2. B. I NFORMATION T ECHNOLOGY (IS) CISB434: D ECISION S UPPORT S YSTEMS Chapter 5: Modeling & Analysis

3. L EARNING O BJECTIVES Explain the basic concepts of management support system (MSS) modeling Describe how MSS models interact with data and the user Explore some different, well-known model classes Demonstrate how spreadsheets can be used for MSS modeling and solution 2

4. L EARNING O BJECTIVES Understand how to structure decision making with a few alternatives Articulate the basic concepts of optimization, simulation, and heuristics, and when to use them Describe how to structure a linear programming model Describe how to handle multiple goals Explain what is meant by sensitivity analysis what-if analysis goal seeking simulation 3

5. M ODELING & A NALYSIS Basic Concepts of Management Support System (MSS) Modeling

6. C ONCEPT OF MSS M ODELING L ESSONS FROM E XPERIENCE DuPont accurately model and simulate 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 with known alternative solutions 5

7. C ONCEPT OF MSS M ODELING L ESSONS FROM E XPERIENCE Procter & Gamble used DSS composed of several models to support strategic decisions in the company Models are integrated Models may be decomposed and simplified Suboptimization may be appropriate Human judgment is an important aspect of using models in decision making 6

8. C ONCEPT OF MSS M ODELING L ESSONS FROM E XPERIENCE Model developer must balance model’s simplification and representation to capture enough reality to make it useful Applying models to real-world situations can save millions of dollars or generate millions of dollars in revenue 7

9. C URRENT M ODELING I SSUES P ROBLEM I DENTIFICATION Environmental Scanning & Analysis monitoring, scanning and interpretation of collected information need to analyze the scope of domain and the forces and dynamics of the environment to identify the culture of decision-making process 8

10. C URRENT M ODELING I SSUES V ARIABLE I DENTIFICATION What variables are involved and their relationships Influence Diagrams can help the identification process a simple visual representation of a decision problem 9

11. I NFLUENCE D IAGRAM 10 http://www.lumina.com/software/influencediagrams.html

12. I NFLUENCE D IAGRAM - E XAMPLE This simple influence diagram shows how decisions about the marketing budget and product price influence expectations about its uncertain market size and market share. These, in turn, influence costs and revenues, which affect the overall profit. The product manager, VP of marketing, and market analyst may work together to draw such a diagram to develop a shared understanding of the key issues. The diagram provides a high-level qualitative view under which the analyst builds a detailed quantitative model.

13. C URRENT M ODELING I SSUES F ORECASTING Predicting the future Important for constructing and manipulating models Predictive Analytics systems predicts the most profitable customers the worst customers and focus on identifying products and services at appropriate prices to appeal to them 12

14. M ODELING & A NALYSIS Model Classes

15. M ODEL C LASSES M ULTIPLE M ODELS A DSS can include several models to represent a different part of the decision-making problem e.g. product-strategy model demand-forecasting model cost-generation model financial-simulation model etc. 14

16. M ODEL C LASSES M ODEL C ATEGORIES Seven groups of DSS models Can be applied to a static or dynamic models Can be constructed under environments of certainty, uncertainty and risk 15

17. M ODEL C LASSES M ODEL C ATEGORIES CategoryProcess & ObjectiveTechniques Optimization with few alternatives Find the best solution from a small no. of alternatives Decision Tables Decision Trees Optimization via al- gorithm Find the best solution using a step-by-step improvement pro- cess Mathematical programming, network models Optimization via an analytic formula Find the best solution using a formula Some inventory models 16

18. M ODEL C LASSES M ODEL C ATEGORIES CategoryProcess & ObjectiveTechniques SimulationFinding a good enough solution using experimentation Several types of simulation HeuristicsFind a good enough solution using rules Heuristic programming Predictive modelsPredict the future for a given scenario Forecasting models, Markov analysis Other modelsSolve a what-if case using a formula Financial modeling, waiting lines 17

19. M ODEL C LASSES M ODEL M ANAGEMENT Models must be managed to maintain integrity and applicability Need a model management system Model Base Management System, MBMS Analogous to DBMS 18

20. M ODEL C LASSES K NOWLEDGE - BASED M ODELING Most DSSs use quantitative models e.g. mathematical model Knowledge-based modeling uses quali-tative models e.g. expert systems 19

21. S TATIC M ODELS Models that describe a single interval of a situation e.g. to outsource or make a product Annual income statement Static models are presumed to handle identical conditions 20

22. D YNAMIC M ODELS Models whose input data are changed over time e.g. a five-year profit or loss projection e.g. how many checkout points to open in a supermarket Dynamic models use, represent or ge-nerate trends and patterns over time 21

23. C ERTAINTY, U NCERTAINTY & R ISK Decision situations are based on what the decision maker knows about the results Three classification of knowledge Certainty Risk Uncertainty 22

24. C ERTAINTY, U NCERTAINTY & R ISK 23

25. C ERTAINTY, U NCERTAINTY & R ISK Certainty A condition under which it is assumed that future values are known for sure and only one result is associated with an action simplifies model and tractable produce optimal solutions Occurs most often in structured problems with short time horizon 24

26. C ERTAINTY, U NCERTAINTY & R ISK Uncertainty Several outcomes are possible for each course of action Decision maker does not know or cannot esti-mate the probability of occurrence of possible outcomes Modeling involves assessing decision makers attitude toward risks 25

27. C ERTAINTY, U NCERTAINTY & R ISK Risk A probabilistic or stochastic decision situa-tion Decision maker must consider several possible outcomes for each alternatives, each with a given probability of occurrence 26

28. C ERTAINTY, U NCERTAINTY & R ISK Risk analysis A decision-making method that analyzes the risk based on assumed known probabilities associated with different alternatives Also known as Calculated Risk 27

29. M ODELING & A NALYSIS Modeling with Spreadsheets

30. M ODELING WITH S PREADSHEETS Models can be developed with many programming languages and systems The Spreadsheet is the most popular end-user modeling tool Incorporates many powerful functions financial statistical mathematical 29

31. M ODELING WITH S PREADSHEETS 30

32. M ODELING WITH S PREADSHEETS Other important spreadsheet features what-if analysis goal seeking data management programmability 31

33. M ODELING WITH S PREADSHEETS Most spreadsheet packages provide fairly seamless integration they read and write common file structures easily interfaced with databases and other tools Static or dynamic models can be built in a spreadsheet 32

34. M ODELING & A NALYSIS Decision Analysis: Decision Making with few Alternatives

35. D ECISION A NALYSIS A method for determining the solution to a problem typically when it is inappropriate to use iterative algorithms 34

36. D ECISION A NALYSIS D ECISION T ABLES A table used to represent knowledge and prepare it for analysis in treating uncertainty treating risk 35

37. 36 D ECISION A NALYSIS D ECISION T ABLES : E XAMPLE Printer Troubleshooter Rules Conditions Printer does not print YYYYNNNN A red light is flashing YYNNYYNN Printer is unrecognized YNYNYNYN Actions Check the power cable X Check the printer-computer cable X X Ensure printer software is installed X X X X Check/replace ink XX XX Check for paper jam X X

38. D ECISION A NALYSIS D ECISION T REES Decision tree A graphical presentation of a sequence of interrelated decisions to be made under assumed risk 37

39. D ECISION A NALYSIS D ECISION T REES : E XAMPLES 38

40. M ODELING & A NALYSIS Basic Concepts of Optimization, Simulation, & Heuristics

41. T HE S TRUCTURE OF M ATHEMATICAL M ODELS FOR D ECISION S UPPORT 40

42. T HE S TRUCTURE OF M ATHEMATICAL M ODELS FOR D ECISION S UPPORT 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 41

43. T HE S TRUCTURE OF M ATHEMATICAL M ODELS FOR D ECISION S UPPORT Decision variable A variable of a model that can be changed and manipulated by a decision maker It corresponds to the decisions to be made such as quantity to produce and amounts of resources to allocate 42

44. T HE S TRUCTURE OF M ATHEMATICAL M ODELS FOR D ECISION S UPPORT Uncontrollable variable (parameter) A factor that affects the result of a decision but beyond the control of the decision maker These variables can be internal e.g. related to technology, policies or external e.g., related to legal issues, climate 43

45. T HE S TRUCTURE OF M ATHEMATICAL M ODELS FOR D ECISION S UPPORT Intermediate result variable A variable that contains the values of intermediate outcomes in mathematical models e.g. employee salaries Intermediate outcome – employee satisfaction Final outcome – increase productivity level 44

46. M ATHEMATICAL P ROGRAMMING O PTIMIZATION Mathematical programming A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing acti-vities to optimize a measurable goal 45

47. M ATHEMATICAL P ROGRAMMING O PTIMIZATION Optimal solution A best possible solution to a modeled problem 46

48. M ATHEMATICAL P ROGRAMMING O PTIMIZATION 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 47

49. M ATHEMATICAL P ROGRAMMING O PTIMIZATION Every LP problem is composed of Decision variables Objective function Objective function coefficients Constraints Capacities Input/output (technology) coefficients 48

50. M ATHEMATICAL P ROGRAMMING L INEAR P ROGRAMMING P ROBLEM A linear programming problem is one in which we are to find the maximum or minimum value of a linear expression ax + by + cz +... This expression - called the objective function - is subjected to a number of linear constraints 49

51. M ATHEMATICAL P ROGRAMMING L INEAR P ROGRAMMING P ROBLEM The constraints are of the form Ax + By + Cz +...≤ N or Ax + By + Cz +...≥ N The largest or smallest value of the objective function, i.e. N, is the optimal value 50

52. M ATHEMATICAL P ROGRAMMING L INEAR P ROGRAMMING P ROBLEM A collection of values of x, y, z,... that gives the optimal value constitutes an optimal solution The variables x, y, z,... are called the decision variables 51

53. M ATHEMATICAL P ROGRAMMING LP P ROBLEM : E XAMPLE Determine the optimum number of Lec-turers for CoIT given the following maximum no. of students maximum no. of cr. hrs. maximum no. of cr. hrs taught by different categories of Lecturers etc. 52

54. M ATHEMATICAL P ROGRAMMING LP P ROBLEM : E XAMPLE 53

55. M ATHEMATICAL P ROGRAMMING O PTIMIZATION 54

56. M ODELING & A NALYSIS Handling Multiple Goals

57. M ULTIPLE G OALS Refers to a decision situation in which alternatives are evaluated with several, sometimes conflicting, goals 56

58. S ENSITIVITY A NALYSIS A study of the effect of a change in one or more input variables on a proposed solution 57

59. S ENSITIVITY A NALYSIS Sensitivity analysis tests relationships such as the impact of changes in external (uncon-trollable) variables and parameters on the outcome variable(s) the impact of changes in decision variables on the outcome variable(s) 58

60. S ENSITIVITY A NALYSIS Sensitivity analysis tests relationships such as the effect of uncertainty in estimating exter-nal variables the effects of different dependent inter-actions among variables the robustness of decisions under chang-ing conditions 59

61. S ENSITIVITY A NALYSIS 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 60

62. S ENSITIVITY A NALYSIS Sensitivity analyses are used for: altering a real-world system to reduce actual sensitivities accepting and using the sensitive real world, leading to the continuous and close monitoring of actual results 61

63. S ENSITIVITY A NALYSIS Two types of sensitivity analyses automatic trial-and-error 62

64. S ENSITIVITY A NALYSIS A UTOMATIC Automatic sensitivity analysis is per-formed in standard quantitative model implementations such as LP 63

65. S ENSITIVITY A NALYSIS T RIAL - AND -E RROR The impact of changes in any variable, or in several variables, can be deter-mined through a simple trial-and-error approach 64

66. W HAT -I F A NALYSIS A process querying a computer, the effect of changing some of the input data or parameters 65

67. W HAT -I F A NALYSIS 66

68. G OAL S EEKING Querying a computer the values certain variables must have in order to attain desired goals 67

69. G OAL S EEKING 68

70. G OAL S EEKING Computing a break-even point by using goal seeking Involves determining the value of the decision variables that generate zero profit 69

71. S IMULATION An imitation of reality Major characteristics of simulation A technique for conducting experiments Descriptive rather than normative method Normally used only when a problem is too complex to be treated using numerical optimization techniques 70

72. S IMULATION C OMPLEXITY A measure of how difficult a problem is in terms of its formulation for optimization its required optimization effort, or its stochastic nature 71

73. THE END T HANK Y OU FOR LISTENING