hashemite university department of civil engineering

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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pro

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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?