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

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Presentation on theme: "Chapter 1 Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Copyright 2001 Prentice Hall."— Presentation transcript:

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

2 INTRODUCTION TO MODELING Modeling Approach to Decision Making: Uses spreadsheet software such as Excel® This approach is easy for managers to use, Results in better management decisions, Provides important insights into problem. Involves spreadsheet based management models

3 THE MODELING PROCESS Managerial Approach to Decision Making Manager analyzes situation (alternatives) Makes decision to resolve conflict Decisions are implemented Consequences of decision These steps Use Spreadsheet Modeling

4 Management Situation Decisions Model Analysis Results Intuition Abstraction Interpretation Real World Symbolic World as applied to the first two stages of decision making. THE MODELING PROCESS

5 Management Situation Decisions Model Analysis Results Intuition Abstraction Interpretation Real World Symbolic World The Role of Managerial Judgment in the Modeling Process: Managerial Judgment THE MODELING PROCESS

6 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. THE MODELING PROCESS

7 TYPES OF MODELS Physical Model 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

8 Analog Model (A set of relationships through a different, but analogous, medium.) TYPES OF MODELS 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

9 Symbolic Model (Relationships are represented mathematically.) TYPES OF MODELS 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

10 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.

11 MORE ON DECISION MODELS Decision models typically include an explicit performance measure that gauges the attainment of that objective. In summary, decision models 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). 1. selectively describe the managerial situation. 2. designate decision variables. 3. designate performance measure(s) that reflect objective(s).

12 BUILDING MODELS 1. Study the Environment to Frame the Managerial Situation A problem statement involves possible decisions and a method for measuring their effectiveness. To model a situation, you first have to frame it (i.e., develop an organized way of thinking about the situation). Steps in modeling: 2. Formulate a selective representation 3. Construct a symbolic (quantitative) model

13 1. Studying the Environment 2. Formulation Select those aspects of reality relevant to the situation at hand. 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

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

15 3. Model Construction The next step is to construct a symbolic model. Mathematical relationships are developed. Graphing the variables may help define the relationship. Var. X Var. Y Cost A Cost B A + B To do this, use “Modeling with Data” technique. BUILDING MODELS

16 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.

17 CLASSIFICATIONS OF MODELS Decision making models are classified by the business function they address or by the discipline or industry involved. Classification Examples Business Function Finance, Marketing, Cost Accounting, Operations Discipline Science, Engineering, Economics Industry Military, Transportation, Telecommunications, Non-Profit Time Frame One Time Period, Multiple Time Periods Organizational Level Strategic, Tactical, Operational Mathematics Linear Equations, Non-Linear Equations Representation Spreadsheet, Custom Software, Paper and Pencil Uncertainty Deterministic, Probabilistic

18 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.

19 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. often used for strategic decision making involving an organization’s relationship to its environment. very useful when there are only a few uncertain model inputs and few or no constraints. DETERMINISTIC AND PROBABILISTIC MODELS

20 ITERATIVE MODEL BUILDING DEDUCTIVE MODELING INFERENTIAL MODELING PROBABILISTIC MODELS DETERMINISTIC MODELS Model Building Process Models Decision Modeling (‘What If?’ Projections, Decision Analysis, Decision Trees, Queuing) Decision Modeling (‘What If?’ Projections, Optimization) Data Analysis (Forecasting, Simulation Analysis, Statistical Analysis, Parameter Estimation) Data Analysis (Data Base Query, Parameter Evaluation

21 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. focuses on the variables as reflected in existing data collections. tends to be “data poor” initially. Inferential Modeling 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. places importance on modeler’s prior knowledge and judgments of both mathematical relationships and data values. ITERATIVE MODEL BUILDING

22 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.

23 MODELING AND REAL WORLD DECISION MAKING Modeling Term Management Lingo Formal DefinitionExample Decision Variable Lever Controllable Exogenous Investment Input Quantity Amount Parameter Gauge Uncontrollable Exogenous Interest Rate Input Quantity Consequence Outcome Endogenous Output Commissions Variable Variable Paid Performance Yardstick Endogenous Variable Return on Measure Used for Evaluation Investment (Objective Function Value)

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