Introduction to Modeling

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Introduction to Modeling
Monte Carlo Simulation
Provides Virtual Experience

• Expensive
• Not always practical
• Time consuming
• Impossible for all situations
• Can be complex

• Great teacher
• Many situations
• Deal with the unexpected
• Thorough understanding of processes
• Broader knowledge

More Pros

• Expensive
• Not always practical
• Time consuming
• Impossible for all situations
• Can be complex

• Cheap
• Flexible
• Fast
• Adaptable
• Simplifying

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Introduction to Modeling
Monte Carlo Simulation

• Allow for interactivity and experimentation by
  the modeler
• Generates a range of possibilities from
  criteria given rather than optimizing the
  goal
• Applicable to short run, temporary and specific
  behavior Analytic (statistical) models predict
  average, or steady state, long run behavior
• Deals well with uncertainty
• Can deal with complicating factors that make
  analytical modeling difficult or impossible
  to estimate uncertainty, risk, multiple
  locations, volatile sales
• Inexpensive, relatively simple process using
  software like Excel and Crystal Ball

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Introduction to Modeling
Monte Carlo Simulation
Monte Carlo Simulation - named for the roulette
wheels of Monte Carlo As in roulette, variable
values are known with uncertainty Unlike
roulette, specific probability distributions
define the range of outcomes
Crystal Ball - an application specializing in
Monte Carlo simulation
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Introduction to Modeling
Monte Carlo Simulation
Generating Random Variables
CRYSTAL BALL

• Generates random variables across a
  distribution specified by the user
• Lets users select distributions from a
  gallery or generate their own
• Generates a report containing all of the
  models assumptions

EXAMPLE
Normal Distribution of random variables
having a mean value of 3.0 generated by the
equation is X2
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Introduction to Modeling
Monte Carlo Simulation
Generating Other Distributions
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Introduction to Modeling
Monte Carlo Simulation

• The User
• Defines distribution assumptions
• Selects the number of trials
• Sets the forecast variables
• Crystal Ball
• Repeats the simulation for the predetermined
  number of trials
• Calculates forecast values for each trial
• Reports the results

Monte Carlo Simulation Via Crystal Ball
1) Specify the models equation(s) 2) Define the
variable distributions 3) Define the forecasts 4)
Select number of trials 5) Run the Monte Carlo
Simulation 6) Interpret the results 7) Make
decisions
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Introduction to Modeling
Monte Carlo Simulation
Distribution of Outcomes
Distribution of outcomes depends on the
distributions chosen for the assumption variables
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Introduction to Modeling
Monte Carlo Simulation
Sensitivity Analysis and Risk
One of Crystal Balls best features it can
easily and quickly perform sensitivity and risk
analysis.
Goal Determine the likelihood that, given the
normal distribution used, the result will equal
at least 1.
Result Drag the arrow to where the frequency
chart equals 1 and the probability will be
calculated by Crystal Ball.
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Introduction to Modeling
Monte Carlo Simulation
Sensitivity Analysis and Risk
Probability that the result will equal at least 1
is 53.60
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Introduction to Modeling
Break-Even Simulation
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Introduction to Modeling
Decision Tree Simulation