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Understanding Simulation in Decision Support Systems

Explore the world of simulation in Decision Support Systems (DSS) through this comprehensive guide covering methodologies, types, advantages, and applications. Learn how simulation can save companies millions and improve decision-making processes.

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Understanding Simulation in Decision Support Systems

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  1. Simulation Sesi 12 Dosen Pembina: Danang Junaedi IF-UTAMA

  2. Simulation • In DSS, simulation refers to a technique for conducting experiments with a computer on a model of a management system. • Major Characteristics of Simulation • Simulation imitates reality and capture its richness • Simulation is a technique for conducting experiments • It can describe and/or predict the characteristics of a given system under different circumstances. • Simulation is a descriptive not normative tool • Simulation is often used to solve very complex, risky problems IF-UTAMA

  3. What is Simulation IF-UTAMA

  4. Case : Simulation Saves Siemens Millions Problem: • Siemens Solar Industries (SSI), the world’s largest maker of solar electric products, suffered continuous problems in poor material flow, unbalanced resource use, bottlenecks in throughput & schedule delays. Solution: • SSI built a cleanroom contamination-control technology. • The simulation provided a virtual laboratory for engineers to experiment with various configurations before the physical systems were constructed. Results: • SSI improved their manufacturing process significantly. • The cleanroom facility saved SSI over $75 million/ year. IF-UTAMA

  5. Advantages and Disadvantages of Simulation • Slow and costly construction process • Cannot transfer solutions and inferences to solve other problems • So easy to sell to managers, may miss analytical solutions • Software is not so user friendly IF-UTAMA

  6. Simulation Methodology • Set up a model of a real system and conduct repetitive experiments 1. Problem Definition 2. Construction of the Simulation Model 3. Testing and Validating the Model 4. Design of the Experiments 5. Conducting the Experiments 6. Evaluating the Results 7. Implementation IF-UTAMA

  7. Simulation Types • Probabilistic Simulation • Discrete distributions : systems monitor the systems each time a change in its state takes place • Continuous distributions : system monitor changes in a state of system at descret points in time • Probabilistic simulation via Monte Carlo technique • Time Dependent versus Time Independent Simulation • Simulation Software • Visual Simulation • Object-oriented Simulation IF-UTAMA

  8. Simulation Development IF-UTAMA

  9. Some Applications of Simulation IF-UTAMA

  10. Visual Spreadsheets • User can visualize models and formulas with influence diagrams • Not cells--symbolic elements 10 IF-UTAMA

  11. Visual Interactive Modeling (VIM) and Visual Interactive Simulation (VIS) • Visual interactive modeling (VIM) Also called • Visual interactive problem solving • Visual interactive modeling • Visual interactive simulation • Use computer graphics to present the impact of different management decisions. • Can integrate with GIS • Users perform sensitivity analysis • Static or a dynamic (animation) systems 11 IF-UTAMA

  12. Generated Image of Traffic at an Intersection from the Orca Visual Simulation Environment (Courtesy Orca Computer, Inc.) 12 IF-UTAMA

  13. Visual Interactive Simulation (VIS) • Decision makers interact with the simulated model and watch the results over time • Visual interactive models and DSS • Queueing 13 IF-UTAMA

  14. Monte Carlo Simulation IF-UTAMA

  15. Monte Carlo Technique IF-UTAMA

  16. Step 1. Probability Distribution IF-UTAMA

  17. Step 2. Building a Cumulative Probability Distribution IF-UTAMA

  18. Step 3. Setting Random Number Interval IF-UTAMA

  19. Step 4. Generating Random Numbers IF-UTAMA

  20. Step 5. Simulating the Experience IF-UTAMA

  21. IF-UTAMA

  22. Simulation of Queuing Problem IF-UTAMA

  23. Queuing Problem IF-UTAMA

  24. Dist 1.Interval Arrival Times Dist 2. Unloading Times IF-UTAMA

  25. Example IF-UTAMA

  26. Example-contd : Some Simple Statistic IF-UTAMA

  27. Simulation and Inventory Analysis The Basic Model IF-UTAMA

  28. Referensi • Dr. Mourad YKHLEF,2009,Decision Support System-Simulation, King Saud University • Richard K. Min.2002.Information Systems for Management. OUR LADY OF THE LAKE UNIVERSITY SCHOOL OF BUSINESS • Insoo Hwang.-. Modeling and Analysis. Department of MIS, Jeonju university • Efraim Turban and Jay E. Aronson.2001. Decision Support Systems and Intelligent Systems 6th edition. Prentice Hall IF-UTAMA

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