DECISION MODELING WITH MICROSOFT EXCEL Chapter 1 Introduction to Modeling (cont) Copyright 2001 Prentice Hall MORE ON MODELS A model is a carefully selected abstraction of reality. Symbolic models 1. always simplify reality. 2. incorporate enough detail so that • the result meets your needs, • it is consistent with the data you have available, • it can be quickly analyzed. Decision models are symbolic models in which some of the variables represent decisions that must or could be made. Decision variables are variables whose values you can control, change or set. 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 To model a situation, you first have to frame it (i.e., develop an organized way of thinking about the situation). A problem statement involves possible decisions and a method for measuring their effectiveness. Steps in modeling: 1. Study the Environment to Frame the Managerial Situation 2. Formulate a selective representation 3. Construct a symbolic (quantitative) model BUILDING MODELS 1. Studying the Environment Select those aspects of reality relevant to the situation at hand. 2. Formulation Specific assumptions and simplifications are made. Decisions and objectives must be explicitly identified and defined. Identify the model’s major conceptual ingredients using “Black Box” approach. BUILDING MODELS The “Black Box” View of a Model Decisions (Controllable) Parameters (Uncontrollable) Model Performance Measure(s) Consequence Variables MODELING AND REAL WORLD DECISION MAKING Management Modeling Term Lingo Formal Definition Example Decision Variable Lever Controllable Exogenous Input Quantity Parameter Gauge Uncontrollable Exogenous Interest Rate Input Quantity Consequence Variable Outcome Performance Measure Yardstick Endogenous Output Variable Endogenous Variable Used for Evaluation (Objective Function Value) Investment Amount Commissions Paid Return on Investment BUILDING MODELS 3. Model Construction The next step is to construct a symbolic model. Var. Y Mathematical relationships are developed. Graphing the variables may help define the relationship. Var. X To do this, use “Modeling with Data” technique. 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. allows for managerial interpretation of results. constrained optimization provides useful way to frame situations. will help develop your ability to formulate models in general. 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. Course Overview Modeling Techniques Deterministic Models Basic Profit Models Breakeven Pricing for Max Profit Crossover Time Series Forecasting Naive / Exp. Smoothing Regression (trend) Classical Decomposition (trend + seasonality) Probabilistic Models Decision Analysis Alternatives, States, Payoffs Decision Criteria Decision Trees Bayes Theorem Simulation Random numbers Distributions Discrete Variables Continuous Variables ITERATIVE MODEL BUILDING DEDUCTIVE MODELING Models Models Model Building PROBABILISTIC MODELS DETERMINISTIC MODELS Process Models Models INFERENTIAL MODELING 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. MODELING WITH DATA DATA – numbers that reflect quantitative facts about the Environment of a managerial situation Which comes first – data or models - Both Managerial Decision First - Marketer knows from experience what will drive sales and Designs a system to capture those variables Data First - See next slide MODELING WITH DATA Consider the following data. Graphs are created to view any relationship(s) between the variables. This is the first step in formulating the equations in the model. Creating the Symbolic Model Predicting Sales Based on Marketing Expenditures Sales Revenue (y) 3000 y = 3.5853x + 357.7 R2 = 0.9316 2500 2000 1500 1000 500 0 0 100 200 300 400 500 Marketing Expenses (x) 600 700 MODELING AND REAL WORLD DECISION MAKING Four Stages of applying modeling to real world decision making: Stage 1: Study the environment, formulate the model and construct the model. Stage 2: Analyze the model to generate results. Stage 3: Interpret and validate model results. Stage 4: Implement validated knowledge.