Post on 15-Dec-2015
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)