© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-1 Chapter 4 Modeling and Analysis Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-2 Learning Objectives Understand basic concepts of MSS modeling. Describe MSS models interaction. Understand different model classes. Structure decision making of alternatives. Learn to use spreadsheets in MSS modeling. Understand the concepts of optimization, simulation, and heuristics. Learn to structure linear program modeling.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-3 Learning Objectives Understand the capabilities of linear programming. Examine search methods for MSS models. Determine the differences between algorithms, blind search, heuristics. Handle multiple goals. Understand terms sensitivity, automatic, what-if analysis, goal seeking. Know key issues of model management.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-4 DuPont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette DuPont used simulation to avoid costly capital expenditures for rail car fleets as customer demands changed. Demand change could include: 1.Rail car purchases. 2.Better management of the existing fleet. 3.Fleet size reduction. The old analysis method, past experience and the conventional wisdom led managers to feel that the fleet size should be increased. Where the real problem was the ineffective use for the existing rail cars.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-5 DuPont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette The solution come when using a commercial modeling software which helped in modeling the system easily, quickly and using what if analyses. The simulation involved the entire rail transportations, many scenarios were developed. The simulation examine the system and the solution not the alternative one (buy new ones). A simulation model can provide a virtual environment in which experimentation with various policies that affect the physical transportation system can be performed before real changes are made.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-6 DuPont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette ProModel simulation created representing entire transport systemProModel Applied what-if analyses Visual simulation // graphically Identified varying conditions Identified bottlenecks Allowed for downsized fleet without downsizing deliveries

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-7 MSS Modeling Key element in DSS Many classes of models Specialized techniques for each model Allows for rapid examination of alternative solutions Multiple models often included in a DSS Trend toward transparency –Multidimensional modeling exhibits as spreadsheet

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-8 Simulations Explore problem at hand Identify alternative solutions Can be object-oriented Enhances decision making View impacts of decision alternatives

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-9 DSS Models Algorithm-based models Statistic-based models Linear programming models Graphical models Quantitative models Qualitative models Simulation models

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-10 Major Modeling Issues Problem identification –Environmental scanning and analysis –Business intelligence tools // they can help identifying the problem by scanning for them. Identify variables and relationships –Influence diagrams –Cognitive maps Forecasting –Fueled by e-commerce –Increased amounts of information available through technology

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-11 Major Modeling Issues Identify variables and relationships –Influence diagrams // is a simple visual representation of a decision problem –Cognitive maps

Multiple Models A DSS can include several models the next table will classify DSS models into 7 groups and lists several representative techniques for each category. Each technique can be applied to either a static or a dynamic model, which can be constructed under assumed environments of certainty, uncertainty, or risk. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-12

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-13

Static & Dynamic Models A decision to make or buy a product is static in nature. In which static model take a static single snapshot of a situation so it has a single interval. It is presumed to be repeated with identical conditions. Dynamic model represent scenarios that change over time. It is time dependent they used to generate trends. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-14

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-15 Static Models Single photograph of situation Single interval Time can be rolled forward, a photo at a time Usually repeatable Steady state –Optimal operating parameters –Continuous –Unvarying –Primary tool for process design

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-16 Dynamic Model Represent changing situations Time dependent Varying conditions Generate and use trends Occurrence may not repeat

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-17 Decision-Making Certainty –Assume complete knowledge –All potential outcomes known –Easy to develop –Resolution determined easily –Can be very complex

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-18 Decision-Making Uncertainty –Several outcomes for each decision –Probability of occurrence of each outcome unknown –Insufficient information –Assess risk and willingness to take it –Pessimistic / optimistic approaches

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-19 Decision-Making Probabilistic Decision-Making –Decision under risk –Probability of each of several possible outcomes occurring –Risk analysis Calculate value of each alternative Select best expected value

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-20 Influence Diagrams Graphical representation of model Provides relationship framework Examines dependencies of variables Any level of detail Shows impact of change Shows what-if analysis

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-21 Influence Diagrams Decision Intermediate or uncontrollable Variables: Result or outcome (intermediate or final) Certainty Uncertainty Arrows indicate type of relationship and direction of influence Amount in CDs Interest earned Price Sales

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-22 Influence Diagrams Random (risk) Place tilde above variable’s name ~ Demand Sales Preference (double line arrow) Graduate University Sleep all day Ski all day Get job Arrows can be one-way or bidirectional, based upon the direction of influence

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-23 Profit Model Example Profit = income – expenses Income = units sold * unit price Units sold = 0.5 * amount used in ads Expenses = unit cost * unit sold + fixed cost The influence diagram is shown next.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-24

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-25 Modeling with Spreadsheets Flexible and easy to use End-user modeling tool Allows linear programming and regression analysis Features what-if analysis, data management, macros Transparent Incorporates both static and dynamic models

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-26

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-27 Decision Tables Single goal situations can be modeled with decision tables or decision tree. Decision tables are a convenient way to organize information in a systematic manner. Since we have one goal we will use decision table, if we have more than one goal we have multi-criteria decision analysis.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-28 Decision Tables Ex. There is a company who wants to choose an area of investment 1.bonds سندات 2.stocks أسهم 3.Certificates of deposit. شهادات ادخار في البنك One goal : maximize the yield on investment after one year. The yield depends on the state of economy which can be solid growth, stagnation, or inflation.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-29 Decision Tables Experts estimate the following annual yields: 1.If there is solid growth in the economy, bonds will yield 12 present, stocks will yield 15 present, and time deposit will yield 6.5 present. 2.If stagnation prevails, bonds will yield 6 present, stocks will yield 3 present, and time deposit will yield 6.5 present. 3.If inflation prevails, bonds will yield 3 present, stocks will bring a loss of 2 present, and time deposit will yield 6.5 present.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-30 Decision Tables Uncertainty: We don’t know the probabilities of each state of nature. 1.Optimistic approach: best of the best = stocks (15%) 2.Pessimistic approach: best of the worst = CDs (6.5%) State of Nature (Uncontrollable variables) Inflation (%)Stagnation (%)Solid Growth (%)Alternative Bonds Stocks 6.5 CDs

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-31 Decision Tables Risk: We know the probabilities of each state of nature. –Select the alternative with the greatest expected value. –Investing in Bonds yields an expected return of: (12 * 0.5) + (6 * 0.3) + (3 * 0.2) = 8.4 % Expected Value (%) Inflation 20% Stagnation 30% Solid Growth 50%Alternative 8.4 (max.) Bonds Stocks 6.5 CDs

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-32 Decision Tables Risk: Select the alternative with the greatest expected value can sometimes be deceiving: –Investment amount: 1,000 –Gain: 1,000 with probability 99.99% –Loss: 500,000 with probability 0.01% Expected value of investment: (2,000 – 1,000) (-500,000 – 1,000) = Good expected value but if loss happens, it is catastrophic.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-33 Decision Tables Multiple criteria decision analysis Features include: –Decision variables (alternatives) –Uncontrollable variables –Result variables Applies principles of certainty, uncertainty, and risk

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-34 Decision Tree Graphical representation of relationships Multiple criteria approach Demonstrates complex relationships Cumbersome, if many alternatives