turban_dss9e_ch04 (2).pptx

Decision Support and Business Intelligence

Systems(9th Ed., Prentice Hall)

Chapter 4:Modeling and Analysis

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-2

Learning Objectives 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 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


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 in spreadsheets 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

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

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 >>>

Model management


Categories of ModelsCategory Objective Techniques

Optimization of problems with few alternatives

Find the best solution from a small number of alternatives

Decision tables, decision trees

Optimization via algorithm

Find the best solution from a large number of alternatives using a step-by-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 “common-sense” rules

Heuristic programming and expert systems

Predictive and other models

Predict future occurrences, what-if analysis, …

Forecasting, Markov chains, financial, …


Static and Dynamic Models Static Analysis

Single snapshot of the situation Single interval Steady state

Dynamic Analysis 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

Assume complete knowledge 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

relationshipsRectangle = a decision variableCircle = uncontrollable or intermediate variableOval = result (outcome) variable: intermediate or final

Variables are connected with arrows indicates the direction of influence (relationship)


Influence Diagrams: Relationships

Amount inCDs

InterestCollected

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 inAdvertisement

Unit Price

Units Sold

Unit Cost

Fixed Cost

Income

Expenses

Profit

An influence diagram for the profit model

Profit = Income – ExpenseIncome = UnitsSold * UnitPriceUnitsSold = 0.5 * Advertisement ExpenseExpenses = 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 Spreadsheet: most popular end-user modeling

tool Flexible and easy to use Powerful functions

Add-in functions and solvers 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

1)1()1(

)1(

n

n

n

iiiPA

iPF


Excel spreadsheet - Dynamic model example: Simple loan calculation of monthly payments and effects of prepayment


Decision Analysis: A Few AlternativesSingle 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 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) 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


Investment Example: Treating Uncertainty Optimistic approach Pessimistic approach Treating Risk:

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

MathematicalRelationships

UncontrollableVariables

Result Variables

Non-Quantitative Models (Qualitative) Captures symbolic relationships between decision variables,

uncontrollable variables and result variables Quantitative Models: Mathematically links decision

variables, uncontrollable variables, and result variables

Decision variables describe alternative choices. Uncontrollable variables are outside decision-maker’s control Result variables are dependent on chosen combination of decision

variables and uncontrollable variables

Independent Variables

Dependent Variables

IntermediateVariables


Optimization via Mathematical Programming 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 Linear programming (LP): A mathematical

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


LP Problem Characteristics1.Limited quantity of economic resources2.Resources are used in the production of

products or services3.Two or more ways (solutions, programs)

to use the resources4.Each activity (product or service) yields

a return in terms of the goal5.Allocation is usually restricted by

constraints


Line

Linear Programming Steps 1. Identify the …

Decision variables Objective function Objective function coefficients Constraints

Capacities / Demands

2. Represent the model LINDO: Write mathematical formulation EXCEL: Input data into specific cells in

Excel

3. Run the model and observe the results


LP ExampleThe Product-Mix Linear Programming Model MBI Corporation Decision: How many computers to build next month? Two types of mainframe computers: CC7 and CC8 Constraints: Labor limits, Materials limit, Marketing

lower limits

CC7 CC8 Rel LimitLabor (days) 300 500 <= 200,000 /moMaterials ($) 10,000 15,000 <= 8,000,000 /moUnits 1 >= 100Units 1 >= 200Profit ($) 8,000 12,000 Max

Objective: Maximize Total Profit / Month


LP Solution


LP Solution Decision Variables:

X1: unit of CC-7X2: unit of CC-8

Objective Function:Maximize Z (profit)Z=8000X1+12000X2

Subject To300X1 + 500X2 200K10000X1 + 15000X2 8000KX1 100X2 200


Sensitivity, What-if, and Goal Seeking Analysis Sensitivity

Assesses impact of change in inputs on outputs

Eliminates or reduces variables Can be automatic or trial and error

What-if Assesses solutions based on changes in

variables or assumptions (scenario analysis) Goal seeking

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 6 60 9 20,160

20 1.22 1018


When to Use HeuristicsWhen 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


Tabu search Intelligent search algorithm

Genetic algorithms Survival of the fittest

Simulated annealing Analogy to Thermodynamics

Modern Heuristic Methods


Simulation 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 The theory is fairly straightforward Great deal of time compression Experiment with different alternatives The model reflects manager’s

perspective Can handle wide variety of problem

types Can include the real complexities of

problems Produces important performance

measures Often it is the only DSS modeling tool

for 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 Model real system and conduct repetitive experiments. Steps:

1. Define problem 5. Conduct experiments2. Construct simulation model 6. Evaluate results3. Test and validate model 7. Implement

solution4. Design experiments


Simulation Types Stochastic vs. Deterministic Simulation

In stochastic simulations: We use distributions (Discrete or Continuous probability distributions)

Time-dependent vs. Time-independent Simulation

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

Discrete event vs. Continuous simulation Steady State vs. Transient Simulation

Simulation Implementation Visual simulation Object-oriented simulation


Visual interactive modeling (VIM)Also called Visual interactive problem solving Visual interactive modeling Visual interactive simulation

Uses computer graphics to present the impact of different management decisions

Often integrated with GIS Users perform sensitivity analysis Static or a dynamic (animation) systems

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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mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

turban_dss9e_ch04 (2).pptx

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