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

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-2 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

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-3 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

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-4 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, forecasting demand, scheduling production per product type, etc.

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-5 Major Modeling Issues Problem identification and environmental analysis (information collection) Variable identification Influence diagrams, cognitive maps 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

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-6 Categories of Models CategoryObjectiveTechniques 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 SimulationFind a good enough solution by experimenting with a dynamic model of the system Several types of simulation HeuristicsFind a good enough solution using “common-sense” rules Heuristic programming and expert systems Predictive and other models Predict future occurrences, what-if analysis, … Markov chains, financial, …

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-7 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 More realistic: Extends static models

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-8 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)

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-9 Certainty, Uncertainty and Risk

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-10 Influence Diagrams 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)

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-11 Influence Diagrams: Relationships The shape of the arrow indicates the type of relationship

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-12 Analytica Influence Diagram of a Marketing Problem: The Marketing Model

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-13 Analytica: The Price Submodel

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-14 Analytica: The Sales Submodel

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-15 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 Simple database management Seamless integration of model and data Include both static and dynamic models Examples: Microsoft Excel, Lotus 1-2-3

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-16 Excel spreadsheet - static model example: Simple loan calculation of monthly payments

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-17 Excel spreadsheet - Dynamic model example: Simple loan calculation of monthly payments and effects of prepayment

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-18 Heuristic Programming Cuts the search space Gets satisfactory solutions more quickly and less expensively Finds good enough feasible solutions to very complex problems Traveling Salesman Problem >>>

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-19 Heuristic Programming - SEARCH

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-20 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 CitiesTNUR ,

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-21 When to Use Heuristics Inexact or limited input data Complex reality For making quick decisions Limitations of Heuristics Cannot guarantee an optimal solution

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-22 Tabu search Intelligent search algorithm Genetic algorithms Survival of the fittest Simulated annealing Analogy to Thermodynamics Modern Heuristic Methods

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-23 Simulation Technique for conducting experiments with a computer on a complate model of the behavior of a system Frequently used in DSS tools

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-24 Imitates reality and capture its richness Technique for conducting experiments Descriptive 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 !

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-25 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

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-26 Limitations of Simulation Cannot guarantee an optimal solution Slow and costly construction process

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-27 Simulation Methodology Model real system and conduct repetitive experiments. Steps: 1. Define problem 5. Conduct experiments 2. Construct simulation model6. Evaluate results 3. Test and validate model 7. Implement solution 4. Design experiments

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-28 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

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-29 End of the Chapter Questions / Comments…

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-30 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. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall