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

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Presentation on theme: "Desktop Business Analytics -- Decision Intelligence l Time Series Forecasting l Risk Analysis l Optimization."— Presentation transcript:

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

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

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

4 Spreadsheets - Pros l Easy to use l Popular l Flexible model-building tool

5 What-if Analysis l 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

6 What is missing? l The ability to know the range of possible outcomes and their likelihood of occurrence l 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

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

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

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

10 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

11 Crystal Ball Demonstration 2+2 = 4 ?

12 Crystal Ball Pro l Decision Intelligence l Includes n Crystal Ball n Optimization n Extenders n Developer Kit

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

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

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

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

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

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

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

20 A Picture is Worth... l A thousand What-ifs

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

22 Monte Carlo l 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.

23 Deterministic v. Stochastic Fixed Data 7% Fixed Outcomes $1,200,00 Variable data Variable Outcomes Deterministic Stochastic

24 Statistics l Normal Distribution, Mean and Standard Deviation Mean Standard Deviation

25 Retirement Example Uncertainty

26 Define Assumptions

27 Retirement Example - Assumptions

28 Retirement Example Assumptions

29 Retirement Example- Forecasts

30 Retirement Example Forecasts

31 Communicating Results l Get the client to understand alternatives l Take action

32 Uncertainty over time

33 Compare Alternatives

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