021
D4 = 2.282
D3 = 0
UCLR = D4R
LCLR = D3R
UCLR = 2.282 (0.0021) = 0.00479 in.
LCLR = 0 (0.0021) = 0 in.
Example 7.1
Range Chart Special Metal
Screw
Figure 7.9
Control Charts
for Variables
Control Charts—Special Metal Screw
X-Charts
UCLx = x= + A2R
LCLx = x= - A2R
Example 7.1
R = 0.0021
x= = 0.5027
Control Charts
for
Variables
Control Chart
Factors
Factor for UCL Factor for
Factor
Control
- Special
Metal
Screw UCL for
Size of Charts
and LCL
for
LCL for
Sample
R-Charts
R-Charts
R = 0.0020
x - Charts x-Charts
(n)
(A2) x = 0.5025 (D3)
(D4)
2
1.880
UCL
=
x
+
A
R
x
2
3
1.023
LCL
4 x = x - A0.729
2R
5
6
7
Example 7.1
0.577
0.483
0.419
0
0
0
0
0
0.076
3.267
2.575
2.282
2.115
2.004
1.924
Control Charts
for Variables
Control Charts—Special Metal Screw
x-Charts
R = 0.0021
x= = 0.5027
A2 = 0.729
UCLx = x= + A2R
LCLx = x= - A2R
UCLx = 0.5027 + 0.729 (0.0021) = 0.5042 in.
LCLx = 0.5027 – 0.729 (0.0021) = 0.5012 in.
Example 7.1
x-Chart—
Special Metal
Screw
Figure 7.10
x-Chart—
Special Metal
Screw
Figure 7.10
x-Chart—
Special Metal
Screw
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Figure 7.10
Measure the process
Find the assignable cause
Eliminate the problem
Repeat the cycle
Control Charts
for Variables Using 
=
UCLx = x + zx
=
LCL = x – z
x
x = / n
Example 7.2
x
Control Charts
for Variables Using 
=
UCLx = x + zx
=
LCL = x – z
=
x = 5.0 minutes
x = / n
n = 6 customers
z = 1.96
x
Example 7.2
x
Sunny Dale Bank
 = 1.5 minutes
Control Charts
for Variables Using 
=
UCLx = x + zx
=
LCL = x – z
=
x = 5.0 minutes
x = / n
n = 6 customers
z = 1.96
x
x
Sunny Dale Bank
 = 1.5 minutes
UCLx = 5.0 + 1.96(1.5)/
6 = 6.20 min
UCLx = 5.0 – 1.96(1.5)/
6 = 3.80 min
Control charts for attributes
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p-chart, for the population proportion
defective
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take a random sample, inspect
plot the sample proportion defective
compare with UCL and LCL to see
whether the process is in control
Attribute Measurements (P-Charts)
p = Total # of Defectives/Total Sample
p (1 - p)
p =
n
UCL = p + Z  p
LCL = p - Z  p
Example
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n
100
50
100
100
75
100
100
50
100
100
100
100
100
100
100
Defects
4
2
5
3
6
4
3
8
1
2
3
2
2
8
3
1.
Calculate the sample proportion, p, for
each sample.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n
100
50
100
100
75
100
100
50
100
100
100
100
100
100
100
Defects
4
2
5
3
6
4
3
8
1
2
3
2
2
8
3
p
0.04
0.04
0.05
0.03
0.08
0.04
0.03
0.16
0.01
0.02
0.03
0.02
0.02
0.08
0.03
2. Calculate the average of the sample proportions.
56
 p=
= 0.04073
1375
3. Calculate the standard deviation of the
sample proportion ....
p (1 - p)
04073(1 - .04073)
p =
=
= .02065
91.6667
n
4. Calculate the control limits.
p+/- Z p
0.04073 +/- 3(0.02065)
UCL = 0.10268
LCL = -0.02122 (or 0)
5.
Plot the individual sample proportions, the average
of the proportions, and the control limits ....
UCL
LCL
When To Take Action
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A single point outside limits
Two consecutive points near a limit
A five point trend toward a limit
A run of five points above or below the
average
Erratic behavior
Process Capability
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Used to assess the degree to which the
output of a process conforms to
specifications
Natural spread of a process is defined as
6 sigma
Specification limits or tolerance limits
(LSL, USL) or (LTL, UTL)
Natural Spread vs. Tolerance
Spread
(a)
(b)
Natural Spread
Tolerance Spread
(c)
Process Capability Ratio
Cp= Tolerance Spread/Natural Spread
= (USL-LSL)/6sigma
•
•
•
A cookie machine produces cookies with mean
sugar content of 4.28; std of 0.122
Tolerance Limits for the Product [3.98,4.98]
Process Capability Ratio =
Process Capability Index, Cpk
X - LSL
USL - X 

C pk = min
or
3 
 3
Shifts in Process Mean
....
3.914
4.28
3.98
4.646
4.98
Example
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Mean of the Process = 4.28; STD = 0.122
Tolerance Limits for the Product [3.98,4.98]
Process Capability Ratio
=(4.98-3.98)/6*0.122 = 1.366
Process Capability Index =
4.28 - 3.98 4.98 - 4.28
min{
,
}
3 * 0.122
3 * 0.122
= min{0.81, 1.91}=0.81
Why Six Sigma?
If natural spread = tolerance spread
we will have:
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> 20,000 wrong prescriptions/yr.
> 15,000 babies accidentally dropped
each year by nurses and obstetricians
500 incorrect surgical operations each
week
Six Sigma Quality
X-Sigma
Quality
Cp
W/O shift in
mean (ppm)
With shift in
mean (ppm)
3
1
2,700
66,803
4
1.33
63
6,200
5
1.67
0.57
233
6
2
0.002
3.4
When Cp=2, it is called six-sigma quality
ppm: parts per million
Building Process Capability
Division Management
Mobilize the Entire Organization
Marketing, Design,
and Engineering
Improve Performance by
Increasing the Numerator
Cpk
SOURCE: Smith (1990).
Manufacturing, Delivery,
and Service
Improve Performance by
Decreasing the Denominator
Robust Quality
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Is it enough to be "in spec" ?
Robustness comes from consistency
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Consistent deviation vs scattered
deviation
More deviation from the target means
greater quality losses

Quality Loss Function: losses proportional
to the square of deviation
Taguchi’s View of Variation
Non-conformance to
design cost
$$$
Actual value->
0
Lower
Tolerance
Design
Spec
Upper
Tolerance
Traditional View
Lower
Tolerance
Design
Spec
Upper
Tolerance
Taguchi’s View
Controlling Variation
Fear?
Control?
Handcuffs?
Loss of Options?
Can Variations be
Controlled in Health Care?
Tool for Medical Field
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Care path: standardized guidelines for a
particular diagnosis or procedure
High volume and resource use
Caregivers now have a recipe to follow
Interdisciplinary
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merging the medical and nursing plans of care with those of other
disciplines, such as physical therapy, nutrition, or mental health.
Benchmarks permit effectiveness assessment
Examples
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Massachusetts General Hospital
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The University of Texas Medical Center
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Coronary Artery Bypass Graft Surgery (CABG)
Colon cancer
Shouldice Hospital
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Hernia surgery