MBA7020_01.ppt/June 13, 2005/Page 1 Georgia State University - Confidential MBA 7020 Business...

MBA7020_01.ppt/June 13, 2005/Georgia State University - Confidential

MBA 7020

Business Analysis Foundations

Introduction - Why Business Analysis

June 13, 2005


Agenda

Business Analysis -

Models

The Modeling Process

Introduction to Decision Sciences


Decision Sciences: Conceptualized!

Information Technology

Analytical Methods

Decision Making


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


Agenda

Business Analysis -

Models




MBA 7020 Business Analysis FoundationsCourse Overview

B reakevenP ric in g fo r M ax P ro fitC rossover

B as ic P ro fit M od e ls

N a ive / E xp . S m ooth in gR eg ress ion (tren d )C lass ica l D ecom p os it ion(tren d + season a lity)

T im e S eries F orecas tin g

D eterm in is tic M od e ls

A lte rn a tives , S ta tes , P ayo ffsD ec is ion C rite riaD ec is ion TreesB ayes Th eorem

D ec is ion A n a lys is

R an d om n u m b ersD is trib u tion sD isc re te V ariab lesC on tin u ou s V ariab les

S im u la tion

P rob ab ilis t ic M od e ls

M od e lin g Tech n iq u es


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.


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.


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

Methodologies1. Qualitative Delphi Methods

2. Quantitative - Non-statistical Using “comparables”

3. Quantitative - Statistical Time-series Regression


Analytical Methods

Quantitative Methods

• Mathematical / Algebraic / Calculus Methods

• Statistical Modeling and Analysis

• Management Science / Operations Research Techniques

• Accounting / Financial Modeling

Qualitative Methods

• Nominal Group Techniques

• Heuristic Based Methods

• Expert Systems / AI


UNCERTAINTY

• Facts not known

• Gather Information

• Fact Finding /.Analysis

DATABASED

COMPLEXITY

• Too many facts

• Generate Information

• Simulation/Synthesis

MODELBASED

EQUIVOCALITY

• Facts not Clear

• Interpret Information

• Application of Expertise

KNOWLEDGEBASED

Decision Environment


Decision Making Process

INTELLIGENCE•Fact Finding•Problem/Opportunity Sensing•Analysis/Exploration

DESIGN

•Formulation of Solutions•Generation of Alternatives•Modeling/Simulation

CHOICE

•Alternative Selection•Goal Maximization•Decision Making•Implementation


Types and Levels of Decisions

STRATEGIC

TACTICAL

OPERATIONAL

UNSTRUCTURED

STRUCTURED TRANSACTION

PROCESSING

MANAGEMENT

INFORMATION

DECISION

SUPPORT


Applications of Information Technology

• Transaction Processing Systems

• Management Information Systems

• Decision Support Systems


Decision Support Systems

Data

Base

Model

Base

Knowledge

Base

User Interface


Agenda

Business Analysis -

Models




Managing OrganizationsInformed decision making as a prerequisite for success

Action

Vision

Mission

Organizational

Context

Policies, Goals, and Objectives

Givens

Values, Purpose, Structure, Politics, Environment, etc.

What should be done ?

Analytics, Decision Making

When and how ??

StrategicDirection

DecisionMaking

Implementation Project Management


Managerial Decision MakingInformation Technology Solutions for Improving Effectiveness

ComplexityWhat does it add up to?Uncertainty

What can happen?

INTELLIGENCE

CHOICE

DESIGN

DATAMODELS

Variables (Measures and Estimates)

Probabilities and Estimates

Structuring Relationships

Problem Representation

Generation of Alternatives

Decision Analysis and Influence Diagrams for Visualizing Models and Choices

Spreadsheet Models

for managing complex relationships and detail


Modeling Decision SituationsProcess for Developing Meaningful and Robust Models

Objective Hierarchies

Variables and Measures

Modeling Relationships

Situation Structuring

Testing and Validation

Implementation and UseDSS

Fundamental and Means Objectives (feasible?)

Decision, Intermediate, and Outcome Variables

Data, Probabilities, Distributions

Communicate

Influence Diagrams and Decision Trees

Spreadsheet Modeling

Statistical, OR, Financial, Acctg. Models

Values, Goals, Strategies, etc

Analyze & Synthesize


The Modeling Process Quantitative - Statistical

Variables and Attributes

ObjectiveHierarchies

Influence Diagrams

Mathematical Representation

Testing and Validation

Implementationand use

• Describe Problem / opportunity

• Identify Overall Objective

• Organize Sub-Objectives into a hierarchy

• Identify Model’s Objective

• Determine all variables and their attributes

• Decide on Measurement / Data Collection

• Graphically depict relationships among variables

• Distinguish between Decision and outcome variables

• Determine mathematical relationships among variables

• Develop mathematical model(s)

• Evaluate reliability and validity

• Understand limitations

• Implement models in DSSs

• Clarify assumptions, inputs, and outputs


The Decision Analysis ProcessTools for Visualizing and Evaluating Alternatives

Identify decision situation

and understand objectives

Identify alternatives

Decompose and model

• problem structure

• uncertainty

• preferences

Choose best alternative

Sensitivity Analyses

Implement Decision

Model Representation

Deterministic Analysis

Probabilistic Analysis

Evaluation of Alternatives

Decision, Chance, and Consequence Variables

Arcs and Relationship Formulas

Tornado Diagrams

N-way Sensitivity

Uncertainty Assessment

Risk Profiles

EMV, NPV, etc.


The Modeling Process Quantitative – Non-Statistical

Managerial Approach to Decision Making

Manager analyzes situation (alternatives)

Makes decision toresolve conflict

Decisions are implemented

Consequences of decision

These stepsUse

SpreadsheetModeling

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