Situation awareness in airway facilities : replacement of maintenance control centers with operations control centers.

Truitt, Todd R. (Todd Richard), 1967-; Ahlstrom, Vicki · 2000 · ROSA P / William J. Hughes Technical Center (U.S.)

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This technical note examines the impact of the Federal Aviation Administration’s (FAA) plan to consolidate 42 Maintenance Control Centers (MCCs) into three Operations Control Centers (OCCs) on specialists’ situation awareness (SA). The consolidation aims to increase efficiency and standardize procedures by centralizing operations. The study, conducted by the National Airspace System Human Factors Branch, evaluates two proposed organizational models for the OCCs: the Area-Specialist Plan and the Technical-Specialist Plan. The Area-Specialist Plan maintains the current structure where specialists monitor multiple technical systems within a specific geographical area. The Technical-Specialist Plan reorganizes responsibilities so that specialists focus on specific technical domains (e.g., communications, navigation aids) across a much larger geographical area covering one-third of the country. The authors analyzed SA by reviewing existing job task analyses and identifying tasks directly and indirectly related to SA, such as monitoring facility status and coordinating maintenance resources. They also evaluated Knowledge, Skills, and Abilities (KSAs) relevant to SA, highlighting the importance of recognizing equipment trends, maintaining wide scope awareness, and possessing both site-specific environmental knowledge and in-depth technical expertise. The study notes that current MCC specialists often rely on delayed information rather than real-time monitoring, suggesting that SA is heavily dependent on expertise and the ability to predict future system states. The analysis reveals significant tradeoffs between the two plans regarding SA. The Area-Specialist Plan preserves area-specific knowledge, such as understanding local weather and terrain impacts, which is critical for predicting outages. However, it requires specialists to distribute attention across multiple systems. The Technical-Specialist Plan enhances technical expertise, allowing specialists to better recognize anomalous parameters and potentially prevent outages through superior technical knowledge. However, it risks degrading SA initially because specialists lack the area-specific knowledge required to anticipate environmental impacts on facilities across a vast region. The authors identify potential solutions for the Technical-Specialist Plan, including creating databases of area-specific knowledge, detailing specialists to MCCs for training, or implementing structured on-the-job training, though these approaches present challenges regarding workload, time, and knowledge retention. The study concludes that the choice between plans involves balancing area-specific knowledge against technical expertise. If area-specific knowledge is prioritized, the Area-Specialist Plan may be preferable to maintain predictive SA. If technical expertise is deemed more critical for preventing outages, the Technical-Specialist Plan could improve NAS reliability, provided that mechanisms are established to help specialists acquire necessary area-specific knowledge. The document serves as a foundation for future human-in-the-loop testing and provides decision-makers with insights into the cognitive implications of restructuring airway facility operations.

Key finding

The Technical-Specialist Plan risks initial degradation of situation awareness due to insufficient area-specific knowledge, potentially increasing unplanned outages, whereas the Area-Specialist Plan preserves existing local expertise but limits technical specialization.

Methodology

review

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
discover success rosap 2 2026-05-23
archive success 1 2026-05-23
extract success cached 2 2026-06-10
clean success 1 2026-06-01
chunk success 1 2026-06-01
embed success 1 2026-06-02
enrich success 1 2026-05-23
promote success 1 2026-05-23
summarize success llm qwen3.6-27b-prismaquant summ-v5 3 2026-06-10
tag success vector_similarity 24 2026-06-11
verify success 2 2026-06-10

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.