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Modeling and Analysis in Decision Support Systems: Techniques and Approaches

This chapter explores the critical components of modeling and analysis within Decision Support Systems (DSS). We examine the steps involved in defining the principle of choice, conducting environmental analysis, and utilizing modeling techniques such as influence diagrams, factor analysis, and regression. The chapter delineates between static, dynamic, and multidimensional models, while also highlighting optimization methods, decision analysis, and simulation techniques. The significance of sensitivity analysis and the concepts of certainty, uncertainty, and risk in modeling are also discussed, alongside practical examples and exercises.

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Modeling and Analysis in Decision Support Systems: Techniques and Approaches

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  1. Chapter 4 MODELING AND ANALYSIS

  2. Model component • Data component provides input data • User interface displays solution • It is the model component of a DSS that actually solves the problem – it is the heart of any DSS

  3. Modeling Steps • Determine the Principle of Choice (or Result / Dependent variable) Eg. Profit • Perform Environmental Scanning & Analysis to identify all Decision / independent variables • For this, • one can use Influence diagrams (Cognitive modeling) • how did you model the car loan payment ? (assignment #2) • Identify an existing model that relate the dependent and independent variables • If needed, develop a new model from scratch • Eg. Factor analysis • Multiple models: If needed divide the problem into sub- problems and fit a model for each sub-problem • Eg. Factor analysis, followed by Regression

  4. Eg. Economy

  5. Static, Dynamic, Multi-Dimensional Models • Static models Models describing a single interval (Fig 4.2). Parameter values may be considered stable (eg. Interest rate) • Dynamic models Models whose input data are changed over time. E.g., a five-year profit or loss projection; a spreadsheet model may capture inflation, business cycle of economy; see also Fig 4.3. • Multidimensionalmodels A modeling method that involves data analysis in several dimensions

  6. Multi-dimensional modeling in Excel

  7. Multi-dimensional view (ABC Hardware, Laptop, Full warranty)=1000 units Equipment type Warranty type Vendor

  8. Model Categories • Optimization • Algorithms (Simplex in LP) • Decision Analysis • Decision-Table/Tree • Simulation • Uses experimentation, random generator • Predictive • Forecasting using regression, time-series analysis • Heuristics • Logical deduction using if-then rules (eg. Expert Systems) • This is a qualitative model • Other • What if, goal-seeking, multiple goals

  9. Optimization • Every LP problem is composed of: • Decision variables • Objective function • Constraints • Capacities

  10. Optimization • Do Exercise #7

  11. Sensitivity analysis • A study of the effect of a change in an input variable on the overall solution • By studying each variable in turn, one can identify the ‘sensitive’ variables • Helps evaluate robustness of decisions under changing conditions • Revising models to eliminate too-large sensitivities

  12. Matching model & decision environments • Certainty A condition under which it is assumed that only one result is associated with a decision (easier to model) • Uncertainty For a given decision, possible outcomes are unknown; even if known, probabilities cannot be calculated due to lack of data. (most difficult to model) Eg. Testing a new rocket / product • Risk Possible outcomes are known & data is available to calculate probabilities of occurrence of each outcome for a given decision

  13. Decision Tables under Risk/Uncertainty Choose Decision D3 since it has the largest Expected Monetary Value.

  14. DecisionTrees under risk/uncertainty

  15. Decision trees in Excel using Precision-Tree Add-in

  16. Simulation • An imitation of reality(eg. market fluctuations) • Creates random scenarios • Major characteristics • Simulation is a technique for conducting experiments • Simulation is a descriptive rather than a normative/prescriptive method • Simulation is normally used only when a problem is too unstructured to be treated using numerical optimization techniques

  17. Simulation • Advantages • A great amount of time compression can be attained • Simulation can handle an extremely wide variety of problem types (eg. queuing, inventory, market returns, product demand variations) • Simulation produces many important performance measures • Disadvantages • 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

  18. Simulation

  19. Simulation Exercise Enter this data as shown. Select cell C20. Type, =RAND(), Enter. Copy C20 all the way down to C34. Select D20. Type, VLOOKUP(C20,$C$7:$D$16,2). Copy cell D20 all the way down to D34. Select F24. Type, =Average(D20:D34). Select F25. Calculate SD.

  20. What-if, Goal-seek, Multiple goals • What-if: Similar to sensitivity analysis, but focus is on generating the revised solution when an input value is changed. • Goal-seek: Calculates the value of an input necessary to achieve a desired level of output (goal). Eg. How many hours to study to get an A? • Multiple goals: Finds a compromise solution. Eg. Group decision environments, usually based on utility analysis (Analytical Hierarchy Process-Chapter 10)

  21. Goal-seek Exercise

  22. Scenarios • A statement of assumptions about the operating environment of a particular system at a given time; a narrative description of the decision-situation setting • Scenarios are especially helpful in simulations and what-if analyses • Possible scenarios • The worst possible scenario • The best possible scenario • The most likely scenario • The average scenario Do Exercise #8

  23. Problem solving search methods • DSS uses these in the Design & Choice phases Eg. LP Eg. Chess (large RAM) Eg. Chess Eg. Med diagnosis

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