10.15.2013 Prj & Port Mgmt SftDev - Investment Analyzer - Projection and Simulations

IT and Engineering
Portfolio & Strategy Management
AAO-CO202:

Jim Densmore

Executive IT Specialist
IT Architect and Technical Sales Specialist
Rational Brand, SWG

23 October 2013

© 2013 IBM Corporation

IBM
2013 – Project and Portfolio Management in Systems and Software Development

Rational Focal Point Investment Analysis Component
Projection and Simulations

15 October 2013

Jim Densmore

Murray Cantor, Ph.D.

Sr. Certified IT Specialist
IBM Rational Software
jdensmore@us.ibm.com

Distinguished Engineer
IBM Rational Software
mcantor@us.ibm.com



2

IBM

Objectives
 Motivate and understand an ROI-based method for you and
your clients to align the development organization (IT,
Engineering, etc.) with the business
 See how IBM Rational provides a mechanism for clients to
do this more easily
– Process
– Tooling
– Use cases


3

IBM

Agenda
• Business Challenges
• Aligning IT or Engineering with the Business
• An ROI based Solution
• Focal Point and its Investment Analysis Component
• Use Cases for Investment Analysis


4

IBM

Agenda


5

IBM

Business Challenges
Zero-growth budgets

2011 Demand for IT Services

Do More!

“The economic downturn forced deep cuts in IT budgets. Now, as CIO’s plan
for the recovery, they are facing unprecedented demand for IT services from
the business. At the same time, organizations are still keeping spending tightly
under control.”
CIOUpdate.com August 3, 2010
http://www.cioupdate.com/budgets/article.php/3896646/How-to-Get-the-Budget-You-Need-in-2011.htm

Executive Programs CIO Survey
Increases in CIO Budgets Over Previous Year, 1998-2009 (Worldwide)

18.0%
16.0%

With Less!

15.0%

15.9%

14.0%
12.0%

9.7% 10.1%

10.0%
8.0%
6.0%
4.0%

1.3%

2.0%
0.0%

Source: Gartner

1.6%

2.5%

2.7%

3.0%

3.2%
0.16%

0.0%
1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Trend Line


6
6

IBM

Business Response
Must (re!)balance resource allocations to support business innovation

IT Spend

1/3

2/3

 Often, ongoing operations & maintenance consume > 2/3 of budgets
 This may leave too little for innovation
Original graphic created by David Puzas, WW Marketing Executive for IBM Enterprise Services, 2007


7
7

IBM

Agenda


8

IBM

Alignment of IT (or Engineering) & Business Value is Crucial

A disciplined, strategic translation is key to this alignment
• Translation management and maintenance?
• What is their common denominator?

What challenges must we meet?
• Business values evolve
• The mapping to capabilities changes

We can’t fund both
projects. Which one
do we keep?

• Capabilities are always in flux


9

IBM

Value

The conventional wisdom for estimating the worth of an
incomplete development effort (project) provides little insight

Conventional wisdom:
no value until shipped

0

6

Ship date

? ? ? ?

 Fails to acknowledge work already done

 What scale do you use?

 Provides no opportunity for ongoing
value management

 How does one avoid gaming?

?

 Have you ever killed a project? How?


10

IBM

Agenda


11

IBM

Bringing IBM’s math department to bear
• Different activities  different analytics & productivity measures
• Improve odds of delivery and predictability across the lifecycle

Variance in cost, schedule…

Inception Elaboration

Construction

Transition/Maintenance
Industry spend
ROI

New
platform

New capability
on existing
platform

High Variance

Maintenance and
small change
requests

Medium Variance

Low Variance

Time

12

IBM

This led to the approach: what might someone pay for it?
 Imagine selling your incomplete development program
– Buyer would spend now/today
– Obtains the option to invest in its completion
– Once completed, receives hoped-for benefits
– So … buy the option to spend to receive uncertain benefits
• This is like a call option with an uncertain strike price!
• No wonder ROI can be difficult to measure

 Reason about a fair price using incomplete market
reasoning:  Expected cost to complete over time,
 Expected value received over time,
 The estimated risk (uncertainty) over time

13

IBM

Example
 For what might Airbus sell their
A350 XWB program?
– In testing with reportedly good results
– Maiden flight completed (June 2013)
– Many orders at varying levels of firmness
• 682 a/c ordered by 33 global customers
• As of July 2013 (wikipedia)

 Certainly not zero!

 How much value do you see in your
portfolio of incomplete projects?
Does the value grow over time?

? ? ? ?

?

 (What’s wrong with this image?
Why?)

14

IBM

Agenda


15

IBM

Rational Focal Point

Hardwiring the linkage between strategy and execution
• The Rational Team Concert integration
allows users to prioritize and manage project
scope and rollup project status
• The System Architect integration connects
the Enterprise Architecture perspective to the
portfolio management perspective in Focal
Point
• FP’s Investment Analysis component
assists users with financial modeling and
business case assessment
• Users can take advantage of advanced
resource management allowing skill-based
supply and demand tracking and balancing
• Configuration templates included in the
product helps users get up and running
quickly


16

IBM

The best common scale of value is “Money”
Investment Analysis provides financial measures for everyday use


17

IBM

IA presents streams with quarter-by-quarter values
Explicit values
(others are interpolated)


18

IBM

Each input assumption is found using simple input estimates
 A triangular distribution is used
– Commonly used in business decision making
– Values are simple to understand
– (Remember, the area of the triangle is 1 by definition)

0

L
L, the lowest monetary value you believe
could occur (no chance of a lower value)

E

H

E, the most likely or expected monetary value
H, the highest monetary value you believe
could occur (no chance of a higher value)


19

IBM

Vignette: How long will it take?
 You’re a development manager

 You assign your developer a task

 “How long will it take?” He hems & haws …
 “Oh … about 20 weeks!”

 The developer returns to his cube
 His buddy prairie dogs & says, “Hey. Did you get
the job?” “Yeah.” “How long will it take?” “Oh,
about 8 weeks!”
 … What happened?


20

IBM

Vignette: How long will it take?
 You’re a development manager
Conclusion: ask for all three numbers!
The
 You assign your developer a task three-number (triangular distribution)
answer is both more honest as well as
more proactive;
 “How long will it take?” He hems & haws … it changes the game
 “Oh … about 20 weeks!”

 The developer returns to his cube
 His buddy prairie dogs & says, “Hey. Did you get
the job?” “Yeah.” “How long will it take?” “Oh,
about 8 weeks!”
0
8
12

20

 … What happened?


21

IBM

IA represents uncertainty with 3 values for each quarter (period)


22

IBM

You assign a discount rate on each (stakeholder) page

Discount rate determination is an
entire, important subject on its own:
 It should never be zero (the
default, by the way)
 Should costs and benefits have
the same or different rate?
 Too high and long-term
investment is discouraged
 Too low? Might be worth doing,
but mañana is soon enough!

? ? ? ?

 How do you handle discount rates?

?

23

IBM

A view (page) can be developed for each Stakeholder

Revenue

Devel. Costs

Maint. Costs

Combined


24

IBM

Monte Carlo analysis permits the needed arithmetic
operations on random variables
 Run thousands of simulations of the investment value
(the NPV)
– Each time a value is picked from each triangularly
distributed random variable
– Each value chosen is based on the likelihood of that
value occurring, according to the distribution of the
associated random variable
 For each simulation,
the investment value
is calculated

 Finally, we build up
the histogram of
investment values to
obtain its distribution


25

IBM

A practical demonstration of the value of risk management

Revenue

Devel. Costs

Maint. Costs

Combined
What is the effect of narrowing cost variance?

26

IBM

This shows Monte Carlo analysis demonstrating
empirically the value of narrowing uncertainties in cost


27

IBM

Thus, our model for estimating NPV and ROI has some
useful properties
 Investment value varies continuously in time
 Investment value improves when we invest:
– In improving likelihood of delivery (reducing uncertainty in costs)
– In improving the range of value, e.g. building reuse into
solutions
(that is, increasing the upside variance of benefits)
Value

Investment
Value

0

Conventional
Wisdom

6

Ship date


28

IBM

Bubble chart: standard way to represent a project portfolio
“better”

Strategic Value

Radius = Cost

7

5

2

4

3

5

4
3

Project 6

8

6

$

1

9

2

1
0

1
0

Traditionally, value & risk
xx
are based mostly on
perceptions, opinions

Risk

5

“worse”

 Doesn’t show that incomplete projects have value
 Creates a discontinuity in thinking
 Rarely accounts for timing and total costs of ownership

29

IBM

This Investment Value Model puts monetary number$
on value and risk
Investment xValue = Mean

2000
1500

Project 10

1000
Project 3

$ Value

Project 2

2

500

5

3

Project 4

4

Project 5

0

1
Project 1

7

Project 6

8

6

Project 7

9

Project 9

-500

Project 8

10

-1000

0

Standard Deviation

?

Normalized Risk

1

IV = Mean
Normalized Risk = (Scaled) Standard Deviation


30

IBM

Agenda


31

IBM

These two key questions support
value-based decision making

 How do you compare routine and
innovative efforts to each other?

? ? ? ?

 How do you manage project risk?

?

 How do you motivate architectural
robustness and reuse?

Are we creating value?

Is this program worth continuing?

Current value,
ROI to date
Program onset: T0

Likely value at delivery,
& likely ROI at delivery
Today: T1

Program delivery: Td

Management Decisions Supported:

Monitoring

Investment

 Is program healthy?

 Is program still needed?

 Intervene?

 Should we adjust content?

 Cut losses?

 Should we continue to invest?


32

IBM

The Model permits more objective management of the portfolio
in the usual resource-constrained environment

$ Value

$5k

7

$4k

2

3

5

4

$3
k

Legend

Project 6

8




1

9

Questionable



6

Keep
Cut?

$2k
1
0
$1k
0

Normalized Risk

1


33

IBM

The model can help choose among build scenarios
Scenario A – Cobbled
together, quick to field,
minimum investment
$ Value

Scenario B – More
reusable, extensible,
lower O&M costs

B

$5k

$4k

A

$3
k
$2k

$1k
0

Normalized Risk


1

34

IBM

Another use is to track improvement in value, and reduction
of risk, throughout the project lifecycle
 T1 is project onset; T2
and T3 are later times in
the lifecycle
$ Value

 Movement from lower
right to upper left shows
that the investment
(development) is
delivering value

T3

$5k

$4k

T2

$3
k

T1

$2k

$1k
0

Normalized Risk


1

35

IBM

IA is a key part of Delivering on these Five Keys to Success
Blockers
Its difficult to link an IT project portfolio to the
Its difficult to link an IT project portfolio to the
realization of business benefits
realization of business benefits

Five keys to success
1. Understand the value of investments and
tradeoffs on content rather than becoming
“bogged down” in project or resource detail

Organizations do not have the ability to
Organizations do not have the ability to
reconcile both project delivery and
reconcile both project delivery and
architectural perspectives
architectural perspectives

2. Build a stronger connection between
customers, Enterprise Architecture and
portfolio management disciplines to enable a
broader viewpoint for decisions

Organizations want to manage lifecycles, not
Organizations want to manage lifecycles, not
just projects
just projects

3. Leverage lifecycle management of products,
services or IT applications with customer,
competitive and capability viewpoints that show
how projects deliver against the lifecycle

Project managers report on the status of a
Project managers report on the status of a
project, but this does not help improve
project, but this does not help improve
delivery capabilities within the organization
delivery capabilities within the organization
Project management tools focus on the needs
Project management tools focus on the needs
of the project manager helping them manage
of the project manager helping them manage
schedules, costs and resources but creates
schedules, costs and resources but creates
overhead for practitioners

4. Gain insight into how delivery practices
impact project success, to inform process and
skill improvements and organizational maturity
5. Provide teams with a collaborative platform
that allows software delivery projects to become
more automated, transparent and predictable
across all disciplines


36

IBM

AAO-CO202


37

IBM

AAO-CO202

www.ibm.com/software/rational
© Copyright IBM Corporation 2013. All rights reserved. The information contained in these materials is provided for informational purposes only, and is provided AS IS without
warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, these materials. Nothing contained in these
materials is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the
applicable license agreement governing the use of IBM software. References in these materials to IBM products, programs, or services do not imply that they will be available in all
countries in which IBM operates. Product release dates and/or capabilities referenced in these materials may change at any time at IBM’s sole discretion based on market
opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. IBM, the IBM logo, Rational, the Rational logo, Telelogic, the
Telelogic logo, and other IBM products and services are trademarks of the International Business Machines Corporation, in the United States, other countries or both. Other company,
product, or service names may be trademarks or service marks of others.


38

IBM

For more interaction …
 Join and collaborate on the TLE Community.

– TLE Community:
http://w3.ibm.com/connections/communities/service/html/communityview?co

 TLE events are being scheduled continuously, so check the
website frequently.
– TLE Website: http://tle.atlanta.ibm.com/home.html


39

IBM

Alignment is Difficult
• Concerns flow down the organization while measures (and data) flow up
• We need tools to plan, track, and deliver on our commitments at every level

Concerns

Senior Manager

commits to
measures

commits to
measures

Project Manager or Team
Lead

commits to
measures

Profit, Internal Rate of Return

Delivery of business value through
the optimal use of resources

Measures

Line of Business Executive

Project deliverables, cost and
schedule


40

IBM

Motivators and Blockers

Line of Business
Executive

Senior Manager

Project Manager
or Team Lead

Need more
effective support for
strategic decision
making

Linking IT project portfolio to
Linking IT project portfolio to
business benefit realization
business benefit realization
Reconciling project delivery
Reconciling project delivery
& architectural perspectives
& architectural perspectives
Want to manage lifecycles,
Want to manage lifecycles,
not just projects
not just projects

Need better metrics
& analytics for
continuous process
improvement

PMs’ status reports don’t
PMs’ status reports don’t
improve delivery capabilities
improve delivery capabilities

Must connect better
with practitioners,
enhance production

PM tools focus on PM
PM tools focus on PM
needs but increase
needs but increase


41

Monte Carlo Simulation (Wikipedia

IBM
1

has a good article at

http://en.wikipedia.org/wiki/Monte_Carlo_method)

Monte Carlo methods (or Monte Carlo experiments) are a class of computational algorithms that rely on repeated random sampling to
Monte Carlo methods (or Monte Carlo experiments) are a class of computational algorithms that rely on repeated random sampling to
compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems. These methods are most
compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems. These methods are most
suited to calculation by a computer and tend to be used when it is infeasible to compute an exact result with a deterministic algorithm.
suited to calculation by a computer and tend to be used when it is infeasible to compute an exact result with a deterministic algorithm.
This method is also used to complement the theoretical derivations.
This method is also used to complement the theoretical derivations.
 Monte Carlo methods are especially useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered
 Monte Carlo methods are especially useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered
materials, strongly coupled solids, and cellular structures (see cellular Potts model). They are used to model phenomena with significant
materials, strongly coupled solids, and cellular structures (see cellular Potts model). They are used to model phenomena with significant
uncertainty in inputs, such as the calculation of risk in business. They are widely used in mathematics, for example to evaluate
uncertainty in inputs, such as the calculation of risk in business. They are widely used in mathematics, for example to evaluate
multidimensional definite integrals with complicated boundary conditions. When Monte Carlo simulations have been applied in space
multidimensional definite integrals with complicated boundary conditions. When Monte Carlo simulations have been applied in space
exploration and oil exploration, their predictions of failures, cost overruns and schedule overruns are routinely better than human intuition
exploration and oil exploration, their predictions of failures, cost overruns and schedule overruns are routinely better than human intuition
or alternative "soft" methods.
or alternative "soft" methods.
 The Monte Carlo method was coined in the 1940s by John von Neumann, Stanislaw Ulam and Nicholas Metropolis, while they were
 The Monte Carlo method was coined in the 1940s by John von Neumann, Stanislaw Ulam and Nicholas Metropolis, while they were
working on nuclear weapon projects in the Los Alamos National Laboratory. It was named in homage to Monte Carlo casino, a famous
working on nuclear weapon projects in the Los Alamos National Laboratory. It was named in homage to Monte Carlo casino, a famous
casino, where Ulam's uncle would often gamble away his money.
casino, where Ulam's uncle would often gamble away his money.
Introduction and example: Monte Carlo method applied to approximating the value of π
Introduction and example: Monte Carlo method applied to approximating the value of π
 Monte Carlo methods vary, but tend to follow a particular pattern:
 Monte Carlo methods vary, but tend to follow a particular pattern:
– Define a domain of possible inputs.
– Define a domain of possible inputs.
– Generate inputs randomly from a probability distribution over the domain.
– Generate inputs randomly from a probability distribution over the domain.
– Perform a deterministic computation on the inputs.
– Perform a deterministic computation on the inputs.
– Aggregate the results.
– Aggregate the results.








For example, given that a circle inscribed in a square and the square itself have a ratio of areas that is π/4, the value of π can be
For example, given that a circle inscribed in a square and the square itself have a ratio of areas that is π/4, the value of π can be
approximated using a Monte Carlo method:
approximated using a Monte Carlo method:
– Draw a square on the ground, then inscribe a circle within it.
– Draw a square on the ground, then inscribe a circle within it.
– Uniformly scatter some objects of uniform size (grains of rice or sand) over the square.
– Uniformly scatter some objects of uniform size (grains of rice or sand) over the square.
– Count the number of objects inside the circle and the total number of objects.
– Count the number of objects inside the circle and the total number of objects.
– The ratio of the two counts is an estimate of the ratio of the two areas, which is π/4. Multiply the result by 4 to estimate π.
– The ratio of the two counts is an estimate of the ratio of the two areas, which is π/4. Multiply the result by 4 to estimate π.
In this procedure the domain of inputs is the square that circumscribes our circle. We generate random inputs by scattering grains over
In this procedure the domain of inputs is the square that circumscribes our circle. We generate random inputs by scattering grains over
the square then perform a computation on each input (test whether it falls within the circle). Finally, we aggregate the results to obtain our
the square then perform a computation on each input (test whether it falls within the circle). Finally, we aggregate the results to obtain our
final result, the approximation of π.
final result, the approximation of π.
To get an accurate approximation for π this procedure should have two other common properties of Monte Carlo methods. First, the
To get an accurate approximation for π this procedure should have two other common properties of Monte Carlo methods. First, the
inputs should truly be random. If grains are purposefully dropped into only the center of the circle, they will not be uniformly distributed,
inputs should truly be random. If grains are purposefully dropped into only the center of the circle, they will not be uniformly distributed,
and so our approximation will be poor. Second, there should be a large number of inputs. The approximation will generally be poor if only
and so our approximation will be poor. Second, there should be a large number of inputs. The approximation will generally be poor if only
a few grains are randomly dropped into the whole square. On average, the approximation improves as more grains are dropped.
a few grains are randomly dropped into the whole square. On average, the approximation improves as more grains are dropped.


42

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10.15.2013 Prj & Port Mgmt SftDev - Investment Analyzer - Projection and Simulations