turban_dss9e_ch04.ppt

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

Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 4: Modeling and Analysis -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 -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 Opening Vignette: “Model-Based Auctions Serve More Lunches in Chile”  Background: problem situation  Proposed solution  Results  Answer and discuss the case questions Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -5 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -6 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)… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -7 Major Modeling Issues   Problem identification and environmental analysis (information collection) Variable identification   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   Influence diagrams, cognitive maps Categories of models >>> Model management Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -8 Categories of Models Category 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 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall other models occurrences, what-if chains, financial, … -9 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -10 Decision Making: Treating Certainty, Uncertainty and Risk  Certainty Models     Uncertainty     Assume complete knowledge All potential outcomes are known May yield optimal solution 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 -11 Certainty, Uncertainty and Risk Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -12 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) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -13 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Influence Diagrams: Example An influence diagram for the profit model Unit Price ~ Amount used in Advertisement Income Units Sold Profit Profit = Income – Expense Unit Cost Income = UnitsSold * UnitPrice UnitsSold = 0.5 * Advertisement Expense Expenses = UnitsCost * UnitSold + FixedCost -14 Expenses Fixed Cost Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -15 Influence Diagrams: Software  Analytica, Lumina Decision Systems   DecisionPro, Vanguard Software Co.   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 Integrates influence diagrams and Excel, also supports Monte Carlo simulations PrecisionTree, Palisade Co.  Creates influence diagrams and decision trees directly in an Excel spreadsheet Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -16 Analytica Influence Diagram of a Marketing Problem: The Marketing Model Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -17 Analytica: The Price Submodel Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -18 Analytica: The Sales Submodel Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -19 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -20 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   Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -21 Excel spreadsheet Dynamic model example: Simple loan calculation of monthly payments and effects of prepayment Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -22 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 Demonstrates complex relationships Cumbersome, if many alternatives exists Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -23 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -24 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% Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -25 Investment Example: Decision Table     Payoff Decision variables (alternatives) Uncontrollable variables (states of economy) Result variables (projected yield) Tabular representation: Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -26 Investment Example: Treating Uncertainty    Optimistic approach Pessimistic approach Treating Risk:   Use known probabilities Risk analysis: compute expected values Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -27 Decision Analysis: A Few Alternatives  Other methods of treating risk   Simulation, Certainty factors, Fuzzy logic Multiple goals  Yield, safety, and liquidity Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -28 MSS Mathematical Models  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 les b ria a V Uncontrollable nt e d Variables en p e Ind Decision Variables Dependent Variables Mathematical Relationships Intermediate Variables Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Result Variables -29 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -30 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -31 Linear Programming Steps  1. Identify the …     Decision variables Objective function Objective function coefficients Constraints   2. Represent the model    Capacities / Demands LINDO: Write mathematical formulation EXCEL: Input data into specific cells in Excel 3. Run the model and observe the results Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Line -32 LP Example The 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 Labor (days) Materials ($) Units Units Profit ($) CC7 CC8 300 500 10,000 15,000 1 1 8,000 12,000 Rel <= <= >= >= Max Limit 200,000 /mo 8,000,000 /mo 100 200 Objective: Maximize Total Profit / Month Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -33 LP Solution Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -34 LP Solution  Decision Variables: X1: unit of CC-7 X2: unit of CC-8  Objective Function: Maximize Z (profit) Z=8000X1+12000X2  Subject To 300X1 + 500X2  200K 10000X1 + 15000X2  8000K X1  100 X2  200 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -35 Sensitivity, What-if, and Goal Seeking Analysis  Sensitivity     What-if   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) Goal seeking    Backwards approach, starts with goal Determines values of inputs needed to achieve goal Example is break-even point determination Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -36 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 >>> Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -37 Heuristic Programming - SEARCH Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -38 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -39 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -40 Modern Heuristic Methods  Tabu search   Genetic algorithms   Intelligent search algorithm Survival of the fittest Simulated annealing  Analogy to Thermodynamics Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -41 Simulation  Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system  Frequently used in DSS tools Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -42 Major Characteristics of Simulation     ! 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -43 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -44 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -45 Simulation Methodology   Model real system and conduct repetitive experiments. Steps: 1. Define problem 5. Conduct experiments 2. Construct simulation model 6. Evaluate results 3. Test and validate model 7. Implement solution 4. Design experiments Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -46 Simulation Types  Stochastic vs. Deterministic Simulation   Time-dependent vs. Time-independent Simulation     In stochastic simulations: We use distributions (Discrete or Continuous probability distributions) 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -47 Visual Interactive Modeling (VIM) / Visual Interactive Simulation (VIS)  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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -48 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -49 End of the Chapter  Questions / Comments… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall -50 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall