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Process Flow Diagram (BPMN)

NP-RM-09 BPMN diagram
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L4 Process Steps

StepStep NameRole / Swim LaneSystem InputOutputKPIDec?Exc?
Phase 1
1.1
Trigger post-departure PSS revenue extract Revenue Reporting Analyst Amadeus Altéa PSS Flight departure confirmation signal from Altéa DCS Raw booking & ticketing record extract per flight Extract available within 30 min of block-in; ≥99.5% flight coverage N N
1.2 Pull ancillary revenue feeds from ancillary platform Revenue Reporting Analyst Amadeus Altéa Ancillary (Ancillary Services Manager) Completed flight leg; ancillary transaction logs Per-flight ancillary revenue breakdown (bags, seat upgrades, extras) Ancillary revenue captured per passenger ≥ airline target (e.g. USD 28/pax industry benchmark) N Y
1.3 Validate booking-to-flown passenger completeness Revenue Reporting Analyst Amadeus Altéa PSS DCS final manifest; booking record extract Reconciled flown passenger count with no-show & offload flags Booking-to-manifest match rate ≥99.8% before proceeding Y Y
Phase 2
2.1
Reconcile ticketed revenue vs. flown revenue Revenue Accounting Analyst SAP S/4HANA Finance (FI/CO) Altéa ticketing extract; flown passenger manifest Flight-level ticketed vs. flown revenue variance report Revenue variance tolerance ≤1% of flight revenue before exception flag Y Y
2.2 Match BSP & interline settlement amounts Revenue Accounting Analyst IATA BSPlink BSP billing files; interline prorate agreements Settled interline revenue allocation per flight segment BSP settlement matched within 48 hours of departure; dispute rate <0.5% N Y
2.3 Validate upgrade & no-show revenue capture Revenue Accounting Analyst Amadeus Altéa PSS No-show records; upgrade transaction log from DCS Confirmed no-show penalty revenue and upgrade delta postings No-show revenue leakage <0.2% of total flight revenue Y Y
Phase 3
3.1
Aggregate O&D revenue by fare class & cabin Revenue Management Analyst Amadeus Revenue Management (NRM) Reconciled flight revenue records; booking class mapping O&D revenue matrix by fare class, cabin, and market pair O&D attribution completeness ≥98% of total flight revenue N N
3.2 Calculate RASM, PRASM, and load factor vs. forecast Revenue Management Analyst Amadeus Revenue Management (NRM) O&D revenue matrix; capacity data from Amadeus SkyWORKS Flight-level RASM, PRASM, load factor actuals vs. forecast delta RASM actuals within ±3% of pre-departure NRM forecast; load factor ≥83% N N
3.3 Identify revenue spoilage and dilution events Revenue Management Analyst Amadeus Revenue Management (NRM) RASM/PRASM variance report; fare class booking curve Spoilage flag (unsold high-yield seats) or dilution flag (over-discounted load) Spoilage rate ≤4% of available seats on constrained routes; dilution rate ≤2% Y Y
Phase 4
4.1
Generate flight-level post-departure P&L report Revenue Reporting Manager SAP S/4HANA Finance (FI/CO) Reconciled revenue; ancillary revenue; prorate settlements Flight-level revenue P&L report with actuals vs. plan variance Report generation within 4 hours of block-in for priority routes N N
4.2 Publish variance dashboard for RM and Finance Revenue Reporting Manager Tableau Flight P&L report; RASM/PRASM variance data Interactive revenue variance dashboard published to BI portal Dashboard refresh latency ≤1 hour from report generation; stakeholder access rate ≥90% N N
4.3 Route significant variances to Revenue Director Revenue Reporting Manager SAP S/4HANA Finance (FI/CO) Variance dashboard; threshold breach flags Escalation notification with variance summary and root-cause annotation Escalation triggered within 2 hours for variances ≥5% of planned flight revenue Y N
4.4 Distribute approved reports to RM and Finance teams Revenue Director AWS S3 Approved flight-level revenue reports Published report package to S3 data lake for downstream consumption Distribution to all stakeholders within 6 hours of block-in for same-day flights N N
Phase 5
5.1
Feed actuals into NRM for forecast comparison Revenue Management Analyst Amadeus Revenue Management (NRM) Finalised flight revenue actuals from S3 data lake Actuals posted to NRM historical performance database Actuals feed completed within 24 hours of departure; data completeness ≥99% N Y
5.2 Assess forecast model error vs. recalibration threshold Revenue Management Analyst Amadeus Revenue Management (NRM) Actuals vs. NRM forecast delta; rolling MAPE statistics Recalibration decision flag: proceed or trigger model update MAPE ≤8% on 30-day rolling basis; trigger recalibration if MAPE >10% for 5 consecutive days Y Y
5.3 Trigger NRM demand model recalibration job RM Systems Administrator Amadeus Revenue Management (NRM) Recalibration trigger flag; accumulated actuals dataset Updated NRM demand model parameters; recalibration completion log Recalibration job completes within 6-hour maintenance window; model improvement verified by ≥1% MAPE reduction N Y
5.4 Archive finalised flight revenue record to data lake Revenue Reporting Analyst AWS S3 Validated & reconciled flight revenue package Immutable flight revenue archive record in S3 with audit trail 100% of departed flights archived within 24 hours; retention compliance per IATA Resolution 787 (7-year minimum) N N
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Process Attributes

Identification

Process IDNP-RM-09
L1 DomainNetwork Planning & Scheduling
L2 ProcessRevenue Management
L3 NamePost-Departure Revenue Reporting
L4 Steps17 across 5 phases
Decision Gates6 (all with iteration loops)
Exceptions9 documented

Swim Lanes (Roles)

Revenue Reporting Analyst
Revenue Accounting Analyst
Revenue Management Analyst
Revenue Reporting Manager
Revenue Director
RM Systems Administrator

Systems & Tools

Amadeus Altéa PSSAmadeus Altéa Ancillary (Ancillary Services Manager)SAP S/4HANA Finance (FI/CO)IATA BSPlinkAmadeus Revenue Management (NRM)TableauAWS S3

Key Performance Indicators

Trigger post-departure PSS revenue extractExtract available within 30 min of block-in; ≥99.5% flight coverage
Pull ancillary revenue feeds from ancillary platformAncillary revenue captured per passenger ≥ airline target (e.g. USD 28/pax industry benchmark)
Validate booking-to-flown passenger completenessBooking-to-manifest match rate ≥99.8% before proceeding
Reconcile ticketed revenue vs. flown revenueRevenue variance tolerance ≤1% of flight revenue before exception flag
Match BSP & interline settlement amountsBSP settlement matched within 48 hours of departure; dispute rate <0.5%
Validate upgrade & no-show revenue captureNo-show revenue leakage <0.2% of total flight revenue
Aggregate O&D revenue by fare class & cabinO&D attribution completeness ≥98% of total flight revenue
Calculate RASM, PRASM, and load factor vs. forecastRASM actuals within ±3% of pre-departure NRM forecast; load factor ≥83%

Airline-Specific Risks & Pain Points

Altéa extract latency spikes during high-frequency bank departures, delaying same-day reporting windows
Ancillary items sold via NDC API Gateway may not post to Altéa in real time, creating gaps in same-day reporting
Last-minute irregular operations (IROPs) create orphan booking records that misstate flown revenue
Multi-coupon interline tickets generate partial-revenue allocations that SAP FI cannot auto-match without manual MITA settlement codes
Prorate disputes with interline partners under MITA/BIMA can take 30–90 days to resolve, overstating interim revenue
Oversold flights with denied boarding (IDB/VDB) generate compensation cost offsets that are frequently mis-posted against revenue rather than cost accounts

Inputs / Outputs

Primary InputFlight departure confirmation signal from Altéa DCS
Primary OutputImmutable flight revenue archive record in S3 with audit trail
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