Executive Information System (EIS) for theAviation System Risk Model (ASRM)

M.S. Thesis Defense

Student: Nathan Greenhut, M.S. StudentAdvisor: James T. Luxhøj, Ph.D.

Thesis Committee: Dr. Coit and Dr. Boucher

Department of Industrial and Systems EngineeringRutgers UniversityMarch 23rd, 2005

Research OverviewInitial Results

#1 Knowledge DisplaysCase DisplaysHaddon MatricesMulti-Factor Plots

#2 GAP AnalysisTaxonomyHaddon Matrices

#3 Sensitivity Analysis (SA)Birnbaum ImportanceIF Tool

#4 OptimizationAlgorithmsLinear ProgramGenetic Algorithm

Research Outcome (EIS)Further Remarks

Problem StatementHistorical Accident Rate

Overview of Risk ManagementFAA Office of System SafetyNASA Aviation Safety and Security Program

Literature SurveyRisk AnalysisBayesian Belief ModelsKnowledge Management

Aviation System Risk ModelV 1.0 Explanation of DSSV 2.0 Explanation of EIS

Problem Statement

To develop and disseminate methods, tools and feedback necessary to evaluate the risk impact of the NASA AvSP product portfolio (48) upon the 1990-1996 aviation accident baseline period.

An Executive Information System is required for the Aviation System Risk Model (ASRM) to gain an improved understanding of the interactions of the NASA AvSP products.

Aggregate data from the 20 ASRM risk models needs to be displayed in a meaningful way to NASA analysts, managers and executives.

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Boeing Summary of Commercial Airplane Accidents

Source: Boeing “Statistical Summary of Commercial Jet Airplane Accidents Worldwide Operations 1959-2003”

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Source: Boeing “Statistical Summary of Commercial Jet Airplane Accidents Worldwide Operations 1959-2003”

Boeing Summary of Commercial Airplane AccidentsProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Literature SurveyRisk Analysis

Bayesian Belief Models

Knowledge Management

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Decision Support System ASRM 1.0

(Jalil, 2003)

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

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

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Masters Thesis Research ProjectsResearch Project 1 Knowledge Displays

- Useful combination, presentation and dissemination of multiple streams of information collected.

Research Project 2 GAP Analysis- Provide summary statistics to Level 2 managers at NASA

and gain feedback for model development.

Research 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.

Research Project 4 Optimization- Use Genetic Algorithms, Simulated Annealing, and Tabu-

Search in combination with developed database to give budgetary and accident rate optimization.

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

More Detailed Representation of Thesis

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Research Project 1 Comparisons of KD’sProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Research Project 4 OptWorks Search

Research Projects StatusResearch Project 1 Knowledge Displays- Initial Case Displays, All Case Haddon & Multi-Factor- Final Result Model & Case Suite Haddon & Multi-Factor

Research Project 2 Gap Analysis- Initial All Case Gap Analysis- Final Result Model & Case Suite Gap Analysis, Pareto Chart

Research Project 3 Sensitivity Analysis- Initial Database Design and Opening Screen- Final Result Drill-down from case type to causal factor

Research Project 4 Optimization- Initial Compare A1 and A2 for LOC 405- Final Result Compare A1 and A2 for LOC 405, Any Model

Optimization*** Result EIS Prototype ***

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

EIS PrototypeProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

EIS Prototype – Multi-FactorProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Case Displays – Multiple EIS ViewsProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

EIS Prototype – Gap AnalysisProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

EIS Prototype – Pareto Causal FactorsProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Number of Analyses for Causal Factors

12.00

10.00 10.00

8.00 8.007.00 7.00 7.00 7.00

6.00 6.00 6.00 6.00 6.00

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

SBEInad

equa

te_QA

MAIN

AMSInhe

rent_d

esign

FAA_Inadeq

uate_res

ource

s

Inappro

priate_

Process

es

MFG_OP_Commun

icatio

nInfra

ction

FAA_Boeing

FAA_Oversi

ght

Inadeq

uate_

design

OrgProc

ess

Pre_flig

ht_Prep

aration

Causal Factors

Num

ber o

f Ana

lyse

s

EIS Prototype – Pareto ProductsProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Number of Analyses for Products

5855

50

40

2624 23 23

19

0

10

20

30

40

50

60

70

ASMM_1 SWAP_7 SWAP_6 ASMM_1_2 AM_1 SVS_1_4 SWAP_1 ASMM_3_6 SWAP_4

Product

Num

ber o

f Ana

lyse

s

Contributions from Thesis ResearchProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

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

Future ResearchProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Extensions to EISExtension 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

EIS ReviewNumerous FAA Technical CentersAircraft Owners and Pilots AssociationChief System Engineer, FAA National Airspace Architecture and Systems Engineering FAA Washington ARTCC MITREEmbry-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 ASRM models

Problem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Resulting Papers and PresentationsProblem

Statement

Historical

Accident

Rate

Risk Mgmt.

Literature Survey

ASRMV1 DSSV2 EIS

Projects#1 KD#2 GAP#3 SA#4 Opt.

Research

Further Remarks

Present Papers and PresentationsGreenhut, N. and Luxhøj, J. “Gap Analysis of Developed Models for an Aviation Technologies Decision Support System.” Industry, Engineering, Management Symposium. March 2003. Cocoa Beach, FL.Greenhut, N., Luxhøj, J. and Andres, D. “Graphical Enhancements to the Executive Information System (EIS) for the Aviation System Risk Model (ASRM).” Aviation Technology Integration Organization. September 2004. Chicago, IL.Greenhut, N., Luxhøj, J. and Coit, D. “Integer Program and Birnbaum'sImportance Measure for Aviation Technology Decision Support System (DSS)” INFORMS. October 2004. Denver, CO.Greenhut, N., Luxhøj, J. and Bariether, C. “Feedback Support for Gap Analysis of Aviation Technologies” Industry, Engineering, Management Symposium. March 2005. Cocoa Beach, FL.

Papers and PresentationsGreenhut, N., Luxhøj, J. and Bariether, C. “’What-If’ and Sensitivity Analysis for the Aviation System Risk Model” IIE National Meeting, IERC. May 2005. Atlanta, GA.

Questions?