Decision support and BI systems chapter 04

Modeling and Analysis Topics Modeling for MSS a critical component  Static and dynamic models  Treating certainty, uncertainty, and risk  Influence diagrams in the posted PDF file...

Decision Support and Business Intelligence

 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

Modeling and Analysis Topics

 Modeling for MSS (a critical component)

 Static and dynamic models

 Treating certainty, uncertainty, and risk

 Influence diagrams (in the posted PDF file)

MSS Modeling

scheduling production per product type, etc.

 Fiat, Pillowtex (…operational efficiency)…

Major Modeling Issues

 Problem identification and environmental analysis (information collection)

 Variable identification

 Influence diagrams, cognitive maps

 Forecasting/predicting

 More information leads to better prediction

 Multiple models: A MSS can include several models, each of which represents a

different part of the decision-making problem

 Categories of models >>>

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Decision tables, decision trees

Optimization via

algorithm Find the best solution from a large number of

alternatives using a by-step process

step-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 sense” rules

“common-Heuristic programming and expert systems

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

Static and Dynamic Models

 Represents trends and patterns over time

 More realistic: Extends static models

Decision Making:

Treating Certainty, Uncertainty and Risk  Certainty Models

 All potential outcomes are known

 May yield optimal solution

 Uncertainty

 Several outcomes for each decision

 Probability of each outcome is unknown

 Knowledge would lead to less uncertainty

 Risk analysis (probabilistic decision making)

 Probability of each of several outcomes occurring

 Level of uncertainty => Risk (expected value)

Certainty, Uncertainty and Risk

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)

Influence Diagrams:

Relationships

Amount in CDs

Interest Collected

Price

Sales

Sales

~ Demand

CERTAINTY UNCERTAINTY

RANDOM (risk) variable: Place a tilde (~) above the variable’s name

The shape of the arrow indicates the

type of relationship

Influence Diagrams: Example

~

Amount used in Advertisement

An influence diagram for the profit model

Profit = Income – Expense

Income = UnitsSold * UnitPrice

UnitsSold = 0.5 * Advertisement Expense

Expenses = UnitsCost * UnitSold + FixedCost

Influence Diagrams: Software

 Analytica , Lumina Decision Systems

 Supports hierarchical (multi-level) diagrams

 DecisionPro , Vanguard Software Co.

 Supports hierarchical (tree structured) diagrams

 DATA Decision Analysis , TreeAge Software

 Includes influence diagrams, decision trees and simulation

 Definitive Scenario , Definitive Software

 Integrates influence diagrams and Excel, also supports Monte Carlo simulations

 PrecisionTree , Palisade Co.

 Creates influence diagrams and decision trees directly

in an Excel spreadsheet

Analytica Influence Diagram of a

Marketing Problem: The Marketing Model

Analytica: The Price Submodel

Analytica: The Sales Submodel

MSS Modeling with Spreadsheets

tool

 Flexible and easy to use

 Powerful functions

 Add-in functions and solvers

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

1 (

) 1 (

) 1 (

n n n

i

i i

P A

i P

F

Decision Analysis: A Few Alternatives

Single Goal Situations Decision tables

 Multiple criteria decision analysis

 Features include decision variables (alternatives), uncontrollable variables, result variables

 Decision trees

 Graphical representation of relationships

 Multiple criteria approach

relationships

alternatives exists

Decision Tables

 One goal: maximize the yield after one year

 Yield depends on the status of the economy

(the state of nature)

 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%

 Payoff Decision variables (alternatives)

 Uncontrollable variables (states of economy)

 Result variables (projected yield)

 Tabular representation:

Investment Example:

Decision Table

 Use known probabilities

 Risk analysis: compute expected values

Decision Analysis: A Few Alternatives

 Other methods of treating risk

 Simulation, Certainty factors, Fuzzy logic

 Multiple goals

 Yield, safety, and liquidity

MSS Mathematical Models

Decision Variables

Mathematical Relationships

Uncontrollable Variables

Result Variables

 Non-Quantitative Models (Qualitative)

uncontrollable variables and result variables

 Quantitative Models: Mathematically links decision

variables, uncontrollable variables, and result variables

variables and uncontrollable variables

Optimization via Mathematical Programming

A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal

 Optimal solution: The best possible solution

to a modeled problem

for the optimal solution of resource allocation problems All the relationships are linear

LP Problem Characteristics

1.Limited quantity of economic resources 2.Resources are used in the production of products or services

3.Two or more ways (solutions, programs)

to use the resources 4.Each activity (product or service) yields

a return in terms of the goal 5.Allocation is usually restricted by constraints

results

LP Example

The Product-Mix Linear Programming Model

LP Solution

Sensitivity, What-if, and Goal Seeking Analysis

 Assesses impact of change in inputs on outputs

 Eliminates or reduces variables

 Can be automatic or trial and error

 Assesses solutions based on changes in variables or assumptions (scenario analysis)

 Backwards approach, starts with goal

 Determines values of inputs needed to achieve goal

 Example is break-even point determination

Heuristic Programming

 Cuts the search space

 Gets satisfactory solutions

more quickly and less

expensively

 Finds good enough feasible

solutions to very complex

problems

 Heuristics can be

 Quantitative

 Qualitative (in ES)

 Traveling Salesman Problem

>>>

Heuristic Programming - SEARCH

Traveling Salesman Problem

 What is it?

 A traveling salesman must visit customers

in several cities, visiting each city only once, across the country Goal: Find the shortest possible route

 Total number of unique routes (TNUR):

TNUR = (1/2) (Number of Cities – 1)!

Number of Cities TNUR

5 12

9 20,160

20 1.22 10 18

When to Use Heuristics

When to Use Heuristics

 Inexact or limited input data

 Complex reality

 Reliable, exact algorithm not available

 Computation time excessive

 For making quick decisions

Limitations of Heuristics

 Cannot guarantee an optimal solution

 Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system

 Frequently used in DSS tools

 Imitates reality and capture its richness

 Technique for conducting experiments

 Descriptive, not normative tool

 Often to “solve” very complex problems

Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques

Major Characteristics of Simulation

!

Advantages of Simulation

problems

non-structured problems

Limitations of Simulation

 Cannot guarantee an optimal solution

 Slow and costly construction process

 Cannot transfer solutions and inferences

to solve other problems (problem specific)

 So easy to explain/sell to managers, may lead overlooking analytical

solutions

 Software may require special skills

Simulation Methodology

2 Construct simulation model 6 Evaluate results

4 Design experiments

Simulation Types

 In stochastic simulations: We use distributions (Discrete

or Continuous probability distributions)

 Time independent stochastic simulation via Monte Carlo technique (X = A + B)

 Visual simulation

 Object-oriented simulation

 Visual interactive modeling (VIM) Also called

impact of different management decisions

Visual Interactive Modeling (VIM) / Visual Interactive Simulation

(VIS)

Model Base Management

 MBMS: capabilities similar to that of DBMS

 But, there are no comprehensive model base management packages

 Each organization uses models somewhat differently

 There are many model classes

 Within each class there are different solution approaches

 Relations MBMS

 Object-oriented MBMS

End of the Chapter

 Questions / Comments…

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Copyright © 2011 Pearson Education, Inc  

Publishing as Prentice Hall