Modeling by keara

VIEWS: 128 PAGES: 29

									MGS3100 General Modeling

Chapter 1: Introduction

THE MODELING PROCESS
Managerial Approach to Decision Making
Manager analyzes situation (alternatives) Makes decision to resolve conflict
Decisions are implemented
These steps Use Spreadsheet Modeling

Consequences of decision

A Detailed View of the Modeling Process
1. 2.
3. 4. 5. 6. 7. 8. 9. 10. 11.

Diagnose the problem Select relevant aspects of reality Organize the facts; identify the assumptions, objectives, and decisions to be made Select the methodology Construct the model Solve the model (generate alternatives) Interpret results (in “lay” terms!) Validate the model (does it work correctly?) Do sensitivity analysis (does the solution change?) Implement the solution Monitor results

THE MODELING PROCESS
Model
Symbolic World Real World

Analysis

Results
Interpretation

Management Situation

Abstraction

Intuition

Decisions

The Modeling Process
Analysis

Model
Symbolic World

Results

Real World

Managerial Judgment

Management Situation

Decisions
Intuition

Interpretation

Abstraction

Reasons for Using Models


Models force you to:
 Be

explicit about your objectives  Think carefully about variables to include and their definitions in terms that are quantifiable  Identify and record the decisions that influence those objectives  Identify and record interactions and trade-offs among those decisions

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

Types of Models

Building Models
The “Black Box” View of a Model

Decisions (Controllable) Parameters (Uncontrollable)

Model

Performance Measure(s) Consequence Variables

MODELING VARIABLES
Modeling Term
Decision Variable

Management Lingo
Lever

Formal Definition
Controllable Exogenous Input Quantity

Example
Investment Amount Interest Rate

Parameter

Gauge

Uncontrollable Exogenous Input Quantity
Endogenous Output Variable Endogenous Variable Used for Evaluation (Objective Function Value)

Consequence Variable Performance Measure

Outcome

Commissions Paid Return on Investment

Yardstick

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 to shield future taxes


Costs are linear in the short term

Building Models
Symbolic Model Construction
Mathematical relationships are developed from data. Graphing the variables may help define the relationship.

Var. Y

Var. X

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.

Creating the Symbolic Model
Predicting Sales Based on Marketing Expenditures
3000

Sales Revenue (y)

2500

y = 3.5853x + 357.7 R2 = 0.9316

2000

1500

1000

500

0 0 100 200 300 400 500 600 700

Marketing Expenses (x)

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.

DETERMINISTIC AND PROBABILISTIC 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.

ITERATIVE MODEL BUILDING
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.
places importance on modeler’s prior knowledge and judgments of both mathematical relationships and data values. tends to be “data poor” initially.

Inferential Modeling
focuses on the variables as reflected in existing data collections. 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.

ITERATIVE MODEL BUILDING
DEDUCTIVE MODELING

Models

Models

Model Building

PROBABILISTIC MODELS
Models

Process

DETERMINISTIC MODELS
Models

INFERENTIAL MODELING

Philosophy of Modeling


Realism
A

model is valuable if you make better decisions when you use it than when you don’t. manager’s intuition arbitrates the content of the abstraction, resulting model, analysis, and the relevance and interpretation of the results.



Intuition
A

MGS3100 General Modeling

Chapter 11: Implementation

INTRODUCTION
Just as knowledge of Excel is insufficient without modeling concepts, your knowledge of spreadsheet modeling alone is insufficient for truly affecting decision making in organizations. Creating a model itself, although an important first step, is far from sufficient in the process of systematically improving decision making in the real world of business enterprise. Inadequate modeling is just one of the reasons why decision-makers do not make good decisions.

The purpose of this chapter is to help you understand why improving the quality of modeling alone will not necessarily lead to improved real-world decisions. This chapter will cover critical oversights that users new to the concepts of modeling make in attempting to move forward to apply those ideas in actual decision-making situations. The upside and downside potential risks of applying modeling concepts will be discussed so that you will come away with a balanced perspective of the pros and cons of applying modeling in business practical situations.

WHAT, AFTER ALL, IS A MODEL?
It is difficult to define a model. One definition might be:
A model is an abstraction of a business situation suitable for spreadsheet analysis to support decision making and provide managerial insights.

To many managers, a model is exquisitely crafted and professionally polished in appearance, highly intuitive, self-documenting, easy to use, completely validated and generalizable enough to be applied in a variety of settings by many people. Consider the following evolution of a model:

A Prototype Model Complete Debugged Runable by Its Author Validated with Test Data Believed to Deliver Value
Effort: 1X

An Institutionalized Model Sustained by the Organization Integrated into Organization's Decision Processes Coordinated in Function with Other Models and Systems Useable by Other Managers Maintainable and Extensible by Others Need Data Supplied and Maintained by Others
Effort: 10X-100X

A Modeling Application Usable by a Client Manager Well Documented Hardened to Reject Unusual Data Inputs Extendable by Author or Client Manager Validated with Real-World Data Known to Deliver Value
Effort: 10X

An Institutionalized Modeling Application

Effort: 100X – 1000X

The Separation of Players Curse
This framework is a variation of one originally proposed by C. West Churchman, et. al.
Modeler

Modeler, Project Manager, Decision Maker, Client

Curse of
Player Separation

Client

Decision Maker

Project Manager

The Curse of Scope Creep
Narrow Modeling Project Wide Modeling Project Single Model Multiple (Replicated) Models Single Objective Multiple Objectives Focused Activity Diffused Activity Few Players Many Players Few Stakeholders Many Stakeholders Curse of Low Effort High Effort Low Cost High Cost Scope Creep Low Development Risk High Development Risk Informal Coordination & Project Formal Coordination & Project Management Management Low Project Visibility High Project Visibility Scale Diseconomies in Scale economies in Information Information Systems for Model Systems for Model Scale Diseconomies in Model & Scale Economies in model & Database Maintenance Database Maintenance Deterioration in Model Use as Support for Model Use Early Adopters Move on Low Independent of Early Adopters Potential Organization-wide High Potential OrganizationalImpact wide Impact

Other Frequent Sources of Implementation Failure
Easily addressed issues in modeling failure are model logic, model inadequacy, etc.

However, inadequate attention to political issues that arise from the use of a model is far more prevalent as a source of failure in modeling. When a model fails, it is all too common to blame the model when in fact, it was due to inadequacies of the whole process of developing and implementing the model.

Another problem is the potential loss of continuity either during the development of a model itself or later during implementation caused by departure of key players, or the loss of organizational memory of a successful model.
A source of difficulty in modeling is the attempt to develop a modeling application before assessing issues of the data availability necessary to support that application. An important consideration early in the model development phase is the matching of available data to a possibly less-adequate model as a way of avoiding implementation problems later.

An infrastructure must be created that guarantees that the data and systems will be maintained in a way that serves the users of the model. A more subtle and insidious shortcoming of modeling concerns the identification of shortcomings at one level of an organization as being caused by failures or inadequacies at a higher, often more abstract, level of the organization. In this case, the best thing to do is to tune the model to work well given other organizational inadequacies that might be addressed more effectively at a later time.


								
To top