Turban Dss9e Ch04

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

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

Learning ObjectivesUnderstand the basic concepts of management support system (MSS) modelingDescribe how MSS models interact with data and the usersUnderstand the well-known model classes and decision making with a few alternativesDescribe how spreadsheets can be used for MSS modeling and solutionExplain the basic concepts of optimization, simulation and heuristics; when to use which


Learning ObjectivesDescribe how to structure a linear programming modelUnderstand how search methods are used to solve MSS modelsExplain the differences among algorithms, blind search, and heuristicsDescribe how to handle multiple goalsExplain what is meant by sensitivity analysis, what-if analysis, and goal seekingDescribe the key issues of model management


Opening Vignette:Model-Based Auctions Serve More Lunches in ChileBackground: problem situationProposed solutionResultsAnswer and discuss the case questions


Modeling and Analysis TopicsModeling for MSS (a critical component)Static and dynamic modelsTreating certainty, uncertainty, and riskInfluence diagrams (in the posted PDF file)MSS modeling in spreadsheetsDecision analysis of a few alternatives (with decision tables and decision trees)Optimization via mathematical programmingHeuristic programmingSimulationModel base management


MSS ModelingA 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 decisionsLocating distribution centers, assignment of DCs to warehouses/customers, forecasting demand, scheduling production per product type, etc.Fiat, Pillowtex (operational efficiency)


Major Modeling IssuesProblem identification and environmental analysis (information collection)Variable identificationInfluence diagrams, cognitive mapsForecasting/predictingMore information leads to better predictionMultiple models: A MSS can include several models, each of which represents a different part of the decision-making problemCategories of models >>>Model management


Categories of Models

CategoryObjectiveTechniquesOptimization of problems with few alternativesFind the best solution from a small number of alternativesDecision tables, decision treesOptimization via algorithmFind the best solution from a large number of alternatives using a step-by-step processLinear and other mathematical programming modelsOptimization via an analytic formulaFind the best solution in one step using a formulaSome inventory modelsSimulationFind a good enough solution by experimenting with a dynamic model of the systemSeveral types of simulationHeuristicsFind a good enough solution using common-sense rulesHeuristic programming and expert systemsPredictive and other modelsPredict future occurrences, what-if analysis, Forecasting, Markov chains, financial,


Static and Dynamic ModelsStatic AnalysisSingle snapshot of the situationSingle intervalSteady stateDynamic AnalysisDynamic modelsEvaluate scenarios that change over timeTime dependentRepresents trends and patterns over timeMore realistic: Extends static models


Decision Making:Treating Certainty, Uncertainty and RiskCertainty ModelsAssume complete knowledgeAll potential outcomes are knownMay yield optimal solutionUncertaintySeveral outcomes for each decisionProbability of each outcome is unknownKnowledge would lead to less uncertaintyRisk analysis (probabilistic decision making)Probability of each of several outcomes occurringLevel of uncertainty => Risk (expected value)


Certainty, Uncertainty and Risk


Influence Diagrams (Posted on the Course Website)Graphical representations of a modelModel of a modelA tool for visual communicationSome influence diagram packages create and solve the mathematical modelFramework 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: RelationshipsThe shape of the arrow indicates the type of relationship

Amount in CDs

Interest Collected

Price

Sales

~Demand

Sales

CERTAINTY

UNCERTAINTY

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


Influence Diagrams: ExampleAn influence diagram for the profit modelProfit = Income ExpenseIncome = UnitsSold * UnitPriceUnitsSold = 0.5 * Advertisement ExpenseExpenses = UnitsCost * UnitSold + FixedCost

~Amount used in Advertisement

Unit Price

Units Sold

Unit Cost

Fixed Cost

Income

Expenses

Profit


Influence Diagrams: SoftwareAnalytica, Lumina Decision SystemsSupports hierarchical (multi-level) diagramsDecisionPro, Vanguard Software Co.Supports hierarchical (tree structured) diagramsDATA Decision Analysis, TreeAge Software Includes influence diagrams, decision trees and simulationDefinitive Scenario, Definitive SoftwareIntegrates influence diagrams and Excel, also supports Monte Carlo simulationsPrecisionTree, 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 SpreadsheetsSpreadsheet: most popular end-user modeling toolFlexible and easy to usePowerful functions Add-in functions and solversProgrammability (via macros)What-if analysisGoal seekingSimple database managementSeamless integration of model and dataIncorporates both static and dynamic modelsExamples: Microsoft Excel, Lotus 1-2-3


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


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 analysisFeatures include decision variables (alternatives), uncontrollable variables, result variables

Decision treesGraphical representation of relationshipsMultiple criteria approachDemonstrates complex relationshipsCumbersome, if many alternatives exists


Decision TablesInvestment example

One goal: maximize the yield after one year

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


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 UncertaintyOptimistic approachPessimistic approachTreating Risk:Use known probabilitiesRisk analysis: compute expected values


Decision Analysis: A Few AlternativesOther methods of treating riskSimulation, Certainty factors, Fuzzy logicMultiple goals Yield, safety, and liquidity


MSS Mathematical ModelsDecision VariablesMathematicalRelationshipsUncontrollableVariablesResult VariablesNon-Quantitative Models (Qualitative) Captures symbolic relationships between decision variables, uncontrollable variables and result variablesQuantitative Models: Mathematically links decision variables, uncontrollable variables, and result variablesDecision variables describe alternative choices.Uncontrollable variables are outside decision-makers controlResult variables are dependent on chosen combination of decision variables and uncontrollable variablesIntermediateVariables


Optimization via Mathematical ProgrammingMathematical 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


LineLinear Programming Steps1. Identify the Decision variables Objective function Objective function coefficients Constraints Capacities / Demands

2. Represent the modelLINDO: Write mathematical formulationEXCEL: 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 CC8Constraints: Labor limits, Materials limit, Marketing lower limits CC7CC8RelLimit Labor (days)300500=200 Profit ($)8,00012,000Max Objective: Maximize Total Profit / Month


LP Solution


LP SolutionDecision Variables:X1: unit of CC-7X2: unit of CC-8Objective Function:Maximize Z (profit)Z=8000X1+12000X2Subject To300X1 + 500X2 200K10000X1 + 15000X2 8000KX1 100X2 200


Sensitivity, What-if, and Goal Seeking AnalysisSensitivityAssesses impact of change in inputs on outputsEliminates or reduces variablesCan be automatic or trial and errorWhat-ifAssesses solutions based on changes in variables or assumptions (scenario analysis)Goal seekingBackwards approach, starts with goalDetermines values of inputs needed to achieve goalExample is break-even point determination


Heuristic ProgrammingCuts the search spaceGets satisfactory solutions more quickly and less expensivelyFinds good enough feasible solutions to very complex problemsHeuristics can be QuantitativeQualitative (in ES)

Traveling Salesman Problem >>>


Heuristic Programming - SEARCH


Traveling Salesman ProblemWhat is it?A traveling salesman must visit customers in several cities, visiting each city only once, across the country. Goal: Find the shortest possible routeTotal number of unique routes (TNUR):TNUR = (1/2) (Number of Cities 1)!Number of CitiesTNUR 5 12 6 60 9 20,160 20 1.22 1018


When to Use HeuristicsWhen to Use HeuristicsInexact or limited input dataComplex realityReliable, exact algorithm not availableComputation time excessiveFor making quick decisions

Limitations of HeuristicsCannot guarantee an optimal solution


Tabu searchIntelligent search algorithm

Genetic algorithmsSurvival of the fittestSimulated annealingAnalogy to ThermodynamicsModern Heuristic Methods


SimulationTechnique 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 richnessTechnique for conducting experimentsDescriptive, not normative toolOften 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 SimulationThe theory is fairly straightforwardGreat deal of time compressionExperiment with different alternativesThe model reflects managers perspectiveCan handle wide variety of problem types Can include the real complexities of problems Produces important performance measuresOften it is the only DSS modeling tool for non-structured problems


Limitations of SimulationCannot guarantee an optimal solutionSlow and costly construction processCannot transfer solutions and inferences to solve other problems (problem specific)So easy to explain/sell to managers, may lead overlooking analytical solutionsSoftware may require special skills


Simulation MethodologyModel real system and conduct repetitive experiments. Steps:1. Define problem 5. Conduct experiments2. Construct simulation model6. Evaluate results3. Test and validate model 7. Implement solution4. Design experiments


Simulation TypesStochastic vs. Deterministic SimulationIn stochastic simulations: We use distributions (Discrete or Continuous probability distributions)Time-dependent vs. Time-independent SimulationTime independent stochastic simulation via Monte Carlo technique (X = A + B)Discrete event vs. Continuous simulationSteady State vs. Transient Simulation

Simulation Implementation Visual simulationObject-oriented simulation


Visual interactive modeling (VIM)Also calledVisual interactive problem solvingVisual interactive modelingVisual interactive simulationUses computer graphics to present the impact of different management decisionsOften integrated with GIS Users perform sensitivity analysisStatic or a dynamic (animation) systemsVisual Interactive Modeling (VIM) / Visual Interactive Simulation (VIS)


Model Base ManagementMBMS: capabilities similar to that of DBMSBut, there are no comprehensive model base management packagesEach organization uses models somewhat differentlyThere are many model classesWithin each class there are different solution approachesRelations MBMSObject-oriented MBMS


End of the Chapter

Questions / Comments


All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, 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

*************************************************

Documents

Turban Dss9e Ch04