Chapter 1

1. DECISION MODELING OVERVIEW Chapter 1

2. MGS 3100 Business Analysis • Why is this class worth taking? • Knowledge of business analysis and MS Excel are core skills that can be applied to almost any job. • What is this class about? • Applying models in support of decision making within a business.

3. What is a model • A model is a carefully selected abstraction of reality.

4. Physical Model Characteristics Tangible Easy to Comprehend Difficult to Duplicate and Share Difficult to Modify and Manipulate Lowest Scope of Use Examples Model Airplane Model House Model City TYPES OF MODELS

5. Characteristics Intangible Harder to Comprehend Easier to Duplicate and Share Easier to Modify and Manipulate Wider Scope of Use Analog Model Examples Road Map Speedometer Pie Chart TYPES OF MODELS (A set of relationships through a different, but analogous, medium.)

6. Characteristics Intangible Hardest to Comprehend Easiest to Duplicate and Share Easiest to Modify and Manipulate Widest Scope of Use Examples Symbolic Model Simulation Model Algebraic Model Spreadsheet Model TYPES OF MODELS (Relationships are represented mathematically.)

7. be explicit about your objectives. 1. identify and record the types of decisions that influence those objectives. 2. identify and record interactions and trade-offs among those decisions. 3. think carefully about which variables to include. 4. consider what data are pertinent and their interactions. 5. recognize constraints or limitations on the values. 6. Models allow communication of your ideas and understanding to facilitate teamwork. 7. THE MODELING PROCESS Decision Support Models force you to Models allow us to use the analytical power of spreadsheets hand in hand with the data storage and computational speed of computers.

8. MORE ON DECISION MODELS Decision models typically include an explicit performance measure that gauges the attainment of that objective. For example, the objective may be to maximize profit or minimize cost in relation to a performance measure (such as sales revenue, interest income, etc). In summary, decision models 1. selectively describe the managerial situation. 2. designate decision variables. 3. designate performance measure(s) that reflect objective(s).

9. Model Decisions (Controllable) Performance Measure(s) Endogenous Variables Exogenous Variables Parameters (Uncontrollable) Consequence Variables BUILDING MODELS The “Black Box” View of a Model

10. ITERATIVE MODEL BUILDING • Deductive Modeling • focuses on the variables themselves before data are collected. • variables are interrelated based on assumptions about algebraic relationships and values of the parameters. • places importance on modeler’s prior knowledge and judgments of • both mathematical relationships and data values. • tends to be “data poor” initially. • Inferential Modeling • focuses on the variables as reflected in existing data collections. • variables are interrelated based on an analysis of data to determine relationships and to estimate values of parameters. • available data need to be accurate and readily available. • tends to be “data rich” initially.

11. The 3 Steps of Model Building • Study the environment • Formulate and construct the model –black box and influence diagrams • Do the Math –develop the mathematical relationships in Excel

12. are models in which all relevant data are assumed to be known with certainty. can handle complex situations with many decisions and constraints. are very useful when there are few uncontrolled model inputs that are uncertain. are useful for a variety of management problems. are easy to incorporate constraints on variables. software is available to optimize constrained models. DETERMINISTIC AND PROBABILISTIC MODELS Deterministic Models

13. are models in which some inputs to the model are not known with certainty. uncertainty is incorporated via probabilities on these “random” variables. very useful when there are only a few uncertain model inputs and few or no constraints. often used for strategic decision making involving an organization’s relationship to its environment. DETERMINISTIC AND PROBABILISTIC MODELS Probabilistic (Stochastic) Models

14. Summary • What is a model? A model is a carefully selected abstraction of reality. • How can models help business? allows a run-through of the situation

15. Summary • Why are there different types of models? Different situations demand models with different types of characteristics Tangible Comprehensible Ease of modification and manipulation Scope of use • What are the types of models? Physical Analog Symbolic

16. Summary • Exogenous Variables Quantitative variables whose values are determined external to a symbolic model (i.e. inputs to a symbolic model) • Endogenous Variables Quantitative variables whose values are determined by the relationships of a symbolic model (i.e. outputs of a symbolic model)

17. Appendix: The Role of Data in Decision Modeling • The definition of data: numbers that reflect quantitative facts about the environment of a managerial situation • Data is a paradox because: • Managerial decisions are based on the interpretation of data • Data is gathered through models designed by managerial decision • This leads to the question of which came first –data or models.

18. The Role of Data in Decision Modeling con’t • Both can come first… • Example of data first • By combining variables like value of car, length of time at job and number of credit cards, a retailer can automatically decide whether to grant you credit. • Example of managerial decisions first • A marketer knows from experience what will drives sales and designs a system to capture those variables.