1. LMI Use of Generalized Activity Network Models for Analysis of European ATM Development Projects Peter Kostiuk LMI Patrick Ky Eurocontrol Experimental Centre Scott Houser LMI ATM-2003 5 th USA/Europe ATM R&D Seminar June 23-28, 2003

2. LMI Agenda Estimating schedule risk for ATM development projects Introducing generalized activity networks Application and results for a European ATM project

3. LMI Risks for ATM Development Projects Schedule and cost risk driven by technical challenges –Hardware –Software –Integration Political risk –Projects require cooperation among developers and stakeholders How can we analyze alternative technical approaches and political processes to generate insight into cost and schedule risk?

4. LMI Specific Risks for European Projects “Think global, act local” Legacy and transition issues Distributed programme management and integration Co-opetition between different stakeholders

5. LMI Typical Development Process

6. LMI Generalized Activity Networks GANs handle both feedback and external effects

7. LMI And All arcs must execute to continue Inclusive or Continue after any arc completes Exclusive or Must complete exactly one arc to continue May follow Arcs execute with assigned probabilities Must follow All arcs execute GAN Junctions

8. LMI A Simple GAN Iterate Work i s a Finalize Abort

9. LMI System-Level ATM Development Project Operating Specs Integrate C*C* B*B* A*A* * Implement System Standard System-Wide Standards

10. LMI System-Level ATM Development Project Operating Specs Integrate C*C* B*B* A*A* * Implement System Standard Local stakeholders opt against system standard… System-Wide Standards

11. LMI Local-Level Standard Development ** Develop Local Standards IntegrateReworkIntegrate C ** B* * A* * …local stakeholders decide to implement local standards

12. LMI European ATM Development Project ** Develop Local Standards Operating Specs Integrate C*C* B*B* A*A* ReworkIntegrate C ** B* * A* * * Implement System Standard System-Wide Standards

13. LMI Quantitative Analysis Specify distributions for each step –For this application, a mixture of Weibulls and uniform distributions Specify probability of rework Perform a Monte Carlo analysis to generate simulation results for time to complete

14. LMI Alternative Weibull Distributions Time (t) P(t) Low Risk High Risk

15. LMI Weibull Descriptive Parameters Risk Ratio of Mode to Most Likely Low ValueProb(t>t mode ) Low1.150.65 Medium1.250.70 High1.500.80

16. LMI ATM Development Project Scenario 1: a European centralised approach –Low, medium, high risk for standards development phase Scenario 2: a Localised approach Scenario 3: a Typical European scenario

17. LMI Simulation Results for Moderate Risk Under Centralized Development (Scenario 1)

18. LMI Duration for a Localised and Independent Approach (Scenario 2)

19. LMI Simulation Results Under a “Typical European Approach” (Scenario 3)

20. LMI Summary of Results Scenario Mean Duration (Years) 25-75% Range (Years) Centralised Approach (Scenario 1) Low Risk14.914 -16 Moderate Risk15.915 - 17 High Risk16.916 - 18 Local and Independent Approach (Scenario 2) 18.716 - 22 Typical European Approach (Scenario 3) 21.018 - 24

21. LMI Conclusions A centralised approach for European projects is the best A mixed approach is generally worse GANs provide a relatively quick and computationally tractable method to analyze schedule risks Adding costs will provide a useful enhancement to the analysis