1. 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 user  Understand some different, well-known model classes  Understand how to structure decision making with a few alternatives

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

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

5. 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

6. 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  A demand –forecasting model(statistics based)  A geographical information system

7. 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

8. MSS Modeling  Current modeling issues  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

9. MSS Modeling  Current modeling issues  Variable identification  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

10. 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

11. MSS Modeling  Model categories  Optimization of problems with few alternatives Find the best solution from a small number of alternatives  Optimization via algorithm Find the best solution from a large number of alternatives using a step-by-step improvement process

12. MSS Modeling  Optimization via an analytic formula Find the best solution in one step, using a formula  Simulation Find a good enough solution or the best among The alternatives checked Using experimentation  Predictive models Predict the future for a given scenario  Other models solve a what-if case using a formula

13. MSS Modeling  Current modeling issues  Model management model like data, must be managed to maintain their integrity and thus their applicability 。  Knowledge-based modeling Some knowledge is necessary to construct solvable(and therefore usable)models 。  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

14. 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

15. Static and Dynamic Models  Static models Models that describe a single interval of a situation 。 Most static decision-making situations are presumed to repeat with identical conditions  Dynamic models It is represent scenarios that change over time 。 Models whose input data are changed over time (e.g., a five-year profit or loss projection)

16. Certainty, Uncertainty, and Risk Decision Support And Business Intelligence System /turban 著 /2005 年

17. Certainty, Uncertainty, and Risk  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

18. Certainty, Uncertainty, and Risk  Risk A probabilistic or stochastic decision situation  Risk analysis A decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives. Also known as calculated risk

19. 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

20. MSS Modeling with Spreadsheets Decision Support And Business Intelligence System /turban 著 /2005 年

21. 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

22. MSS Modeling with Spreadsheets Decision Support And Business Intelligence System /turban 著 /2005 年

23. 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 Experts estimated the following annual yield ： ． If there is sold growth in the economy, bonds will yield 12 percent, stocks 15 percent, and time deposits 6.5 percent 。

24. Decision Analysis with Decision Tables and Decision Trees -If stagnation prevails, bonds will yield 6 percent, stocks 3 percent, and the time deposits 6.5 percent 。 -If inflation prevails bonds will yield 3 percent, stocks will bring a loss of 2 percent, and time deposits will yield 6.5 percent 。

25. Decision Analysis with Decision Tables and Decision Trees  Decision table A table used to represent knowledge and prepare it for analysis in:  Treating uncertainty There are several methods of handling uncertainty 。 There are serious problems with every approach for handling uncertainty 。  Treating risk This approach can sometime be a dangerous strategy because the utility of each potential outcome may be different from the value 。

26. 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

27. The Structure of Mathematical Models for Decision Support Decision Support And Business Intelligence System /turban 著 /2005 年

28. 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

29. 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

30. 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

31. 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

32. Mathematical Programming Optimization  Every LP problem is composed of:  Decision variables  Objective function  Objective function coefficients  Constraints  Capacities  Input/output (technology) coefficients

33. Mathematical Programming Optimization Decision Support And Business Intelligence System /turban 著 /2005 年

34. Mathematical Programming Optimization Decision Support And Business Intelligence System /turban 著 /2005 年

35. 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

36. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking  Several methods of handling multiple goals can be used when working with MSS 。 The most common ones are ：  Utility theory  Goal programming  Expression of goal as constraints, using LP  A points system

37. 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

38. 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

39. 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

40. 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 For example ： What will happen to the total inventory cost if the cost of carrying invertories increases by 10 percent?

41. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking Decision Support And Business Intelligence System /turban 著 /2005 年

42. 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 For example ： what annual R&D budget is needed for an annual growth rate of 15 percent by 2009?

43. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking Decision Support And Business Intelligence System /turban 著 /2005 年

44. 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 。  The goal to be achieved is NPV equal to zero, which determines the internal rate of return(IRR)of this cash flow, including the investment 。

45. Problem-Solving Search Methods Decision Support And Business Intelligence System /turban 著 /2005 年

46. Problem-Solving Search Methods  Analytical techniques use mathematical formulas to derive an optimal solution directly or to predict a certain result  An algorithm may use algorithms to increase the efficiency of the search, and it is a step-by-step search process for obtaining an optimal solution

47. Problem-Solving Search Methods Decision Support And Business Intelligence System /turban 著 /2005 年

48. 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

49. 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)

50. 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

51. Simulation  Simulation is the appearance of reality, And it also is 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

52. 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

53. 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

54. 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

55. 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

56. Simulation Decision Support And Business Intelligence System /turban 著 /2006 年

57. 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

58. Simulation  Simulation types  Probabilistic simulation  Discrete distributions involve a situation with a limited number of events that can take on only a finite number of values 。  Continuous distributions are situation with unlimited numbers of possible events that follow density functions, such as the normal distribution  Time-dependent versus time-independent simulation  Object-oriented simulation  Visual simulation  Simulation software

59. 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

60. 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

61. 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

62. 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

63. 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