Desktop Business Analytics -- Decision Intelligence

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Desktop Business Analytics -- Decision
Intelligence

• Time Series Forecasting
• Risk Analysis
• Optimization

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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

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Monte Carlo Applications

• Capital Budgeting
• New Venture Planning
• Manufacturing Planning
• Marketing Planning
• Quality Design
• Environmental Risk
• Petroleum Exploration

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Spreadsheets - Pros

• Easy to use
• Popular
• Flexible model-building tool

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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

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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

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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

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Advantages of Simulation

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

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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

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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
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Crystal Ball Demonstration

• 22 4 ?

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Crystal Ball Pro

• Decision Intelligence
• Includes
• Crystal Ball
• Optimization
• Extenders
• Developer Kit

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Optimization Model

• Decision Variables
• Quantities over which you have control (Accept or
  reject each project)
• Upper and lower bounds
• Continuous or discrete

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Optimization
Function
X
F(X) Y
Find the possible input values that make the
output as large or as small as possible
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Project Selection
Model
Project Mix
Combined NPV
Find the project mix that generates the highest
combined NPV
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A Realistic Model

• Uncertainty analysis
• Constraints and Requirements
• We will us the simplifying assumption of applying
  a budgetary constraint to limit investment

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The Flaw of Averages

• Never try to walk across a river just because it
  has an average depth of four feet.
• Milton Friedman

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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.

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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...
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A Picture is Worth...

• A thousand What-ifs

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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

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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.

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Deterministic v. Stochastic
Deterministic
Fixed Data 7
Fixed Outcomes 1,200,00
Stochastic
Variable data
Variable Outcomes
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Statistics

• Normal Distribution, Mean and Standard Deviation

Mean
Standard Deviation
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Retirement Example
Uncertainty
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Define Assumptions
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Retirement Example - Assumptions
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Retirement ExampleAssumptions
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Retirement Example- Forecasts
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Retirement ExampleForecasts
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Communicating Results

• Get the client to understand alternatives
• Take action

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Uncertainty over time
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