Quality Management
“It costs a lot to produce a bad product.”
Norman Augustine
Cost of quality
1. Prevention costs
2. Appraisal costs
3. Internal failure costs
4. External failure costs
5. Opportunity costs
What is quality management all about?
Try to manage all aspects of the organization
in order to excel in all dimensions that are
important to “customers”
Two aspects of quality:
features: more features that meet customer needs
= higher quality
freedom from trouble: fewer defects = higher
quality
The Quality Gurus – Edward Deming
Quality is
“uniformity and
dependability”
Focus on SPC
and statistical
tools
“14 Points” for
management
1986
PDCA method
1900-1993
The Quality Gurus – Joseph Juran
Quality is
“fitness for use”
Pareto Principle
Cost of Quality
General
management
approach as well
as statistics
1951
1904 - 2008
History: how did we get here…
• Deming and Juran outlined the principles of Quality
Management.
• Tai-ichi Ohno applies them in Toyota Motors Corp.
• Japan has its National Quality Award (1951).
• U.S. and European firms begin to implement Quality
Management programs (1980’s).
• U.S. establishes the Malcolm Baldridge National
Quality Award (1987).
• Today, quality is an imperative for any business.
What does Total Quality Management encompass?
TQM is a management philosophy:
• continuous improvement
• leadership development
• partnership development
Cultural
Alignment
Customer
Technical
Tools
(Process
Analysis, SPC,
QFD)
Developing quality specifications
Input
Design
Design quality
Process
Output
Dimensions of quality
Conformance quality
Six Sigma Quality
 6
• A philosophy and set of methods companies use to
eliminate defects in their products and processes
• Seeks to reduce variation in the processes that lead to
product defects
• The name “six sigma” refers to the variation that
exists within plus or minus six standard deviations of
the process outputs
Six Sigma Quality
Six Sigma Roadmap (DMAIC)
Next Project
Celebrate
Project $
Control
Define
Customers, Value, Problem Statement
Scope, Timeline, Team
Primary/Secondary & OpEx Metrics
Validate
Project $
Current Value Stream Map
Voice Of Customer (QFD)
Measure
Assess specification / Demand
Measurement Capability (Gage R&R)
Correct the measurement system
Process map, Spaghetti, Time obs.
Document process (WIs, Std Work)
Mistake proof, TT sheet, CI List
Analyze change in metrics
Value Stream Review
Prepare final report
Measure OVs & IVs / Queues
Validate
Project $
Validate
Project $
Improve
Optimize KPOVs & test the KPIVs
Redesign process, set pacemaker
5S, Cell design, MRS
Visual controls
Value Stream Plan
Analyze (and fix the obvious)
Validate
Project $
Root Cause (Pareto, C&E, brainstorm)
Find all KPOVs & KPIVs
FMEA, DOE, critical Xs, VA/NVA
Graphical Analysis, ANOVA
Future Value Stream Map
Six Sigma Organization
Quality
Quality Improvement
Traditional
Time
Continuous improvement philosophy
1. Kaizen: Japanese term for continuous improvement.
A step-by-step improvement of business processes.
2. PDCA: Plan-do-check-act as defined by Deming.
Plan
Do
Act
Check
3. Benchmarking : what do top performers do?
Tools used for continuous improvement
1. Process flowchart
Tools used for continuous improvement
2. Run Chart
Performance
Time
Tools used for continuous improvement
3. Control Charts
Performance Metric
Time
Tools used for continuous improvement
4. Cause and effect diagram (fishbone)
Machine
Man
Environment
Method
Material
Tools used for continuous improvement
5. Check sheet
Item
A
------------- √ √ √
-------
B
C
√√
D
E
√
√√√
√√
√
√√
F
√
√
√
G
√
√√
Tools used for continuous improvement
6. Histogram
Frequency
Tools used for continuous improvement
7. Pareto Analysis
100%
75%
50
40
50%
30
20
10
25%
0%
A
B
C
D
E
F
Percentage
Frequency
60
Summary of Tools
1. Process flow chart
2. Run diagram
3. Control charts
4. Fishbone
5. Check sheet
6. Histogram
7. Pareto analysis
Case: shortening telephone waiting time…
• A bank is employing a call answering service
• The main goal in terms of quality is “zero waiting time”
- customers get a bad impression
- company vision to be friendly and easy access
• The question is how to analyze the situation and improve quality
The current process
Custome
rA
Custome
rB
Operator
Receiving
Party
How can we reduce
waiting time?
Fishbone diagram analysis
Absent receiving
party
Working system of
operators
Absent
Too many phone calls
Lunchtime
Out of office
Not at desk
Not giving receiving
party’s coordinates
Complaining
Absent
Lengthy talk
Does not know
organization well
Takes too much time to
explain
Leaving a
message
Customer
Does not
understand
customer
Operator
Makes
custome
r wait
Reasons why customers have to wait
(12-day analysis with check sheet)
Daily
average
Total
number
A
One operator (partner out of office)
14.3
172
B
Receiving party not present
6.1
73
C
No one present in the section receiving call
5.1
61
D
Section and name of the party not given
1.6
19
E
Inquiry about branch office locations
1.3
16
F
Other reasons
0.8
10
29.2
351
Pareto Analysis: reasons why customers have to wait
Frequency
Percentage
300
87.1%
250
71.2%
200
49%
150
100
0%
A
B
C
D
E
F
Ideas for improvement
1. Taking lunches on three different shifts
2. Ask all employees to leave messages when leaving desks
3. Compiling a directory where next to personnel’s name
appears her/his title
Results of implementing the recommendations
…After
Before…
Percentage
Frequency
Percentage
Frequency
100%
87.1%
300
300
71.2%
200
Improvement
200
49%
100
100
100%
0%
A
B
C
D
E
F
0%
B
C
A
D
E
F
In general, how can we monitor quality…?
By observing
variation in
output measures!
1. Assignable variation: we can assess the cause
2. Common variation: variation that may not be possible to
correct (random variation, random noise)
Statistical Process Control (SPC)
Every output measure has a target value and a level of
“acceptable” variation (upper and lower tolerance limits)
SPC uses samples from output measures to estimate the
mean and the variation (standard deviation)
Example
We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ
Question:
How do we define the output measures?
In order to measure variation we need…
The average (mean) of the observations:
1
X 
N
N
x
i 1
i
The standard deviation of the observations:
N
 
2
(
x

X
)
 i
i 1
N
Average & Variation example
Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27
Average:
Standard Deviation:
Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23
Average:
Standard Deviation:
Which pizza would you rather have?
When is a product good enough?
High
a.k.a
Upper/Lower Design Limits
(UDL, LDL)
Upper/Lower Spec Limits
(USL, LSL)
Upper/Lower Tolerance Limits
(UTL, LTL)
Incremental
Cost of
Variability
Zero
Lower Target
Tolerance Spec
Upper
Tolerance
Traditional View
The “Goalpost” Mentality
But are all ‘good’ products equal?
High
Incremental
Cost of
Variability
Taguchi’s View
“Quality Loss Function”
(QLF)
Zero
Lower
Spec
Target
Spec
Upper
Spec
LESS VARIABILITY implies BETTER PERFORMANCE !
Capability Index (Cpk)
It shows how well the performance measure
fits the design specification based on a given
tolerance level
A process is k capable if
X  k  UTL and
UTL  X
1
k
and
X  k  LTL
X  LTL
1
k
Capability Index (Cpk)
Another way of writing this is to calculate the capability index:
C pk
 X  LTL UTL  X 
 min 
,

k 
 k
Cpk < 1 means process is not capable at the k level
Cpk >= 1 means process is capable at the k level
Accuracy and Consistency
We say that a process is accurate if its mean
the target T.
X is close to
We say that a process is consistent if its standard deviation
is low.
Example 1: Capability Index (Cpk)
X = 10 and σ = 0.5
LTL = 9
UTL = 11
LTL
X
UTL
11 10 
 10  9
C pk  min 
or
 0.667

3 0.5 
 3 0.5
Example 2: Capability Index (Cpk)
X = 9.5 and σ = 0.5
LTL = 9
UTL = 11
LTL
X
UTL
Example 3: Capability Index (Cpk)
X = 10 and σ = 2
LTL = 9
UTL = 11
LTL
X
UTL
Example
Consider the capability of a process that puts
pressurized grease in an aerosol can. The design
specs call for an average of 60 pounds per square
inch (psi) of pressure in each can with an upper
tolerance limit of 65psi and a lower tolerance limit
of 55psi. A sample is taken from production and it
is found that the cans average 61psi with a standard
deviation of 2psi.
1. Is the process capable at the 3 level?
2. What is the probability of producing a defect?
Solution
LTL = 55 UTL = 65 X  61
C pk
C pk
=2
X  LTL UTL  X
 min(
,
)
3
3
61  55 65  61
 min(
,
)  min( 1,0.6667)  0.6667
6
6
No, the process is not capable at the 3 level.
Solution
P(defect) = P(X<55) + P(X>65)
=P(X<55) + 1 – P(X<65)
=P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2)
=P(Z<-3) + 1 – P(Z<2)
=G(-3)+1-G(2)
=0.00135 + 1 – 0.97725 (from standard normal table)
= 0.0241
2.4% of the cans are defective.
Example (contd)
Suppose another process has a sample mean of 60.5 and
a standard deviation of 3.
Which process is more accurate? This one.
Which process is more consistent? The other one.
Control Charts
Upper Control Limit
Central Line
Lower Control Limit
Control charts tell you when a process measure is
exhibiting abnormal behavior.
Two Types of Control Charts
•
X/R Chart
This is a plot of averages and ranges over time
(used for performance measures that are variables)
•
p Chart
This is a plot of proportions over time (used for
performance measures that are yes/no attributes)
Statistical Process Control with p Charts
When should we use p charts?
1. When decisions are simple “yes” or “no” by inspection
2. When the sample sizes are large enough (>50)
Sample (day)
Items
Defective
Percentage
1
200
10
0.050
2
200
8
0.040
3
200
9
0.045
4
200
13
0.065
5
200
15
0.075
6
200
25
0.125
7
200
16
0.080
Statistical Process Control with p Charts
Let’s assume that we take t samples of size n …
total number of "defects"
p
(number of samples)  (sample size)
sp 
p (1  p )
n
UCL  p  zs p
LCL  p  zs p
Statistical Process Control with p Charts
80
1
p
  0.066
6  200 15
0.066(1  0.066)
sp 
 0.017
200
UCL  0.066  3 0.017  0.117
LCL  0.066  3 0.017  0.015
Statistical Process Control with p Charts
UCL = 0.117
p = 0.066
LCL = 0.015
Statistical Process Control with X/R Charts
When should we use X/R charts?
1. It is not possible to label “good” or “bad”
2. If we have relatively smaller sample sizes (<20)
Statistical Process Control with X/R Charts
Take t samples of size n (sample size should be 5 or more)
1 n
X   xi
n i 1
X is the mean for each sample
R  max {xi }  min {xi }
R is the range between the highest and the lowest for each sample
Statistical Process Control with X/R Charts
1 t
X  Xj
t j 1
X is the average of the averages.
1 t
R   Rj
t j 1
R is the average of the ranges
Statistical Process Control with X/R Charts
define the upper and lower control limits…
UCLX  X  A2 R
LCLX  X  A2 R
UCLR  D4 R
LCLR  D3 R
Read A2, D3, D4 from
Table TN 8.7
Example: SPC for bottle filling…
Sample
Observation (xi)
Average
1
11.90
11.92
12.09
11.91
12.01
2
12.03
12.03
11.92
11.97
12.07
3
11.92
12.02
11.93
12.01
12.07
4
11.96
12.06
12.00
11.91
11.98
5
11.95
12.10
12.03
12.07
12.00
6
11.99
11.98
11.94
12.06
12.06
7
12.00
12.04
11.92
12.00
12.07
8
12.02
12.06
11.94
12.07
12.00
9
12.01
12.06
11.94
11.91
11.94
10
11.92
12.05
11.92
12.09
12.07
Range (R)
Example: SPC for bottle filling…
Calculate the average and the range for each sample…
Sample
Observation (xi)
Average
Range (R)
1
11.90
11.92
12.09
11.91
12.01
11.97
0.19
2
12.03
12.03
11.92
11.97
12.07
12.00
0.15
3
11.92
12.02
11.93
12.01
12.07
11.99
0.15
4
11.96
12.06
12.00
11.91
11.98
11.98
0.15
5
11.95
12.10
12.03
12.07
12.00
12.03
0.15
6
11.99
11.98
11.94
12.06
12.06
12.01
0.12
7
12.00
12.04
11.92
12.00
12.07
12.01
0.15
8
12.02
12.06
11.94
12.07
12.00
12.02
0.13
9
12.01
12.06
11.94
11.91
11.94
11.97
0.15
10
11.92
12.05
11.92
12.09
12.07
12.01
0.17
Then…
X  12.00
is the average of the averages
R  0.15
is the average of the ranges
Finally…
Calculate the upper and lower control limits
UCLX  12.00  0.58  0.15  12.09
LCLX  12.00  0.58  0.15  11.91
UCLR  2.11 0.15  1.22
LCLR  0  0.15  0
The X Chart
UCL = 12.10
X = 12.00
LCL = 11.90
The R Chart
UCL = 0.32
R = 0.15
LCL = 0.00
The X/R Chart
UCL
X
LCL
What can you
conclude?
UCL
R
LCL