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

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-2 Outline 1. Modeling for DSS 2. Static and Dynamic models 3. Treating certainty, uncertainty 4. Influence diagrams 5. Modeling with spreadsheets 6.Decision Tables and Decision trees 7.MSS mathematical models 8. Search approaches 9.Simulation 10. Model base management system

3. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-3 1.Modeling for DSS Modeling is key element in DSS Many classes of models –Simulation is an example Specialized techniques for each model Allows for rapid examination of alternative solutions Multiple models often included in a DSS Trend toward model transparency –Multidimensional modeling exhibits as spreadsheet

4. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-4 Some major modeling issues Identification of the problem and environment analysis Variable identification –Decision variables –Uncontrollable variables –Result variables, etc. Note: using influence diagrams and cognitive maps to identify variables and relationships. Forecasting: DSS is designed to determine what will be. –Time series forecasting –There exist several forecasting packages. Multiple models

5. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-5 DSS Models Algorithm-based models Statistic-based models Linear programming models Graphical models Quantitative models Qualitative models Simulation models

6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-6

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-7 2. Static and Dynamic Models Static Models Single snapshot of situation Single interval Time can be rolled forward, a snapshot at a time Usually repeatable Steady state –Optimal operating parameters –Continuous –Unvarying –Primary tool for process design

8. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-8 Dynamic Models Represent changing situations Time dependent Varying conditions Generate and use trends and patterns over time Occurrence may not repeat

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-9 3.Treating certainty, uncertainty and risk Decision making under certainty –Assume complete knowledge –All potential outcomes known –Easy to develop –Resolution determined easily –Can be very complex

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-10 Decision-Making under uncertainty 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

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-11 Decision-Making under risk 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

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-12 4. Influence Diagrams Graphical representation of model Provides relationship framework Examines dependencies of variables Any level of details Shows impact of change Shows what-if analysis

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

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-14 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

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-15 Example Profit = income – expenses Income = units sold  unit price Units sold = 0.5  amount used in advertisement Expenses = unit cost  unit sold + fixed cost

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-16

17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-17 5.Modeling with Spreadsheets Flexible and easy to use End-user modeling tool Allows linear programming and regression analysis (as add-ins to the software package) Features what-if analysis, data management, macros Seamless and transparent Incorporates both static and dynamic models Excel and Lotus 1-2-3 are two popular spreadsheet software package.

18. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-18

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-19 6.Decision Tables Multiple criteria decision analysis Features include: –Decision variables (alternatives) –Uncontrollable variables –Result variables Applies principles of certainty, uncertainty, and risk

20. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-20 Table 2 Investment Problem Decision Table Model State of Nature (uncontrollable variables) --------------------------------------------------------------- Alternative Solid growth (%) Stagnation (%) Inflation(%) ------------------------------------------------------------------------------- Bonds 12.0 6.0 3.0 Stocks15.03.06.5 CDs6.56.56.5

21. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-21 Decision Tree Graphical representation of relationships Multiple criteria approach Demonstrates complex relationships Cumbersome, if many alternatives

22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-22 7. MSS Mathematical Models Link decision variables, uncontrollable variables, parameters, and result variables together –Decision variables describe alternative choices. –Uncontrollable variables are outside decision- maker’s control. –Fixed factors are parameters. –Intermediate outcomes produce intermediate result variables. –Result variables are dependent on chosen solution and uncontrollable variables.

23. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-23 MSS Mathematical Models Nonquantitative models –Symbolic relationship –Qualitative relationship –Results based upon Decision selected Factors beyond control of decision maker Relationships amongst variables

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

25. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-25 Mathematical Programming Tools for solving managerial problems Decision-maker must allocate resources amongst competing activities Optimization of specific goals Linear programming –Consists of decision variables, objective function and coefficients, uncontrollable variables (constraints), capacities, input and output coefficients

26. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-26 Example of Linear programming Objective function: Max Z = 45x 1 + 12x 2 Constraints: 1x 1 + 1x 2  300 3x 1 + 0x 2  250 x 1 and x 2 are decision variables Constraints in the form of linear inequalities or equalities. Lingo and Lindo are two best-known software packages used for Linear and Integer Programming.

27. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-27 Multiple Goals Simultaneous, often conflicting goals sought by management Determining single measure of effectiveness is difficult Handling methods: –Utility theory –Goal programming –Linear programming with goals as constraints –Point system Some software packages for multi-goal decision making: Analytic Hierarchy Process (AHP) and Expert Choice.

28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-28 Sensitivity, What-if, and Goal Seeking Analysis Sensitivity –Assesses impact of change in inputs or parameters on solutions –Allows for adaptability and flexibility –Eliminates or reduces variables –Can be automatic or trial and error What-if –Assesses solutions based on changes in variables or assumptions Goal seeking –Backwards approach, starts with goal –Determines values of inputs needed to achieve goal –Example is break-even point determination

29. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-29 8. Problem solving search approaches Analytical techniques (algorithms) for structured problems –General, step-by-step search –Obtains an optimal solution Blind search –Complete enumeration All alternatives explored –Incomplete Partial search –Achieves particular goal –May obtain optimal goal

30. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-30 Search Approaches Heurisitic –Repeated, step-by-step searches –Rule-based, so used for specific situations –“Good enough” solution, but, eventually, will obtain optimal goal –Examples of heuristics Hill Climbing Tabu search –Remembers and directs toward higher quality choices Simulated annealing Genetic algorithms –Randomly examines pairs of solutions and mutations Ant colony optimization (ACO)

31. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-31

32. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-32 9.Simulations Imitation of reality Allows for experimentation and time compression Descriptive, not normative Can include complexities, but requires special skills Handles unstructured problems Optimal solution not guaranteed Methodology –Problem definition –Construction of model –Testing and validation –Design of experiment –Experimentation –Evaluation –Implementation

33. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-33 Simulations Probabilistic independent variables –Discrete or continuous distributions Time-dependent or time-independent Visual interactive modeling –Graphical –Decision-makers interact with simulated model –may be used with artificial intelligence Can be objected-oriented

34. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-34

35. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-35 Advantages of Simulation The theory of a simulation is straightforward. A great amount of time compression can be attained. Managers can use a trial-and-error approach to problem solving and can do so faster, cheaper and more accuracy with simulation. Simulation helps to gain a better understanding of the problem and the potential decisions available. Simulation can handle a wide variety of problem types. Simulation is often the only DSS method that can handle unstructured problems.

36. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-36 Disadvantages of Simulation An optimal solution cannot be guaranteed Simulation model construction can be a slow and costly process Solutions and inferences from a simulation are not transferable to other problems. Simulation is so easy to explain to managers that analytic methods are often overlooked. Simulation software requires special skills.

37. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-37 Quantitative software packages Statistical packages: –SPSS, Systat, SAS, TSP. Operation research packages: –ILOG, OR-Objects, Lingo, Lindo, OSL (Optimization System Library), CPLEX –GPSS, SIMULA, SIMSCRIPT, SLAM. Revenue Management packages Spreadsheet add-ins

38. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-38 10.Model-Base Management System Software that allows model organization with transparent data processing Some desirable MBMS capabilities –DSS user has control –Flexible in design –Gives feedback –GUI based –Reduction of redundancy –Increase in consistency –Communication between combined models

39. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-39 Model-Base Management System Modeling languages –Lingo, AMPL, GAMS (General Algebraic Modeling Systems) Relational model base management system –Virtual file –Virtual relation Object-oriented model base management system –Logical independence Database and MIS design model systems –Data diagram, ERD diagrams managed by CASE tools