# Decision support and BI systems chapter 04

Decision Support and
Systems
(9th Ed., Prentice Hall)
Chapter 4:

Modeling and Analysis

Learning Objectives

4-2

Understand the basic concepts of
management support system (MSS) modeling
Describe how MSS models interact with data
and the users
Understand the well-known model classes and
decision making with a few alternatives
Describe how spreadsheets can be used for
MSS modeling and solution
Explain the basic concepts of optimization,
simulation and heuristics; when to use which

Learning Objectives

4-3

Describe how to structure a linear
programming model
Understand how search methods are used to
solve MSS models
Explain the differences among algorithms,
blind search, and heuristics
Describe how to handle multiple goals
Explain what is meant by sensitivity analysis,
what-if analysis, and goal seeking
Describe the key issues of model management

Opening Vignette:
“Model-Based Auctions Serve More
Lunches in Chile”
 Background: problem situation
 Proposed solution
 Results
 Answer and discuss the case questions

4-4

Modeling and Analysis Topics

4-5

Modeling for MSS (a critical component)
Static and dynamic models
Treating certainty, uncertainty, and risk
Influence diagrams (in the posted PDF file)
Decision analysis of a few alternatives (with
decision tables and decision trees)
Optimization via mathematical programming
Heuristic programming
Simulation
Model base management

MSS Modeling

A key element in most MSS
Leads to reduced cost and increased
revenue

DuPont Simulates Rail Transportation System
and Avoids Costly Capital Expenses

Procter & Gamble uses several DSS models
collectively to support strategic decisions

4-6

Locating distribution centers, assignment of DCs to
warehouses/customers, forecasting demand,
scheduling production per product type, etc.

Fiat, Pillowtex (…operational efficiency)…

Major Modeling Issues

Problem identification and environmental
analysis (information collection)
Variable identification

Forecasting/predicting

4-7

Multiple models: A MSS can include several
models, each of which represents a
different part of the decision-making
problem

Influence diagrams, cognitive maps

Categories of models >>>

Model management

Categories of Models
Category

4-8

Objective

Techniques

Optimization of
problems with few
alternatives

Find the best solution from Decision tables,
a small number of
decision trees
alternatives

Optimization via
algorithm

Find the best solution from
a large number of
alternatives using a stepby-step process

Linear and other
mathematical
programming
models

Optimization via
an analytic formula

Find the best solution in
one step using a formula

Some inventory
models

Simulation

Find a good enough
solution by experimenting
with a dynamic model of
the system

Several types of
simulation

Heuristics

Find a good enough
solution using “commonsense” rules

Heuristic
programming and
expert systems

Predictive
and
Predict future
Forecasting, Markov
other models
occurrences, what-if
chains, financial, …

Static and Dynamic Models

Static Analysis

Dynamic Analysis

4-9

Single snapshot of the situation
Single interval
Dynamic models
Evaluate scenarios that change over time
Time dependent
Represents trends and patterns over time
More realistic: Extends static models

Decision Making:
Treating Certainty, Uncertainty and
Risk
 Certainty Models

Uncertainty

Several outcomes for each decision
Probability of each outcome is unknown
Knowledge would lead to less uncertainty

Risk analysis (probabilistic decision making)

4-10

Assume complete knowledge
All potential outcomes are known
May yield optimal solution

Probability of each of several outcomes
occurring
Level of uncertainty => Risk (expected value)

Certainty, Uncertainty and Risk

4-11

Influence Diagrams
(Posted on the Course Website)

Graphical representations of a model
“Model of a model”
A tool for visual communication
Some influence diagram packages create and
solve the mathematical model
Framework for expressing MSS model
relationships
Rectangle = a decision variable
Circle = uncontrollable or intermediate variable
Oval = result (outcome) variable: intermediate or final
Variables are connected with arrows  indicates the
direction of influence (relationship)

4-12

Influence Diagrams:
Relationships
CERTAINTY
Amount in
CDs

Interest
Collected

UNCERTAINTY
Price
Sales

The shape of
the arrow
indicates the
type of
relationship

RANDOM (risk) variable: Place a tilde (~) above the variable’s name
~
Demand
Sales
4-13

Influence Diagrams: Example
An influence diagram for the profit model
Unit Price
~
Amount used in

Income
Units Sold
Profit

Profit = Income – Expense
Unit Cost
Income = UnitsSold * UnitPrice
Expenses = UnitsCost * UnitSold + FixedCost

Expenses

Fixed Cost

4-14

Influence Diagrams: Software

Analytica, Lumina Decision Systems

DecisionPro, Vanguard Software Co.

Integrates influence diagrams and Excel, also supports
Monte Carlo simulations

4-15

Includes influence diagrams, decision trees and
simulation

Definitive Scenario, Definitive Software

Supports hierarchical (tree structured) diagrams

DATA Decision Analysis, TreeAge Software

Supports hierarchical (multi-level) diagrams

Creates influence diagrams and decision trees directly

Analytica Influence Diagram of a
Marketing
Problem: The Marketing Model

4-16

Analytica: The Price Submodel

4-17

Analytica: The Sales Submodel

4-18

tool
Flexible and easy to use
Powerful functions

4-19

Programmability (via macros)
What-if analysis
Goal seeking
Simple database management
Seamless integration of model and data
Incorporates both static and dynamic models
Examples: Microsoft Excel, Lotus 1-2-3

Excel spreadsheet - static model example:
Simple loan calculation of monthly
payments

F = P(1 + i ) n
 i (1 + i ) n 
A = P

n
(
1
+
i
)

1

4-20

example:
Simple loan
calculation of
monthly payments
and effects of
prepayment

4-21

Decision Analysis: A Few
Alternatives
Single Goal Situations
Decision tables

Decision trees

4-22

Multiple criteria decision analysis
Features include decision
variables (alternatives),
uncontrollable variables, result
variables

Graphical representation of
relationships
Multiple criteria approach
Demonstrates complex
relationships
Cumbersome, if many
alternatives exists

Decision Tables

Investment example

One goal: maximize the yield after one year

Yield depends on the status of the economy
(the state of nature)

4-23

Solid growth
Stagnation
Inflation

Investment Example:
Possible Situations
1. If solid growth in the economy, bonds yield
12%; stocks 15%; time deposits 6.5%
2. If stagnation, bonds yield 6%; stocks 3%;
time deposits 6.5%
3. If inflation, bonds yield 3%; stocks lose 2%;
time deposits yield 6.5%

4-24

Investment Example:
Decision Table

4-25

Payoff Decision variables (alternatives)
Uncontrollable variables (states of economy)
Result variables (projected yield)
Tabular representation: