mgs3100_01.ppt/aug 25, 2015/page 1 georgia state university - confidential mgs 3100 business...

MGS3100_01.ppt/Aug 25, 2015/Georgia State University - Confidential

MGS 3100

Business Analysis

Introduction - Why Business Analysis

Aug 25 and 26, 2015


Agenda

Business Analysis -

Models

The Modeling Process

Introduction to Decision Sciences


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




MGS 3100 Business AnalysisCourse 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


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


Agenda

Business Analysis -

Models




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 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



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



ManagementSituation

Decisions

Model

Analysis

Results

Intuition

Ab

stra

ctio

n

Inte

rpre

tati

on

Real World

Symbolic World Managerial

Judgment

The Role of Managerial Judgment in the Modeling Process:


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:

1. Study the Environment to Frame the Managerial Situation

2. Formulate a selective representation

3. Construct a symbolic (quantitative) model


Building Models

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

2. 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.

PerformanceMeasure(s)

Decisions(Controllable)

Parameters(Uncontrollable)E

xoge

nou

sV

aria

bles

ModelConsequence Variables

End

ogenous

Variables

The “Black Box” View of a Model


Building Models

3. 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 BA + B


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


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 Term

Management Lingo Formal Definition Example

Decision Variable Lever Controllable Exogenous InvestmentInput Quantity Amount

Parameter Gauge Uncontrollable Exogenous Interest RateInput Quantity

Consequence Outcome Endogenous Output Commissions Variable Variable Paid

Performance Yardstick Endogenous Variable Return onMeasure Used for Evaluation Investment

(Objective Function Value)

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