Airline flight networks, cycle times, and profitability: 2004–2006

Airline flight networks, cycle times, and profitability:2004–2006

David West & John Bradley

Received: 28 August 2008 /Revised: 7 January 2009 /Accepted: 3 February 2009 /Published online: 20 February 2009# Springer Science + Business Media, LLC 2009

Abstract This research investigates the relationships betweenairline flight networks, aircraft cycle times, and carrierprofitability for ten large US domestic airlines. We find thatdirect point-to-point flight networks and short cycle times areoperational factors that airlines should exploit to improveprofitability. These findings are based on the analysis of 11.9million flight records from 2004 through 2006. The resultscontradict earlier research validating performance advantagesof hub-and-spoke flight networks. Today, the advantages ofpassenger consolidation at hub airports are lost to lower aircraftutilization and productivity problems created by the extendedcycle times at hubs. We also find that airline operating profitcan be increased by improving the efficiency of the aircraftturnaround process and decreasing aircraft fleet complexity.This research also provides an estimate of the marginalopportunity cost of ground time. A 1-min reduction in aircraftfleet ground time increases the average sized carriers’operating income by $12 to $18 million.

Keywords Cycle time . Hub-and-spoke .

Operational strategy . Profitability

1 Introduction

Two key operational factors that critically impact airlineprofitability are the design of a network of daily flightsfrom sources to destinations (Nero 1999) and the opera-tional capability to turn around aircraft quickly (which wewill refer to as the cycle time) (Gittell 2001; VanLandehanand Beuselinck 2002). Since deregulation of the US airlineindustry, the dominant flight schedule network of domesticcarriers has been the hub-and-spoke (HS) strategy usingmultiple flight segments. With a HS network, a passenger isrouted from an origin to a hub airport where the passengerconnects to an outbound flight for the final destination. Thisstrategy has been favored for its ability to aggregatepassenger loads creating economies of traffic density,economies of scale, and economies of scope. Toh andHiggins (1985) report weak evidence that a hub-and-spokeflight schedule increases airline profitability (based onoperational data for 1982). Bania et al. (1998) also reportsempirical evidence that large multi-hub networks are aneffective strategy for an airline to secure a competitiveadvantage.

Typical arguments for hub-and-spoke networks (Nero,1999) cite aggregation efficiencies but overlook degrada-tion of performance from congestion and capacity con-straints at hubs and Also overlooked are costs to thetraveling passengers whose itineraries are significantlylonger as two flight segments are required with extendedlayovers. Hub airports have become heavily congested withwaves of arriving flights followed by waves of outboundflights scheduled at convenient travel times for passengers.As we intuitively expect (and this research confirms), thehubs require longer cycle times to turn around aircraft. Theextension of cycle times at hub airports undermines airlineproductivity and alienates customers. It is also noteworthy

Oper Manag Res (2008) 1:129–140DOI 10.1007/s12063-009-0014-6

D. West (*)College of Business, Department of Marketing and Supply ChainManagement, East Carolina University,Greenville, NC 27858-4353, USAe-mail: [email protected]

J. BradleyCollege of Business, Department of Management InformationSystems, East Carolina University,Greenville, NC 27858-4353, USAe-mail: [email protected]

that airline revenue per passenger mile flown has beenconsistently declining over the past decade (Lee 2003). Atsome point the airlines lose the ability to recover the costsof the two flight segments required by the HS networkstrategy. Revenue pressures are also aggravated by theincreasing popularity of commuter jets that can directlyserve smaller markets (Savage and Scott 2004).

The purpose of this research is to investigate therelationships between airline flight networks, aircraft cycletimes, and airline profitability for large US domesticcarriers. Our first research hypothesis is that aircraft cycletimes are largely determined by (1) the design of the flightnetwork, and (2) the relative efficiency of the airline in theaircraft turnaround process. The first factor is airportspecific and the second is an airline organizational factor.We hypothesize that centralized networks and inefficientturnaround processes result in longer cycle times. Oursecond hypothesis is that longer cycle times are associatedwith lower operational profitability. We expect cycle timesto have a significant effect on a key airline productivitymeasure: costs per seat mile. Shorter cycle times result inmore time flying and more seat miles flown per aircraft. Itis also possible that differences in cost structures cancontribute to differences in productivity and profitability.We include sensitivity analysis which will focus on differ-ences in aircraft fleet complexity, differences in fuelconsumption and cost, and differences in frills (i.e., foodcosts). In examining cost differences we note that the airlineindustry does exhibit a high degree of “sameness.” Thissimilarity results from the fact that their aircraft are fromtwo manufacturers (Airbus and Boeing), pilots and crew arehired from the same population of applicants, and the keyraw material (jet fuel) is purchased from a commoditymarketplace. The nature of competition in the industry islargely price and schedule convenience. No airline has beenable to successfully differentiate and create a niche market.For all these reasons, we anticipate that cycle time will bethe dominant determinant of operational profitability.

In the next section we review prior research on airlinenetwork design, aircraft cycle times, and profitability. Thisis followed by a description of our experimental design, adiscussion of results, and implications for operationalprofitability in the airline industry.

2 Operational models of US airline industry

2.1 Designing flight networks

The design of a flight network for an airline is criticallyimportant; it determines a large portion of an airline’s cost(Barnhardt and Cohn 2004; Dobson and Lederer 1993). Acommon flight network strategy in the airline industry is the

hub-and-spoke (HS) strategy, which is defined by Rosenbergeret al. (2002) as a flight network with a large percentage ofthe flight segments into or out of a small subset of stationscalled hubs. A number of studies report that HS networksallow airlines to improve economic returns by exploitingeconomies of scale, economies of scope, and flightfrequency, enhancing monopolistic market power at dom-inant hub locations (Adler and Smilowitz 2007; Gillen andKanafani 2005; Brueckner and Zhang 2001; Barla andConstantatos 2000; Nero 1999; Dobson and Lederer 1993).The economic rationale for HS network is that the airlinescan aggregate demand, increase frequency, reduce airfares,and prevent entry of competitors into the marketplace(Adler 2001).

There are a number of studies that focus on theadvantages of HS network flight schedules. Button (2002)specifically highlights HS as protective of the airlines thatcenter their operations on large hubs. Nero (1999) exploresthe extent to which airlines operating large hub-and-spokenetworks secure a competitive advantage. Other research onthe deployment of HS networks in the airline industryincludes Gillen and Kanafani (2005), Sasaki et al (1999),and Kelly and Bryan (1998).

Recent research emphasizes emerging problems with HSflight schedules. For example, Rietveld and Brons (2001)describe how carriers schedule inbound and outboundflights in banks during desirable travel times, therebycreating schedule delays at hub airports. This producescongestion during peak hours, delays that spill over to largeportions of the airlines’ flight network. Congestion anddelays also lead to low daily utilization of aircraft due tolong hub turnaround, which produces higher costs per seatmile as aircraft and crew productivity are suboptimal. Adisruption at a hub station can prevent passengers fromflying their original itinerary and impact the airline’s entirenetwork (Adler and Smilowitz 2007). Lee (2003) concludesthat overall airline prices have decreased significantly inreal terms throughout the 1990s. The implication ofreduced real prices on HS flight schedules is critical. Withdeclining revenue, an airline reaches a point where it cannotrecover the costs of the necessary two flight segments in aHS strategy. A survey of airline performance factors byGudmundsson (1999, 2004) suggests that airline manage-ment is beginning to recognize that hub-and-spoke flightdesigns are not positively related to performance. Gud-mundsson (2004) measured management’s perceptions ofthe importance of aircraft utilization and hub-and-spokeoperations for 36 US airlines. Using an 11 point Likertscale (where 0 is of no importance and 10 is highlyimportant), aircraft utilization, which is directly related tocycle time, received a mean response of 7.78 and HSnetwork received a mean of 3.98. It is interesting to notemanagement perceives that a hub-and-spoke flight schedule

130 D. West, J. Bradley

is considerably less important than aircraft utilization. Wespeculate this explicitly recognizes the fact that delays athubs increase aircraft cycle time and thereby lower aircraftutilization by keeping aircraft on the ground for extendedperiods of time.

Southwest is one airline that does not use a HS strategyto schedule flights, instead using a point-to-point (PP)network where a high percentage of passengers are routeddirectly from origin to destination. There is a thread ofresearch studying the viability of multiple schedulingstrategies in the airline industry. Using a game-theoreticalframework for two carriers, Alderighi et al. (2005) showthat asymmetric equilibrium may exist when carrierscompete in designing their network configurations and thatHS, PP, and multi-hub strategies may coexist. They find“two main stable outcomes, which depend on the size of theinternal market. First, when the internal markets are small,PP network strategies are played by both carriers, while fora specific subset of parameters a collusive equilibrium in aHS configuration can be derived. Second, when the size ofthe internal markets is large, asymmetric configurations,where one carrier chooses a HS strategy and the otherchooses a PP strategy, are the only stable equilibrium”(Alderighi et al 2005).

The only prior study to directly relate airline networkdesign to profitability was conducted by Toh and Higgins(1985) based on operational data from 1982. They report apositive relationship between hub-and-spoke network cen-trality and airline profitability but report a model R2 of only0.1. We note this study conducted with data from 1982represents an industry with a very different macroeconomicenvironment. Revenue per passenger mile has declinedprecipitously since then, hubs are no longer protectedfortresses for legacy carriers, and low cost operators haveinvaded key markets bringing down fares (the “Southwesteffect”) (Vowles 2001).

2.2 The aircraft turnaround process

Gittell (2001) argues that preparing flights for departure isone of the core processes of an airline’s operations, repeatedhundreds of times daily in dozens of locations. Thedeparture process is a set of twelve interdependent tasksperformed by a cross-functional group consisting of gateagents, ticketing agents, ramp agents, baggage handlers,cabin cleaners, caterers, fuelers, freight agents, operationsagents, pilots, flight attendants, and mechanics (Gittell2001). The success or failure of the aircraft turnaroundprocess can make or break an airline’s reputation for bothreliability and profitability by impacting productivity of theairline’s most costly assets, employees and aircraft. Hult etal. (2002) communicate the importance of speed as acompetitive weapon and concludes that regardless of the

nature of the industry, cycle time is a significant key tosuccess. Despite the critical nature of cycle times in leanmanufacturing systems, we find no published researchlinking cycle times and profitability in service industries,including the airline industry. There are a number ofresearch studies of the process of boarding passengers, aportion of the turnaround effort. For example, Van Landeghemand Beuselinck (2002) recognize that turnaround times forairplanes are under constant pressure to be reduced, andprovide a comprehensive simulation study of a number ofpotential boarding processes.

The contribution of this research is to empirically test therelationships between flight network design, aircraft cycletimes, and airline profitability in a multi-year time period.Performance is measured on 11.9 million flight records forten domestic carriers. This represents every domestic flightfrom 2004–2006.

3 Data warehouse description and experimentalmethodology

Data warehouses are generally designed around one of twoplans. The first is a centralized single repository. Thesecond is a set of smaller data warehouses that reduceresponse times and can be distributed to select users. Somedata warehouses are designed using a three-tiered architec-ture with highly aggregated data in the top tier, data that isfocused on a particular theme or functional area in thesecond tier, and more detailed data in the third tier.Databases in the second tier are known as data marts.Making data marts available to analysts is a moresuccessful strategy than giving everyone access to a singlerepository (Chenoweth et al. 2006).

The Bureau of Transportation Statistics of the Researchand Innovative Technology Administration in the U.S.Department of Transportation provides a data mart calledAirline On-Time Performance Data that contains scheduledand actual departure and arrival times reported by certifiedUS air carriers, where the carrier accounts for at least onepercent of domestic scheduled passenger revenues (www.transtats.bts.gov). The data warehouse also contains domaintables that provide the acceptable values for many of theattributes. The primary table contains departure delays andarrival delays for non-stop domestic flights by major aircarriers and provides such additional items as origin anddestination airports, flight numbers, scheduled and actualdeparture and arrival times, cancelled or diverted flights,taxi-out and taxi-in times, wheels-off and wheels-on times,air time, and non-stop distance. The On-time Performancedata mart currently contains over 114 million records forflights from 1987 to the present. For this study, a total of21.4 million records with data from 2004 to 2006 were

Airline flight networks, cycle times, profitability: 2004–2006 131

http://www.transtats.bts.gov

http://www.transtats.bts.gov

downloaded into monthly csv-formatted files. We wrote aprogram to open the csv-formatted file and process eachflight segment, transforming the data and storing it in SQLServer tables.

Since each flight segment was reported separately andcontained air time (not ground time), the transformationincluded sorting the data by flight date, time, and tail number.Each flight segment record was then matched chronologicallyto the subsequent flight segment based on date/time and tailnumber. When correctly matched, the difference between thearrival time (wheels-on) of the first segment and the departuretime (wheels-off) of the second segment provided the totaltime spent on the ground (the cycle time). For example, ifflight segment #1 to Dallas Fort Worth arrived at 3:30 P.M. andflight segment #2 departed at 4:15 P.M., the cycle time wouldbe the difference, 45 min. When a flight segment arrived latein the day (i.e., 11:30 P.M.) and departed early the nextmorning (i.e., 12:30 A.M.), the transformation algorithmcompensated for the change in date to calculate the correctground time of 1 h. Cycle times were not calculated forflights that were held overnight for morning departures.Minor inconsistencies like two tail numbers going to twolocations at the same time were eliminated. The transforma-tion resulted in 11.9 million useable cycle times. Using SAS10.0, our SQL table was defined as a data set for input intothe analyses conducted in the next section.

The ten major US carriers investigated in this study areidentified in Table 2. Commuter airlines, international

flights, and charter operations were excluded. The metricused to measure the relative reliance on HS flight design isthe percentage of an airline’s routes that originate from thetwo most frequently used airports. This metric is also usedby Bania et al. (1998) to measure the degree of centraliza-tion in route structure. We maintain this is an appropriateproxy for the degree of network centralization, as theairlines investigated have at most two hubs.

4 Discussion of results

4.1 Cycle time at hub airports

Each domestic carrier independently schedules a network offlights between city pairs served and determines whichroutes will be served by consolidating passengers at hubairports. The operation of hubs to consolidate passengersnecessitates scheduling a wave of inbound flights followedby a wave of departures, creating extreme variations indemands on airport resources. Figure 1 depicts the dailyflight activity (both arrivals and departures) at Atlanta’sHartsfield airport during a period of normal operation in June2006. The number of arriving and departing flights issummarized by the histogram depicting 30 min time intervals.During most of the day, activity levels vary from a low of 20flights to a high of 100 flights during 30 min intervals.Departure delays are plotted on the right axis of Fig. 1. It is

Time of Day (minutes)

Fig. 1 Flight frequency anddeparture delays Atlanta, June2006


evident that departing flights are delayed by at least 5 minthroughout the day. The impact of these delays cumulates bymidday, reaching as much as 20 min; this level is sustainedthroughout most of the remainder of the day. These delayshave direct costs at the hub operations, but also propagatethrough the airline networks.

The 14 most frequently used hub airports, based on theirflight histories from 2004 to 2006, are identified in Table 1.The hub with the longest cycle time is Houston, whereflights of domestic carriers averaged 130.5 min on theground for each aircraft turnaround. Seven of the 14 hubshad cycle times in excess of 100 min including Minneapolis,Charlotte, Detroit, Atlanta, Chicago, and Dallas Fort Worth.The average cycle time for the 14 hub airports is 97.6 min,while all other major airports average 72.2 min. In a laterdiscussion, we estimate the airline breakeven cycle time tobe approximately 85 min, well below the average cycletime of all airlines at the 14 hub airports. The cycle timedifference of 25.4 min between hubs and non-hubs isessentially a penalty associated with hub networks and isstatistically significant: p<0.001. This cycle time penalty athubs increases the cost of consolidating passengers; weestimate this penalty to be $205,000,000/year for theaverage domestic carrier based on an opportunity costdefined later in this section.

4.2 Cycle time by airline

Each of the ten domestic carriers investigated in thisresearch is identified in Table 2 along with the twodomestic airports they utilize most frequently. For example,Air Tran conducted 47,572 flights through Atlanta and8,343 flights through Baltimore–Washington. These two

airports constitute 38.3% and 6.7% of all Air Tran flightsrespectively for a two-airport concentration of 45.0%. Theempirically calculated cycle time is reported for airlinecarrier at the carriers two most frequently used airports andcontrasted to the average cycle time of all carriers using theairport. Air Tran has a cycle time of 82.7 min for 47,572flights through Atlanta, substantially below the 106.9 minaverage for all ten carriers with Atlanta flights. By contrast,Delta with 162,460 Atlanta flights, has a cycle time of115.7 min, somewhat larger than the Atlanta average.Table 2 includes a calculation of the relative efficiency ofeach airline in the aircraft turnaround process at the two mostfrequently used airports. The relative efficiency of an airlineat a given airport is calculated as the average cycle time atthe airport for all airlines, minus the carriers cycle time at theairport, divided by the airport’s average cycle time. Thevalues reported in Table 2 are two airport averagesweighted by the respective number of flights at eachairport. Positive efficiencies reflect organizations that canturnaround the aircraft faster than the airport’s averagecycle time. The renowned efficiency of Southwest’sturnaround process is evidenced by an efficiency of0.286. This confirms Gittell’s (2001) claim that Southwestachieved the highest levels of teamwork and relationalcoordination, enabling employees to turn aircraft quicklyat the gate, thus maximizing the time that aircraft are in theair earning revenue.

The ten carriers we investigate can be grouped into HS,PP, and multi-hub network strategies. Figure 2 is a Paretochart of the airport frequencies of Air Tran’s networkstrategy. We characterize this as a single-hub HS network,with 38% of flights at the Atlanta hub and all other sourcesnear or below 5%. Other airlines that exhibit single-hub

Airport Major carrier Number departures/year Average cycle time (min)

Houston Continental 231,311 130.5

Minneapolis Northwest 299,773 117.9

Charlotte US Air 249,677 115.6

Detroit Northwest 297,446 113.5

Atlanta Delta, Air Tran 663,958 106.1

Philadelphia US Air 241,074 103.0

Chicago O’Hare American, United 500,994 102.5

Dallas Fort Worth American 465,584 100.7

Los Angeles 290,024 89.7

Denver United 292,507 87.3

Phoenix America West 407,643 86.0

Kansas City 239,866 73.6

Las Vegas America West 338,264 68.9

Baltimore Washington Air Tran 224,688 60.1

Total Hub 4,742,809 97.6

Total Non-Hub 10,374,013 72.2

Table 1 Cycle time for majorhub airports 2004–2006


strategies include Delta and Jet Blue. We might argue thatNew York’s JFK is more a dominant base than a hub forJetBlue but the same implications apply, namely a highpercentage of flights scheduled thorough a congestedairport prone to delays and long cycle times. The averagecycle time for the five carriers employing the single hub HSnetwork strategy is 88.1 min.

Figure 3 portrays the two-hub network strategy ofAmerican Airlines, with hubs in Dallas Fort Worth andChicago accounting for 45% of American domestic flights.Other carriers with two-hub networks include Continental,

America West, Northwest, United, and US Air. The averagecycle time for the 6 carriers employing the two-hubnetwork strategy is 100.1 min, a statistically significantincrease of 12 min longer than those airlines employingsingle hubs. The decision to employ multi-hubs adds to thecycle time and costs of aggregating passengers. Thisconfirms the theoretical findings of Wojahan (2001) thatmulti-hub strategies are not cost efficient.

The PP network structure is depicted in Fig. 4, thehistogram of Southwest’s airport frequency. The twobusiest airports for Southwest constitute only 7% and 6%

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Fig. 2 Single hub-and-spokenetwork strategy showing theproportion of flights (bars) ateach airport and the cumulativepercentage (curve)

Table 2 Hubs by carrier (2004)

Airline Hubs Flights Percent Cumulative percent Carrier cycle time (min) Hub cycle time (min) Relative Efficiency

Air Tran ATL 47,572 0.3832 0.3832 82.7 106.9 0.155

BWI 8,343 0.0672 0.4504 78.1 62.3

American West PHX 50,889 0.3998 0.3998 116.7 85.2 −0.328LAS 18,635 0.1464 0.5462 84.0 69.1

American DFW 128,458 0.2892 0.289 106.7 104.0 −0.009ORD 71,473 0.1609 0.450 100.6 102.9

Continental IAH 62,635 0.3407 0.340 143.7 131.1 −0.104EWR 28,798 0.1566 0.497 129.0 115.2

Delta ATL 162,460 0.3435 0.343 115.7 106.9 −0.065CVG 45,678 0.0965 0.440 98.7 98.4

Jet Blue JFK 21,259 0.3634 0.363 98.9 107.1 0.061

FLL 5,263 0.0899 0.453 79.3 79.1

Northwest DTW 94,324 0.2585 0.258 117.0 112.6 −0.043MSP 92,068 0.2523 0.510 123.1 117.6

Southwest LAS 55,481 0.0698 0.069 54.0 69.1 0.286

PHX 49,166 0.0618 0.131 54.4 85.2

United ORD 92,797 0.2557 0.255 107.1 102.9 −0.037DEN 72,987 0.2011 0.456 90.6 87.7

US CLT 73,029 0.2389 0.2389 111.5 109.1 −0.052PHL 52,124 0.1705 0.4094 119.4 109.1


of Southwest flights respectively. Southwest, with a cycletime of 46.5 min, is the only carrier of the ten major airlinesstudied with a PP network strategy. Credit for some ofSouthwest’s short cycle time is attributable to their networkstrategy of avoiding congested airports. We estimate thisavoidance strategy by itself results in an expected cycletime of 63 min. This estimate is a weighted average ofSouthwest’s scheduled airport frequency and average cycletime of all carriers at the respective airports. The differencebetween the expected cycle time and Southwest’s achievedcycle time of 46.5 min, a difference of 16.5 min, isattributed to Southwest’s supervision of the complexinternal process required for aircraft turnaround (Gittell2001).

Our first research hypothesis is (1) an airline’s cycle timeincreases with the decision to concentrate flight schedulesat hub airports and (2) when an airline’s processes to turnaround aircraft are relatively inefficient. The univariate

regression of carrier cycle time on two-airport concentrationis shown in Fig. 5. The PP network strategy of SouthwestAirlines is clearly evident with a concentration of 0.13 anda cycle time of 46.5 min. The HS and multi-hub carriersform a cluster, with higher concentrations and significantlylonger cycle times. The regression model has a slope of141.8 (p=0.003), confirming that the higher airportconcentrations of HS network strategies is associated withlonger aircraft cycle times and lower aircraft utilizationrates. An increase in airport concentration of 0.10 (achievedby scheduling more flights through dominant hub airports)increases aircraft cycle time by an average of 14.18 min perflight. The multivariate regression of cycle time on hubconcentration and relative turnaround efficiency also yieldsa significant model. The model R2 is 0.75, suggesting that75% of the variance in airline cycle times is explained bythe flight network and turnaround efficiency. The hubcoefficient is 84.8 (p=0.003) and the estimate of the

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American Airlines Hub ConcentrationFig. 3 Multi-hub networkstrategy showing the proportionof flights (bars) at each airportand the cumulative proportion(curve)


turnaround efficiency is −48.03 (p=0.022). The negativecoefficient reflects the fact that higher efficiencies result inlower turnaround times. The empirical evidence providesstrong support for our first hypothesis that network flightconcentration and inefficient turnaround processes increaseaircraft cycle time.

4.3 Operating profitability by airline

Our second hypothesis is that increased airline cycle time isassociated with lower annual operating profits. To test this,we isolate the financial performance of each carrier’sdomestic operations and calculate the ratio of domesticoperating profit or loss to the assets employed in domesticoperations. This ratio is given in Table 3, where the carriersare listed in ascending order of cycle time. Figure 6 showsthe regression model of operating profit/assets on carriercycle time. The estimate of the slope is −0.0018 (p=0.000),confirming our hypothesis that increasing aircraft cycle time

is associated with declining carrier profitability. The eco-nomic implication of this result is that the opportunity costsof the longer cycle times required to aggregate passengers athub airports exceed the benefits of scale achieved byaggregation. The regression model facilitates the calculationof a breakeven cycle time of 84.4 min. Only two carriers,Southwest and Air Tran, have cycle times below thebreakeven requirement. JetBlue is essentially breakeven,and all other carriers are significantly above breakeven. Thereader should note that this breakeven volume is calculatedat the operating profit level, not at the net income level.

5 Sensitivity to cost structure differences

The prior discussion empirically confirms the ability ofshorter airline cycle times to increase operating profitability.The primary mechanism is the opportunity to increase seatmiles flown per aircraft and thereby lower a key produc-

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Fig. 5 Cycle time vs hubconcentration 2004–2006

Number flights Percent Cycle time (min) Op-profit/assets

Southwest 794,507 23.0 46.54 0.049

Air Tran 124,122 3.6 73.86 0.036

Jet Blue 58,485 1.7 86.13 0.040

US Air 305,649 8.9 93.44 −0.042America West 127,271 3.7 94.37 −0.002United 362,845 10.5 97.45 −0.055American 444,233 12.9 100.23 −0.017Delta 472,938 13.7 100.62 −0.075Northwest 364,862 10.6 101.34 −0.023Continental 183,834 5.3 113.64 −0.027

Table 3 Carrier cycle times andprofitability


tivity metric, costs per seat mile. It is also possible thatsystematic differences between airlines’ cost structures mayaffect costs per seat mile and profitability. To test thesensitivity to cost structure differences we expand theprofitability model to include differences in fuel consump-tion and fuel costs, aircraft fleet complexity, and passengerfood costs. We do not anticipate substantial differencesfrom these cost elements, as airlines exhibit a high degreeof similarity. Competition in the airline industry focusesprimarily on price and convenience; no airline hassuccessfully differentiated themselves from the industryon value added services. All domestic airlines fly a fairlysimilar fleet of aircraft manufactured by either AirbusIndustries or Boeing. The key raw material, jet fuel, is acommodity item purchased at comparable prices by allindustry participants. With the exception of Jet Blue, allairlines have unions representing the interests of employeesincluding machinists, pilots, and flight attendants. Insummary, few industries are characterized by participantsthat compete in such similar natures and whose factors of

service production are so similar. Table 4 depicts typicaloperating cost structures of a legacy carrier (American), anda low-cost airline (Southwest). The biggest element in thecost structure is employee salaries and benefits, whichrange from 29.9% of operating costs for American to39.2% for Southwest. Fuel expenses are a major costelement representing 26.5% of total operating costs forAmerican and 28% for Southwest. Food costs are 1.91% forAmerican versus 0.28% for Southwest, while advertising is0.72% for American and 2.24% for Southwest. Insurancecosts and landing fees are fairly comparable between the twoairlines.

The cost of jet fuel for the period 2004–2006 issummarized by airline in Table 5. During this period, thecost of this basic commodity has nearly doubled. Table 4also shows that the significance of fuel costs in the airlineoperating budget has increased markedly during this period.Inspection of Table 4 also reveals that all airlines are payingcomparable prices for jet fuel supplies with the exception ofSouthwest Airlines, The lower costs of fuel for Southwest

Table 4 Typical cost structure

Cost element American $(000) Percent of total (%) Southwest $(000) Percent of total (%)

Salaries and benefits $4,180,424 29.92 $3,200,077 39.25

Fuel $3,700,993 26.49 $2,284,441 28.02

Maintenance materials $215,107 1.54 $80,304 0.99

Food $266,955 1.91 $23,114 0.28

Advertising $101,155 0.72 $182,246 2.24

Insurance $66,176 0.47 $59,937 0.74

Landing fees $322,208 2.31 $220,567 2.71

Other $5,118,262 36.63 $2,101,354 25.78

Total operating expense $13,971,280 100.00 $8,152,040 100.00

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Airlines result from their forward purchases to hedgeincreasing fuel costs. The forward purchases are a signif-icant advantage for Southwest; in 2006, their average fuelcost was $1.53 versus a price close to $2.00 for all otherairlines.

Statistics for fuel consumption by airline for calendaryear 2006 are summarized in Table 6. Fuel consumption isexpressed in gallons per seat mile flown, and range from alow of 0.014 for Jet Blue to a high of 0.019 for Northwest.The fuel consumption values are largely determined by theaircraft fleet flown by the airline. The relatively inefficientfuel consumption per mile of American and Northwest aredue to their reliance on aging MD 80 aircraft. The mostimportant statistic in Table 6 is the fuel cost per seat mileflown. The lowest fuel cost per seat mile are $0.025 forSouthwest and $0.028 for Jet Blue, attributable to their fuelefficient fleet of Airbus A320 aircraft. The other airlineshave reasonably comparable fuel costs ranging from $0.031to $0.039 per seat mile. To account for fuel cost differencesin the profitability model, we include fuel cost per seat mileby airline by year for the period 2004–2006.

The number of distinct types of aircraft in an airline’sfleet is known to increase the operating costs through

increased complexity of operations. Fleet complexitycreates higher costs associated with the scheduling, train-ing, and maintenance of multiple aircraft designs, and insome cases multiple aircraft manufacturers. Table 7 dem-onstrates differences in fleet complexity for American andSouthwest Airlines. American’s fleet consists of sixdifferent aircraft designs from three manufacturers: Boeing(737, 757, 767, and 777), McDonnell Douglas DC9, andAirbus A300. By contrast, Southwest’s fleet consists of asingle aircraft design, the Boeing 737. To account for fleetcomplexity in the profitability model, we include an ordinalvariable for the number of different aircraft designs in thefleet by year for the period 2004–2006.

Another notable difference in the airlines’ cost structureis the strategy for frills offered to passengers, mostly in theform of food and beverage. We noted in an earlierdiscussion that the magnitude of these costs in the operatingcost structure are relatively minor (1–2%) compared to thecosts of employees and fuel. The total food expenditure issummarized by airline in Table 8. Food costs range from alow of $9.2 million for Air Tran to a high of $267 millionfor American. Expressed on a per passenger basis, thelowest food cost is $0.45 for Air Tran versus $3.75 forContinental. A regression of food cost per passenger onaverage flight length by airline suggests an intuitiveconclusion: food cost per passenger is positively correlatedwith flight length. We therefore account for differences infood costs in the profitability model by including values forfood cost per passenger per mile by airline by year for theperiod 2004–2006.

The multivariate profitability model with cost structuredifferences includes variables for fuel costs per seat mile,an ordinal variable reflecting fleet complexity, and a foodcost per passenger per mile. Estimates for this model revealthat differences in food costs, p=0.476, and fuel cost perseat mile, p=0.801, are not statistically significant. It islikely that food costs are not significant because (1) they area smaller cost element comprising only 1–2% of operating

2004 2005 2006

Carrier $/gal fuel % Op cost $/gal fuel % Op cost $/gal fuel % Op cost

AirTran 1.163 23.7 1.766 32.1 1.977 35.7

American 1.13 19.0 1.672 24.2 1.926 26.5

Continental 1.127 12.3 1.711 16.5 1.987 17.9

Delta 1.132 14.2 1.708 20.3 2.088 21.7

JetBlue 1.059 22.2 1.61 29.7 1.994 35.0

Northwest 1.169 16.4 1.711 20.1 2.023 24.6

Southwest 0.831 18.5 1.035 21.7 1.535 28.0

United 1.187 15.9 1.749 19.9 2.066 22.2

US Airways 1.062 12.2 1.706 18.8 1.992 22.1

Table 5 Fuel cost summary

Table 6 Fuel consumption 2006

Carrier $/gal fuel Gal/seat mile Fuel cost/seat mile

AirTran $1.977 0.016 0.035

American $1.926 0.017 0.034

Continental $1.987 0.015 0.031

Delta $2.088 0.015 0.033

JetBlue $1.994 0.014 0.028

Northwest $2.023 0.019 0.039

Southwest $1.535 0.015 0.025

United $2.066 0.015 0.033

US Airways $1.992 0.017 0.036


expenses, and (2) food costs are correlated with flightlength. The longer flights yield secondary profitabilitybenefits of higher revenues per mile and economies ofscale associated with fewer setups and longer flights. Wespeculate that the fuel differences are not significant becausea significant portion of the savings from Southwest’s knownand controllable forward fuel contracts are passed on toconsumers as lower fares. We also note that airlines withfleets of newer fuel efficient aircraft encounter someoffsetting costs from the acquisition of these newer aircraft.

Eliminating food and fuel cost variables from the modelproduces a statistically significant model (R2=0.717), with72% of the differences in operating profitability explainedby differences in cycle time and differences in fleetcomplexity. The model estimate for the coefficient of cycletime is −0.00124, a lower estimate than the −0.00182 fromthe univariate model. Cycle time is still statisticallysignificant with p=0.002. The coefficient for fleet com-plexity is estimated at −0.0066, with p=0.033. The negativecoefficient for fleet complexity suggests that lower fleetcomplexity is associated with higher operating profits. Theprimary benefit of a more complex fleet of aircraft is theability to match aircraft configuration to market character-istics and achieve high load factors and operating econom-ics. If fleet complexity decisions in the airline industry areoptimal decisions, then we would expect the fleet com-plexity variable to be statistically insignificant in the model.The fact that fleet complexity is significant suggests thatairlines are employing too many aircraft designs in theirfleet and are unable to recover the costs of more complex

fleets with the benefits achieved by matching supply anddemand in a complex network of city pair flight segments.

Empirical analysis of the operating data for the domesticairline industry for the period 2004–2006 confirms thatincreased levels of network concentration of HS strategiesincreases the airlines cycle time, and the higher cycle timesare associated with lower operating profitably. We also findthat the effectiveness of the aircraft turnaround processlowers cycle times and is associated with higher profitabil-ity levels. Operating profitability can also be increased bysimplifying the number of aircraft designs utilized in theflight schedule.

6 Conclusions and implications

The results of this research are based on 11.9 milliondomestic flight records for calendar year 2004–2006. Thedata for this period reveals that the scheduling designdecision to aggregate passengers through a hub networkresults in significantly longer aircraft cycle times. We alsofind that longer cycle times undermine the profitability ofthe carrier. The financial significance of cycle timereductions can be quantified from our regression models;the slope of −0.0018 represents the amount that the averagecarriers operating profits per dollar of assets invested willchange with a 1 min increase/decrease in cycle time. Theaverage domestic airline has approximately $10 billion inoperating assets; a reduction of 1 min in cycle time will, onaverage, increase operating profits by $12–$18 million. The

Carrier Food cost $(000) Cost/passenger Average flight segment miles Cost/passenger/mile

Air Tran $9,188 $0.45 652 $0.0007

American $266,955 $3.43 1,076 $0.0032

Continental $134,668 $3.75 1,132 $0.0033

Delta $218,010 $3.40 918 $0.0037

JetBlue $13,035 $0.72 1,185 $0.0006

Northwest $59,857 $1.31 778 $0.0017

Southwest $23,114 $0.22 622 $0.0004

United $160,910 $2.76 1,097 $0.0025

US Air $35,492 $1.09 736 $0.0015

Table 8 Airline food costsummary

American Southwest

Boeing 737-800 Boeing 737-700/700lr

Boeing 757-200 Boeing 737-500

Boeing 767-200/Er/Em Boeing 737-300

Boeing 767-300/300er

Boeing 777-200/200lr/233lr

McDonnell Douglas Dc9 Super 80/Md81/2/3/7/8

Airbus Industries A300-600/R/Cf/Rcf

Table 7 Aircraft fleetcomplexity


reader is cautioned that this savings is only realized whencumulative reductions in cycle time are large enough topermit scheduling additional flight segments in the dailyschedule. Nevertheless, there are clear incentives forairlines to reduce cycle times. Airlines should concentrateon standardizing and improving current turnaround oper-ations (Gittell 2001). During the time period investigated,more efficient turnaround processes were associated withshorter cycle times. The authors’ recent anecdotal evidenceof aircraft turnaround process problems include a departuredelay caused by the failure to clean an aircraft even thoughit was at the gate for 2 h, and in a second case, the inabilityto locate a wheelchair to disembark a passenger. Both areexamples of poorly functioning turnaround processes.

The airlines could also benefit by reassessing strategicdecisions in the schedule design of their networks.Scheduling more direct flights and decreasing reliance oncongested hubs will increase operating profits. A reductionin hub concentration of 0.10 reduces average aircraft cycletime by 13.67 min, which results in an estimated $246million increase in operating profits.

Finally, we might consider a priority investment in theNational Airspace System to create several strategicallylocated hub-only airports. These airports, funded by thefederal government, would not be located in metropolitanareas and would be designed with the express purpose offacilitating rapid landings, direct taxi approaches to and fromgate areas, and fast transfer of passengers and baggage.

A secondary benefit of this research is the estimate of themarginal value of airtime relative to ground time. Thisopportunity cost can be used in econometric modelsdeveloped to assess the benefits of proposed infrastructureinvestment in the National Airspace System, including AirTraffic Management and Communications-Navigation-Surveillance. Hansen et al. (2001) report difficulties inevaluating the benefits of National Airspace infrastructureinvestments; many studies use a somewhat arbitrary valueof $25/min based on aircraft direct operating costs.

The findings of this research apply to the domesticoperations of US carriers and should not be generalized tointernational flight operations. It should also be noted thatincreases in direct scheduling of flights will be constrainedby capacity of our National Airways System; upgrading theNational Airspace System infrastructure is a critical need.Customer welfare has not been explicitly addressed in thisresearch but we would argue that including customerwelfare would strengthen the findings of this research.

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Airline flight networks, cycle times, and profitability: 2004–2006