Reducing the Time and Cost Overruns for Aerospace Development Programs Using Precedence Networks Patterns Open Access
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Many commercial and defense product development programs are large-scale, complex, and contain significant innovation. Such programs often suffer cost and schedule over-runs. The goal of this work is to increase the production rate of well-ordered precedence networks for complex, large-scale aerospace product development. Earlier work in correspondence pattern automation enabled the automatic selection of inputs based on name matching. This praxis pursued broadening the scope to evaluate patterns of nodes and inputs. This research will accelerate the identification and reuse of patterns resident in project precedence networks, enabling a reduction in time and cost overruns for aerospace Product Development.This work has applied link prediction techniques developed for social network analysis to a recommendation system which augments human selection of inputs during precedence network development. This work also introduces the Triad Significance Profile for a project management precedence network.Using the link prediction techniques developed in social network analysis, and using a triadic closure algorithm developed in this praxis, it is shown that inputs may be predicted for the studied product development network, with reasonable prediction accuracy.