Draft Statement of Objectives - OrchestrateSI

DRAFT

OrchestrateSI: Integrated Statement of Objectives (SOO)

1. Background, Purpose, and Operational Vision

1.1 Program Background — TSA manages passenger throughput across more than 430 federalized commercial airports. Over 70 decentralized scheduling offices manually coordinate tens of thousands of Transportation Security Officers using disparate legacy systems and static bidding procedures, which limits frontline agility, inflates overtime costs, and complicates labor-policy compliance. Checkpoint layouts vary by airport category and terminal design, and frontline supervisors currently rely on manual, reactive observation to spot queue surges — meaning bottlenecks are often identified only after throughput has already degraded. Passengers also face unpredictability across their curb-to-gate journey because transit, traffic, flight, and border data remain fragmented across isolated platforms, leaving both passengers and TSA leadership without reliable advance demand indicators. 1.2 Combined Purpose and Strategic Vision — TSA is seeking to evolve toward an integrated, data-driven security operations ecosystem that synchronizes Demand Intelligence, Tactical Checkpoint Visibility, and Workforce Supply Optimization. By unifying external arrival signals, intra-checkpoint sensor telemetry, and automated personnel scheduling into one operational framework, TSA aims to shift from reactive bottleneck mitigation to predictive operational mastery — lowering costs, maintaining passenger wait times at or below target thresholds, enhancing accountability, and reinforcing national security screening standards.

2. Individual Objectives and Performance Metrics

TSA is seeking a consolidated solution, or set of integrated solutions, unified under a single operational architecture but comprising three distinct technical workstreams.
2.1 Workstream 1 – Workforce Orchestration: Objectives include SI-driven predictive scheduling using historical throughput, seasonal patterns, and real-time flight data; an automated bidding engine supporting leave bidding, senioritybased shift bidding, shift trades, call-outs, and split shifts up to 14 months in advance; a dynamic "what-if" capacity simulation environment for leadership to model lane configurations, equipment availability, and budget constraints; automated tracking of Paid Not Worked (PNW), Overtime (OT), FTE burn rates, training, and certifications; and bidirectional data exchange with existing HR, payroll, and credentialing systems. Performance metrics include measurable