Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Chapter 1 Introduction to Modeling Copyright 2001 Prentice Hall

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.

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

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

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

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

THE MODELING PROCESS Decision Support Models force you to 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. Models allow us to use the analytical power of spreadsheets hand in hand with the data storage and computational speed of computers.

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).

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

DETERMINISTIC AND PROBABILISTIC MODELS Deterministic Models 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 Probabilistic (Stochastic) Models 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.

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

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

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)