1. Introduction to Modeling Introduction Management Models Simulate business activities and decisions Feedback about and forecast of outcomes Minimal risk or cost Why Model?

2. Implementation Introduction to ModelingThe Modeling Process The Managerial Approach to decision making Management Situation DecisionPayoff Should our baking company make cookies in addition to cakes? Owner has been a baker for 50 years and thinks the cookies will sell: “Taip!” The company spends 50,000 litae on new machinery and advertising. The cookies sell well! But fewer cakes can be baked Net profit falls Relying solely on intuition is risky No feedback until the final outcome

3. Introduction to ModelingThe Modeling Process The Managerial Approach to decision making Using a Model! Managerial judgment - intuition - essential aspect of process Management Situation Decisions Symbolic World Real World Model Abstraction Interpretation Results Analysis Intuition Managerial Judgment

4. Implementation Introduction to ModelingThe Modeling Process Decision Payoff The Managerial Approach to decision making Using a Model! Interpretation of model results Intuition of Management situation

5. Introduction to ModelingTypes of Models Model TypeCharacteristicsExamples Physical Model Analog Model Symbolic Model  Tangible  Comprehend: Easy  Duplicate/Share: Difficult  Modify/Manipulate: Difficult  Range of uses: Lowest  Intangible  Comprehend: Harder  Duplicate/Share: Easier  Modify/ Manipulate: Easier  Range of uses: Wider  Intangible  Comprehend: Hardest  Duplicate/Share: Easiest  Modify/ Manipulate: Easiest  Range of uses: Widest  Model Airplane  Model House  Model City  Road Map  Speedometer  Pie Chart  Simulation Model  Algebraic Model  Spread Sheet Model

6. Introduction to ModelingFormulation The Model Decisions (Controllable) Parameters (Uncontrollable) Performance Measure(s) Consequence Variables Exogenous Variables Endogenous Variables { { Black Box View of the Model The Model The Model The Model The Model The Model

7. Introduction to ModelingDecision Models DeterministicProbabilistic Models PhysicalAnalogSymbolic Non-decisionDecision Symbolic Decision DeterministicProbabilistic Assumed: all elements known with certainty Highest value: few uncertain uncontrolled model inputs Assumed: Some elements not known with certainty Incomplete knowledge: Uncertainty must be incorporated into the model

8. Optimization Forecasting Monte Carlo Simulation Decision Trees Introduction to ModelingDecision Models DeterministicProbabilistic Models PhysicalAnalogSymbolic Non-decisionDecision Symbolic Decision DeterministicProbabilistic

9. Introduction to ModelingDecision Analysis Decision Theory Decision Vs. Nature Decision Analysis Payoff Table State of Nature Decision r11r11 1 d1d1 The result (return) of one decision depends on another player’s (nature’s) action over which you have no control

10. Introduction to ModelingDecision Analysis Decision Theory Decision Vs. Nature Decision Analysis Payoff Table State of Nature Decision1 r11r11 d1d1 2 d2d2 r12r12 r21r21 r22r22 r13r13 r31r31 3 d3d3 r23r23 r32r32 r33r33 r1mr1m rn1rn1 m dndn r2mr2m rn2rn2 r3mr3m rn3rn3 rnmrnm The result (return) of one decision depends on another player’s (nature’s) action over which you have no control

11. Introduction to ModelingDecision Models Decision Model A B C The outcome of nature Decisions Under Certainty Decisions Under Risk Decisions Under Uncertainty Three Classes of Decision Models

12. Introduction to Modeling Decision Under Certainty Decision Under Certainty occurs in situations where you know which state of nature will occur. 1 Decision Analysis Payoff Table State of Nature Decision r11r11 d1d1 d2d2 r21r21 r31r31 d3d3 rn1rn1 dndn

13. Introduction to Modeling Decision Under Risk Decision Under Risk occurs in situations where the decision maker can arrive at a probability estimate for the occurrence for each of the various states of nature. Decision Analysis Payoff Table State of Nature Decision1 r 11 = 50 Lt d1d1 2 d2d2 r 12 = 70 Lt r21r21 r22r22 r 13 = 125 Lt r31r31 3 d3d3 r23r23 r32r32 r33r33 r 1m = 30 Lt rn1rn1 m dndn r2mr2m rn2rn2 r3mr3m rn3rn3 rnmrnm Probabilities of States of Nature (SON) P 1 =.2 P 2 =.45 P 3 =.05 P m =.3 R 1 =.2(r 11 ) +.45(r 12 ) +.05(r 13 ) +.3(r 1m ) = Expected Value 56.75Lt =.2(50) +.45(70) +.05(125) +.3(30)

14. Introduction to Modeling Decision Under Risk High Middle Low Risky Safe Risky Start 24 possibilities after only one three-way and 3 two-way decisions Assign probabilities at these points

15. Introduction to ModelingMonte Carlo Simulation Great teacher Many situations Deal with the unexpected Thorough understanding of processes Broader knowledge Expensive Not always practical Time consuming Impossible for all situations Can be complex Cons Pros Experience

16. Introduction to ModelingMonte Carlo Simulation Expensive Not always practical Time consuming Impossible for all situations Can be complex Cons Pros Experience More Pros Expensive Not always practical Time consuming Impossible for all situations Can be complex Cheap Flexible Fast Adaptable Simplifying Simulation Provides “Virtual Experience” Great teacher Many situations Deal with the unexpected Thorough understanding of processes Broader knowledge

17. Introduction to ModelingMonte Carlo Simulation Key Points of Simulation Models 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

18. Introduction to ModelingMonte 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

19. Introduction to ModelingMonte Carlo Simulation Generating Random Variables Normal Distribution 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 model’s assumptions CRYSTAL BALL: EXAMPLE: Normal Distribution of random variables having a mean value of 3.0 generated by the equation is X 2

20. Introduction to ModelingMonte Carlo Simulation Generating Other Distributions Triangle Distribution Lognormal Distribution Uniform Distribution Custom Distribution

21. Introduction to ModelingMonte 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 model’s 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

22. Introduction to ModelingMonte Carlo Simulation Distribution of Outcomes Distribution of outcomes depends on the distributions chosen for the assumption variables Outcome Frequency Chart - Normal DistributionOutcome Frequency Chart - Lognormal Distribution

23. Introduction to ModelingMonte Carlo Simulation Sensitivity Analysis and Risk One of Crystal Ball’s 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.

24. Introduction to ModelingMonte Carlo Simulation Sensitivity Analysis and Risk Probability that the result will equal at least 1 is 53.60%

25. Introduction to ModelingDecision Tree Analysis

26. Introduction to ModelingMonte Carlo Simulation