Quality Management
Chapter 8
1
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Learning Goals
 Statistical
Process Control
 X-bar, R-bar, p charts
 Process variability vs. Process specifications
 Yields/Reworks and their impact on costs
 Just-in-time philosophy
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Steer Support for the Scooter
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Steer Support Specifications
Go-no-go
gauge
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4
Control Charts
79.98
79.97
79.96
X-bar
79.95
79.94
79.93
79.92
79.91
79.9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
0.09
0.08
0.07
0.06
R
0.05
0.04
0.03
0.02
0.01
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
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Statistical Process Control (SPC)

SPC: Statistical evaluation
of the output of a process during production/service

The Control Process
–
–
–
–
–
–
Define
Measure
Compare to a standard
Evaluate
Take corrective action
Evaluate corrective action
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The Concept of Consistency:
Who is the Better Target Shooter?
Not just the mean is important, but also the variance
Need to look at the distribution function
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Statistical Process Control
Capability
Analysis
Eliminate
Assignable Cause
Conformance
Analysis
Investigate for
Assignable Cause
Capability analysis
• What is the currently "inherent" capability of my process when it is "in control"?
Conformance analysis
• SPC charts identify when control has likely been lost and assignable cause
variation has occurred
Investigate for assignable cause
• Find “Root Cause(s)” of Potential Loss of Statistical Control
Eliminate assignable cause
• Need Corrective Action To Move Forward
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8
Statistical Process Control
 Shewhart’s
classification of variability:
– Common (random) cause
– assignable cause
 Variations
and Control
– Random variation: Natural variations in the output of
process, created by countless minor factors
» temperature, humidity variations, traffic delays.
– Assignable variation: A variation whose source can be
identified. This source is generally a major factor
» tool failure, absenteeism
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Two Types of Causes for Variation
Common Cause Variation (low level)
Common Cause Variation (high level)
Assignable Cause Variation
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Mean and Variance

Given a population of numbers, how to compute the
mean and the variance?
Population  {x1 , x2 ,..., x N }
N
Mean   
x
i 1
i
N
N
Variance   2 
2
(
x


)
 i
i 1
N
Standard deviation  
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Sample for Efficiency and Stability
 From
a large population of goods or services (random
if possible) a sample is drawn.
– Example sample: Midterm grades of OPRE6302 students
whose last name starts with letter R {60, 64, 72, 86}, with
letter S {54, 60}
»
»
»
»
x 
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Sample size= n
Sample average or sample mean= x
Sample range= R
Standard deviation of sample means=

n
where  : Standard deviation of the population
12
Sampling Distribution
Sampling distribution is the distribution of sample means.
Sampling distribution
Variability of the average scores of
people with last name R and S
Process distribution
Variability of the scores
for the entire class
Mean
Grouping reduces the variability.
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13
Normal Distribution
normdist(x,.,.,1)
normdist(x,.,.,0)
Probab


Mean
x


95.44%
99.74%
Excel statistica l functions : normdist ( x, mean, st _ dev,0) normal pdf at x.
Excel statistica l functions : normdist ( x, mean, st _ dev,1) normal cdf at x.
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Cumulative Normal Density
1
prob
normdist(x,mean,st_dev,1)
0
x
norminv(prob,mean,st_dev)
Excel statistica l functions :
Cumulative function (cdf) at x : normdist ( x, mean, st _ dev,1)
Inverse function of cdf at " prob": norminv ( prob, mean, st _ dev)
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Normal Probabilities: Example
 If
temperature inside a firing oven has a normal
distribution with mean 200 oC and standard deviation of
40 oC, what is the probability that
– The temperature is lower than 220 oC
=normdist(220,200,40,1)
– The temperature is between 190 oC and 220oC
=normdist(220,200,40,1)-normdist(190,200,40,1)
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Control Limits
Process is in control if sample mean is between control limits.
These limits have nothing to do with product specifications!
Sampling
distribution
Process
distribution
Mean
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LCL
Lower
control
limit
UCL
Upper
control
limit
17
Setting Control Limits:
Hypothesis Testing Framework



Null hypothesis: Process is in control
Alternative hypothesis: Process is out of control
Alpha=P(Type I error)=P(reject the null when it is true)=
P(out of control when in control)
 Beta=P(Type II error)=P(accept the null when it is false)
P(in control when out of control)

If LCL decreases and UCL increases, we accept the null more easily.
What happens to
– Alpha?
– Beta?

Not possible to target alpha and beta simultaneously,
– Control charts target a desired level of Alpha.
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Type I Error=Alpha
Sampling distribution
/2
/2
Mean
Probability
of Type I error
LCL
UCL
LCL  norminv( /2, mean, st_dev)
UCL  norminv(1 - /2, mean, st_dev)
The textbook uses Type I error=1-99.74%=0.0026=0.26%.
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Statistical Process Control: Control Charts
Process
Parameter
• Track process parameter over time
- mean
- percentage defects
Upper Control Limit (UCL)
• Distinguish between
- common cause variation
(within control limits)
- assignable cause variation
(outside control limits)
Center Line
Lower Control Limit (LCL)
Time
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• Measure process performance:
how much common cause variation
is in the process while the process
is “in control”?
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Control Chart
Abnormal variation
due to assignable sources
Out of
control
UCL
Mean
Normal variation
due to chance
LCL
Abnormal variation
due to assignable sources
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15
Sample number
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Observations from Sample Distribution
UCL
LCL
1
2
3
4
Sample number
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Parameters for computing UCL and LCL
the Table method
Number of
Observations
in Sample
Sample size (n)
2
3
4
5
6
7
8
9
10
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Factor for Xbar Chart
(A2)
1.88
1.02
0.73
0.58
0.48
0.42
0.37
0.34
0.31
Factor for
Lower
control Limit
in R chart
(D3)
0
0
0
0
0
0.08
0.14
0.18
0.22
Factor for
Factor to
Upper
estimate
control limit
Standard
in R chart
deviation, (d2)
(D4)
3.27
1.128
2.57
1.693
2.28
2.059
2.11
2.326
2.00
2.534
1.92
2.704
1.86
2.847
1.82
2.970
1.78
3.078
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The X-bar Chart: Application to Call Center
Period
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
x1
x2
1.7
2.7
2.1
1.2
4.4
2.8
3.9
16.5
2.6
1.9
3.9
3.5
29.9
1.9
1.5
3.6
3.5
2.8
2.1
3.7
2.1
3
12.8
2.3
3.8
2.3
2
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x3
1.7
2.3
2.7
3.1
2
3.6
2.8
3.6
2.1
4.3
3
8.4
1.9
2.7
2.4
4.3
1.7
5.8
3.2
1.7
2
2.6
2.4
1.6
1.1
1.8
6.7
x4
3.7
1.8
4.5
7.5
3.3
4.5
3.5
2.1
3
1.8
1.7
4.3
7
9
5.1
2.1
5.1
3.1
2.2
3.8
17.1
1.4
2.4
1.8
2.5
1.7
1.8
x5
3.6
3
3.5
6.1
4.5
5.2
3.5
4.2
3.5
2.9
2.1
1.8
6.5
3.7
2.5
5.2
1.8
8
2
1.2
3
1.7
3
5
4.5
11.2
6.3
2.8
2.1
2.9
3
1.4
2.1
3.1
3.3
2.1
2.1
5.1
5.4
2.8
7.9
10.9
1.3
3.2
4.3
1
3.6
3.3
1.8
3.3
1.5
3.6
4.9
1.6
Average
Mean
Range
2.7
2
2.38
1.2
3.14
2.4
4.18
6.3
3.12
3.1
3.64
3.1
3.36
1.1
5.94
14.4
2.66
1.4
2.6
2.5
3.16
3.4
4.68
6.6
9.62
28
5.04
7.1
4.48
9.4
3.3
3.9
3.06
3.4
4.8
5.2
2.1
2.2
2.8
2.6
5.5
15.1
2.1
1.6
4.78
10.4
2.44
3.5
3.1
3.4
4.38
9.5
3.68
5.1
3.81
5.85
• Collect samples over time
• Compute the mean:
x1  x2  ...  xn
X
n
• Compute the range:
R  max{ x1 , x2 ,...xn }
 min{ x1 , x2 ,...xn }
as a proxy for the variance
• Average across all periods
- average mean
- average range
• Normally distributed
24
Control Charts: The X-bar Chart
The Table method
• Define control limits
12
UCL= X +A2 × R =3.81+0.58*5.85=7.19
LCL= X -A2 × R =3.81-0.58*5.85=0.41
10
8
• Constants are taken from a table
6
• Identify assignable causes:
- point over UCL
- point below LCL
- many (6) points on one side of center
4
2
0
1
3
5
7
mean
st-dev
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11 13 15 17 19 21 23 25 27
CSR 1
2.95
0.96
CSR 2
3.23
2.36
• In this case:
- problems in period 13
- new operator was assigned
CSR 3
7.63
7.33
CSR 4
3.08
1.87
CSR 5
4.26
4.41
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Range Control Chart
UCL  D4 R  A multiple of the average of sample ranges
LCL  D3 R  A multiple of the average of sample ranges
Multipliers D4 and D3 depend on n and are available in Table 8.2.
EX: In the last five years, the range of GMAT scores of incoming PhD class is
88, 64, 102, 70, 74. If each class has 6 students, what are UCL and LCL for
GMAT ranges?
R  (88  64  102  70  74) / 5  79.6. For n  6, D 4  2, D3  0.
UCL  D4 R  2 * 79.6  159.2 LCL  D3 R  0 * 79.6  0
Are the GMAT ranges in control?
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Control Charts: X-bar Chart and R-bar Chart
For the Call Center
12
10
X-Bar
8
6
4
2
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
1
3
5
7
9
11
13
15
17
19
21
23
25
27
30
25
R
20
15
10
5
0
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X-bar and Range Charts: Which?
(process mean is
shifting upward)
Sampling
Distribution
UCL
Detects shift
x-Chart
LCL
UCL
R-chart
Does not
detect shift
LCL
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X-bar and Range Charts: Which?
Sampling
Distribution
(process variability is increasing)
UCL
Does not
reveal increase
x-Chart
LC
L
UCL
R-chart
Reveals increase
LC
L
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Control Charts: The X-bar Chart
The Direct method
• Compute the standard deviation of the sample averages
• stdev(2.7, 2.38, 3.14, 4.18, 3.12, 3.64, 3.36, 5.94, 2.66, 2.6, 3.16, 4.68, 9.62,
5.04, 4.48, 3.3, 3.06, 4.8, 2.1, 2.8, 5.5, 2.1, 4.78, 2.44, 3.1, 4.38, 3.68)=1.5687
• Use
type I error of 1-0.9974
  0.0026
LCL  norminv( /2, mean, st_dev)
 norminv(0. 0013,3.81,1.5687)  -0.91
UCL  norminv(1 - /2, mean, st_dev)
 norminv(0. 9987,3.81,1.5687)  8.53
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Process Capability
Let us Tie Tolerances and Variability

Tolerances/Specifications
– Requirements of the design or customers

Process variability
– Natural variability in a process
– Variance of the measurements coming from the process

Process capability
– Process variability relative to specification
– Capability=Process specifications / Process variability
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Process Capability:
Specification limits are not control chart limits
Lower
Specification
Upper
Specification
Process variability matches
specifications
Lower
Specification
Sampling
Distribution
is used
Upper
Specification
Process variability well within
Lower
Upper
specifications
Specification Specification
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Process variability exceeds
specifications
32
Process Capability Ratio
When the process is centered, process capability ratio
Cp 
Upper specificat ion level - Lower specificat ion level
6 X
A capable process has large Cp.
Example: The standard deviation, of sample averages of the
midterm 1 scores obtained by students whose last names start
with R, has been 7. The SOM requires the scores not to
differ by more than 50% in an exam. That is the highest
score can be at most 50 points above the lowest score.
Suppose that the scores are centered, what is the process
capability ratio?
Answer: 50/42
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3 Sigma and 6 Sigma Quality
Upper
specification
Lower
specification
Process
mean
+/- 3 Sigma
+/- 6 Sigma
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The Statistical Meaning of Six Sigma
Lower
Specification (LSL)
Upper
Specification (USL)
Process A
(with st. dev A)
X-3A
X-2A
X-1A
X
X+1A
X+2
Process B
(with st. dev B)
X
Cp
P{defect}
1
0.33
0.317
2
0.67
0.0455
3
1.00
0.0027
4
1.33
0.0001
5
1.67
0.0000006
6
2.00
2x10-9
X+3A
3
X-6B
x
X+6B
• Estimate standard deviation: ̂ =R /d2
• Or use the direct method with the excel function stdev()
• Look at standard deviation relative to specification limits
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Use of p-Charts

p=proportion defective, assumed to be known

When observations can be placed into two categories.
– Good or bad
– Pass or fail
– Operate or don’t operate
– Go or no-go gauge
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Attribute Based Control Charts: The p-chart
Period
n
1
300
2
300
3
300
4
300
5
300
6
300
7
300
8
300
9
300
10
300
11
300
12
300
13
300
14
300
15
300
16
300
17
300
18
300
19
300
20
300
21
300
22
300
23
300
24
300
25
300
26
300
27
300
28
300
29
300
30
300
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defects
18
15
18
6
20
16
16
19
20
16
10
14
21
13
13
13
17
17
21
18
16
14
33
46
10
12
13
18
19
14
p
0.060
0.050
0.060
0.020
0.067
0.053
0.053
0.063
0.067
0.053
0.033
0.047
0.070
0.043
0.043
0.043
0.057
0.057
0.070
0.060
0.053
0.047
0.110
0.153
0.033
0.040
0.043
0.060
0.063
0.047
• Estimate average defect
percentage
p =0.052
• Estimate Standard Deviation
̂ =
p(1  p)
Sample Size
=0.013
• Define control limits
UCL= p + 3̂ =0.014
LCL= p- 3̂ =0.091
37
Attribute Based Control Charts: The p-chart
0.180
0.160
0.140
0.120
0.100
0.080
0.060
0.040
0.020
0.000
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
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Inspection

Where/When
» Raw materials
» Finished products
Inputs
Acceptance
sampling
Transformation
Process
control
Outputs
Acceptance
sampling
» Before a costly operation, PhD comp. exam before candidacy
» Before an irreversible process, firing pottery
» Before a covering process, painting, assembly

Centralized vs. On-Site, my friend checks quality at cruise lines
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Discovery of Defects and the Costs
Process
Step
Defect
occurred
Defect
detected
Cost of
defect
End of
Process
Bottleneck
Defect
detected
Defect
detected
$
$
Based on labor and
material cost
Based on sales
price (incl. Margin)
Market
Defect
detected
$
Recall, reputation,
warranty costs
Recall Alert
CPSC, Segway LLC Announce Voluntary Recall to
Upgrade Software on Segway™ Human
U.S. Consumer Product Safety Commission Transporters
Office of Information and Public Affairs
The following product safety recall was conducted by the
Washington, DC 20207
firm in cooperation with the CPSC.
September 26, 2003
Name of Product: Segway Human Transporter (HT)
Units: Approximately 6,000
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Examples of Inspection Points
Type of
business
Fast Food
Inspection
points
Cashier
Counter area
Eating area
Building
Kitchen
Hotel/motel Parking lot
Accounting
Building
Main desk
Supermarket Cashiers
Deliveries
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Characteristics
Accuracy
Appearance, productivity
Cleanliness
Appearance
Health regulations
Safe, well lighted
Accuracy, timeliness
Appearance, safety
Waiting times
Accuracy, courtesy
Quality, quantity
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The Concept of Yields
Yield of Resource =
Yield of Process =
90%
Flow rate of units processed correctly at the resource
Flow rate
Flow rate of units processed correctly
Flow rate
80%
90%
100%
90%
Line Yield: 0.9 x 0.8 x 0.9 x 1 x 0.9
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Rework / Elimination of Flow Units
Rework:
Step 1
Test 1
Step 2
Test 2
Step 3
Test 3
Rework
Step 1
Test 1
Step 2
Test 2
Step 3
Defects can be corrected
Same or other resource
Leads to variability
Examples:
- Readmission to Intensive Care Unit
Test 3
Loss of Flow units:
Step 1
Test 1
Step 2
Test 2
Step 3
Test 3
Defects can NOT be corrected
Leads to variability
To get X units, we have to start
X/y units
Examples:
- Interviewing
- Semiconductor fab
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Why Having a Process is so Important:
Two Examples of Rare-Event Failures
Case 1: Process does not matter in most cases
• Airport security
• Safety elements (e.g. seat-belts)
“Bad” outcome only happens
Every 100*10,000 units
1 problem every 10,000 units
99% correct
Case 2: Process has built-in rework loops
• Double-checking
99%
Good
99%
99%
“Bad” outcome happens
with probability (1-0.99)3
1%
1%
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1%
Bad
Learning should be driven by process deviations, not by defects
44
Rare events are not so rare:
Chances of a Jetliner Crash due to Engine Icing

Engine flameout due to crystalline icing: Engine
stops for 30-90 secs and hopefully starts again.

Suppose 150 single engine flameouts over 19902005 and 15 dual engine flameouts over 20022005. What are the annualized single and dual
engine flameouts?
10=150/15 and 5=15/3

Let N be the total number of widebody jetliners
flying through a storm per year. Assume that
engines ice independently to compute N.
Set Prob(2 engine icing)=Prob(1 engine icing)2
(5/N)=(10/N)2 which gives N=20

There are 1200 widebody jetliners worldwide. It
is safe to assume that each flies once a day.
Suppose that there are 2 storms on their path
every day, which gives us about M=700
widebody jetliner and storm encounter very year.
How can we explain M=700 > N=20?
The engines do not ice independently. With M=700,
Prob(1 engine icing)=10/700=1.42% and Prob(2 engine
icing)=5/700=0.71%. Because of dependence Prob(2
engine icing) >> Prob(1 engine icing) 2 .
Unjustifiable independence leads to underestimation of the failure probabilities
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in operations, finance, engineering, flood control, etc.
45
Just-in-Time Philosophy
 Pull
the operations rather than pushing them
– Inventory reduction
– JIT Utopia
» 0-setup time
» 0-non value added operations
» 0-defects

Discover and reduce process variability
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Push vs Pull System

What instigates the movement of the work in the system?

In Push systems, work release is based on downstream demand
forecasts
– Keeps inventory to meet actual demand
– Acts proactively
» e.g. Making generic job application resumes today (e.g.: exempli gratia)

In Pull systems, work release is based on actual demand or the
actual status of the downstream customers
– May cause long delivery lead times
– Acts reactively
» e.g. Making a specific resume for a company after talking to the recruiter
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Push/Pull View of Supply Chains
Procurement,
Manufacturing and
Replenishment cycles
PUSH PROCESSES
Customer Order
Cycle
PULL PROCESSES
Customer
Order Arrives
Push-Pull boundary
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Pull Process with
Kanban Cards
Direction of production flow
upstream
downstream
Authorize
production
of next unit
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Pareto Principle or 20-80 rule
Absolute
Number
Cause of Defect
Percentage
Cumulative
Browser error
43
0.39
0.39
Order number out of sequence
29
0.26
0.65
Product shipped, but credit card not billed
16
0.15
0.80
Order entry mistake
11
0.10
0.90
Product shipped to billing address
8
0.07
0.97
Wrong model shipped
3
0.03
1.00
Total
110
Number of
defects
100 Cumulative
percents of
defects
100
75
50
50
Wrong model
shipped
Product shipped to
billing address
Order entry
mistake
Product shipped, but
credit card not billed
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Order number out
off sequence
Browser
error
25
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Reduce Variability in the Process
Taguchi: Even Small Deviations are Quality Losses
Taguchi’s view of Quality loss
Traditional view of Quality loss
Quality
Loss
Quality
Loss
Performance
Metric, x
High
Low
Lower
Specification
Limit
Target
value
Upper
Specification
Limit
Performance
Metric
Target
value
Performance
Metric
•It is not enough to look at “Good” vs “Bad” Outcomes
•Only looking at good vs bad wastes opportunities for learning; especially as failures become
rare (closer to six sigma) you need to learn from the “near misses”
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Accommodate Residual (Common) Variability
Through Robust Design
• Double-checking (see Toshiba)
• Fool-proofing, Poka yoke (see Toyota)
• Computer plugs
• Set the watch 5 mins ahead
• Process recipe (see Brownie)
• Recipes help standardize
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Ishikawa (Fishbone) Diagram
Specifications /
information
Machines
Cutting
tool worn
Dimensions incorrectly
specified in drawing
Vise position
set incorrectly
Clamping force too
high or too low
Machine tool
coordinates set incorrectly
Part incorrectly
positioned in clamp
Dimension incorrectly coded
In machine tool program
Vice position shifted
during production
Part clamping
surfaces corrupted
Steer support
height deviates
from specification
Extrusion temperature
too high
Error in
measuring height
Extrusion stock
undersized
Extrusion die
undersized
People
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Extrusion
rate
too high
Material
too soft
Materials
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Summary
 Statistical
Process Control
 X-bar, R-bar, p charts
 Process variability vs. Process specifications
 Yields/Reworks and their impact on costs
 Just-in-time philosophy
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Process Failure in Healthcare:
The Case of Jesica Santillan
Jesica Santillan died after a bungled heart-lung transplant in 2003. In an operation
Feb. 7, Jesica was mistakenly given organs of the wrong blood type.
Her blood type was 0 Rh+.
Organs come from A Rh- blood type.
Her body rejected the organs, and a matching transplant about two weeks later
came too late to save her. She died Feb. 22 at Duke University Medical Center.
Line of Causes leading to the mismatch
• On-call surgeon on Feb 7 in charge of pediatric heart transplants,
James Jaggers, did not take home the list of blood types
Later stated, "Unfortunately, in this case, human errors were made during the
process. I hope that we, and others, can learn from this tragic mistake."
• Coordinator initially misspelled Jesica’s name
• Once UNOS (United Network for Organ Sharing) identified Jesica,
no further check on blood type
• Little confidence in information system / data quality
• Pediatric nurse did not double check
• Harvest-surgeon did not know blood type
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Process Failure in Healthcare:
The Case of Jesica Santillan
- We didn’t have enough checks.
Ralph Snyderman, Duke University Hospital
- As a result of this tragic event, it is clear to us at Duke that we need to have
more robust processes internally and a better understanding of the
responsibilities of all partners involved in the organ procurement process.
William Fulkerson, M.D., CEO of Duke University Hospital.
Jesica is not the first death in organ transplantation because of blood type mismatch.
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The Three Steps in the Case of Jesica
Step 1: Define and map processes
- Jaggers had probably forgotten the list with blood groups 20 times before
- Persons involved in the process did not double-check,
everybody checked sometimes
- Learning is triggered following deaths / process deviations are ignored
Step 2: Reduce variability
- quality of data (initially misspelled the name)
Step 3: Robust Design
- color coding between patient card / box holding the organ
- information system with no manual work-around
- let the technology help
RFID tagged patients: Tag includes blood type and other info
Electronic medicine box: Alarming for the obsolete medicine
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How do you get to a Six Sigma Process?
Do Things Consistently (ISO 9000)
1. Management Responsibility
2. Quality System
3. Contract review
4. Design control
5. Document control
6. Purchasing / Supplier evaluation
7. Handling of customer supplied material
8. Products must be traceable
9. Process control
10. Inspection and testing
11. Inspection, Measuring, Test Equipment
12. Records of inspections and tests
13. Control of nonconforming products
14. Corrective action
15. Handling, storage, packaging, delivery
16. Quality records
17. Internal quality audits
18. Training
19. Servicing
20. Statistical techniques
Examples: “The design process shall be planned”,
“production processes shall be defined and planned”
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The System of Lean Production
Principles
Zero Inventories
Zero Defects
Flexibility / Zero set-ups
Zero breakdowns
Zero handling / non
value added
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Organization
Autonomation
Competence and Training
Continuous Improvement
Quality at the source
Methods
Just-in-time Production
• Kanban
• Classical Push
• “Real” Just-in-time
Mixed Production
Set-up reduction
Pardon the French, caricatures are from Citroen.
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Principles of Lean Production:
Zero Inventory and Zero Defects
Inventory in process
Buffer argument:
“Increase inventory”
• Avoid unnecessary inventory
• To be seen more as an ideal
• To types of (bad) inventory:
a. resulting from defects / rework
b. absence of a smooth process flow
• Remember the other costs of inventory (capital, flow time)
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Toyota argument:
“Decrease inventory”
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ITAT: Information Turnaround Time
7
8
5
4
6
3
1
Defective unit
2
Good unit
ITAT=7*1 minute
4
1
3
2
ITAT=2*1 minute
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Principles of Lean Production:
Zero Set-ups, Zero NVA and Zero Breakdowns
Avoid Non-value-added activities,
specifically rework and set-ups
• Flexible machines with short set-ups
• Allows production in small lots
• Real time with demand
• Large variety
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• Maximize uptime
• Without inventory, any breakdown
will put production to an end
• preventive maintenance
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Methods of Lean Production: Just-in-time
Push: make to forecast Pull: Synchronized production
• Classical MRP way
• Based on forecasts
• Push, not pull
• Still applicable for
low cost parts
• Part produced for specific
order (at supplier)
• shipped right to assembly
• real-time synchronization
• for large parts (seat)
• inspected at source
Pull: Kanban
• Visual way to implement a pull system
• Amount of WIP is determined by
number of cards
• Kanban = Sign board
• Work needs to be authorized by demand
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Methods of Lean Production:
Mixed Production and Set-up reduction
Production with large batches
Cycle
Inventory
Cycle
Inventory
Beginning of
Month
End of
Month
Production with small batches
Produce Sedan
Produce Station wagon
Month
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Beginning of
Month
End of
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Organization of Lean Production:
Autonomation and Training
• Automation with a human touch
• Create local decision making rather
than pure focus on execution
• Use machines / tools, but avoid the
lights-off factory
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• Cross training of workers
• Develop problem solving skills
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Organization of Lean Production:
Continuous Improvement and Quality-at-the-source
• Solve the problems where they occur
- this is where the knowledge is
- this is the cheapest place
Defect found
End User
Own Process Next Process End of Line Final
Inspection
$
$
$
$
$
• very minor • minor
delay
• Rework
• Significant
• Reschedule
Rework
• Delayed
Defect fixed
delivery
• Overhead
• Warranty
cost
• recalls
• reputation
• overhead
• Traditional: inspect and rework
at the end of the process
• Once problem is detected, send
alarm and potentially stop
the production
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