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MGS 3100 Business Analysis Introduction - Why Business Analysis Aug 23, 2010. Introduction to Decision Sciences. Agenda. Business Analysis - Models. The Modeling Process. What is Decision Sciences . Grocery Industry Kroger Travel Industry Delta SkyMiles Marriott Rewards

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Mgs 3100 business analysis introduction why business analysis aug 23 2010 l.jpg

MGS 3100Business AnalysisIntroduction - Why Business AnalysisAug 23, 2010


Agenda l.jpg

Introduction to Decision Sciences

Agenda

Business Analysis - Models

The Modeling Process


What is decision sciences l.jpg
What is Decision Sciences

  • Grocery Industry

  • Kroger

  • Travel Industry

  • Delta SkyMiles

  • Marriott Rewards

  • Gambling Industry

  • MGM Mirage Players Club

    • The Mirage

    • Treasure Island

    • Bellagio

    • New York New York

    • MGM Grand

  • Retail Business

  • Best Buy

  • Circuit City

  • Macy


Agenda4 l.jpg
Agenda

Introduction to Decision Sciences

Business Analysis - Models

The Modeling Process



Deterministic models vs probabilistic stochastic models l.jpg
Deterministic Models vs.Probabilistic (Stochastic) 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.


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Deterministic Models vs.Probabilistic (Stochastic) 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.


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Classification of Models

  • By problem type

    • Forecasting

    • Decision Analysis

    • Constrained Optimization

    • Monte Carlo Simulation

  • By data type

    • Time series

      • Exponential smoothing

      • Moving average

    • Cross sectional

      • Multiple linear regression

  • By causality

    • Causal: causal variable

    • Non-causal: surrogate variable

  • Methodologies

  • 1. Qualitative

    • Delphi Methods

    • 2. Quantitative - Non-statistical

    • Using “comparables”

  • 3. Quantitative - Statistical

    • Time-series

    • Regression


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Reasons for Using Models

  • Models force you to:

  • Be explicit about your objectives

  • Identify and record the decisions that influence those objectives

  • Identify and record interactions and trade-offs among those decisions

  • Think carefully about variables to include and their definitions in terms that are quantifiable

  • Consider what data are pertinent for quantification of those variables and determining their interactions

  • Recognize constraints (limitations) on the values that those quantified variables may assume

  • Allow communication of your ideas and understanding to facilitate teamwork


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Introduction to Decision Sciences

Agenda

Business Analysis - Models

The Modeling Process


The modeling process quantitative statistical l.jpg
The Modeling Process Quantitative - Statistical

  • Describe Problem / opportunity

  • Identify Overall Objective

  • Organize Sub-Objectives into a hierarchy

Objective

Hierarchies

Variables

and Attributes

  • Identify Model’s Objective

  • Determine all variables and their attributes

  • Decide on Measurement / Data Collection

Influence

Diagrams

  • Graphically depict relationships among variables

  • Distinguish between Decision and outcome variables

Mathematical

Representation

  • Determine mathematical relationships among variables

  • Develop mathematical model(s)

Testing and

Validation

  • Evaluate reliability and validity

  • Understand limitations

Implementation

and use

  • Implement models in DSSs

  • Clarify assumptions, inputs, and outputs


The modeling process quantitative non statistical l.jpg

Manager analyzes

situation (alternatives)

These steps

Use

Spreadsheet

Modeling

Makes decision to

resolve conflict

Decisions are

implemented

Consequences of decision

The Modeling Process Quantitative – Non-Statistical

Managerial Approach to Decision Making


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The Modeling Process

As applied to the first two stages of decision making

Model

Results

Analysis

Symbolic

World

Abstraction

Interpretation

Real

World

Management

Situation

Decisions

Intuition


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The Modeling Process

The Role of Managerial Judgment in the Modeling Process:

Analysis

Model

Results

Symbolic

World

Managerial

Judgment

Abstraction

Interpretation

Real

World

Management

Situation

Decisions

Intuition


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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:

    • Study the Environment to Frame the Managerial Situation

    • Formulate a selective representation

    • Construct a symbolic (quantitative) model


Building models16 l.jpg

Model

Decisions

(Controllable)

Performance

Measure(s)

Endogenous

Variables

Exogenous

Variables

Parameters

(Uncontrollable)

Consequence

Variables

Building Models

  • Studying the Environment

    • Select those aspects of reality relevant to the situation at hand.

  • 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.

The “Black Box” View of a Model


Building models17 l.jpg

A + B

Cost B

Var. Y

Cost A

Var. X

Building Models

  • Study the Environment to Frame the Managerial Situation

    • The next step is to construct a symbolic model.

    • Mathematical relationships are developed. Graphing the variables may help define the relationship.

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


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Iterative Model Building

DEDUCTIVE MODELING

Decision Modeling

(‘What If?’ Projections,

Optimization)

Decision Modeling

(‘What If?’ Projections, Decision

Analysis, Decision Trees, Queuing)

Models

Models

Model Building

Process

PROBABILISTIC

MODELS

DETERMINISTIC

MODELS

Models

Models

Data Analysis

(Forecasting, Simulation

Analysis, Statistical Analysis,

Parameter Estimation)

Data Analysis

(Data Base Query,

Parameter Evaluation

INFERENTIAL MODELING


Modeling and real world decision making l.jpg
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 making20 l.jpg
Modeling and Real World Decision Making

Management

Lingo

ModelingTerm

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)


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