205420 crystal ball case studies

Crystal Ball Case studies
Success & failures using simulation models and
Crystal Ball and what you can learn from them
Huybert Groenendaal, PhD, MBA
Managing Partner
EpiX Analytics
www.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 = black

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 $30
COGS[-]
Success launch [+]
Fixed costs [-]
Marketing[+]
Price trends[+]
Competitor reaction[+]
Yield [+]
Market size [+]
eNPV (value ofproject in US$ million)
Importance of uncertaintyof parameters to projectvalue (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&DProject 10
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Expectedreturn
Risk around financial returns
Risks-returncharts R&D projects
(size of bubble proportional to investment amount)

Example Crystal Ball applications
Inventory optimization
ReliabilitySales commission
estimation
Pricing decisions
R&D portfolio forecasting
Schedule 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

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 Model
Develop 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 Groenendaal
Managing Partner
EpiX Analytics
Huybert@epixanalytics.com

205420 crystal ball case studies

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a 205420 crystal ball case studies

Similar a 205420 crystal ball case studies (20)

Más de p6academy

Más de p6academy (20)

Último

Último (20)

205420 crystal ball case studies