1. MGS 3100 Business Analysis Introduction - Why Business Analysis Jan 14, 2016

2. Introduction to Decision Sciences
Agenda
Introduction to Decision Sciences
Business Analysis - Models
The Modeling Process

3. 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

4. Business Analysis - Models
Agenda
Introduction to Decision Sciences
Business Analysis - Models
The Modeling Process

5. MGS 3100 Business Analysis Course Overview

6. Deterministic Models vs. Probabilistic (Stochastic) 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.

7. Deterministic Models vs. 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.

8. 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

9. 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

10. Agenda Introduction to Decision Sciences Business Analysis - Models
The Modeling Process

11. 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

12. The Modeling Process Quantitative – Non-Statistical
Managerial Approach to Decision Making
These steps
Use
Spreadsheet
Modeling
Manager analyzes
situation (alternatives)
Makes decision to
resolve conflict
Decisions are
implemented
Consequences of decision

13. 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

14. 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

15. 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

16. 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
Performance
Measure(s)
Decisions
(Controllable)
Parameters
(Uncontrollable)
Exogenous
Variables
Model
Consequence
Endogenous

17. 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.
Var. X
Var. Y
Cost A
Cost B
A + B

18. Iterative Model Building
DEDUCTIVE MODELING
(‘What If?’ Projections,
Decision Modeling
Optimization)
(‘What If?’ Projections, Decision
Analysis, Decision Trees, Queuing)
Decision Modeling
Models
Models
Model Building
Process
PROBABILISTIC
MODELS
DETERMINISTIC
MODELS
Models
Models
Analysis, Statistical Analysis,
(Forecasting, Simulation
Data Analysis
Parameter Estimation)
Data Analysis
(Data Base Query,
Parameter Evaluation
INFERENTIAL MODELING

19. 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.

20. Modeling and Real World Decision Making
Management
Lingo
Modeling Term
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)