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 th USA/Europe ATM R&D Seminar June 23-28, 2003

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

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?

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

LMI Typical Development Process

LMI Generalized Activity Networks GANs handle both feedback and external effects

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

LMI A Simple GAN Iterate Work i s a Finalize Abort

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

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

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

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

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

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

LMI Weibull Descriptive Parameters Risk Ratio of Mode to Most Likely Low ValueProb(t>t mode ) Low Medium High

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

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

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

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

LMI Summary of Results Scenario Mean Duration (Years) 25-75% Range (Years) Centralised Approach (Scenario 1) Low Risk Moderate Risk High Risk Local and Independent Approach (Scenario 2) Typical European Approach (Scenario 3)

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