hashemite university department of civil engineering
TRANSCRIPT
Project Quality Management- Introduction
Part 3
Construction Project
Management
(CE 110401346)
Hashemite University
Department of Civil Engineering
▪Quality is the ability of a product or service to consistently
meet or exceed customer expectations
▪Quality in Engineering Sense conveys the concepts of:
✓Conformance to requirements
✓Value for money
✓Fitness for purpose
✓Customer satisfaction
9 - 2
Quality
▪ Perceived quality is governed by the gap between
customers’ expectations and their perceptions of the
product or service
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Perceived quality is poor
Perceived quality is good
Expectations > perceptions
Expectations = perceptions
Expectations < perceptions
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Perception of Quality
9 -12
▪Precision: Consistency that the value of repeated
measurements are clustered and have little scatter
▪Accuracy: Correctness that the measured value is very
close to the true value
9 - 4
Precision vs. Accuracy
▪Quality Control (QC): A set of activities or techniques whose
purpose is to ensure that all quality requirements are being
met by monitoring of processes and solving performance
problems✓Monitoring work results
✓Inspections and tests
▪ Quality Assurance (QA): Emphasis on finding and
correcting defects before reaching market.
9 - 5
QC vs. QA
The Quality Cycle
Refine
site
Quality
Contro
l
Quality
Assurance
Qualityoutput
input
Test
results
Create
site
Test
site
▪BS5750 Quality Management first introduced in Britain in 1979
▪IS0 (the International Organization for Standardization) is a worldwide federation of national standards bodies (IS0member bodies).
▪Adopted by the International Standards Organisation (ISO) in Geneva and was reborn as ISO 9000 Quality Management and Quality Assurance Standards in 1987
▪Updated in 1994, 2000, and 2008
9 - 7
ISO 9001
ISO 9001
▪ ISO 9000:2005: Quality management systems
— Fundamentals and vocabulary
▪ ISO 9001:2008: Quality management systems
— Requirements
▪ ISO 9004:2000: Quality management systems
—Guidelines for performance improvements▪ ISO 10005:2005: Quality management systems —
Guidelines for quality plans
▪ ISO 10006:2003: Quality management systems —
Guidelines for quality management in projects9 - 8
9 - 9
▪Six Sigma means a failure rate of 3.4 parts per million
or 99.9997% perfect
▪A philosophy and set of methods companies use to eliminate defects in their products and processes
▪It is essentially based on three underlying facts:▪ You can manage what you measure
▪ You can measure what you can define
▪ You can define what you understand.
Six Sigma
9 -10
▪The objective of six sigma is to improve profits
through variability and defect reduction, yield
improvement, improved consumer satisfaction and
best-in-class product / process performance.
▪3 or 6 sigma – represents level of quality✓ +/- 1 sigma equal to 68.26%
✓ +/- 2 sigma equal to 95.46%
✓ +/- 3 sigma equal to 99.73%
✓+/- 6 sigma equal to 99.99%
Six Sigma
Quality Improvement
Traditional
Time
Qu
ality
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
2. Run Chart
T im e (Ho urs )
0.44
0.46
0.48
0.5
0.52
0.54
0.56
0.58
1 2 3 4 5 6 7 8 9 10 11 12
Time (Hours)
Dia
mete
r
3. Control Charts
Performance Metric
970
980
990
1000
1010
1020
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UCL
LCL
UCL
LCL
LCLLCL
UCLUCL
Process not centered
and not stable
Process centered
and stable
Additional improvements
made to the process
4. Cause and effect diagram
(fishbone)
Effect
MaterialsMethods
EquipmentPeople
Environment
Cause
Cause
CauseCause
Cause
CauseCause
Cause
CauseCause
Cause
Cause
Environment
Machine Man
Method Material
5. Check sheet
Item A B C D E F G
-------
-------
-------
√ √ √
√ √
√ √
√
√
√ √
√ √ √
√
√
√
√
√ √
6. Histogram
Frequency
7. Pareto Analysis
A B C D E F
Fre
qu
en
cy
Pe
rce
nta
ge
50
%
100%
0%
75
%
25
%10
20
30
40
50
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
Customer
B
OperatorCustomer
A
Receiving
Party
How can we reduce
waiting time?
Makes
customer
wait
Absent receiving
party
Working system of
operators
Customer Operator
Fishbone diagram analysis
Absent
Out of office
Not at desk
Lunchtime
Too many phone
calls
Absent
Not giving
receiving party’s
coordinates
Complaining
Leaving a
message
Lengthy talk
Does not know
organization
wellTakes too much time
to explain
Does not
understand
customer
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
Reasons why customers have to wait
(12-day analysis with check sheet)
Pareto Analysis: reasons why customers have
to wait
A B C D E F
Frequency Percentage
0%
49%
71.2%
100
200
300 87.1%
150
250
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
A B C D E F
Frequency Percentage
100%
0%
49
%
71.2
%
100
200
300 87.1
%
100%
B C A D E F
Frequency Percentage
0%
100
200
300
Before… …After
Improvement
In general, how can we monitor quality…?
1. Assignable variation: we can assess the cause
2. Common variation: variation that may not be possible to
correct (random variation, random noise)
By observing
variation in
output measures!
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:
=
=N
i
ixN
X1
1
The standard deviation of the
observations:
N
XxN
i
i=
−
= 1
2)(
Average & Variation example
Number of vegetable piece per pizza: 25, 25, 26, 25, 23, 24, 25, 27
Average: 25
Standard Deviation:
Number of vegetable piece 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?
Incremental
Cost of
Variability
High
Zero
Lower
Tolerance
Target
Spec
Upper
Tolerance
Traditional View
The “Goalpost” Mentality
a.k.a
Upper/Lower Design Limits
(UDL, LDL)
Upper/Lower Spec Limits
(USL, LSL)
Upper/Lower Tolerance Limits
(UTL, LTL)
But are all ‘good’ products equal?
Incremental
Cost of
Variability
High
Zero
Lower
Spec
Target
Spec
Upper
Spec
Taguchi’s View
“Quality Loss Function”
(QLF)
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
LTLkXUTLkX −+ and
1and1 −−
k
LTLX
k
XUTL
Capability Index (Cpk)
Cpk < 1 means process is not capable at the k level
Cpk >= 1 means process is capable at the k level
−−
= k
XUTL
k
LTLXCpk ,min
Another way of writing this is to calculate the capability index:
Accuracy and Consistency
We say that a process is accurate if its mean is
close to the target T.
We say that a process is consistent if its standard
deviation is low.
X
Example 1: Capability Index (Cpk)
X = 10 and σ = 0.5LTL = 9
UTL = 11
667.05.03
1011or
5.03
910min =
−
−=pkC
UTLLTL X
Example 2: Capability Index (Cpk)
X = 9.5 and σ = 0.5
LTL = 9
UTL = 11
UTLLTL X
Example 3: Capability Index (Cpk)
X = 10 and σ = 2LTL = 9
UTL = 11
UTLLTL X
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 = 2 61=X
6667.0)6667.0,1min()6
6165,
6
5561min(
)3
,3
min(
==−−
=
−−=
pk
pk
C
XUTLLTLXC
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
Control charts tell you when a process
measure is exhibiting abnormal behavior.
Upper Control Limit
Central Line
Lower Control Limit
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)
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
Statistical Process Control with p Charts
Statistical Process Control with p Charts
Let’s assume that we take t samples of size n…
size) (samplesamples) ofnumber (
defects"" ofnumber total
=p
n
ppsp
)1( −=
p
p
zspLCL
zspUCL
−=
+=
066.015
1
2006
80==
=p
017.0200
)066.01(066.0=
−=ps
015.0 017.03 066.0
117.0 017.03 066.0
=−=
=+=
LCL
UCL
Statistical Process Control with p Charts
LCL = 0.015
UCL =
0.117
p = 0.066
Statistical Process Control with p 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)
=
=n
i
ixn
X1
1
}{min }{max ii xxR −=
R is the range between the highest and the lowest
for each sample
Statistical Process Control with X/R Charts
X is the mean for each sample
=
=t
j
jXt
X1
1
=
=t
j
jRt
R1
1
Statistical Process Control with X/R Charts
X is the average of the averages.
R is the average of the ranges
RAXLCL
RAXUCL
X
X
2
2
−=
+=
define the upper and lower control limits…
RDLCL
RDUCL
R
R
3
4
=
=
Statistical Process Control with X/R Charts
Read A2, D3, D4 from
Table TN 8.7
Example: SPC for bottle filling…
Sample Observation (xi) Average Range (R)
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
Example: SPC for bottle filling…
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
Calculate the average and the range for each
sample…
Then…
00.12=X
is the average of the averages
15.0=R
is the average of the ranges
Finally…
91.1115.058.000.12
09.1215.058.000.12
=−=
=+=
X
X
LCL
UCL
Calculate the upper and lower control limits
015.00
22.115.011.2
==
==
R
R
LCL
UCL
LCL = 11.90
UCL =
12.10
The X Chart
X = 12.00
The R Chart
LCL = 0.00
R = 0.15
UCL = 0.32
The X/R Chart
LCL
UCL
X
LCL
R
UCLWhat can
you
conclude?