Desktop Business Analytics --
Decision Intelligence
 Time Series Forecasting
 Risk Analysis
 Optimization

Current Products
 Crystal Ball
®
 Excel-based Monte Carlo simulation
 Crystal Ball Pro
 Integrated Optimization and Monte Carlo simulation
 CB Predictor
 Integrated Time-Series Forecasting with Monte
Carlo
 CB Turbo
 Distributed Processing capability to speed up simulations

Monte Carlo Applications
 Capital Budgeting
 New Venture Planning
 Manufacturing Planning
 Marketing Planning
 Quality Design
 Environmental Risk
 Petroleum Exploration

Spreadsheets - Pros
 Easy to use
 Popular
 Flexible model-building tool

What-if Analysis
 Methodically entering even increments of values to view the projected outcomes
Pros: Reveals incremental range of possible outcomes
Cons: Time-consuming, Results in a mountain of data, Reveals what is possible, not what is probable

What is missing?
 The ability to know the range of possible outcomes and their likelihood of occurrence
 As a result, we use Monte Carlo Simulation as a system that uses random numbers to measure the effects of uncertainty on our decision-making process

What is Simulation?
 Modeling a real system to learn about its behavior
 The model is a set of mathematical and logical relationships
 You can vary conditions to test different scenarios

Advantages of Simulation
 Inexpensive to evaluate decisions before implementation
 Reveals critical components of the system
 Excellent tool for selling the need for change

Disadvantages of Simulation
 Results are sensitive to the accuracy of input data
 Garbage in, Garbage out
 Intelligent agents using secret rules
 Investment in time and resources

The Five Steps of
Model Development
1. Develop a system flow diagram
2. Write an Excel spreadsheet to model the system
3. Use Crystal Ball to model uncertainty
4. Run the simulation and analyze the output
5. Improve the model and/or make decisions

Crystal Ball Demonstration
2+2 = 4 ?

 Decision Intelligence
 Includes
 Crystal Ball
 Optimization
 Extenders
 Developer Kit
Crystal Ball Pro

Optimization Model
 Decision Variables
 Quantities over which you have control
(Accept or reject each project)
– Upper and lower bounds
– Continuous or discrete

Optimization
X
Function
Find the possible input values that make the output as large or as small as possible
F(X) = Y

Project Selection
Project Mix
Model
Find the project mix that generates the highest combined NPV
Combined
NPV

A Realistic Model
 Uncertainty analysis
 Constraints and Requirements
 We will us the simplifying assumption of applying a budgetary constraint to limit investment

The ‘Flaw’ of Averages
“Never try to walk across a river just because it has an average depth of four feet.”
 Milton Friedman

Academic v. Real World
 Professors and students have used many techniques
 Inaccessible
 Difficult to implement
 Clients do not understand the results
 Decisioneering makes Monte Carlo easy to use in everyday spreadsheet modeling.

How are you handling uncertainty?
 Do you use low, middle and high values?
 Do you do What-if analysis?
Multiple What-if scenarios confuse as much as enlighten
...

A Picture is Worth...
 A thousand What-ifs

Decisioneering, Inc.
 Provider of Analytic Tools since 1986
 Headquartered in Denver, Colorado,
USA
 More than 70,000 Users
 85% of Fortune 500 Companies
 45 of Top 50 Business Schools
 65% CAGR over 3 Years

Monte Carlo
 Random number generation simulates the uncertainty in the assumptions. The program selects a value for the assumption, recalculates the spreadsheet, plots the forecast and repeats.

Deterministic v. Stochastic
Fixed
Data
7%
Variable data
M onthly S a v ings
3 5 0 .0 0 4 2 5 .0 0 5 0 0 .0 0 5 7 5 .0 0 6 5 0 .0 0
Deterministic
Fixed Outcomes
$1,200,00
Stochastic
500 Trials
. 0 9 4
. 0 7 1
. 0 4 7
. 0 2 4
. 0 0 0
$ 3 0 0 , 0 0 0
Variable
Outcomes
Forecast: Scenario A Retirement Portfolio
Frequency Chart
$ 5 2 5 , 0 0 0
M e a n = $ 6 4 6 , 1 9 8
$ 7 5 0 , 0 0 0
D o l l a r s
$ 9 7 5 , 0 0 0
6 Outliers
4 7
3 5 .2 5
2 3 . 5
1 1 .7 5
$ 1 ,2 0 0 , 0 0 0
0

Statistics
 Normal Distribution, Mean and Standard
Deviation
M o n th ly S a v in g s
Mean
3 5 0 .0 0
Standard Deviation
4 2 5 .0 0 5 0 0 .0 0 5 7 5 .0 0 6 5 0 .0 0

Retirement Example
Monthly Dollar Saving
Number of Years
Annual Growth Rate
Value at Retirement
$ 500
20
12%
$ 432,315
Uncertainty

Define Assumptions

Retirement Example -
Assumptions

Retirement Example
Assumptions

Retirement Example-
Forecasts

Retirement Example
Forecasts

Communicating Results
 Get the client to understand alternatives
 Take action

Uncertainty over time

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