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Using Revenue Management Forecasts for Fleet Assignment

Using Revenue Management Forecasts for Fleet Assignment. Karl Isler Nese Akkaya. Introduction. Focus of Current Scheduling Activity Fleet reassignment due to prevailing demand level or operational setting. Introduction. Problems of Current Procedure RM requests mostly equipment upgrades

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Using Revenue Management Forecasts for Fleet Assignment

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  1. Using Revenue Management Forecasts for Fleet Assignment Karl Isler Nese Akkaya

  2. Introduction • Focus of Current Scheduling Activity Fleet reassignment due to prevailing demand level or operational setting

  3. Introduction • Problems of Current Procedure • RM requests mostly equipment upgrades • Emphasis on local rather than global optimality • Requests rather late in the planning process

  4. Introduction

  5. Introduction • Study to Address Central Issues • The quantification of the contribution of reassignments to overall profitability • The assessment of YM demand forecasts as one of the main drivers of refleeting decisions • Optimal fleet assignment based on YM demand forecasts and the actual value of that assignment.

  6. Simulation • Flight set A320 & AR1: flight legs to be operated with AR1 or a A320 family (1751 flight legs) • Flight set A320: flight legs to be operated with the A320 family (1333 flight legs)

  7. Simulation • Optimization step: Optimal fleet assignment based on RM demand forecast with standard fleet assignment tool • Evaluation step: Value of the proposed optimal fleet assignment at actual demand

  8. Simulation Results

  9. Simulation Results • 50 % Realized as Early as 60 Days Prior • Substantial Opportunity Costs of Product Constraints (AR1) • Convertible Seats Have Marginal Effect • 30% - 40% of Flights Changed • 50% of Changes Provide 80% of Benefit

  10. Simulation Results: RM Forecast

  11. Simulation Results: RM Forecast Conclusions • RM forecast performance monitoring • Forecast targets for RM • Need better forecast for group classes • Attention to RM “less relevant” classes

  12. Live Trial (CA3T) Optimal assignment of aircraft from A320 family and AR1 for trial week under real technical and operational constraints 58 days prior • MGT and block time restrictions • Total seat limitations • Product restrictions • Crew requirements • Maintenance requirements • Payload restrictions

  13. Decision Process • Generation of three scenarios by extending the basic scenario to enforce • Swissair crew links of severity level 3 (CA3T1) • All Swissair crew links and Crossair crew rotational preferences (CA3T3) • Comparison of incremental profit and incremental crew costs for each scenario

  14. Decision Process Comparison of incremental profit and crew costs

  15. Findings

  16. Findings • Existence of profit potential is evident. • 1.7% of profit increase realized (corresponds to ~1% of net revenue increase on the A320 & AR1 set) • Important inputs to improve current scheduling process and IT architecture • Forecasting demand for special events and for feeder legs needs more attention.

  17. Outlook Anticipate decrease in profit potential due to • Effective usage of optimal fleet assignment at long term schedule and network planning stage • Implementation of subseasonal planning with possible forecast leveling between long term and (aggregated/averaged) fully dated demand forecasts.

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