Decision Support Systems

1. Decision Support Systems Toby Wex October 31, 2007

2. Toby Wex • Graduating: December 2007 • Degrees: BS in Industrial Engineering • Emphasis: Engineering Management • BS in Computer Science • Emphasis: Computer Technologies • Minor: Mathematics • Work Experience: • Swiss Colony Co-op, Assistant Project Manager of Fulfillment Co-op • Developed inventory slotting program • Researched and implemented facility layout changes

3. Decisions and Decision Modeling • Types of Decisions • Human Judgment and Decision Making • Biases • Modeling Decisions • Components

4. Decisions • Types of Decisions1 • Simple • A choice among several alternative. • Intermediate • Addition of the process for constructing alternative. • Complete • Includes active searching for opportunities for decisions. 1. Druzdzel, M. and Flynn, R. Decision Support Systems.

5. Data vs. Information • Data is a collection of facts from which conclusions may be drawn.2 • Information is the organization of the data so conclusion may be drawn. • Data Processing/Conversion 2. Data. (2007). http://wordnet.princeton.edu/perl/webwn?s=data

6. Data vs. Information • How to get the information needed: • Phase 1: Define Strategy3 • Step 1: Educational Grounding • Step 2: Diagnostics • Step 3: Strategy 3. Kuhn, M., Lopata, I. and Todd, G.. From Data to Decision.

7. Data vs. Information • How to get the information needed: • Phase 2: Supporting the Strategy3 • Step 1: Governance • Step 2: Data • Step 3: Storage • Step 4: Delivery 3. Kuhn, M., Lopata, I. and Todd, G.. From Data to Decision.

8. Decision Making • “The cognitive process leading to the selection of a course of action among variations4.” • Psychological construct • Cannot “see” a decision but can see the effects of a decision. 4. Wikipedia. (2007). Decision making.

9. Decision Making Style • Myers-Briggs Type Indicator4 • Thinking versus Feeling • Extroversion versus Introversion • Judgment versus Perception • Sensing versus Intuition • Combination makes up Decision Making Style • Unassisted decisions are biased to some degree. 4. Wikipedia. (2007). Decision making.

10. Decision Models • Simplified set of variables of an usually complex, real-world system used to analyze and improve the system. • Simple linear programming has been shown to be superior to human intuitive judgment5. 5. Druzdzel, M. and Flynn, R. Decision Support Systems.

11. Decision Model Components6 • Preference • Not all outcomes are equally attractive. • Available Decision Options • Enumerated list or continuous values of policy variable. • Uncertainty • One of the most inherent and prevalent properties of knowledge. 6. Druzdzel, M. and Flynn, R. Decision Support Systems.

12. Good Decisions and Good Outcomes • Poor decisions can lead to good outcomes. • Good decisions can lead to poor outcomes.

13. Decision Models • Probabilistic Models • Naïve Bayes • MYCIN’S Certainty Factors • Prospector’s Bayesian Model • Dempster-Shafer Theory • Bayesian Networks • Influence Diagrams • Fuzzy Logic and Fuzzy Sets • Rough Sets • Non-monotonic Logics

14. Enhancing Management Decision • Overview of Management Information Systems7 • Levels of Information 7. Laudon, K., and Laudon, J. Management information systems

15. Enhancing Management Decision • Types of Management Information Systems • Decision Support Systems (DSSs) • Strategic • Executive

16. Enhancing Management Decision • Types of Decision Support System • Model-driven DSS • Data-driven DSS • Communication-driven DSS • Document-driven DSS • Knowledge-driven DSS

17. Decision Support Systems • Definition • Components • Applications • Interfaces

18. Decision Support Systems • A computer system that aims to assist in the making of a decision, providing support to the choice, model and analyze systems, identify decision opportunities, and structuring decision problems. • History • Reason for development

19. Decision Support System Components Laudon-Laudon Druzdel-Flynn • DSS Database • Transaction processing system • External data • DSS Software system • User interface • Database management system • Model-base management system • Dialog generation and management system

20. Decision Support Systems • Applications • Energy and environment • Aerospace/defense • Health and pharmaceutical • Consumer • Automotive • Consultants • Higher education

21. Decision Support Systems • Interfaces • PrecisionTree • Palisade Corporation • www.palisade.com/precisiontree/ • GeNIe and SMILE • Decision Systems Laboratory, University of Pittsburgh • genie.sis.pitt.edu • Analytica! • Lumina Decision Systems • www.lumina.com

22. PrecisionTree • Decision Analysis using Microsoft Excel • PrecisionTree Nodes • PrecisionTree allows you to build decision trees by defining nodes in Excel spreadsheets. • Node types offered by PrecisionTree include: • Chance nodes • Decision nodes • End nodes • Logic nodes • Reference nodes

23. PrecisionTree Features • Intuitive and easy to learn • Fully integrated with spreadsheet model • Build decision trees and influence diagrams directly in Excel

24. PrecisionTree Features • Graphs and reports customized using standard Excel features • Automatic formatting of influence diagrams and decision trees • Influence diagrams show results without being converted to a decision tree

25. PrecisionTree Features • Decision analysis results updated automatically as model is changed • Perform Sensitivity Analyses, one-way and two-way, on any value in decision tree or influence diagram • Use with @RISK software for complete Monte Carlo simulation

26. PrecisionTree Decision Tree

27. GeNIe and SMILE • Development environment for building graphical decision-theoretic models • GeNIe is implemented in Visual C++ • This makes it not easily portable, although it runs under Windows operating systems

28. GeNIe and SMILE • GeNIe allows for building models of any size and complexity, limited only by the capacity of the operating memory of your computer • Models developed using GeNIe can be embedded into any applications and run on any computing platform, using SMILE, which is fully portable • SMILE is Structural Modeling, Inference, and Learning Engine

29. GeNIe Model

30. GeNIe Diagnosis

31. Analytica! • “Visual tool for creating, analyzing, and communicating decision models.” • Its intuitive influence diagramslet you create a model the way you think, and communicate clearly with colleagues and clients

32. Analytica! • Intelligent Arrays™ let you create and manage multidimensional tables with an ease and reliability unknown in spreadsheets. • Efficient Monte Carlo simulator lets you quickly evaluate risk and uncertainty, and find out what variables really matter and why.

33. Analytica! Reviews • User Review • Tony Cox, Consultant, Cox & Associates, Boulder, Colorado • "Analytica is very easy to learn. ... Once the software has been learned, it is delightful to use. The number of mouse-clicks and key strokes required to produce desired results is minimal, yet the process to follow is obvious."

34. Analytica! Reviews • Software Reviews • PC Week • "Everything that's wrong with the common PC spreadsheet is fixed in Analytica.“ • Inc Technology • "A powerful forecasting and business-modeling package does what spreadsheets never could."

35. Analytica! Users

36. R&D and commercialization of a new product Decision Tree Influence Diagram

37. Analytica! Influence Diagram Simplicity

38. Designing a DSS • Information process • Get desired database set • Process data of database or data warehouse • Get good, usable data • Determine type of modeling desired • Model versus data driven DSS • Probabilistic Design Model • User interface • Ease of use a priority for executives

39. Forums for DSS Support • INFORMS • Institute for Operations Research and the Management Sciences • Operations Research • Simulation • Engineering Management • Project Management

40. References • Data. (2007). Retrieved October 31, 2007 from the World Wide Web: http://wordnet.princeton.edu/perl/webwn?s=data • Decision Support Laboratory. (2007). http://dsl.sis.pitt.edu/ • Diez , F. J. and Druzdzel, M. Reasoning Under Uncertainty. In Encyclopedia of Cognitive Science, pages 880-886, Nadel, L. (Ed.), London: Nature Publishing Group, 2003.

41. References • Druzdzel, M. and Flynn, R. Decision Support Systems. In Encyclopedia of Library and Information Science, Vol. 67, Suppl. 30, pages 120-133, Allen Kent (ed.), Marcel Dekker, Inc., New York, 2000. • GeNIe and SMILE. (2007). http://genie.sis.pitt.edu/ • Lumina Decision Systems. (2007). http://www.lumina.com/index.html • PrecisionTree. (2007). http://www.palisade.com/precisiontree/

42. References • Kuhn, M., Lopata, I. and Todd, G.. From Data to Decision: Mastering Information Management. Outlook Journal, June 2005. Link: http://www.accenture.com/Global/Research_and_Insights/Outlook/By_Subject/Business_Intelligence/FromDataToDecision.htm • Laudon, K., and Laudon, J. Management information systems: managing the digital firm. Pearson Prentice Hall: 2004, 8th ed., pages 346-373. • Wikipedia. (2007). Decision making. Retrieved May 23, 2007 from the World Wide Web: http://en.wikipedia.org/wiki/Decision_making • Wikipedia. (2007). Decision support system. Retrieved May 23, 2007 from the World Wide Web: http://en.wikipedia.org/wiki/Decision_support_system

43. References • Wikipedia. (2007). Executive information system. Retrieved May 23, 2007 from the World Wide Web: http://en.wikipedia.org/wiki/Executive_Support_System • Wikipedia. (2007). Strategic information system. Retrieved May 23, 2007 from the World Wide Web: http://en.wikipedia.org/wiki/Strategic_information_system

44. Summary and Conclusion • Decisions and Decision Making • Decision Modeling • Management Information Systems

45. Summary and Conclusion • Decision Support Systems • Analytica! • Designing a DSS • Forums for DSSSupport