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1 CHAPTER 5 Modeling and Analysis. 2 n Major DSS component n Model base and model management n CAUTION - Difficult Topic Ahead –Familiarity with major.

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Presentation on theme: "1 CHAPTER 5 Modeling and Analysis. 2 n Major DSS component n Model base and model management n CAUTION - Difficult Topic Ahead –Familiarity with major."— Presentation transcript:

1 1 CHAPTER 5 Modeling and Analysis

2 2 n Major DSS component n Model base and model management n CAUTION - Difficult Topic Ahead –Familiarity with major ideas –Basic concepts and definitions –Tool--influence diagram –Model directly in spreadsheets Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

3 3 n Structure of some successful models and methodologies –Decision analysis –Decision trees –Optimization –Heuristic programming –Simulation n New developments in modeling tools / techniques n Important issues in model base management Modeling and Analysis Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

4 4 Major Modeling Issues n Problem identification n Environmental analysis n Variable identification n Forecasting n Multiple model use n Model categories or selection (Table 5.1) n Model management n Knowledge-based modeling Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5 5 Static and Dynamic Models n Static Analysis –Single snapshot n Dynamic Analysis –Dynamic models –Evaluate scenarios that change over time –Time dependent –Trends and patterns over time –Extend static models Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

6 6 Treating Certainty, Uncertainty, and Risk n Certainty Models n Uncertainty n Risk Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

7 7 Influence Diagrams n Graphical representations of a model n Model of a model n Visual communication n Some packages create and solve the mathematical model n Framework for expressing MSS model relationships Rectangle = a decision variable Circle = uncontrollable or intermediate variable Oval = result (outcome) variable: intermediate or final Variables connected with arrows Example (Figure 5.1) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

8 8 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

9 9 Analytica Influence Diagram of a Marketing Problem: The Marketing Model (Figure 5.2a) (Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

10 10 MSS Modeling in Spreadsheets n Spreadsheet: most popular end-user modeling tool n Powerful functions n Add-in functions and solvers n Important for analysis, planning, modeling n Programmability (macros) (More) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

11 11 n What-if analysis n Goal seeking n Simple database management n Seamless integration n Microsoft Excel n Lotus n Excel spreadsheet static model example of a simple loan calculation of monthly payments (Figure 5.3) n Excel spreadsheet dynamic model example of a simple loan calculation of monthly payments and effects of prepayment (Figure 5.4) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

12 12 Decision Analysis of Few Alternatives (Decision Tables and Trees) Single Goal Situations n Decision tables n Decision trees Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

13 13 Decision Tables n Investment example n One goal: maximize the yield after one year n Yield depends on the status of the economy (the state of nature) –Solid growth –Stagnation –Inflation Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

14 14 1.If solid growth in the economy, bonds yield 12%; stocks 15%; time deposits 6.5% 2.If stagnation, bonds yield 6%; stocks 3%; time deposits 6.5% 3.If inflation, bonds yield 3%; stocks lose 2%; time deposits yield 6.5% Possible Situations Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

15 15 View Problem as a Two-Person Game Payoff Table 5.2 n Decision variables (alternatives) n Uncontrollable variables (states of economy) n Result variables (projected yield) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

16 16 Table 5.2: Investment Problem Decision Table Model States of Nature Solid Stagnation Inflation Alternatives Growth Bonds12%6% 3% Stocks15% 3% -2% CDs6.5% 6.5% 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

17 17 Treating Uncertainty n Optimistic approach n Pessimistic approach Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

18 18 Treating Risk n Use known probabilities (Table 5.3) n Risk analysis: compute expected values n Can be dangerous Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

19 19 Table 5.3: Decision Under Risk and Its Solution Solid Stagnation InflationExpected GrowthValue Alternatives Bonds12%6% 3% 8.4% * Stocks15% 3% -2% 8.0% CDs6.5% 6.5% 6.5% 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20 20 n Decision Trees n Other methods of treating risk –Simulation –Certainty factors –Fuzzy logic n Multiple goals n Yield, safety, and liquidity (Table 5.4) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

21 21 Table 5.4: Multiple Goals AlternativesYieldSafetyLiquidity Bonds8.4%HighHigh Stocks8.0%Low High CDs6.5%Very High High Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

22 22 Optimization via Mathematical Programming n Linear programming (LP) Used extensively in DSS n Mathematical Programming Family of tools to solve managerial problems in allocating scarce resources among various activities to optimize a measurable goal Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

23 23 LP Allocation Problem Characteristics 1.Limited quantity of economic resources 2.Resources are used in the production of products or services 3.Two or more ways (solutions, programs) to use the resources 4.Each activity (product or service) yields a return in terms of the goal 5.Allocation is usually restricted by constraints Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

24 24 LP Allocation Model n Rational economic assumptions 1. Returns from allocations can be compared in a common unit 2. Independent returns 3. Total return is the sum of different activities’ returns 4. All data are known with certainty 5. The resources are to be used in the most economical manner n Optimal solution: the best, found algorithmically Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

25 25 Linear Programming n Decision variables n Objective function n Objective function coefficients n Constraints n Capacities n Input-output (technology) coefficients Line Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

26 26 Heuristic Programming n Cuts the search n Gets satisfactory solutions more quickly and less expensively n Finds rules to solve complex problems n Finds good enough feasible solutions to complex problems n Heuristics can be –Quantitative –Qualitative (in ES) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

27 27 When to Use Heuristics 1. Inexact or limited input data 2. Complex reality 3. Reliable, exact algorithm not available 4. Computation time excessive 5. To improve the efficiency of optimization 6. To solve complex problems 7. For symbolic processing 8. For making quick decisions Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

28 28 Simulation n Technique for conducting experiments with a computer on a model of a management system n Frequently used DSS tool Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

29 29 Major Characteristics of Simulation n Imitates reality and capture its richness n Technique for conducting experiments n Descriptive, not normative tool n Often to solve very complex, risky problems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

30 30 Simulation Methodology Model real system and conduct repetitive experiments 1. Define problem 2. Construct simulation model 3. Test and validate model 4. Design experiments 5. Conduct experiments 6. Evaluate results 7. Implement solution Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

31 31 Multidimensional Modeling n Performed in online analytical processing (OLAP) n From a spreadsheet and analysis perspective n 2-D to 3-D to multiple-D n Multidimensional modeling tools: 16-D + n Multidimensional modeling - OLAP (Figure 5.6) n Tool can compare, rotate, and slice and dice corporate data across different management viewpoints Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

32 32 Visual Interactive Modeling (VIS) and Visual Interactive Simulation (VIS) n Visual interactive modeling (VIM) (DSS In Action 5.8) Also called –Visual interactive problem solving –Visual interactive modeling –Visual interactive simulation n Use computer graphics to present the impact of different management decisions. n Can integrate with GIS n Users perform sensitivity analysis n Static or a dynamic (animation) systems (Figure 5.7) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

33 33 Visual Interactive Simulation (VIS) n Decision makers interact with the simulated model and watch the results over time n Visual interactive models and DSS –VIM (Case Application W5.1 on book’s Web site) –Queueing Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

34 34 Quantitative Software Packages-OLAP n Preprogrammed models can expedite DSS programming time n Some models are building blocks of other models –Statistical packages –Management science packages –Revenue (yield) management –Other specific DSS applications including spreadsheet add-ins Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

35 35 Model Base Management n MBMS: capabilities similar to that of DBMS n But, there are no comprehensive model base management packages n Each organization uses models somewhat differently n There are many model classes n Within each class there are different solution approaches n Some MBMS capabilities require expertise and reasoning Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ


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