Desktop Business Analytics -- Decision Intelligence l Time Series Forecasting l Risk Analysis l Optimization.
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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
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
Results are sensitive to the accuracy of input data Garbage in, Garbage out Intelligent agents using secret rules
Investment in time and resources
Disadvantages of Simulation
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
The Five Steps of Model Development
Optimization Model
Decision Variables Quantities over which you have control
(Accept or reject each project)– Upper and lower bounds– Continuous or discrete
Optimization
FunctionX F(X) = Y
Find the possible input values that make the output as large or as small as possible
Project Selection
Model
Find the project mix that generates the highest combined NPV
Project Mix Combined NPV
Uncertainty analysis Constraints and Requirements
We will us the simplifying assumption of applying a budgetary constraint to limit investment
A Realistic Model
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...
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%
Fixed Outcomes
$1,200,00
Variable data
Variable Outcomes
Deterministic
Stochastic
350.00 425.00 500.00 575.00 650.00
M onthly S avingsFrequency Chart
D ol l ars
M e an = $6 46,19 8.00 0
.02 4
.04 7
.07 1
.09 4
0
11 .7 5
2 3.5
35 .2 5
4 7
$3 00,00 0 $5 25,00 0 $7 50,00 0 $9 75,00 0 $1 ,2 00,00 0
500 Trials 6 Outliers
Forecast: Scenario A Retirement Portfolio
Statistics
Normal Distribution, Mean and Standard Deviation
350.00 425.00 500.00 575.00 650.00
M onthly S avings
Mean
Standard Deviation
Retirement Example
Monthly Dollar Saving 500$ Number of Years 20Annual Growth Rate 12%
Value at Retirement 432,315$
Uncertainty
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