Week05 slides

Mgt 610 Strategic Perspectives on Project Management
(c) 2013, Thomas Lechler. All rights reserved.For academic use only. 1
MGT610
Lecture 5
Relations between Project Variation and
Project Value
Dr. Thomas Lechler Phone: (201) 216-8174
Morton Room 636 FAX: (201) 216-5385
email: tlechler@stevens.edu

Lecture 5: Evidence
• Analyzing project variation.
• Interpreting project variation.
• What to improve?
• How to improve?

Lecture 5: Topics and Objectives
• Managing Effort Value with SPC (Statistical
Process Control)
• Applying SPC on the Project Level

Lecture 5: Agenda
• 1. Maximizing Effort Value
• 2. Managing Variation
• 3. The Problem
• 4. The Solution
• 5. Implementation
• 6. Case Study
• 7. Interpretation
• 8. Improvement

1. Maximizing Effort Value: CC Limits
• Managing Effort Value:
Effort value means to implement the project by maximizing
the performance of the resources.
• CC helps to minimize the project duration
• but
• CC does NOT explicitly support process improvement

1. Maximizing Effort Value: Reducing Variation
• Definition of World-Class Quality:
• “On-Target with Minimum Variance.”
• Operating “On-Target” requires a different way of thinking
about our processes.
• Operating with “Minimum Variance” is achieved only when a
process displays a reasonable degree of statistical control.
• —Walter A. Shewhart
• Wheeler, Chambers 1992, p. xix

1. Maximizing Effort Value: Reducing Variation
• “If I had to reduce my message for management to just a few
words, I'd say it all had to do with reducing variation”
• —W. Edwards Deming
• “The central problem of management in all its aspects...is to
understand better the meaning of variation, and to extract the
information contained in variation.”
• —Lloyd S. Nelson

2. Managing Variation: Advantages
• Project Processes with less variation :
– Have less variability.
– Are more predictable.
– Have lower costs.
– Achieve faster throughput.
• Variation could mean both: improvement or debasement.
• What are the sources of variation?
• What are the steps for process management?

2. Managing Variation: The Learning Cycle
•How shall we learn?
– learn
– do
– check
– act
•Improvement requires learning
– Improvement is limited by the
rate of learning
– We can improve at learning

2. Managing Variation: 7 Quality Control Tools
Pareto chart
histogram
cause
and
effect
diagram
control chart
check sheet
graphs
scatter plot

3. The Problem: Analyzing Project Variation
• Metrics on Projects:
– Shareholder—Results to achieve on the business level
• The product was defined by a business strategy
– Stakeholder—Results to achieve high customer satisfaction
• The product has to be of value to the customer
– Output—attributes of the product
• You fix a defective product (this is rework)
• The product was produced by a process
– Effort—attributes of the process
• Process improvements to get better project results
• Process improvements to get better products in the future
• There is a problem with all process data

3. The Problem: Process Measurement
• Measuring the Process:
– Reliability Problem:
All metrics come from a measurement process—and it has
variation too. Is our measurement really stable?
If the measurement error is larger than the variation of the
process you are trying to measure, your results are not
meaningful!
– Validity Problem:
Using the right metrics or measures to describe the process.
Do we really measure what we want to measure?

3. The Problem: Analyzing Process Variation
The Process:
All real-world processes vary—
• they have natural “common cause” variation that is
random (“noise”)
• they also vary by a “special cause”
So how do we know if the data is showing us a real change
(from a “special cause” outside the process—a “signal”)
or just noise?

3. The Problem: Consequences
• Summary:
If we can’t differentiate between signals from noise, we can’t tell what
is happening to our process…
=> We can’t tell if it improved, or not…
=> We can’t manage it

4. The Solution: The Inventor of the Control Chart
• Telling signals from noise: control charts
– Dr. Walter Shewhart, the “father of Quality,” developed the control chart
in the 1920’s
– This was the first quality tool developed

5. Implementation: Interpreting the Data
• Process could be:
– stable, predictable, in control
– unstable, unpredictable, out of control
• Process could be changing (improving?):
– Location
– Dispersion (Variation)
• How can we tell? A control chart

4. The Solution: Effort Value with Control Chart
• Aren’t control charts for manufacturing?
In projects we don’t produce the same result over and over…
– Within projects we find similar activities, like the communication
processes, change processes …
– If you use a defined process for the project implementation like the
software development (a methodology), then you produce your
products with a process, and control charts will work
– (If you don’t, you have nothing to improve)
• Control charts don’t depend on the “product” being “the same”
but on the process being “the same”
– … and they will tell you if your process isn’t “the same”

5. Implementation: Choosing the Process Metrics
person week 1 week 2 week 3 week 4 total
Able
Baker
Charlie
Delta
Echo
total
7
5
3
4
3
22
8
2
3
4
3
20
6
3
2
7
2
20
4
7
4
2
3
20
25
17
12
17
11
82
data flow diagram defects

5. Implementation: A Controlled Process
x x
x
x
x
x x
x
x x
x x
Location: OK
Planned average met
Dispersion: OK
Variation tolerable

5. Implementation: An Uncontrolled Process
Location: NOT OK
Planned average not met
Dispersion: NOT OK
Variation not tolerable

5. Implementation: Analyzing the Process Data

5. Individual and Moving Range Charts
• The simplest control charts:
– Moving range (mR) charts
• plot the successive differences between actual data points
– Individual (X) charts
• plot the actual data points
• The strength of control charts:
– NO assumptions about the data!

5. Implementation: X Control Chart
X Control Chart: CHECK LOCATION

5. Implementation: Individual (X) Charts
•Center Line
– the average
•Upper Natural Process Limit
– three sigma limit
•Lower Natural Process Limit
– may be below zero
RXXLNPL
RXXUNPL
N
X
XCL
X
X
N
i
X
660.23
660.23
1







5. Implementation: mR control chart
mR control chart: CHECK DISPERSION

5. Implementation: Moving Range (mR) Charts
• Center Line
– the mean
• Upper Control Limit
– three sigma limit
• Lower Control Limit
– none noneRDLCL
RRDUCL
N
Rj
RCL
R
R
N
j
R







3
4
1
1
27.3
1

6. Case1: Data
Release 1 Release 2 Release 3 Release 4
no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
X 12.0 2.0 12.0 12.0 32.0 2.0 6.0 2.0 10.0 6.0 3.0 2.0 2.0 3.0 3.0 1.0 11.0 11.0 1.0 5.0 10.0 6.0 10.0 3.0
mR 10.0 10.0 0.0 20.0 30.0 4.0 4.0 8.0 4.0 3.0 1.0 0.0 1.0 0.0 2.0 10.0 0.0 10.0 4.0 5.0 4.0 4.0 7.0
Release 5 Release 6 Release 7 Release 8
no. 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
X 4.4 2.9 4.2 5.4 6.6 3.6 1.0 1.3 1.2 1.2 1.0 2.8 2.0 3.0 1.8 2.3 1.3 5.2 4.1 4.3 3.1 2.3 0.9 2.1
mR 1.4 1.5 1.3 1.2 1.2 3.0 2.6 0.3 0.0 0.0 0.2 1.8 0.8 1.0 1.2 0.5 1.0 3.9 1.1 0.2 1.2 0.8 1.4 1.2

6. Case 1: Individual (X) Chart
• c
Improvements
implemented
Days
35
30
25
20
15
10
5
0
Modules
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Work assignment lost
UNPL = 23.27
Mean = 6.96
UNPL = 6.03
Mean = 2.83
New
method
•Development time in days

6. Case1: Moving Range (MR) Chart
Development time in days
Improvements
implemented
Days
30
25
20
15
10
5
0
Modules
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Work assignment lost
UCL = 20.03
Mean = 6.13

6. Case 2: Data
Module coding Time in Hours
no. 1 2 3 4 5 6 7 8 9 10
X 4.4 2.9 4.2 5.4 6.6 3.6 1.0 1.3 1.2 1.2
mR 1.5 1.3 1.2 1.2 3.0 2.6 0.3 0.1 0.0
no. 11 12 13 14 15 16 17 18 19 20
X 1.0 2.8 2.0 3.0 1.8 2.3 1.3 5.2 4.1 4.3
mR 0.2 1.8 0.8 1.0 1.2 0.5 1.0 3.9 1.1 0.2

6. Case 2: mR Control Chart
Coding Time in Hours—moving Range (mR) chart
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Modules
Hours
Mean= 1.21
UCL=3.95

6. Case 2: X Control Chart
Coding Time in Hours—individual (X ) chart
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Modules
Hours
Mean=
2.98
UCL=6.20

7. Interpretation: What’s a signal?
• Western Electric zone rules:
– 1 point more than three sigma
– 2 out of 3 consecutive points more than two sigma
on the same side of the center line
– 4 out of 5 consecutive points more than one sigma
– 8 consecutive points on the same side of the center line
• These tell us the x chart has changed

7. Interpretation: Zone Rule 1
• 1 point more than three sigma
CL
3
2
1
1
2
3
Note: for both individual and moving range charts

• 2 out of 3 points more than two sigma on the same side
of the center line
CL
3
2
1
1
2
3
Note: for individual charts ONLY

• 4 out of 5 points more than one sigma
CL
3
2
1
1
2
3

CL
3
2
1
1
2
3
• 8 on the same side of the center line

7. Interpretation: The Empirical Rule
• For distributions commonly encountered:
– 60-75% of the values will be within
1 sigma of the center line
• So x, mR charts work regardless of the distribution—which we
don’t know…

8. Improvement: Statistical Process Control
• A process in statistical control:
– is stable ("in control")
– you may predict its performance,
or plan based on its past performance
– you may improve it
(and can tell that you have)
– may or may not be satisfactory!
• (it may not be "capable")
• So how do we improve a process?

8. Improvement: Process Improvement…
•A revolutionary idea
– By what method?
• What tools?
Techniques?
– How many last year?
– How many this year?
– Is there a plan?
– Are you improving at
improving?
– Is management doing it?
Why not?

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