Presentation on theme: "Chapter 1 Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Copyright 2001 Prentice Hall Publishers and Ardith E. Baker."— Presentation transcript:
Chapter 1 Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Copyright 2001 Prentice Hall Publishers and Ardith E. Baker
INTRODUCTION TO MODELING Modeling Approach to Decision Making: Uses spreadsheet ________ such as Excel® This approach is easy for managers to use, Results in better management__________, Provides important _________ into problem. Involves __________ based management models
THE MODELING PROCESS Managerial Approach to Decision Making Manager analyzes situation (_____________) Makes decision to Resolve _______ Decisions are ___________ ____________ of decision These steps Use Spreadsheet Modeling
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
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
Decision Support Models force you to be explicit about your_____________. 1. _______ and record the types of decisions that influence those objectives. 2. identify and record ____________and trade-offs among those decisions. 3. think carefully about which __________to include. 4. consider what ____ are pertinent and their interactions. 5. recognize ____________ or limitations on the values. 6. Models allow communication of your ideas and understanding to facilitate_____________. 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
TYPES OF MODELS Physical Model ____________ Easy to Comprehend Difficult to __________and Share Difficult to Modify and ____________ Lowest Scope of Use Characteristics Model Airplane Model House Model City Examples
Analog Model (A set of relationships through a different, but analogous, medium.) TYPES OF MODELS ________________ Harder to Comprehend Easier to __________and Share Easier to Modify and Manipulate Wider Scope of_________ Characteristics Road Map Speedometer Pie Chart Examples
Symbolic Model (Relationships are represented mathematically.) TYPES OF MODELS ______________ Hardest to Comprehend Easiest to Duplicate and Share Easiest to _______and Manipulate Widest Scope of Use Characteristics Simulation Model Algebraic Model Spreadsheet Model Examples
MORE ON MODELS A _______is a carefully selected abstraction of________. Symbolic models 1. always __________reality. 2. incorporate enough __________so that the result meets your needs, it is ____________with the data you have available, it can be quickly analyzed. Decision models are ________models in which some of the variables represent decisions that must or could be made. Decision variables are __________ whose values you can control, change or set.
MORE ON DECISION MODELS Decision models typically include an explicit ____________ measure that gauges the attainment of that___________. In summary, decision models For example, the objective may be to maximize ________ or minimize _______ in relation to a performance measure (such as sales revenue, interest income, etc). 1. selectively describe the ____________situation. 2. designate decision ______________. 3. designate ______________ measure(s) that reflect objective(s).
BUILDING MODELS 1. Study the _____________ to Frame the Managerial Situation A __________________ involves possible decisions and a method for measuring their effectiveness. To model a situation, you first have to ________ it (i.e., develop an _________ way of thinking about the situation). Steps in modeling: 2. Formulate a selective _____________________ 3. Construct a symbolic (quantitative) ____________
1. Studying the Environment 2. Formulation Select those aspects of _________ relevant to the situation at hand. Specific _____________ and simplifications are made. Decisions and ___________ must be explicitly identified and defined. Identify the model’s major conceptual ingredients using “______________” approach. BUILDING MODELS
Performance Measure(s) Decisions (Controllable) Parameters (Uncontrollable) Exogenous Variables Model Consequence Variables Endogenous Variables The “Black Box” View of a Model BUILDING MODELS
3. Model Construction The next step is to construct a______________________. ______________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
MODELING WITH DATA Consider the following data. Graphs are created to view any ______________between the variables. This is the first step in formulating the equations in the model.
CLASSIFICATIONS OF MODELS Decision making models are classified by the ________________ 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
DETERMINISTIC AND PROBABILISTIC MODELS Deterministic Models are models in which all relevant data are assumed to be known with _____________________. can handle ________ 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 ________________ on variables. software is available to optimize constrained models. allows for managerial _______________ of results. constrained optimization provides useful way to frame situations. will help develop your ability to _____________ models in general.
Probabilistic (Stochastic) Models are models in which some _______ to the model are not known with certainty. uncertainty is incorporated via ______________ 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____________________. DETERMINISTIC AND PROBABILISTIC MODELS
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
Deductive Modeling focuses on the ___________ themselves before data are collected. variables are interrelated based on _____________ about algebraic relationships and values of the parameters. focuses on the _________ as reflected in existing data collections. tends to be “____________” initially. Inferential Modeling variables are interrelated based on an __________ of data to determine relationships and to estimate values of parameters. available data needs to be ____________ and readily available. tends to be “________” initially. places importance on modeler’s prior __________ and judgments of both mathematical relationships and data values. ITERATIVE MODEL BUILDING
MODELING AND REAL WORLD DECISION MAKING Four Stages of applying modeling to real world decision making: Stage 1: Study the environment, formulate the_______ and construct the model. Stage 2: Analyze the model to generate _____________. Stage 3: Interpret and ______________ model results. Stage 4: ______________ validated knowledge.
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)