1. Chapter 1: Introduction
MGS3100 General Modeling
Chapter 1: Introduction

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

3. A Detailed View of the Modeling Process
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

4. THE MODELING PROCESS Model Results Management Situation Decisions
Analysis
Symbolic
World
Abstraction
Interpretation
Real
World
Management
Situation
Decisions
Intuition

5. The Modeling Process Managerial Judgment Model Results Management
Analysis
Model
Results
Symbolic
World
Managerial
Judgment
Abstraction
Interpretation
Real
World
Management
Situation
Decisions
Intuition

6. 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

7. 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

8. Types of Models

9. Building Models The “Black Box” View of a Model Model Endogenous
Performance
Measure(s)
Decisions
(Controllable)
Parameters
(Uncontrollable)
Exogenous
Variables
Model
Consequence
Endogenous

10. MODELING VARIABLES 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)

11. 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

12. Building Models Symbolic Model Construction
Mathematical relationships are developed from data.
Graphing the variables may help define the relationship.
Var. X
Var. Y
Cost A
Cost B
A + B

13. 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.

14. Creating the Symbolic Model

15. 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.

16. 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.

17. 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.

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

20. Chapter 11: Implementation
MGS3100 General Modeling
Chapter 11: Implementation

21. 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.

22. 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.

23. 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:

24. 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
A Prototype Model
Complete
Debugged
Runable by Its Author
Validated with Test Data
Believed to Deliver Value
Effort: 1X
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
An Institutionalized
Modeling Application
Effort: 10X
Effort: 100X – 1000X

25. The Separation of Players Curse
This framework is a variation of one originally proposed by C. West Churchman, et. al.
Modeler,
Project
Manager,
Decision
Maker,
Client
Curse of
Player Separation
Modeler
Project Manager
Maker

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

27. 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.

28. 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.

29. 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.