Defining Procedures for Decision Analysis

1. May 02-14 & Engr 466-02A April 30, 2002 Defining Procedures for Decision Analysis • Team Members • Marvin Choo • Dave Cohen • Amy Kalbacken • Natasha Khan • Jesse Smith • Theodore Scott • Client & Faculty Advisors • Dr. Keith Adams • Dr. John Lamont • Dr. Ralph Patterson III

2. Acknowledgments • Faculty Advisors • Dr. Doug Gemmil • Dr. Kenneth Kirkland • Dr. Jo Min • Dr. Ron Nelson • Dr. Steve Russell • Dr. Howard Van Aucken • Dr. Max Wortman

3. Presentation Outline • Problem Statement • Design Objectives • End-Product Description • Assumptions & Limitations • Project Risks & Concerns • Technical Approach • Evaluation of Project Success

4. Presentation Outline • Recommendations for Future Work • Personnel Budgets • Financial Budgets • Lessons Learned • Closing Summary

5. Problem Statement • Problem • Companies often are required to make major decisions regarding the commercialization process for a product, process, or service • How can we maximize efforts most efficiently during the decision-making process? • Goal • Develop a guide that aids users in the decision-making process

6. Design Objectives • Design Constraints • Inaccurate research (especially Internet) • Uncovering all factors • Limited understanding of algorithms

7. Design Objectives • Intended Users & Uses • People in decision-making positions • Gain greater understanding of methods • Software Programmers • Have background reference information • Detailed starting point for developing software

8. End Product Description • The report will aid individuals in conducting a thorough analysis of the decision factors surrounding their particular product, process, or service

9. End Product Description • Written Report • Key factors regarding decision processes • Algorithms used in decision analysis • Examples of Algorithms • Functional Software Specification • Reference Material

10. Assumptions • Considering company goals • Aids in decision-making but will not be the only tool used • Take into account other decision-making factors and considerations • Using decision-making algorithms

11. Assumptions • Use algorithms based on research • Have basic knowledge of decision-making process • For any business interested in decision analysis software • No sophisticated mathematics or statistics are used in algorithms

12. Limitations • Ranking the importance of each factor differently • Not all data accounted for • Selected algorithms may not be applicable to all decisions • Need to apply each process to specific situation

13. Limitations • Limited knowledge of algorithms • Algorithms may require a statistical background or other expertise • All factors & constraints may not be uncovered • Algorithm applicability is based on project requirements & criteria

14. Project Risks & Concerns • Scheduling interviews • Finding information • Losing a team member • Understanding project

15. Technical Approach Purpose • To determine an algorithm for use in creating software that will implement the decision analysis process Process • Determine the basic project process • Compile a list of potential algorithms • Create a set of criteria for evaluating the algorithms • Research the algorithms • Select the most applicable algorithms

16. Technical Approach“Basic Project Process”

17. Technical Approach“List of Algorithms” • Artificial Neural Networks • Bayesian Logic • Decision Matrix • Decision Tree • Fuzzy Logic • Genetic Algorithms • Linear Algebra

18. Technical Approach“Criteria for Evaluating the Algorithms” • What type of problems is the algorithm good for? • What input data is needed? • What kind of control is needed? • How does the algorithm work? • What are the expected outputs? • How easy or difficult is it to implement? • Is there any information on the solution time, problem size, etc. • Are there any examples available for the algorithm? • Are there sufficient conditions for convulgence? • If the algorithm is discovered to be ineffective what are the reasons in support of the determination.

19. Technical Approach“Selecting the Best Algorithms” Artificial neural networks • Able to learn, memorize, and create relationship between data • Able to work with the non-linearities • Used for the accurate prediction of events Decision trees • Useful for handling a lot of complex information Genetic algorithms • Multi objective solutions can be defined

20. Project Success Initial Startup • Identifying key factors • Interview coordination Interview Results & Project Definition • Conduction Interviews • Completing Project Plan

21. Project Success Implementation • Algorithms • Functional Software Specification Testing • Scenario Example • Needs further testing

22. Project Success End Product • Guide • Algorithms Report • Functional Software Specification • Reference Material • Final Report Software Package

23. Recommendations for Further Work • Create detailed models of selected algorithms • Consult with professionals to evaluate algorithms • Develop a functional software package

24. Personnel Budget

25. Budgeted Hours Vs. Actual Hours

26. Financial Budget

27. Lessons Learned • Essential team attributes • Teamwork • Time Management • Brainstorming • Knowledge acquired • Division of labor, team goals, task management • Interview information • Defining scope of project

28. Lessons Learned • Algorithms • Complexity • Need to study more carefully • Issues faced in decision-making process • Time vs. Money • Who is involved in decision-making process • Engineering vs. Business Processes

29. Closing Summary Conclusion • A tool created to aid during the decision-making process would be well worth developing Benefits • Identifies key factors in the decision process • Characterizes the decision-making process • Determines the best decision processes • Aid in further analyzing a particular decision • Narrows in on the optimum decision

30. Questions