QUALITY OF SERVICE INDEX

Transcription

1 QUALITY OF SERVICE INDEX Advanced Presented by: David Dague SH&E, Prinicpal Airports Council International 2010 Air Service & Data Planning Seminar January 26, 2010

2 Workshop Agenda Introduction QSI/CSI Overview QSI Uses Historical Development of QSI Methodologies Factors Affecting Current QSI Application of QSI Methodologies Discussion 1

3 Introduction

4 Air Service Development is a Key Economic Driver for Communities of All Sizes Estimated Annual Economic Impact as Filed in the 2007 U.S. China Route Allocation Proceeding Dallas/Ft. Worth Beijing Detroit Shanghai PEK $180 Million DFW PVG $160 Million DTW Washington Beijing PEK $275 Million WAS 3

5 Airlines Choose New Markets Based on Quantifiable Facts Existing Passenger Demand, Strong Local Component Fare Environment Access to Corporate Travelers Right Fit With Existing Network Optimal Equipment Availability Balanced Originating Demand Long-Term Sustainable Return 4

6 In the International Arena, Air Service Growth is Focused on Asia and the Middle East Forecast Average Annual Growth Rates CY 2008 CY 2013 Middle East 7.7% Asia 2.9% Africa 2.7% Europe 2.7% Latin America 2.2% Average 2.7% 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% Source: IATA Traffic Forecasts, October

7 Which Makes Asia and Middle East Service Incredibly Competitive for U.S. Airports as the More Mature European Markets Already Are for Existing and New Service Restricted Entry Asian Markets Will be Hotly Contested Potential Expanded Open-Skies Opportunities in the Asian Markets Will Further Increase the Competitive Pressures at U.S. Airports Middle Eastern Airlines With Large A380 Fleets are Looking for Blank Checks 6

8 U.S. Airports Don t Compete with Each Other for International Aircraft They Compete Against Airports All Over the World From London Heathrow, the B787 Reaches Most of the World Only Southern Australia Falls Outside Its 8,500 mi. Range Shanghai Manila Tokyo Honolulu Vancouver Los Angeles LHR Mumbai Kuala Lumpur Cairns Perth Sydney Santiago Rio de Janeiro Johannesburg 7

9 To Make Your Airport s New Service Proposition Stand Out Amongst Your Competitors Nationwide or Worldwide, Credible QSI Results Are Key To Set Your Airport Apart, You Should Only Go After Service that the Market Can Support With a Straight Face The Quantity and Quality airline service has long been measured by Quality of Service Index ( QSI ) methodologies QSI Results Will Quantify Market Share, Predicted Passenger Traffic and Ultimately Demonstrate Route Profitability for the Carrier Since Each Airline Uses Its Own Model to Predict Passenger Behavior and Traffic, Having Your Own Opinion Allows You to Engage in Discussion with the Carrier 8

10 Section 1 QSI Overview

11 QSI Methodology is Straightforward Until It s Time to Adjust for the Real World 1. Determine factors that passengers consider when choosing flights 2. Incorporate these factors in quantitative weighting system 3. Calibrate the coefficients based on hard empirical data 4. Apply coefficients to predict how traffic will divide between competing airlines and services 10

12 Section 2 QSI Uses

13 QSI Is Used by Airline Route Planners To Answer a Variety of Questions All Targeted to Predicting Passenger Behavior How many passengers will my flight carry in this market at these times? How much will the market be stimulated with this new service? What percentage of the overall market will I capture? Against whom am I competing? From whom will this flight steal market share? Do I want to take that carrier on? Am I taking passengers off my own flights elsewhere? Is that worth it? What is the makeup of the passengers onboard? Local Connecting 12

14 QSI Results Help You Decide on Which Markets Offer the Most Potential and Aid Airline Route Planners in Making Decisions How much money will this flight make? Revenue by passenger type Profitability Does it contribute positively to my network? How does this flight s performance compare to the other flights that are possible with this aircraft? 13

15 Section 3 Historical Development of QSI Methodologies

16 The CAB QSI methodology was initially developed to determine expected changes in traffic due to changes in service Originally developed to determine expected gain or loss in traffic due to transfer of routes from Trunk to Local Service airlines Later refined to evaluate airline service proposals in route cases involving new or additional competitive services % Increase in Traffic QSI Traffic Stimulation Formula Original QSI methodology used weighting factors for aircraft type and number of stops and were applied only to direct flights CAB Staff conducted many analyses of traffic stimulation associated with increase in QSI % Increase in QSI Traffic Stimulation formula Traffic Chg = QSI Chg / ( (.5561 x QSI Chg) /

17 The CAB QSI methodology has been refined and adapted to the current air transport environment, but many of the basis element are the same Primary reliance on quantifying competitive value of airline services based on published schedules (OAG data) Significant refinements to measure relative value of different types of connections On-line, code share and interline service Trip times and travel circuity More difficult customization of QSI models Differences in fares offered Turnaway / yield management algorithms Adaption to international markets Capture of airline and airport preference weights QSI or similar market share models are used by nearly all airlines in the route system planning process Most are similar in logic and structure, but incorporate different weighting factors 16

18 Section 4 Factors Affecting Current QSI

19 Currently, Factors Affecting QSI are the Result of a Changing Industry and Competitive Environment Emergence of Airline Networks and Connecting Opportunities In many markets, connecting routings carry a significant share of the market This required developing a weighting system for connecting flights In some competitive situations, aircraft size is not an accurate predictor of market share Point-to-point carrier competes against a network carrier Network carriers seat capacity may be diluted with connecting traffic from outside O&D market Often this phenomenon is handled by adjusting the aircraft size weights or assigning a premium to the point-to-point carrier 18

20 Currently, Factors Affecting QSI are the Result of a Changing Industry and Competitive Environment Multiple Airport Markets Multi-airport markets have become more prevalent as congestion and delays have constrained service growth at primary airports serving major cities NYC, LA, SFO Bay Area, Washington/Baltimore, Chicago, etc. Traffic forecasts need to reflect airport choice on a market-by-market basis Ground access, comparative fare levels and service availability all influence the distribution of passengers between airports Credible forecasts should reflect empirical data how has traffic distribution changed with service additions in existing markets? 19

21 Currently, Factors Affecting QSI Are the Result of a Changing Industry and Competitive Environment Array of Fares Offered In the deregulated environment, carriers often compete based on fares QSI formula doesn t generally reflect fare differences Fare differentials can be captured by assigning a QSI premium to low-cost airlines This approach can be used to account for partial or full matching of reduced fares Predicted changes in O&D market fare levels are also necessary to estimate traffic stimulation resulting from new low-cost carrier entry 20

22 Section 5 Application of QSI Methodologies

23 Developing and applying QSI methodology to estimate expected market shares is normally a two step process Baseline QSI Estimates Service Frequency Aircraft type /seat capacity No. of Stops Connection penalty (on-line or interline) Elapsed time factor Routing circuity Calibration process share premium or gap analysis Airline preference factor (e.g., hub dominance, loyalty programs, low or high fares) Airport preference factor (near-in versus more remote airport) Time of day factor Pre-emption of seats in local market due to connecting psgr traffic Passenger turnaway due to high factors Problem of many poor connections picking up too much QSI share points 22

24 Calibration process is based on share premium and share gap analysis for comparable markets where empirical data is available Measure actual market shares by carrier for selected time periods against baseline QSI service shares Determine magnitude and pattern of variances Make logical assumptions of the reasons for premium and/or gap variances and make adjustments to QSI weighting factors Keep adjustments as simple as possible Ability to calibrate depends on data that is available Ideal situation is to have both industry and carrier detailed data 23

25 Examples of typical adjustments to baseline QSI results Problem: Many poor connections pick up too much weight in total market QSI Adjustment: Tighten rules for including flights (e.g., circuity, elapsed times, minimum share for inclusion, roundtrip requirement, etc.) Problem: LCC carrier traffic share consistently above its QSI share Check fare differential versus legacy carriers add carrier preference factor or share adjustment outside of QSI model Check amount of connecting passengers for legacy carriers adjust legacy carriers aircraft capacity value downward Problem: Hub carrier s share variance in hub markets are consistently high or low Apply an airline premium or penalty factor to carrier s baseline QSI Adjustment factors are often made outside the model for transparency 24

26 QSI methodology and expected traffic stimulation The QSI methodology measures changes in the quantity and quality of service it does not directly estimate traffic changes Separate analysis can be performed that quantify and/or account for the stimulation in traffic due to changes in service Traffic changes from traffic data bases Service changes using the QSI methodology for measurement Despite its age, The CAB QSI traffic stimulation formula often provides a good estimate of expected changes in passengers associated with the provision of new services 1st nonstop service in a market is often not well predicted by QSI, and analysis based on a comparable market approach is recommended 25

27 Section 6 Discussion