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Integrators’ Air Transport Networks in Europe

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Abstract

Integrators are shipping carriers that control complete air and road delivery networks and offer a wide range of package delivery services. Despite the increasing relevance of small package delivery services in the European air transport market, very little has been written on integrated carriers’ air transport networks on the Old Continent. In this paper we examine the network configurations of DHL, FedEx, TNT, and UPS in terms of hubs, spokes, and market shares. Our results show that integrators operate hub-and-spoke networks. Network indices and centrality measures confirm that their network structures are more similar to those of full-service passenger carriers rather than those of low-cost carriers. However, the nature of their hub-and-spoke systems is different because freight tons, as compared to passengers, are more easily flown along multiple-stop and circular routes. As a consequence, FedEx, TNT, and UPS operate non-pure star networks with a dominant central hub and a set of intermediate airports acting as stops between the central hub and (usually) one “external airport”. DHL operates a multi-hub architecture, with a main dominant hub in Leipzig and a set of “secondary hubs” that provide several connections to other network nodes. Furthermore, we provide evidence of the most important intra-Europe and long-haul routes for each integrator, showing that DHL seems to have a more developed Europe-Asia connection, and is the only integrator to connect Europe to Sub-Saharan Africa. Finally, we show the high degree of complementarity existing between FedEx and TNT networks and that such complementarity is confirmed also by an analysis of their market shares in the different European sub-markets. Despite the significant level of market concentration, our analysis shows that the recent merger between FedEx and TNT is not expected to significantly modify market equilibrium in Europe.

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Notes

  1. On January 2016, the EC approved the acquisition of TNT Express by FedEx Corporation, although three years earlier, the EC had prohibited the planned acquisition of TNT by UPS. See EC (2013), EC (2016), and Buettner et al. (2013) for a deeper discussion.

  2. Amazon.com, the world’s biggest online retailer, has signed a deal to lease 20 Boeing 767 widebody cargo aircraft to handle more of its own deliveries in the U.S.

  3. We collected data directly from the website. In the section Data/Aircraft each airline has an aircraft fleet associated and the weekly schedule of each single aircraft is available. Freely accessible information is limited to one week ending the current day and starting six days earlier. More specifically, the information collected are (i) flight date and time, (ii) operating airline, (iii) origin and destination, and (iv) aircraft model. Note that we consider an airport as European if, according to the OAG database, the corresponding region is “Western Europe” or “Eastern/Central Europe”.

  4. Unfortunately, no comprehensive official sources exist to help in this matching. Hence we collect information from the websites of the airlines and/or from websites with content dedicated to air cargo (e.g., Flightradar24 and www.airliners.net).

  5. Notice that the actual capacity of freighters may be lower than the nominal payload because integrators’ traffic tends to be low density (i.e. “volumetric” according to the industry jargon). Furthermore, the integrated carriers make also use of belly-hold capacity on passenger airlines. Unfortunately, there are no data sources available to incorporate this “borrowed capacity” in the analysis.

  6. AFTs at each airport are computed as the sum of the payloads of arrival and departure flights. As already explained, payloads were obtained by matching the aircraft models indicated in Flightradar24 data to the corresponding aircraft capacity according to two main sources: the integrators’ websites and Morrell (2011), who indicates the typical payload of the most popular freighters.

  7. The area includes major European economic centres of Benelux, German Rhineland, Southern Germany, French Alsace, Switzerland, and Northern Italy (Palacio and Wojciechowski 2015).

  8. Note that Grubesic et al. (2008 and 2009) use also the concept of nodal regions since they analysed thousands of worldwide origins and destinations provided by hundreds of passenger airlines. The focus of our analysis is instead limited to a single region (Europe) and a single integrated carrier at a time.

  9. Note a second (relatively small) dominant airport in the DHL network represented by Liege LGG. This result is due to the way in which dominant airports are identified. More specifically, Liege LGG dominates some airports without being dominated by any airport in the network.

  10. The data source for passenger airlines network is OAG and data refers to a week of scheduled flights in October 2011.

  11. The gamma index is computed as γ = e / (0.5v)(v-1), where e is the number of edges and v the number of vertices.

  12. Eigenvector and betweenness centrality measures are also largely used in social network analyses. See Fire and Puzis (2016) as an example. Interestingly, the two measures of centrality have been already applied to the air cargo industry in a recent contribution from Wong et al. (2016).

  13. Betweenness centrality has also been used, among other techniques, in Sun et al. (2017) to analyze the criticality of nodes in air transportation.

  14. Connections to Morocco (DHL) and Tunisia (TNT) are not included in Figs. 4 and 6 because of their low volumes.

  15. Note that EU airline ownership and control rules will foster TNT to sell (1) TNT Airways and, (2) Pan Air Lineas Aereas to the Ireland-based ASL Aviation Group. However, these purchases should not significantly affect market equilibrium in Europe given that ASL is expected to operate such flights for TNT (Reuters 2016).

  16. Note that we do not consider smaller package delivery companies (e.g., DPD in France and GLS in the U.K.) in our analysis that combine dense national delivery networks with significant economies of scale. As highlighted in Buettner et al. (2013), their lack of integration into (1) an air network, and (2) a foreign delivery network make them significantly “distant” competitors.

  17. Note that our computation obviously only provides a proxy of the market structure given that it simply takes into account the air cargo space. This means that an aircraft flown from airport A to airport B through airport C generates a higher market share than the same aircraft flown directly from A to B. This basically means that the higher the turnover of parcels at airport C, the more accurate the computed market share.

  18. By “global” we mean that countries outside Europe are also considered. We recall that our dataset includes all of the flights having at least one between origin and destination located in Europe. Hence, freight volumes in countries outside Europe refer to the part originated by intercontinental connections to or from Europe.

  19. Note that even if market shares are computed by considering only available cargo space on aircraft, our results appear to be reasonable—e. g., they are in line with the market shares shown in DHL (2016) and Stevens (2015) for international shipments.

  20. The available freight tonnes arrived at and departed from all the airports in country A are summed to gain the market share in country A.

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Acknowledgments

We would like to thank the participants of the Air Transport Research Society (ATRS) World Conference 2016 for their valuable comments and suggestions.

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Authors and Affiliations

  1. Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy

    Paolo Malighetti, Gianmaria Martini, Renato Redondi & Davide Scotti

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  1. Paolo Malighetti
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Correspondence to Davide Scotti.

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Malighetti, P., Martini, G., Redondi, R. et al. Integrators’ Air Transport Networks in Europe. Netw Spat Econ 19, 557–581 (2019). https://doi.org/10.1007/s11067-018-9390-5

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