Business Analysis- General Modeling

8/3/2019 Business Analysis- General Modeling

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MGS3100

General Modeling

Chapter 1: Introduction


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THE MODELING PROCESS

Managerial Approach to Decision Making

Manager analyzes

situation (alternatives)

Makes decision to

resolve conflict

Decisions are

implemented

Consequences

of decision

These steps

UseSpreadsheet

Modeling


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A Detailed View of the Modeling Process

1. Diagnose the problem

2. Select relevant aspects of reality

3. Organize the facts; identify the assumptions,objectives, and decisions to be made

4. Select the methodology

5. Construct the model

6. Solve the model (generate alternatives)

7. Interpret results (in lay terms!)

8. Validate the model (does it work correctly?)

9. Do sensitivity analysis (does the solution change?)

10. Implement the solution

11. Monitor results


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THE MODELING PROCESS

ManagementSituation

Decisions

Model

AnalysisR

esults

Intuition

Abstraction

Interpretation

Real

World

Symbolic

World


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The Modeling Process

ManagementSituation

Decisions

Model

Analysis

R

esults

Intuition

Abstraction

I

nterpretation

Real

World

Symbolic

World Managerial

Judgment


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Reasons for Using Models Models force you to:

Be explicit about your objectives

Think carefully about variables to include andtheir definitions in terms that are quantifiable

Identify and record the decisions thatinfluence those objectives

Identify and record interactions and trade-offsamong those decisions


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Reasons (cont.) 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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Types of Models


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Building Models

Performance

Measure(s)

Decisions

(Controllable)

Parameters

(Uncontrollable)

ModelConsequence

Variables

The Black Box View of a Model


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MODELING VARIABLES

Modeling Term

Management

Lingo 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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Examples of Decision Model

Assumptions - Profit Models

If it is beyond your control, do not consider

it! Overhead costs - a convenient fiction - we

ignore

Sunk costs - we ignore

Depreciation - only include if we can use toshield future taxes

Costs are linear in the short term


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Building ModelsSymbolic Model Construction

Mathematical relationships are developed from data.

Graphing the variables may help define the relationship.

Var. X

Var.Y


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Modeling with Data

Consider the following data.

Graphs are created to view any relationship(s) between the variables.

This is the first step in formulating the equations in the model.


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Creating the Symbolic Model

Predicting Sales Based on Marketing Expenditures

y = 3.5853x + 357.7

R2 = 0.9316

0

500

1000

1500

2000

2500

3000

0 100 200 300 400 500 600 700

Marketing Expenses (x)

Sa

lesR

evenue(

y)


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DETERMINISTIC AND

PROBABILISTIC 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.

allow for managerial interpretation of results.

will help develop your ability to formulate models in general.


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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.

often used for strategic decision making involving an organizations

relationship to its environment.

very useful when there are only a few uncertain model inputs andfew or no constraints.

DETERMINISTIC ANDPROBABILISTIC MODELS


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Deductive Modeling

focuses on the variables themselves before data are collected.

variables are interrelated based on assumptions about algebraic

relationships and values of the parameters.

focuses on the variables as reflected in existing data collections.

tends to be data poor initially.

Inferential Modeling

variables are interrelated based on an analysis of data to

determine relationships and to estimate values of parameters.

available data need to be accurate and readily available.

tends to be data rich initially.

places importance on modelers prior knowledge and judgments of

both mathematical relationships and data values.

ITERATIVE MODEL BUILDING


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ITERATIVE MODEL BUILDING

DEDUCTIVE MODELING

INFERENTIAL MODELING

PROBABILISTIC

MODELS

DETERMINISTIC

MODELS

Model Building

Process

Models

ModelsModels

Models


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Philosophy of Modeling Realism

A model is valuable if you make better

decisions when you use it than when youdont.

Intuition

A managers intuition arbitrates the content of

the abstraction, resulting model, analysis, andthe relevance and interpretation of the results.


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MGS3100

General Modeling

Chapter 11:

Implementation


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Just as knowledge of Excel is insufficientJust as knowledge of Excel is insufficientwithout modeling concepts, your knowledge ofwithout modeling concepts, your knowledge ofspreadsheet modeling alone is insufficient forspreadsheet modeling alone is insufficient fortruly affecting decision making in organizations.truly affecting decision making in organizations.

INTRODUCTIONINTRODUCTION

Creating a model itself, although an importantCreating a model itself, although an importantfirst step, is far from sufficient in the process offirst step, is far from sufficient in the process ofsystematically improving decision making in thesystematically improving decision making in the

real world of business enterprise.real world of business enterprise.

Inadequate modeling is just one of the reasonsInadequate modeling is just one of the reasonswhy decisionwhy decision--makers do not make goodmakers do not make good

decisions.decisions.


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The purpose of this chapter is to help youThe purpose of this chapter is to help youunderstand why improving the quality ofunderstand why improving the quality of

modeling alone will not necessarily lead tomodeling alone will not necessarily lead toimproved realimproved real--world decisions.world decisions.

This chapter will cover critical oversights thatThis chapter will cover critical oversights thatusers new to the concepts of modeling make inusers new to the concepts of modeling make inattempting to move forward to apply thoseattempting to move forward to apply thoseideas in actual decisionideas in actual decision--making situations.making situations.

The upside and downside potential risks ofThe upside and downside potential risks of

applying modeling concepts will be discussedapplying modeling concepts will be discussedso that you will come away with a balancedso that you will come away with a balancedperspective of the pros and cons of applyingperspective of the pros and cons of applyingmodeling in business practical situations.modeling in business practical situations.


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WHAT, AFTER ALL, IS A MODEL?WHAT, AFTER ALL, IS A MODEL?

It is difficult to define a model. One definitionIt is difficult to define a model. One definitionmight be:might be:

Consider the following evolution of a model:Consider the following evolution of a model:

A model is an abstraction of a business situationA model is an abstraction of a business situationsuitable for spreadsheet analysis to supportsuitable for spreadsheet analysis to support

decision making and provide managerial insights.decision making and provide managerial insights.

To many managers, a model is exquisitelyTo many managers, a model is exquisitelycrafted and professionally polished incrafted and professionally polished in

appearance, highly intuitive, selfappearance, highly intuitive, self--documenting,documenting,easy to use, completely validated andeasy to use, completely validated andgeneralizable enough to be applied in a varietygeneralizable enough to be applied in a varietyof settings by many people.of settings by many people.


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A Prototype ModelA Prototype Model

CompleteCompleteDebuggedDebugged

Runable by Its AuthorRunable by Its AuthorValidated with Test DataValidated with Test DataBelieved to Deliver ValueBelieved to Deliver Value

An Institutionalized ModelAn Institutionalized ModelSustained by the OrganizationSustained by the OrganizationIntegrated into Organization'sIntegrated into Organization's

Decision ProcessesDecision ProcessesCoordinated in Function withCoordinated in Function with

Other Models and SystemsOther Models and SystemsUseable by Other ManagersUseable by Other ManagersMaintainable and ExtensibleMaintainable and Extensible

by Othersby OthersNeed Data Supplied andNeed Data Supplied and

Maintained by OthersMaintained by Others

Effort: 10XEffort: 10X--100X100XEffort: 1XEffort: 1X

A Modeling ApplicationA Modeling ApplicationUsable by a Client ManagerUsable by a Client Manager

Well DocumentedWell Documented

Hardened to Reject Unusual

Hardened to Reject UnusualData InputsData Inputs

Extendable by Author or ClientExtendable by Author or ClientManager Validated withManager Validated with

RealReal--World DataWorld DataKnown to Deliver ValueKnown to Deliver Value

Effort: 10XEffort: 10X

An InstitutionalizedAn InstitutionalizedModeling ApplicationModeling Application

Effort: 100XEffort: 100X 1000X1000X


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This framework is a variation of one originallyThis framework is a variation of one originallyproposed by C. West Churchman, et. al.proposed by C. West Churchman, et. al.

Modeler,Modeler,ProjectProject

Manager,Manager,DecisionDecision

Maker,Maker,ClientClient

Curse ofCurse of

Player SeparationPlayer Separation

ClientClient

ModelerModeler

Project ManagerProject Manager

DecisionDecisionMakerMaker

The Separation of Players CurseThe Separation of Players Curse


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The Curse of Scope CreepThe Curse of Scope Creep

Narrow Modeling ProjectNarrow Modeling ProjectSingle ModelSingle Model

Single ObjectiveSingle Objective

Focused ActivityFocused ActivityFew PlayersFew Players

Few StakeholdersFew StakeholdersLow EffortLow EffortLow CostLow Cost

Low Development RiskLow Development RiskInformal Coordination & ProjectInformal Coordination & Project

ManagementManagement

Low Project VisibilityLow Project VisibilityScale Diseconomies inScale Diseconomies in

Information Systems for ModelInformation Systems for ModelScale Diseconomies in Model &Scale Diseconomies in Model &

Database MaintenanceDatabase MaintenanceDeterioration in Model Use asDeterioration in Model Use asEarly Adopters Move on LowEarly Adopters Move on LowPotential OrganizationPotential Organization--widewide

ImpactImpact

Curse ofCurse of

Scope CreepScope Creep

Wide Modeling ProjectWide Modeling ProjectMultiple (Replicated) ModelsMultiple (Replicated) Models

Multiple ObjectivesMultiple ObjectivesDiffused ActivityDiffused Activity

Many PlayersMany PlayersMany StakeholdersMany Stakeholders

High Effort

High EffortHigh CostHigh Cost

High Development RiskHigh Development RiskFormal Coordination & ProjectFormal Coordination & Project

ManagementManagement

High Project VisibilityHigh Project VisibilityScale economies in InformationScale economies in Information

Systems for ModelSystems for ModelScale Economies in model &Scale Economies in model &

Database MaintenanceDatabase MaintenanceSupport for Model UseSupport for Model Use

Independent of Early AdoptersIndependent of Early AdoptersHigh Potential OrganizationalHigh Potential Organizational--

wide Impactwide Impact


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Other Frequent SourcesOther Frequent Sourcesof Implementation Failureof Implementation Failure

However, inadequate attention to politicalHowever, inadequate attention to politicalissues that arise from the use of a model is farissues that arise from the use of a model is farmore prevalent as a source of failure inmore prevalent as a source of failure inmodeling.modeling.

Easily addressed issues in modeling failure areEasily addressed issues in modeling failure aremodel logic, model inadequacy, etc.model logic, model inadequacy, etc.

When a model fails, it is all too common toWhen a model fails, it is all too common toblame the model when in fact, it was due toblame the model when in fact, it was due toinadequacies of the whole process ofinadequacies of the whole process ofdeveloping and implementing the model.developing and implementing the model.


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Another problem is the potential loss ofAnother problem is the potential loss ofcontinuity either during the development of acontinuity either during the development of a

model itself or later during implementationmodel itself or later during implementationcaused by departure of key players, or thecaused by departure of key players, or theloss of organizational memory of a successfulloss of organizational memory of a successfulmodel.model.

A source of difficulty in modeling is the attemptA source of difficulty in modeling is the attemptto develop a modeling application beforeto develop a modeling application beforeassessing issues of the data availabilityassessing issues of the data availabilitynecessary to support that application.necessary to support that application.

An important consideration early in the modelAn important consideration early in the modeldevelopment phase is the matching of availabledevelopment phase is the matching of availabledata to a possibly lessdata to a possibly less--adequate model as aadequate model as a

way of avoiding implementation problems later.way of avoiding implementation problems later.


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An infrastructure must be created thatAn infrastructure must be created thatguarantees that the data and systems will beguarantees that the data and systems will be

maintained in a way that serves the users ofmaintained in a way that serves the users ofthe model.the model.

A more subtle and insidious shortcoming ofA more subtle and insidious shortcoming ofmodeling concerns the identification ofmodeling concerns the identification of

shortcomings at one level of an organization asshortcomings at one level of an organization asbeing caused by failures or inadequacies at abeing caused by failures or inadequacies at ahigher, often more abstract, level of thehigher, often more abstract, level of theorganization.organization.

In this case, the best thing to do is to tune theIn this case, the best thing to do is to tune themodel to work well given other organizationalmodel to work well given other organizationalinadequacies that might be addressed moreinadequacies that might be addressed more

effectively at a later time.effectively at a later time.

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Business Analysis- General Modeling