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Chapter 4

Chapter 4 . MODELING AND ANALYSIS. 8 th Edition. 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

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Chapter 4

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  1. Chapter 4 MODELING AND ANALYSIS 8th Edition 2nd 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 2nd 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 2nd 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 2nd 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. 2nd 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 2nd 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 2nd semester 2010 Dr. Qusai Abuein

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

  9. (4.2) 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 The problem must be understood 2nd semester 2010 Dr. Qusai Abuein

  10. (4.2) MSS Modeling • Current modeling issues • Variable identification define the variables and the relationship of it • 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 2nd 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 2nd 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 2nd 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 2nd 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) 2nd semester 2010 Dr. Qusai Abuein

  15. (4.4) Certainty, Uncertainty, and Risk 2nd 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. 2nd 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? 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  19. MSS Modeling with Spreadsheets 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  21. MSS Modeling with Spreadsheets 2nd 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 2nd 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 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  25. The Structure of Mathematical Models for Decision Support 2nd 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 2nd 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 2nd 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   2nd 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 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  31. Mathematical Programming Optimization 2nd semester 2010 Dr. Qusai Abuein

  32. Mathematical Programming Optimization 2nd 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 2nd 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 2nd 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 2nd 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 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  38. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  40. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  42. Problem-Solving Search Methods 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  44. Problem-Solving Search Methods 2nd 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 2nd 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) 2nd 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 2nd semester 2010 Dr. Qusai Abuein

  48. Simulation • 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 2nd 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 2nd 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 2nd semester 2010 Dr. Qusai Abuein

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