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

© 2013 IBM Corporation

AAO-CO202: IT and Engineering Portfolio & Strategy Management

Jim DensmoreExecutive IT Specialist

IT Architect and Technical Sales SpecialistRational Brand, SWG

23 October 2013


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2013 – Project and Portfolio Management in Systems and Software Development

Rational Focal Point Investment Analysis ComponentProjection and Simulations 15 October 2013

Jim Densmore Murray Cantor, Ph.D.Sr. Certified IT Specialist Distinguished EngineerIBM Rational Software IBM Rational [email protected] [email protected]



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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


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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


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Business ChallengesZero-growth budgets

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

200819980.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20072006200520042003200120001999

Trend Line

15.0% 15.9%

9.7% 10.1%

2002

1.3%

0.0%1.6% 2.5% 2.7%

3.2%3.0%

Source: Gartner

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

2009

0.16%

2011 Demand for IT Services“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, 2010http://www.cioupdate.com/budgets/article.php/3896646/How-to-Get-the-Budget-You-Need-in-2011.htm

With Less!

Do More!

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Often, ongoing operations & maintenance consume > 2/3 of budgets This may leave too little for innovation

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

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Original graphic created by David Puzas, WW Marketing Executive for IBM Enterprise Services, 2007

IT S

pe

nd

IT S

pe

nd

2/3

1/3


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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

• Capabilities are always in flux

We can’t fund both We can’t fund both projects. Which one projects. Which one

do we keep?do we keep?


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

Fails to acknowledge work already done

Provides no opportunity for ongoing value management

0 6 Ship date

Conventional wisdom: no value until shippedV

alu

e

? ? ? ? What scale do What scale do youyou use? use?

How does one avoid gaming?How does one avoid gaming?

Have you ever killed a project? How?Have you ever killed a project? How?

?


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Inception Elaboration Construction Transition/Maintenance

New capability on existing platform Maintenance and

small change requests

New platform

Medium VarianceHigh Variance Low Variance

Bringing IBM’s math department to bear

Time

Var

ian

ce in

co

st, s

ched

ule

…

• Different activities different analytics & productivity measures

• Improve odds of delivery and predictability across the lifecycle

Industry spend

ROI


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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 valuevalue received over time, The estimated risk (uncertainty) over time


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For what might Airbus sell their For what might Airbus sell their A350 XWB program?A350 XWB program?

– In testing with reportedly good resultsIn testing with reportedly good results

– Maiden flight completed Maiden flight completed (June 2013)(June 2013)

– Many orders at varying levels of firmnessMany orders at varying levels of firmness• 682 a/c ordered by 33 global customers682 a/c ordered by 33 global customers• As of July 2013 (wikipedia)As of July 2013 (wikipedia)

Certainly not zero!Certainly not zero!

Example

? ? ? ? How much value do you see in your How much value do you see in your

portfolio of incomplete projects? portfolio of incomplete projects? Does the value grow over time?Does the value grow over time?

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

?


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Rational Focal PointHardwiring 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


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


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IA presents streams with quarter-by-quarter values

Explicit values

(others are interpolatedothers are interpolated)


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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

E, the most likely or expected monetary value

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

H, the highest monetary value you believe could occur (no chance of a higher value)

L E H


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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?


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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?

0 8 12 20

Conclusion: ask for all three numbers!

The three-number (triangular distribution)

answer is both more honest as well as

more proactive; it changes the game


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


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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?How do you handle discount rates? ?


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A view (page) can be developed for each Stakeholder

Revenue Devel. Costs

Maint. Costs Combined


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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 valueis calculated

Finally, we build up the histogram of investment values toobtain its distribution


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A practical demonstration of the value of risk managementment

Revenue Devel. Costs

Maint. Costs Combined

What is the effect of narrowing cost variance?


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


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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)

0 6 Ship date

Conventional WisdomV

alu

e

Investment

Value


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Bubble chart: standard way to represent a project portfolio

1

2

3

Risk

Str

ateg

ic V

alu

e

0 5

4

5

xx Doesn’t show that incomplete projects have valueCreates a discontinuity in thinkingRarely accounts for timing and total costs of ownership

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

Project 62

10

8

4

3

9

5

6

1

7

$$

Radius = Cost

“better”

“worse”


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

xInvestment Value = Mean

Standard Deviation IV = MeanNormalized Risk = (Scaled) Standard Deviation??

Project 1

Project 2 Project 3

Project 4

Project 5

Project 7

Project 8

Project 9

Project 6

Project 10

2

10

8

4

3

9

5

6

1

7

-1000

-500

0

500

1000

1500

2000

Normalized Risk

Val

ue

0 1

Project 1

Project 2 Project 3

Project 4

Project 5

Project 7

Project 8

Project 9

Project 6

Project 10

2

10

8

4

3

9

5

6

1

7

-1000

-500

0

500

1000

1500

2000

Normalized Risk

Val

ue

0 1

$


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These two key questions support value-based decision making

Are we creating value?Are we creating value? Is this program worth continuing?Is this program worth continuing?

Program onset: T0 Program delivery: Td

Monitoring Is program healthy? Intervene?Cut losses?

Investment Is program still needed?Should we adjust content?Should we continue to invest?

Likely value at delivery, Likely value at delivery, & likely ROI at delivery& likely ROI at delivery

Current value, Current value, ROI to dateROI to date

Management Decisions Supported:

Today: T1

? ? ? ? How do you compare routine and How do you compare routine and

innovative efforts to each other?innovative efforts to each other?

How do you manage project risk?How do you manage project risk?

How do you motivate architectural How do you motivate architectural robustness and reuse?robustness and reuse?

?


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

$1k

$2k

$3k

Normalized Risk

$ V

alu

e

0 1

$4k

$5k

2

10

8

4

3

9

5

6

1

7

Project 62

10

8

4

3

9

5

6

1

7

Legend

Keep

Questionable

Cut?


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The model can help choose among build scenarios

Scenario A – Cobbled together, quick to field, minimum investment

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

$1k

$2k

$3k

Normalized Risk

$ V

alu

e

0 1

$4k

$5k

A

B


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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

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

$1k

$2k

$3k

Normalized Risk

$ V

alu

e

0 1

$4k

$5k

T1

T2

T3


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IA is a key part of Delivering on these Five Keys to Success

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

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

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

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

Its difficult to link an IT project portfolio to the realization of business benefits

Its difficult to link an IT project portfolio to the realization of business benefits

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

architectural perspectives

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

architectural perspectives

Organizations want to manage lifecycles, not just projects

Organizations want to manage lifecycles, not just projects

Project managers report on the status of a project, but this does not help improve

delivery capabilities within the organization

Project managers report on the status of a project, but this does not help improve

delivery capabilities within the organization

Project management tools focus on the needs of the project manager helping them manage schedules, costs and resources but creates

overhead for practitioners

Project management tools focus on the needs of the project manager helping them manage schedules, costs and resources but creates


Blockers Five keys to success


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AAO-CO202


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© 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.

www.ibm.com/software/rational

AAO-CO202


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For more interaction …

Join and collaborate on the TLE Community.

– TLE Community: http://w3.ibm.com/connections/communities/service/html/communityview?communityUuid=eb5b200a-0890-4a21-b06d-dc4141c34f20

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


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Alignment is Difficult

Line of Business ExecutiveLine of Business Executive

Project Manager or Team Lead


Senior ManagerSenior Manager

Profit, Internal Rate of ReturnProfit, Internal Rate of Return

Project deliverables, cost and schedule

Project deliverables, cost and schedule

Delivery of business value through the optimal use of resources

Delivery of business value through the optimal use of resources

• 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

Mea

sure

s

measures

commits to

measures

measures

commits to

commits to


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Motivators and Blockers

Line of Business Executive

Line of Business Executive



Senior ManagerSenior Manager

Need more effective support for strategic decision

making

Need better metrics & analytics for

continuous process improvement

Must connect better with practitioners,

enhance production

Linking IT project portfolio to business benefit realization

Linking IT project portfolio to business benefit realization

Reconciling project delivery & architectural perspectives

Reconciling project delivery & architectural perspectives

Want to manage lifecycles, not just projects

Want to manage lifecycles, not just projects

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

improve delivery capabilities

PM tools focus on PM needs but increase


PM tools focus on PM needs but increase



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Monte Carlo Simulation (Wikipedia1 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 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. 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 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 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 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 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.

Introduction and example: Monte Carlo method applied to approximating the value of π

Monte Carlo methods vary, but tend to follow a particular pattern:– Define a domain of possible inputs. – Generate inputs randomly from a probability distribution over the domain. – Perform a deterministic computation on the inputs. – 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 approximated using a Monte Carlo method:

– 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. – 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.

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 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 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 a few grains are randomly dropped into the whole square. On average, the approximation improves as more grains are dropped.

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 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.

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 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 exploration and oil exploration, their predictions of failures, cost overruns and schedule overruns are routinely better than human intuition 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 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.

Introduction and example: Monte Carlo method applied to approximating the value of π

Monte Carlo methods vary, but tend to follow a particular pattern:– Define a domain of possible inputs. – Generate inputs randomly from a probability distribution over the domain. – Perform a deterministic computation on the inputs. – 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 approximated using a Monte Carlo method:

– 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. – 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.

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 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 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 a few grains are randomly dropped into the whole square. On average, the approximation improves as more grains are dropped.

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

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