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: [email protected]



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

causeandeffectdiagram

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

iX

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

Hour

s

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

Hour

s

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

on the same side of the center line

– 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 on the same side of the center line

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?

Date post:	02-Dec-2014
Category:	Business
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