205420 crystal ball case studies

Crystal Ball Case studiesSuccess & failures using simulation models and

Crystal Ball and what you can learn from them

Huybert Groenendaal, PhD, MBAManaging PartnerEpiX Analyticswww.epixanalytics.com

2013 © EpiX Analytics LLC

Agenda

• What is Crystal Ball and three main ways of how it improves

decision-making

• Case studies:• Creating value from data to decisions (Technology & Services)

• Focus on the decision, not on the tool (Pharmaceutical)

• Best practices of building Crystal Ball capacity (Oil & Gas)

• Conclusions and next steps


About EpiX Analytics

• Specialized simulation modeling consulting, training and

research firm

• Objective = improve decision making under uncertainty

‘From data to decisions’

• Wide range of fields:• Pharmaceuticals

• Mining

• Manufacturing

• Transportation

• Insurance

• Financial industry

• Health / Food safety

• Energy, oil & gas

• Government

• Many others….


• Four marbles in bag, two black and two red

• Investment of $5

• Two marbles are drawn:• Two blacks you’ll win $25

• One black, one red, you’ll win $10

• Two reds, you’ll have to pay me another $5

Investment opportunity


Who invests and why?

Vote:

1. No, it’s nap time… zzzzz…

2. No (I expect to lose money)

3. Yes, (I expect to make money)

4. Yes, I always play when I can…


Decision trees can help…

Invest $5

Do not play

2 blacks

2 red

$25

$-5

$0

1 red, 1 black $10

?

?

?


What is the probability of two hearts?

There are four possibilities:

1. Black, Black

2. Black, red

3. Red, black

4. Red, red


Probability of drawing two black marbles is ¼:

Vote:

1. Yes

2. No

3. Not enough information


Probability tree…

First = red

Second = red

First = black

1/3

Second = black2/4

2/4

2/3

1/3

1/3

1/6

1/6

1/3

2/3

Second = red

Second = black2013 © EpiX Analytics LLC

Invest -$5

Do not play

2 heart

2 spades

$25

$-5

$0

Expected Value = 1/6 * $25 + 2/3 * $10 + 1/6 * -$5 = $10

$10

$5

1 heart, 1 spade $10

1/6

2/3

1/6

Updating the decision tree…


Let’s do the poll again….invest?

Poll:

1. No

2. Yes

3. Still not enough info


Lesson’s learned

• A decision is about something in the future

• Typically, future is uncertain

• Uncertainty is combination of scenarios (possibilities) and

probabilities

• Don’t trust your intuition regarding uncertainty

• For situations that are harder (i.e. almost all real-life

decisions) than the example we just considered, Crystal

Ball allows us to take into account uncertainties


Crystal Ball’s main three uses

Three main decision questions:

1. Valuations of investments with uncertain return;

2. How much uncertainty/risk is there?

3. What is the optimal decision?


1. Valuations

• Transparent way to address risk and uncertainty in

valuation model (eNPV)

• Provides consistent (and disciplined) way to measure value

of investment with uncertainty outcomes (e.g. R&D,

Business Development, M&A, etc.)

• Risk often has negative connotation, but Crystal Ball can

help change discussion about uncertainty to create value

and managing the upside potential

First main use of Crystal Ball


Background:

� Fortune 500 pharma company

� Improve prioritization and financial performance & achieve a risk-balanced pipeline

Challenge:

� No valuation or uncertainty/risk analysis tool available to evaluate pipeline

Approach:

� Identify key risks and variables in R&D process

� Designed Crystal Ball (template) Model

Pharmaceutical client: Portfolio Valuation, Strategy and Prioritization Improvement

Result:

• Better prioritization of projects and improved risk-balanced pipeline

• Crystal Ball critical part of every project valuation


$10 $12 $14 $16 $18 $20 $22 $24 $26 $28 $30

COGS [-]

Success launch [+]

Fixed costs [-]

Marketing [+]

Price trends [+]

Competitor reaction [+]

Yield [+]

Market size [+]

eNPV (value of project in US$ million)

Importance of uncertainty of parameters to project value (eNPV)

Uncertainty drivers

Quantifying uncertainty helps management focus on those

uncertainties that have the greatest impact on value

Market size greatest

uncertainty driver of

value, can change value

from $13M to $28M

COGS not important

uncertainty driver of

value

First main use of Crystal Ball


2. What is realistic? How confident are we about our

numbers?• ‘Traditional’ budgets, sales forecasts, project costs, project

schedules don’t quantitatively take into account uncertainty

• Uncertainty often ignored or alternatively base, low and high

scenarios determined

• Crystal Ball provides way of including expectations and their

uncertainties into estimates;• Provides a “bandwidth” where likely the future will fall;

• Gives management insight into how likely it is that they will reach

their goals/sales/budget/costs;

• Focuses on main risk drivers, instead of a wash-list of many

risks/uncertainties

Second main use of Crystal Ball


Background:

� Fortune 500 pipeline and midstream company

� High CAPEX rate of new pipelines

Challenge:

� Traditional cost estimation had proven inaccurate, not accounting for risk

Approach:

� Used Crystal Ball for cost estimation and project valuation

� Set up internal Crystal Ball team, trained senior management

Natural Gas pipeline project cost estimation

Result:

• Understanding of uncertainty in project costs and schedules and financial results

• No projects approved without Crystal Ball / Monte Carlo analysis of costs and key financial metrics


3. What is optimal decision?

• After understanding how much risk there is, question is what is

optimal?

• With Decision Optimizer (part of Crystal Ball), can support

optimal decisions that include uncertainty

• For example:• Optimal R&D and project Portfolio: What are the right projects to pursue?

• Optimal inventory: What is the best amount of inventory?

• Optimal sales force territories: What are the optimal territories?

• Optimal staffing levels: What is the optimal hiring/firing?

Third main use of Crystal Ball


Background:

� Fortune 500 chemical company

� Investments in R&D with different risk profiles

Challenge:

� What is strategically the right portfolio of R&D projects that balances rewards and risk

Approach:

� Used Crystal Ball for evaluating uncertainties around R&D

� Set up internal Crystal Ball team, trained senior management

R&D of large chemical company

Result:

• Crystal Ball analysis helped senior management understand risk-return profiles of different R&D strategies and portfolios

• Decision Optimizer helped understand optimal R&D project portfolio

R&D Project 1

R&D Project 2

R&D Project 3

R&D Project 4

R&D Project 5

R&D Project 6

R&D Project 7

R&D Project 8

R&D Project 9

R&D Project 10

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%E

xpe

cte

d r

etu

rnRisk around financial returns

Risks-return charts R&D projects(size of bubble proportional to investment amount)


Example Crystal Ball applications


Inventory optimization

ReliabilitySales commission estimation

Pricing decisions

R&D portfolio forecastingSchedule risk estimation

Financial analysis (NPV, IRR etc.)

Sales forecasting

Private equity

Budgeting

Strategic decision-making

Business development

Hedging and insurance

Price forecasting

Value of information

Hiring decisions

Safety

Agenda

• What is Crystal Ball and three main ways of how it improves

decision-making

• Case studies:• Creating value from data to decisions (Technology & Services)

• Focus on the decision, not on the tool (Pharmaceutical)

• Best practices of building Crystal Ball capacity (Oil & Gas)

• Conclusions and next steps


Background:

� Fortune 500 Industrial equipment company

� Selling equipment and providing related services (set-up, maintenance, repair etc.)

Challenge:

� Given that it takes 1 year to train service engineers, how many to hire?

Approach:

� Analysis of historical data, understanding patterns, trends, uncertainties etc.

� Used Crystal Ball (see next slide)

Case study 1: Data to decisions


Overall result:

• Company saved $2 - $3 million in the first year of using the model

• Company now uses Crystal Ball and Decision Optimizer in a number of other applications

Case study 1: Data to decisions

Crystal Ball Model

Sales forecast

Historical service

demands

Equipment in use

Example result:

Need to hire 3 engineers to be 80% confident to meet demand or 4 to be 94%

confident


Background:

� Fortune 500 pharmaceutical firm

� Large number of products in the R&D pipeline, different stages and risks/returns

� Decisions about R&D funding critically important for future

Challenge:

� How to improve prioritization and achieve a risk-balanced pipeline?

� How to develop accurate and useful Crystal Ball model to support decisions around this?

Case study 2: Focus on the decision


Case study 2: Focus on the decision

Crystal Ball ModelDevelop Crystal

Ball model

Failure:

Crystal Ball model was overly complex, too laborious for analysts, didn’t have buy-in and results weren’t

understood

Crystal Ball Model

Develop Crystal

Ball model

Success:

Crystal Ball model now integral part of company’s prioritization and

decisions around R&D portfolio

Management

Marketing

R&D

Legal Operations

Sales


Background:

� Many industries ranging from oil & gas and pharmaceuticals to manufacturing and insurance

Challenge:

� Often only ‘pockets’ of an organization use Crystal Ball

� How to best build more capacity and skills in the use of Crystal Ball throughout the organization?

� How to build understanding by senior management for Monte Carlo simulation and Crystal Ball and create demand for it?

Case study 3: Building CB capacity


Typical successful approach:

• Develop & maintain a core-group of experts and users. This group can be ‘called upon’ by other groups

• Develop process of peer-review with group of other Crystal Ball users. Quality control is very important!

• For decisions that are made regularly (e.g. product launches within pharmaceutical company) create Crystal Ball template model(s)

• Knowledge about Crystal Ball and Monte Carlo outside this group important too, but not everyone within organization needs to know how to develop and run Crystal Ball model;



Approach for management:

• Educate management!

• Increase internal demand with a few highly visible projects

• Involve management in Monte Carlo / Crystal Ball based analysis early on in analysis/projects (not only at final presentation)

• To management, don’t focus on the model (or on complex statistical jargon) but focus on the analysis relevant to decisions

� Also for presentation, use peer-review!



Summary

• Future is uncertain, so don’t ignore uncertainty in your

analysis and decisions!

• Crystal Ball allows us to combine risks and uncertainty to

support a wide range of decisions

• Range of visual graphics for clear communication

• Thinking in probabilistic terms!!


Thanks for your time!

Dr. Huybert GroenendaalManaging Partner

EpiX [email protected]


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