GO-15 · Station Performance & KPI Reporting

📊

Process Flow Diagram (BPMN)

📋

L4 Process Steps

Step	Step Name	Role / Swim Lane	System	Input	Output	KPI	Dec?	Exc?
Phase 1 1.1	Extract gate event data from AMS	Station Performance Analyst	SITA Airport Management System (AMS)	Scheduled reporting period close (daily/weekly trigger)	Raw gate event log: block-in, block-out, stand assignment, delays	Data extraction latency ≤15 min after period close	N	N
1.2	Pull ACARS turnaround event timestamps	Station Performance Analyst	SITA ACARS	AMS gate event log from step 1.1	ACARS-corroborated block times: chocks-on, door-open, door-close, push-back	ACARS coverage ≥95% of narrowbody turns at hub stations	N	Y
1.3	Ingest baggage handling data from BRS	Station Performance Analyst	SITA WorldTracer	Baggage reconciliation system (BRS) export for the period	Baggage mishandling records: late bags, wrong-loaded, offloaded, damaged	WorldTracer file ingestion success rate ≥99%	N	Y
1.4	Validate data completeness across all stations	Station Performance Analyst	AWS S3 / Redshift	Merged AMS, ACARS, and WorldTracer datasets loaded to Redshift staging schema	Data quality report: missing records flagged, coverage % per station	Data completeness ≥98% of operated flights before KPI run proceeds	Y	Y
Phase 2 2.1	Calculate D0 and D15 on-time departure metrics	Station Performance Analyst	AWS Redshift	Validated gate event dataset from phase 1	D0 and D15 departure performance % per station, per fleet type, per hour-of-day	System-wide D0 ≥78%; D15 ≥88% (IATA AHM 810 standard)	N	N
2.2	Compute gate and stand utilisation rate	Station Performance Analyst	SITA Airport Management System (AMS)	Stand assignment and block-time data from AMS	Gate utilisation % per stand per day; peak-hour congestion index per station	Gate utilisation target ≥72% at primary hub gates; congestion index <1.2 at peak	N	N
2.3	Calculate baggage mishandling and delivery KPIs	Station Performance Analyst	SITA WorldTracer	Baggage mishandling records from step 1.3; passenger boarding counts from Amadeus Altéa PSS	Mishandled baggage rate per 1,000 passengers; average delivery-to-belt time (minutes)	Mishandled bag rate ≤3.5 per 1,000 pax (IATA World Baggage Report benchmark); first-bag-to-belt ≤20 min	N	N
2.4	Assess computed KPIs against threshold rules	Station Performance Analyst	AWS Redshift	Computed KPI values from steps 2.1–2.3; threshold configuration table in Redshift	KPI status flags: Green / Amber / Red per metric per station	100% of KPI metrics receive a RAG status within 30 min of data validation	Y	Y
Phase 3 3.1	Build station scorecard dashboard in Tableau	Station Performance Analyst	Tableau	RAG-flagged KPI dataset published to Tableau Server data source	Interactive station scorecard: D0/D15, gate util, bag KPIs, trend sparklines per station	Scorecard refresh latency ≤60 min after reporting period close	N	N
3.2	Identify underperforming stations via threshold alerts	Ground Operations Performance Manager	Tableau	Published station scorecard with RAG flags	Underperformer shortlist: stations with ≥2 Red KPIs or single critical-metric Red flag	Underperformer identification completed within 2 hours of scorecard publication	N	N
3.3	Conduct root cause drill-down for Red-flagged stations	Ground Operations Performance Manager	AWS Redshift	Underperformer shortlist; flight-level delay code breakdown from Redshift	Root cause classification: staffing, equipment, aircraft late-in, ATC, weather, process	Root cause assigned for ≥90% of Red-flagged stations within 4 hours	Y	Y
Phase 4 4.1	Issue performance improvement notice to station manager	Ground Operations Performance Manager	Microsoft Azure Synapse	Root cause classification and supporting KPI evidence from step 3.3	Performance Improvement Notice (PIN) with specific KPI targets and 30-day remediation timeline	PIN issued within 24 hours of Red-flag identification for stations with ground-caused root cause	N	N
4.2	Assess whether VP Ground Ops escalation is required	Ground Operations Performance Manager	Tableau	Station KPI trend data; PIN issuance history; escalation threshold ruleset	Escalation decision: escalate to VP Ground Ops or manage at performance manager level	Escalation decision made within 48 hours of Red-flag identification	Y	N
4.3	Brief VP Ground Ops with station performance summary	Director of Ground Operations	Tableau	Escalated station performance package: KPI trend, root cause, PIN history, remediation plan	VP decision: resource reallocation, contractor review, or formal station audit trigger	VP briefing delivered within 72 hours of escalation trigger	N	N
Phase 5 5.1	Publish daily ops performance briefing	Station Performance Analyst	Tableau	Overnight KPI computation run; previous-day baseline	Daily Ops Briefing email/dashboard: top-5 KPI movers, Red-flag summary, delay code distribution	Daily briefing distributed by 07:00 local time at each hub station	N	Y
5.2	Distribute weekly station scorecard to station managers	Ground Operations Performance Manager	Tableau	7-day rolling KPI dataset; prior week scorecard for trend comparison	Weekly scorecard PDF distributed to station managers and regional directors	Weekly scorecard delivered every Monday by 09:00 local hub time	N	N
5.3	Submit monthly KPI report to senior leadership	Director of Ground Operations	Microsoft Power BI	Monthly KPI summary from Redshift; incident log from AMS; financial impact data from SAP S/4HANA Finance	Monthly Ground Ops Performance Report: trend charts, YoY comparison, cost-of-poor-quality estimate	Monthly report submitted to SVP Operations by the 5th calendar day of the following month	N	N
Phase 6 6.1	Analyse 13-week rolling performance trends	Ground Operations Performance Manager	AWS Redshift	13-week historical KPI dataset from Redshift; seasonality index from Amadeus SkyCAST	Trend analysis report: directional KPI movement, seasonal-adjusted performance, benchmark gap	Trend analysis completed quarterly; covers 100% of hub and focus-city stations	N	N
6.2	Review and update KPI targets in BI platform	Director of Ground Operations	Tableau	13-week trend report; benchmark data from IATA AHM and Airport Council International (ACI)	Updated KPI threshold configuration approved and published to Tableau data source; change log entry	KPI targets reviewed at minimum quarterly; changes approved within 5 business days of review	Y	N

📋

Process Attributes

Identification

Process IDGO-15

L1 DomainGround Operations & Airport Services

L2 ProcessAirport & Gate Management

L3 NameStation Performance & KPI Reporting

L4 Steps19 across 6 phases

Decision Gates5 (all with iteration loops)

Exceptions6 documented

Swim Lanes (Roles)

Station Performance Analyst

Ground Operations Performance Manager

Director of Ground Operations

Systems & Tools

SITA Airport Management System (AMS)SITA ACARSSITA WorldTracerAWS S3 / RedshiftAWS RedshiftTableauMicrosoft Azure SynapseMicrosoft Power BI

Key Performance Indicators

Extract gate event data from AMSData extraction latency ≤15 min after period close

Pull ACARS turnaround event timestampsACARS coverage ≥95% of narrowbody turns at hub stations

Ingest baggage handling data from BRSWorldTracer file ingestion success rate ≥99%

Validate data completeness across all stationsData completeness ≥98% of operated flights before KPI run proceeds

Calculate D0 and D15 on-time departure metricsSystem-wide D0 ≥78%; D15 ≥88% (IATA AHM 810 standard)

Compute gate and stand utilisation rateGate utilisation target ≥72% at primary hub gates; congestion index <1.2 at peak

Calculate baggage mishandling and delivery KPIsMishandled bag rate ≤3.5 per 1,000 pax (IATA World Baggage Report benchmark); first-bag-to-belt ≤20 min

Assess computed KPIs against threshold rules100% of KPI metrics receive a RAG status within 30 min of data validation

Airline-Specific Risks & Pain Points

AMS event timestamps may be entered manually by ramp agents, introducing ±3 min inaccuracies that distort D0 calculations; no auto-capture at many regional stations

Regional and outstations with older ground equipment may have ACARS downlink gaps; fallback to manual log creates 5–8% data voids that inflate reported OTP

WorldTracer data latency at transborder stations (US/Canada/Mexico) can exceed 4 hours due to customs hold scanning delays, making same-day reporting unreliable

Redshift ingestion pipelines from SITA AMS rely on SFTP batch files; file delivery failures at outstations can silently drop entire station's data from the reporting run

Carrier-caused vs. non-carrier delay classification is applied post-hoc via delay codes entered by agents — code 93 (ATC) vs code 15 (late aircraft) misclassification inflates ground ops performance metrics

AMS stand assignment data does not reflect real-time remote-stand tows, causing gate utilisation to be overstated for aerobridged stands and understated for remote apron positions

Inputs / Outputs

Primary InputScheduled reporting period close (daily/weekly trigger)

Primary OutputUpdated KPI threshold configuration approved and published to Tableau data source; change log entry

←PreviousGO-14 · Ground Support Equipment (GSE) Management NextMR-01 · Line Maintenance Planning & Execution→