1 / 22

# MBA 7020 Business Analysis Foundations Introduction - Why Business Analysis June 13, 2005 - PowerPoint PPT Presentation

MBA 7020 Business Analysis Foundations Introduction - Why Business Analysis June 13, 2005. Introduction to Decision Sciences. Agenda. Business Analysis - Models. The Modeling Process. Analytical Methods. Information Technology. Decision Making. Decision Sciences: Conceptualized!.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about 'MBA 7020 Business Analysis Foundations Introduction - Why Business Analysis June 13, 2005' - LeeJohn

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### MBA 7020Business Analysis FoundationsIntroduction - Why Business AnalysisJune 13, 2005

Agenda

Business Analysis - Models

The Modeling Process

Information Technology

Decision Making

Decision Sciences: Conceptualized!

• 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

• Circuit City

• Macy

Introduction to Decision Sciences

Business Analysis - Models

The Modeling Process

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.

• 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

• Qualitative Methods

• Nominal Group Techniques

• Heuristic Based Methods

• Expert Systems / AI

• Quantitative Methods

• Mathematical / Algebraic / Calculus Methods

• Statistical Modeling and Analysis

• Management Science / Operations Research Techniques

• Accounting / Financial Modeling

DATABASED

MODELBASED

KNOWLEDGEBASED

UNCERTAINTY

COMPLEXITY

EQUIVOCALITY

• Facts not known

• Gather Information

• Fact Finding /.Analysis

• Too many facts

• Generate Information

• Simulation/Synthesis

• Facts not Clear

• Interpret Information

• Application of Expertise

INTELLIGENCE

• Fact Finding

• Problem/Opportunity Sensing

• Analysis/Exploration

• Formulation of Solutions

• Generation of Alternatives

• Modeling/Simulation

DESIGN

• Alternative Selection

• Goal Maximization

• Decision Making

• Implementation

CHOICE

TACTICAL

OPERATIONAL

Types and Levels of Decisions

DECISION

SUPPORT

UNSTRUCTURED

MANAGEMENT

INFORMATION

STRUCTURED

TRANSACTION

PROCESSING

Applications of Information Technology

• Transaction Processing Systems

• Management Information Systems

• Decision Support Systems

Base

Decision Support Systems

Model

Base

Knowledge

Base

User Interface

Agenda

Business Analysis - Models

The Modeling Process

Managing OrganizationsInformed decision making as a prerequisite for success

Vision

Mission

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

Givens

Strategic

Direction

Policies, Goals, and Objectives

What should be done ?

Decision

Making

Analytics, Decision Making

When and how ??

Implementation

Project Management

Action

What does it add up to?

Uncertainty

What can happen?

MODELS

INTELLIGENCE

DATA

DESIGN

CHOICE

Managerial Decision MakingInformation Technology Solutions for Improving Effectiveness

Variables (Measures and Estimates)

Probabilities and Estimates

Structuring Relationships

Problem Representation

Generation of Alternatives

Decision Analysis and Influence Diagrams for Visualizing Models and Choices

for managing complex relationships and detail

Modeling Decision SituationsProcess for Developing Meaningful and Robust Models

Values, Goals, Strategies, etc

Fundamental and Means Objectives (feasible?)

Objective Hierarchies

Decision, Intermediate, and Outcome Variables

Data, Probabilities, Distributions

Variables and Measures

Influence Diagrams and Decision Trees

Situation Structuring

Statistical, OR, Financial, Acctg. Models

Modeling Relationships

Testing and Validation

DSS

Implementation and Use

Communicate

Analyze & Synthesize

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 Decision Analysis ProcessTools for Visualizing and Evaluating Alternatives

Identify decision situation

and understand objectives

Decision, Chance, and Consequence Variables

Arcs and Relationship Formulas

Model Representation

Identify alternatives

N-way Sensitivity

Deterministic Analysis

• Decompose and model

• problem structure

• uncertainty

• preferences

Uncertainty Assessment

Risk Profiles

Probabilistic Analysis

Sensitivity Analyses

Choose best alternative

Evaluation of Alternatives

EMV, NPV, etc.

Implement Decision

situation (alternatives)

These steps

Use

Modeling

Makes decision to

resolve conflict

Decisions are

implemented

Consequences of decision

The Modeling Process Quantitative – Non-Statistical

Managerial Approach to Decision Making