Chapter 1

Introduction to Modeling

DECISION MODELING WITHMICROSOFT EXCEL

Copyright 2001Prentice Hall

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 basedmanagement models

THE MODELING PROCESS

Managerial Approach to Decision MakingManager analyzes situation (alternatives)

Makes decision toresolve conflict

Decisions are implemented

Consequences of decision

These stepsUse

SpreadsheetModeling

ManagementSituation

Decisions

ModelAnalysis

Results

Intuition

Ab

stra

ctio

n

Inte

rpre

tati

on

Real World

Symbolic World

as applied to the first two stages of decision making.

THE MODELING PROCESS

ManagementSituation

Decisions

Model

Analysis

Results

Intuition

Ab

stra

ctio

n

Inte

rpre

tati

on

Real World

Symbolic World

The Role of Managerial Judgment in the Modeling Process:

ManagerialJudgment

THE MODELING PROCESS

Decision Support Models force you tobe 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

TYPES OF MODELS

Physical Model

TangibleEasy to ComprehendDifficult to Duplicate and ShareDifficult to Modify and ManipulateLowest 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

IntangibleHarder to ComprehendEasier to Duplicate and ShareEasier to Modify and ManipulateWider Scope of Use

Characteristics

Road Map

Speedometer

Pie Chart

Examples

Symbolic Model(Relationships are represented mathematically.)

TYPES OF MODELS

IntangibleHardest to ComprehendEasiest to Duplicate and ShareEasiest to Modify and ManipulateWidest Scope of Use

Characteristics

Simulation Model

Algebraic Model

Spreadsheet Model

Examples

MORE ON MODELS

A model is a carefully selected abstraction of reality.

Symbolic models1. 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 thevariables 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.

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).

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

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

PerformanceMeasure(s)

Decisions(Controllable)

Parameters(Uncontrollable)E

xogenous

Vari

abl e

s

ModelConsequence Variables

Endogenous

Varia

ble

s

The “Black Box” View of a Model

BUILDING MODELS

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 BA + B

To do this, use “Modeling with Data” technique.

BUILDING MODELS

MODELING WITH DATAConsider 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.

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

DETERMINISTIC ANDPROBABILISTIC 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 inputsthat 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.

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 ANDPROBABILISTIC MODELS

ITERATIVE MODEL BUILDING

DEDUCTIVE MODELING

INFERENTIAL MODELING

PROBABILISTICMODELS

DETERMINISTICMODELS

Model Building

Process

Models

ModelsModels

ModelsDecision M

odeling

(‘What I

f?’ P

rojectio

ns, Decisio

n

Analysis, Decisio

n Tre

es, Queuin

g) Decision Modeling

(‘What If?’ Projections,

Optimization)

Data Analysis

(Forecasting, Simulation

Analysis, Statistical Analysis,

Parameter Estimation)

Data A

nalysis

(Data

Base Q

uery,

Param

eter E

valuatio

n

Deductive Modelingfocuses 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

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.

MODELING AND REAL WORLD DECISION MAKING

Modeling TermManagement

Lingo Formal Definition Example

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)