Executive Information System (EIS) for the Aviation System Risk Model (ASRM) M.S. Thesis Defense

Transcription

1 Executive Information System (EIS) for the Aviation System Model () M.S. Thesis Defense Student: Nathan Greenhut, M.S. Student Advisor: James T. Luxhøj, Ph.D. Thesis Committee: Dr. Coit and Dr. Boucher Department of Industrial and Systems Engineering Rutgers University March 23 rd, 2005

2 Overview Problem Overview of Management FAA Office of System Safety NASA Aviation Safety and Security Program Analysis Bayesian Belief Models Knowledge Management Aviation System Model V 1.0 V 2.0 Explanation of DSS Explanation of EIS Initial Results #1 Knowledge Displays Case Displays Haddon Matrices Multi-Factor Plots Analysis Taxonomy Haddon Matrices #3 Sensitivity Analysis (SA) Birnbaum Importance IF Tool #4 Optimization Algorithms Linear Program Genetic Algorithm Outcome (EIS)

3 Problem Problem To develop and disseminate methods, tools and feedback necessary to evaluate the risk impact of the NASA AvSP product portfolio (48) upon the aviation accident baseline period. An Executive Information System is required for the Aviation System Model () to gain an improved understanding of the interactions of the NASA AvSP products. Aggregate data from the 20 risk models needs to be displayed in a meaningful way to NASA analysts, managers and executives.

4 Boeing Summary of Commercial Airplane s Problem Source: Boeing Statistical Summary of Commercial Jet Airplane s Worldwide Operations

5 Boeing Summary of Commercial Airplane s Problem Source: Boeing Statistical Summary of Commercial Jet Airplane s Worldwide Operations

6 Problem

7 Problem

8 Problem Analysis Bayesian Belief Models Knowledge Management

9 Problem

10 Problem

11 Problem

12 Problem

13 Problem Decision Support System 1.0 (Jalil, 2003)

14 Problem

15 Differences 1.0 (DSS) vs. 2.0 (EIS) Problem

16 Masters Thesis Problem Project 1 Knowledge Displays - Useful combination, presentation and dissemination of multiple streams of information collected. Project 2 GAP Analysis - Provide summary statistics to Level 2 managers at NASA and gain feedback for model development. Project 3 Sensitivity Analysis - Use Birnbaum Sensitivity Analysis measures to determine how much one causal factor affects another. Determines which nodes require more investigation and other methodologies. Project 4 Optimization - Use Genetic Algorithms, Simulated Annealing, and Tabu- Search in combination with developed database to give budgetary and accident rate optimization.

17 More Detailed Representation of Thesis

18 Problem

19 Problem

20 Problem

21 Project 1 Comparisons of KD s Problem

22 Problem

23 Problem

24 Problem

25 Problem

26 Problem

27 Problem

28 Project 4 OptWorks Search

29 Status Problem Project 1 Knowledge Displays - Initial Case Displays, All Case Haddon & Multi-Factor - Final Result Model & Case Suite Haddon & Multi-Factor Project 2 Gap Analysis - Initial All Case Gap Analysis - Final Result Model & Case Suite Gap Analysis, Pareto Chart Project 3 Sensitivity Analysis -Initial Database Design and Opening Screen - Final Result Drill-down from case type to causal factor Project 4 Optimization -Initial Compare A1 and A2 for LOC Final Result Compare A1 and A2 for LOC 405, Any Model Optimization *** Result EIS Prototype ***

30 EIS Prototype Problem

31 EIS Prototype Multi-Factor Problem

32 Case Displays Multiple EIS Views Problem

33 EIS Prototype Gap Analysis Problem

34 Problem EIS Prototype Pareto Causal Factors Number of Analyses for Causal Factors Number of Analyses SBE Inadequate_QA MAIN AMS Inherent_design FAA_Inadequate_resources Inappropriate_Processes MFG_OP_Communication Infraction FAA_Boeing FAA_Oversight Inadequate_design OrgProcess Pre_flight_Preparation Causal Factors

35 EIS Prototype Pareto Products Problem Number of Analyses for Products 70 Number of Analyses ASMM_1 SWAP_7 SWAP_6 ASMM_1_2 AM_1 SVS_1_4 SWAP_1 ASMM_3_6 SWAP_4 Product

36 Contributions from Thesis Problem Developed methodology to compare numerous case studies in an EIS. Meetings with technical monitor and NASA managers to develop and refine. Exposure to top-level management at public companies and government agencies. 5 Papers and Presentations Produced deliverables for NASA contract. Optimization, Sensitivity Analysis, Gap Analysis, Graphical Displays and Graphical User Interface for NASA managers and executives to utilize. Bayesian Belief Network Genetic Algorithm

37 Future Problem Extensions to EIS Extension of Tools ( IF, What-If and Sensitivity Programs C. Bareither) Enterprise Information System Optimization Techniques and Budgetary Linkages Integration of EIS into other areas at NASA and FAA

38 Problem EIS Review Numerous FAA Technical Centers Aircraft Owners and Pilots Association Chief System Engineer, FAA National Airspace Architecture and Systems Engineering FAA Washington ARTCC MITRE Embry-Riddle Aeronautical University NASA Pratt & Whitney Gained feedback on relevance, effectiveness and meaningfulness of EIS from NASA managers and executives. RESULT = Development of EIS Prototype with 20 models

39 Resulting Papers and Presentations Problem Present Papers and Presentations Greenhut, N. and Luxhøj, J. Gap Analysis of Developed Models for an Aviation Technologies Decision Support System. Industry, Engineering, Management Symposium. March Cocoa Beach, FL. Greenhut, N., Luxhøj, J. and Andres, D. Graphical Enhancements to the Executive Information System (EIS) for the Aviation System Model (). Aviation Technology Integration Organization. September Chicago, IL. Greenhut, N., Luxhøj, J. and Coit, D. Integer Program and Birnbaum's Importance Measure for Aviation Technology Decision Support System (DSS) INFORMS. October Denver, CO. Greenhut, N., Luxhøj, J. and Bariether, C. Feedback Support for Gap Analysis of Aviation Technologies Industry, Engineering, Management Symposium. March Cocoa Beach, FL. Papers and Presentations Greenhut, N., Luxhøj, J. and Bariether, C. What-If and Sensitivity Analysis for the Aviation System Model IIE National Meeting, IERC. May Atlanta, GA.

40 Questions?