1. Chapter 4 MODELING AND ANALYSIS 8 th Edition 12nd semester 2010 Dr. Qusai Abuein

2. Learning Objectives Understand the basic concepts of management support system (MSS) modeling Describe how MSS models interact with data and the user Understand some different, well-known model classes Understand how to structure decision making with a few alternatives 22nd semester 2010 Dr. Qusai Abuein

3. Learning Objectives Describe how spreadsheets can be used for MSS modeling and solution Explain the basic concepts of optimization, simulation, and heuristics, and when to use them Describe how to structure a linear programming model 32nd semester 2010 Dr. Qusai Abuein

4. Learning Objectives 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 42nd semester 2010 Dr. Qusai Abuein

5. (4.2) MSS Modeling Lessons from modeling at DuPont –By accurately modeling and simulating its rail transportation system, decision makers were able to experiment with different policies and alternatives quickly and inexpensively –The simulation model was developed and tested known alternative solutions Simulation models can enhance an organization’s decision-making process and enable it to see the impact of its future choice. 52nd semester 2010 Dr. Qusai Abuein

6. (4.2) MSS Modeling Lessons from modeling for Procter & Gamble –DSS can be composed of several models used collectively to support strategic decisions in the company –Models must be integrated –models may be decomposed and simplified –A suboptimization approach may be appropriate –Human judgment is an important aspect of using models in decision making 62nd semester 2010 Dr. Qusai Abuein

7. (4.2) MSS Modeling Lessons from additional modeling applications –Mathematical (quantitative) model A system of symbols and expressions that represent a real situation –Applying models to real-world situations can save millions of dollars or generate millions of dollars in revenue 72nd semester 2010 Dr. Qusai Abuein

8. (4.2) MSS Modeling Current modeling issues: 1.Identification of the problem and environmental analysis 2.Variable identification 3.Forecasting 4.Multiple models 5.Model categories 6.Model management 7.Knowledge-based modeling 82nd semester 2010 Dr. Qusai Abuein

9. (4.2) MSS Modeling Current modeling issues: 1.Identification of the problem and environmental analysis –Environmental scanning and analysis A process that involves conducting a search for and an analysis of information in external databases and flows of information The problem must be understood 92nd semester 2010 Dr. Qusai Abuein

10. (4.2) MSS Modeling Current modeling issues 2.Variable identification define the variables and the relationship of it 3.Forecasting Predicting the future –Predictive analytics systems attempt to predict the most profitable customers, the worst customers, and focus on identifying products and services at appropriate prices to appeal to them 102nd semester 2010 Dr. Qusai Abuein

11. (4.2) MSS Modeling Current modeling issues –Multiple models: A DSS can include several models, each of which represents a different part of the decision-making problem –Model categories Optimization of problems with few alternatives Optimization via algorithm Optimization via an analytic formula Simulation Predictive models Other models 112nd semester 2010 Dr. Qusai Abuein

12. (4.2) MSS Modeling Current modeling issues –Model management –Knowledge-based modeling –Current trends Model libraries and solution technique libraries Development and use of Web tools Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions 122nd semester 2010 Dr. Qusai Abuein

13. (4.2) MSS Modeling Current trends –Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions –Influence diagram A diagram that shows the various types of variables in a problem (e.g., decision, independent, result) and how they are related to each other 132nd semester 2010 Dr. Qusai Abuein

14. (4.3) Static and Dynamic Models Static models Models that describe a single interval of a situation. (e.g., a decision whether to buy or make a product ) Dynamic models Models whose input data are changed over time (e.g., a five-year profit or loss projection) 142nd semester 2010 Dr. Qusai Abuein

15. (4.4) Certainty, Uncertainty, and Risk 152nd semester 2010 Dr. Qusai Abuein

16. (4.4) Certainty, Uncertainty, and Risk It is necessary to predict the future outcome of each proposed alternative. This prediction is classified into: Certainty A condition under which it is assumed that future values are known for sure and only one result is associated with an action Uncertainty In expert systems, a value that cannot be determined during a consultation. Many expert systems can accommodate uncertainty; that is, they allow the user to indicate whether he or she does not know the answer (no enough information) Risk A probabilistic or stochastic decision situation in which the decision maker must consider several possible outcomes for each alternative. 162nd semester 2010 Dr. Qusai Abuein

17. (4.4) Certainty, Uncertainty, and Risk Risk analysis A decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives. Also known as calculated risk –Up to what degree it is risky? 172nd semester 2010 Dr. Qusai Abuein

18. MSS Modeling with Spreadsheets Models can be developed and implemented in a variety of programming languages and systems The spreadsheet is clearly the most popular end-user modeling tool because it incorporates many powerful financial, statistical, mathematical, and other functions 182nd semester 2010 Dr. Qusai Abuein

19. MSS Modeling with Spreadsheets 192nd semester 2010 Dr. Qusai Abuein

20. MSS Modeling with Spreadsheets –Other important spreadsheet features include what-if analysis, goal seeking, data management, and programmability –Most spreadsheet packages provide fairly seamless integration because they read and write common file structures and easily interface with databases and other tools –Static or dynamic models can be built in a spreadsheet 202nd semester 2010 Dr. Qusai Abuein

21. MSS Modeling with Spreadsheets 212nd semester 2010 Dr. Qusai Abuein

22. Decision Analysis with Decision Tables and Decision Trees Decision analysis Methods for determining the solution to a problem, typically when it is inappropriate to use iterative algorithms 222nd semester 2010 Dr. Qusai Abuein

23. Decision Analysis with Decision Tables and Decision Trees Decision table A table used to represent knowledge and prepare it for analysis in: –Treating uncertainty –Treating risk 232nd semester 2010 Dr. Qusai Abuein

24. Decision Analysis with Decision Tables and Decision Trees Decision tree A graphical presentation of a sequence of interrelated decisions to be made under assumed risk Multiple goals Refers to a decision situation in which alternatives are evaluated with several, sometimes conflicting, goals 242nd semester 2010 Dr. Qusai Abuein

25. The Structure of Mathematical Models for Decision Support 252nd semester 2010 Dr. Qusai Abuein

26. The Structure of Mathematical Models for Decision Support Components of decision support mathematical models –Result (outcome) variable A variable that expresses the result of a decision (e.g., one concerning profit), usually one of the goals of a decision-making problem –Decision variable A variable of a model that can be changed and manipulated by a decision maker. The decision variables correspond to the decisions to be made, such as quantity to produce and amounts of resources to allocate 262nd semester 2010 Dr. Qusai Abuein

27. The Structure of Mathematical Models for Decision Support –Uncontrollable variable (parameter) A factor that affects the result of a decision but is not under the control of the decision maker. These variables can be internal (e.g., related to technology or to policies) or external (e.g., related to legal issues or to climate) –Intermediate result variable A variable that contains the values of intermediate outcomes in mathematical models 272nd semester 2010 Dr. Qusai Abuein

28. Mathematical Programming Optimization 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 A best possible solution to a modeled problem 282nd semester 2010 Dr. Qusai Abuein

29. Mathematical Programming Optimization Linear programming (LP) A mathematical model for the optimal solution of resource allocation problems. All the relationships among the variables in this type of model are linear 292nd semester 2010 Dr. Qusai Abuein

30. Mathematical Programming Optimization Every LP problem is composed of: –Decision variables –Objective function –Objective function coefficients –Constraints –Capacities –Input/output (technology) coefficients 302nd semester 2010 Dr. Qusai Abuein

31. Mathematical Programming Optimization 312nd semester 2010 Dr. Qusai Abuein

32. Mathematical Programming Optimization 322nd semester 2010 Dr. Qusai Abuein

33. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking Multiple goals Refers to a decision situation in which alternatives are evaluated with several, sometimes conflicting, goals Sensitivity analysis A study of the effect of a change in one or more input variables on a proposed solution 332nd semester 2010 Dr. Qusai Abuein

34. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking –Sensitivity analysis tests relationships such as: The impact of changes in external (uncontrollable) variables and parameters on the outcome variable(s) The impact of changes in decision variables on the outcome variable(s) The effect of uncertainty in estimating external variables The effects of different dependent interactions among variables The robustness of decisions under changing conditions 342nd semester 2010 Dr. Qusai Abuein

35. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking –Sensitivity analyses are used for: Revising models to eliminate too-large sensitivities Adding details about sensitive variables or scenarios Obtaining better estimates of sensitive external variables Altering a real-world system to reduce actual sensitivities Accepting and using the sensitive (and hence vulnerable) real world, leading to the continuous and close monitoring of actual results –The two types of sensitivity analyses are automatic and trial-and-error 352nd semester 2010 Dr. Qusai Abuein

36. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking Automatic sensitivity analysis –Automatic sensitivity analysis is performed in standard quantitative model implementations such as LP Trial-and-error sensitivity analysis –The impact of changes in any variable, or in several variables, can be determined through a simple trial-and-error approach 362nd semester 2010 Dr. Qusai Abuein

37. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking What-If Analysis A process that involves asking a computer what the effect of changing some of the input data or parameters would be 372nd semester 2010 Dr. Qusai Abuein

38. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 382nd semester 2010 Dr. Qusai Abuein

39. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking Goal seeking Asking a computer what values certain variables must have in order to attain desired goals 392nd semester 2010 Dr. Qusai Abuein

40. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 402nd semester 2010 Dr. Qusai Abuein

41. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking Computing a break-even point by using goal seeking –Involves determining the value of the decision variables that generate zero profit 412nd semester 2010 Dr. Qusai Abuein

42. Problem-Solving Search Methods 422nd semester 2010 Dr. Qusai Abuein

43. Problem-Solving Search Methods Analytical techniques use mathematical formulas to derive an optimal solution directly or to predict a certain result An algorithm is a step-by-step search process for obtaining an optimal solution 432nd semester 2010 Dr. Qusai Abuein

44. Problem-Solving Search Methods 442nd semester 2010 Dr. Qusai Abuein

45. Problem-Solving Search Methods A goal is a description of a desired solution to a problem The search steps are a set of possible steps leading from initial conditions to the goal Problem solving is done by searching through the possible solutions 452nd semester 2010 Dr. Qusai Abuein

46. Problem-Solving Search Methods Blind search techniques are arbitrary search approaches that are not guided –In a complete enumeration all the alternatives are considered and therefore an optimal solution is discovered –In an incomplete enumeration (partial search) continues until a good-enough solution is found (a form of suboptimization) 462nd semester 2010 Dr. Qusai Abuein

47. Problem-Solving Search Methods Heuristic searching –Heuristics Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth –Heuristic programming The use of heuristics in problem solving 472nd semester 2010 Dr. Qusai Abuein

48. Simulation An imitation of reality Major characteristics of simulation –Simulation is a technique for conducting experiments –Simulation is a descriptive rather than a normative method –Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques 482nd semester 2010 Dr. Qusai Abuein

49. Simulation –Complexity A measure of how difficult a problem is in terms of its formulation for optimization, its required optimization effort, or its stochastic nature 492nd semester 2010 Dr. Qusai Abuein

50. Simulation Advantages of simulation –The theory is fairly straightforward. –A great amount of time compression can be attained –A manager can experiment with different alternatives –The MSS builder must constantly interact with the manager –The model is built from the manager’s perspective. –The simulation model is built for one particular problem 502nd semester 2010 Dr. Qusai Abuein

51. Simulation Advantages of simulation –Simulation can handle an extremely wide variety of problem types –Simulation can include the real complexities of problems –Simulation automatically produces many important performance measures –Simulation can readily handle relatively unstructured problems –There are easy-to-use simulation packages 512nd semester 2010 Dr. Qusai Abuein

52. Simulation 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 study are usually not transferable to other problems –Simulation is sometimes so easy to explain to managers that analytic methods are often overlooked –Simulation software sometimes requires special skills 522nd semester 2010 Dr. Qusai Abuein

53. Simulation 532nd semester 2010 Dr. Qusai Abuein

54. Simulation Methodology of simulation 1.Define the problem 2.Construct the simulation model 3.Test and validate the model 4.Design the experiment 5.Conduct the experiment 6.Evaluate the results 7.Implement the results 542nd semester 2010 Dr. Qusai Abuein

55. Simulation Simulation types –Probabilistic simulation Discrete distributions Continuous distributions –Time-dependent versus time-independent simulation –Object-oriented simulation –Visual simulation –Simulation software 552nd semester 2010 Dr. Qusai Abuein

56. Visual Interactive Simulation Conventional simulation inadequacies –Simulation reports statistical results at the end of a set of experiments –Decision makers are not an integral part of simulation development and experimentation –Decision makers’ experience and judgment cannot be used directly –Confidence gap occurs if the simulation results do not match the intuition or judgment of the decision maker 562nd semester 2010 Dr. Qusai Abuein

57. Visual Interactive Simulation Visual interactive simulation or visual interactive modeling (VIM) A simulation approach used in the decision- making process that shows graphical animation in which systems and processes are presented dynamically to the decision maker. It enables visualization of the results of different potential actions 572nd semester 2010 Dr. Qusai Abuein

58. Visual Interactive Simulation Visual Interactive models and DSS –Waiting-line management (queuing) is a good example of VIM –The VIM approach can also be used in conjunction with artificial intelligence –General-purpose commercial dynamic VIS software is readily available 582nd semester 2010 Dr. Qusai Abuein

59. Quantitative Software Packages and Model Base Management Quantitative software packages A preprogrammed (sometimes called ready-made) model or optimization system. These packages sometimes serve as building blocks for other quantitative models 592nd semester 2010 Dr. Qusai Abuein

60. Quantitative Software Packages and Model Base Management Model base management –Model base management system (MBMS) Software for establishing, updating, combining, and so on (e.g., managing) a DSS model base –Relational model base management system (RMBMS) A relational approach (as in relational databases) to the design and development of a model base management system –Object-oriented model base management system (OOMBMS) An MBMS constructed in an object-oriented environment 602nd semester 2010 Dr. Qusai Abuein