Background: Large technology initiatives depend on coordinated work across specialized delivery units, creating technical, informational, and scheduling dependencies whose structure may affect delivery performance. Coordination theory and software-engineering research suggest that dependencies generate coordination requirements, but conventional program controls frequently summarize them as isolated risks rather than an interconnected system. 
Purpose: This study develops and empirically benchmarks a network-based approach for examining whether dependency topology provides information about delivery risk beyond conventional project characteristics. 
Method: The study uses the TAWOS public Jira dataset. The published version contains 508,963 issues from 44 open-source projects, including 267,568 issue links, while public version 1.1 contains 458,232 issues from 39 projects after five projects were removed. Directed dependency networks are specified from explicit Depend issue links, with software components serving as transparent delivery-unit proxies. Planned analyses include degree, betweenness, density, centralization, concentration, clustering, and modularity, linked prospectively to sprint-window misses and issue-resolution duration. 
Results: In the real published aggregate benchmark, median issue-link intensity was 0.379 links per issue and varied from 0.064 to 0.824. Link intensity correlated with comments per issue, ρ=0.536, p<0.001, but not conclusively with change-log intensity or bug share. These findings establish marked dependency heterogeneity and an association with coordination activity, but do not establish a centrality-delay effect. Direct longitudinal coefficient estimates are deliberately withheld until the raw network analysis is executed. 
Contribution: The study formalizes dependencies as network structure rather than isolated entries and provides a reproducible design for testing structural delivery-risk mechanisms. 
Practical implications: Network indicators could complement existing delivery governance by prioritizing structurally consequential dependencies and potential bottlenecks.