Amsterdam University of Applied Sciences
From Full Freighters to Belly Cargo Aircraft: Stochastic Analysis of Alternatives
for Freight in Schiphol Airport
Mujica Mota, Miguel; El Makhloufi, Abdel; Romero , Rodrigo
Publication date
2019
Document Version
Final published version
Published in
Proceedings of the International Forum on Shipping, Ports and Airports (IFSPA) 2019
Link to publication
Citation for published version (APA):
Mujica Mota, M., El Makhloufi, A., & Romero , R. (2019). From Full Freighters to Belly Cargo
Aircraft: Stochastic Analysis of Alternatives for Freight in Schiphol Airport. In P. C-S. Lu, D. T.
L. Yip, & D. D. Yang (Eds.), Proceedings of the International Forum on Shipping, Ports and
Airports (IFSPA) 2019 : Beyond Breakthroughs, Above Excellence (pp. 186-195). The Hong
Kong Polytechnic University.
General rights
It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons).
Disclaimer/Complaints regulations
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please contact the library:
https://www.amsterdamuas.com/library/contact/questions, or send a letter to: University Library (Library of the University of Amsterdam and Amsterdam University of Applied Sciences), Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Proceedings of the
International Forum on Shipping, Ports and Airports (IFSPA) 2019
Beyond Breakthroughs, Above Excellence
20 – 24 May 2019 Hong Kong Edited By: Prof. Chin-Shan Lu Dr T. L. Yip Dr Dong Yang
Proceedings of the International Forum on Shipping, Ports and Airport (IFSPA) 2019 20 – 24 May 2019
Copyright © 2019
C.Y. Tung International centre for Maritime Studies Department of Logistics and Maritime Studies The Hong Kong Polytechnic University All rights reserved.
No part of this publication may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into any language, in any form or by any means without written authorization from The Hong Kong Polytechnic University.
ISBN:
978-988-74084-0-6
The organizers and editors of this publication assume no responsibility or liability for the statements or opinions expressed in papers or presentations by the contributors to this conference or proceedings.
The International Forum on Shipping, Ports and Airports (IFSPA) is an annual international conference jointly organised by the C.Y. Tung International Centre for Maritime Studies, the Department of Logistics and Maritime Studies and Faculty of Business of The Hong Kong Polytechnic University. It aims to invite international academics and practitioners to discuss and exchange views on issues related to global maritime and aviation economics, policy and management. The conference also serves as a good platform for networking and promoting academic-industry collaboration.
The roots of IFSPA can be dated back to 2006 when it was started as a workshop with the objective to promote high-quality research papers. Since then it has experienced significant successes and has attracted more than 900 participants from different countries and regions of the world.
Preface
The 10th International Forum on Shipping, Ports, and Airports (IFSPA) 2019 was successfully held from 20-24 May 2019, in Hong Kong. The proceedings contained a collection of 26 full papers out of 93 presentations presented during the Conference. The topics covered include shipping economics, port strategy, airport management, logistics development, environmental issues in logistics, port efficiency and competition, and maritime safety and security.
The theme of IFSPA 2019 was “Beyond Breakthroughs, Above Excellence”. It aimed at providing an interactive platform for international academics to discuss important issues related to shipping, ports and airports. It also advocated the adoption of innovation management and technology for maximization of competitive advantage, economic benefits and sustainable developments of transport, logistics, shipping and trading industries worldwide.
This year the Forum comprised of 5 Keynote Sessions, 2 Industrial Sessions, 3 Special Sessions, and 20 Parallel Sessions. During the event, world-famous scholars and industry leaders shared with participants their insights on issues relevant to maritime and trade economics, policy and management. More than 100 delegates came from different parts of the world including Canada, Chile, India, Japan, Netherlands, Singapore, South Korea, Taiwan, Thailand, the United Kingdom and the U.S.
Led by the C. Y. Tung International Centre for Maritime Studies of The Hong Kong Polytechnic University, IFSPA is an annual international event devoted to maritime, aviation and logistics studies to discuss and exchange views on contemporary issues facing the sectors, and further advance academia-industry cooperation. Through participation from relevant international and regional organizations, the increased pool of participants has enabled IFSPA to become an important event in the transport logistics sector. We are pleased that the event has secured significant support from local government agencies and institutions to assist with its coordination and implementation. Conference participants now include the world’s leading maritime and aviation experts and professionals.
The IFSPA 2019 Organizing Committee greatly appreciates the invaluable contribution from the invited speakers, paper authors, paper reviewers, conference co-organizers and partners.
Finally, we thank members of the Organizing Committee and Conference Secretariat who had offered both moral and technical support to the conference and this proceeding. In particular, we would like to thank Vera Tsui and Tsz-him Chan.
IFSPA 2019 Organizing Committee Conference Chairman Co-Chairs Committee Members Secretariat Professor Chin-Shan Lu Dr Stephen Y.K. Li Dr Dong Yang Dr T. L. Yip Dr Petrus Choy Dr Achim I. Czerny
Professor Kee-Hung Mike Lai Dr Edward Lee
Dr Meifeng Luo
Professor Chi To Daniel Ng Dr Kelvin Pang
Dr Sik Kwan Tai Dr Shuaian Hans Wang Dr C. L. Johnny Wan Dr Yulai Sarah Wan Dr Zhou Xu Professor Hong Yan Dr Ling Zhu
Miss Cherry Law Miss Vera Tsui
IFSPA 2019 International Steering Committee
Roar Os Ådland Norwegian School of Economics, Norway Peggy Shu-Ling Chen University of Tasmania, Australia Martin Dresner University of Maryland, USA Ying-En Ge Shanghai Maritime University, China David Gillen University of British Columbia, Canada Anne Graham University of Westminster, UK
George Q. Huang The University of Hong Kong, Hong Kong
Manolis Kavussanos Athens University of Economics and Business, Greece Kap Hwan Kim Pusan National University, South Korea
Jasmine Siu Lee Lam Nanyang Technological University, Singapore
Chung-Yee Lee The Hong Kong University of Science and Technology, Hong Kong
Ling Li Old Dominion University
Jing xian Liu Wuhan University of Technology School of Navigation, China
Nan Liu Zhejiang University, China
Becky P.Y. Loo The University of Hong Kong, Hong Kong
Chin-Shan Lu The Hong Kong Polytechnic University, Hong Kong Theo E. Notteboom Shanghai Maritime University, China
Tae Hoon Oum University of British Columbia, Canada Thanos Pallis University of the Aegean, Greece
Harilaos N. Psaraftis Technical University of Denmark, Denmark Kuo-Chung Shang National Taiwan Ocean University, Taiwan Jiuh-Biing Sheu National Taiwan University, Taiwan
Atsuo Suzuki Nanzan University, Japan
Wayne K. Talley Old Dominion University, USA Jose L. Tongzon Inha University, South Korea Stefan Voss University of Hamburg, Germany
Wen-Yao Grace Wang Texas A & M University at Galveston, USA
Fan Wang Sun Yat-Sen University, China
Mariner Wang Ritsumeikan Asia Pacific University, Japan Wesley W. Wilson University of Oregon, USA
Hai Yang The Hong Kong University of Science and Technology, Hong Kong Ching-Chiao Yang National Kaohsiung University of Science and Technology, Taiwan Anming Zhang University of British Columbia, Canada
List of Keynote Speakers
IFSPA 2019 is proud to have invited the following worldwide prestigious scholars and practitioners as Keynote Speakers for this event:
Mr. Albert Yau
Hong Kong Zhuhai Airport Management Co Ltd. Hong Kong Mr. Ting-Yi Tsai
Chairman, Taiwan International Ports Logistics Corporation, Taiwan Professor Juan de Dios Ortúzar Salas
Pontificia Universidad Católica de Chile, Santiago, Chile Professor Qiang Meng
National University of Singapore, Singapore Professor Thanos Pallis
University of the Aegean, Greece Professor Anming Zhang
University of British Columbia, Canada Professor Lu Zhen
Shanghai University, Shanghai, China Professor Liu Jingxian
Wuhan University of Technology, Wuhan, China Dr Shuaian Hans Wang
The Hong Kong Polytechnic University, Hong Kong Professor Ching-Chiao Yang
National Kaohsiung University of Science and Technology, Taiwan Dr Christina W. Y. Wong
List of Paper Reviewers
The Organizing Committee would like to express heartfelt thanks to the following scholars for their invaluable inputs to the peer-review process of the academic papers submitted to IFSPA 2019.
Tat Dat Bui National Taiwan Ocean University, Taiwan Pin-Yen Hsieh National Taiwan Ocean University, Taiwan Shih-Liang Chao National Taiwan Ocean University, Taiwan Sheng Teng Huang National Taiwan Ocean University, Taiwan Sumin Chen Dalian Maritime University, China
Jyh-Fu Jeng National Chengchi University, Taiwan Zhuo Chen Ocean University of China, China Meizhi Jiang Dalian Maritime University, China
Jun-Wen Chen National Kaohsiung University of Science and Technology, Taiwan Dong-Wook Kwak Coventry Business School, UK
Dan Cheng South China University of Technology, China Jasmine Siu Lee Lam Nanyang Technological University, Singapore Seong-Hyun Cho Inha University, Korea
Stephen Y.K. Li The Hong Kong Polytechnic University, Hong Kong Jin Chun Dalian University of Technology, China
Kai-Yuan Li Shanghai Maritime University, China
Achim I. Czerny The Hong Kong Polytechnic University, Hong Kong Xiaoxia Li The Hong Kong Polytechnic University, Hong Kong Shengyun Ding Dalian Maritime University, China
Pei-Chun Lin National Cheng Kung University, Taiwan Angela Du Fort Hays State University, USA
Taih-Cherng Lirn National Taiwan Ocean University, Taiwan
Lixian Fan Shanghai University, China
Jin-Long Lu National Kaohsiung University of Science and Technology, Taiwan Deb Narayan Goswami Middlesex University, UK
Abel El Makhloufi Amsterdam University of Applied Sciences, Netherlands Hidenobu Matsumoto Kobe University, Japan
Xin Wen Shanghai Maritime University, China
Xin Peng Wuhan university of technology, China
Yi Xiao Chung-Ang University, Seoul, Korea
Ching Pong Poo Liverpool John Moores University, UK Chung-Shan Yang Chang Jung Christian University, Taiwan Pan Sheng Shanghai Jiao Tong University, China
Ling Sun Shanghai Maritime University, China
Dong Yang The Hong Kong Polytechnic University, Hong Kong Sik Kwan Tai United International College, BNU-HKBU
Jin Yang Jimei University, China
Ting-Yi Tsai Taiwan International Ports Logistics Corporation, Taiwan Xin Yang Wuhan University of Technology, China
Dan Wang Dalian Maritime University, China
Zhongzheng Yang Ningbo University, China
Chunjuan Wang Shandong Jiao Tong University, China
Wei Yim Yap Singapore University of Social Sciences, Singapore Grace Wang Texas A&M University Galveston, USA
Jin-Ru Yen National Taiwan Ocean University, Taiwan Mariner Wang Ritsumeikan Asia Pacific University, Japan
T. L. Yip The Hong Kong Polytechnic University, Hong Kong Zhaochen Wang Shanghai Jiao Tong University, China
Hang Yu Shanghai Maritime University, China
Contents
Shipping Connectivity for East Asia and the Indian Ocean Region in Relation to the 21st Century Maritime Silk Road
Wei Yim Yap
1
Maritime Autonomous Surface Ships (MASS) and Its Implementation in Hong Kong Y.K. Stephen Li and K.S. Fung
11
The Challenge of Minimizing Financial Losses due to Accidents at Sea – Observing Regulatory Compliance or Making a Profit? Developing a Model Safety Culture in Merchant Shipping Deb Nayaran Goswami, Dr. Stephanie Jones, andDr. Jose Tongzon
20
Online Integrated Optimization Model for Container Rehandling and Pickup Sequence in Container Terminals
Su-min Chen and Qing-cheng Zeng
33
The Outlook of Deep-water Container Terminal at the Port of Kaohsiung Ting-Yi Tsai and Hui-Huang Tai
50
Industry Choice for Airport Economic Zone by Multi-Objective Optimization Dan Wang, Zhongzhen Yang and Lixin Shen
69
Comparative Analysis of Competitiveness of Sino-Euro Railway Express and Liner Shipping in the context of Sulphur Emission Control Convention
Jiaru Jin, Feng Lian and Zhongzhen Yang
80
Evaluation System Design and Index Model Construction for Container Liner Shipping Operation Chun-Juan Wang, Chao Chen and Yang Liu
96
Using Analytic Hierarchy Process to Analyze the Development Strategies of Multi-temperature Logistics Center in Free Trade Zone
Dr. Feng-Ming Tsai, Chih-Hung Wuand Pin-Yen Hsieh
109
Maritime Shipping Digitalization: Blockchain-Based Technology Applications, Future Improvements, and Intention to Use
Chung-Shan Yang
127
Spatial Analysis of AIS-Based LNG Fleet Emission Inventory Hoegwon Kim, Daisuke Watanabe and Shigeki Toriumi
Maritime Accident Risk Probability Prediction of Sea Lanes Based on Dynamic Bayesian Network
Meizhi Jiangand Jing Lu
157
China’s Inland Water Traffic Security Risk Assessment Based on Data Mining Zhaochen Wang and Jingbo Yin
172
From Full Freighters to Belly Cargo Aircraft: Stochastic Analysis of Alternatives for Freight in Schiphol Airport
Miguel Mujica Mota, Abdel El Makhloufi and Rodrigo Romero
186
The Spillover Effect of Liner Shipping Performance on Trade Values for Coastal Countries Po-Yuan Huang, Szu-Yu Kuo, Pei-Chun Lin and Chung-Wei Shen
196
Cooperative Compensation Mechanism under the Balance of ‘Cruise-Port-City’ Benefits of Cruise Ports
Ling Sun,Kai-yuan Li,Mei-feng Luo,Chen-bin Huand Ya-jun Deng
211
Research on Investigation and Treatment Mechanism of Water Traffic Accidents Xin Yang,Jingxian Liu, Zhao Liu,Deng Jian and Zhi Yuan
223
The Moderating Role of Service Recovery on Customer Loyalty in the Context of Cruise Passengers
Ming-Ren Chiou, Shih-Liang Chaoand Hsin-Yu Hsieh
233
Using the Kano Model to Investigate the Quality of a Pharmaceutical Logistics Service: A Study of Taiwanese Health Insurance Pharmacies
Shih-Liang Chao, Chin-Ping Chuang and Hsuan-Yun Lin
246
System Impulse Modeling of the Green Shipping Policies Lixian Fan and Jingbo Yin
255
Climate Change Adaptation for Airports by a Climate Change Risk Indicator (CCRI) Framework Mark Ching-Pong Poo, Zaili Yang, Delia Dimitriu, Zhuohua Qu, Paul Tae-Woo
Lee and Xuehao Feng
268
Impacts of Different Low-carbon Policies on Route Decisions in Intermodal Freight Transportation: The Case of the West River Region in China
Xing-Qun Cheng, Chun Jin , Cong Wang and Yuliya Mamatok
Environmental Efficiency of China’s Transportation Sectors Young-Tae Chang, Donggyu Jeon and Seong-Hyun Cho
294
When is a Good Time? Market Entry Timing from Competitive Dynamics Perspective: Case of Asia-Pacific Low Cost Airlines
Don Jyh-Fu Jeng and Le Truong Bao Tran
319
Evolution and Emerging Trends in Cruise Research Grace Wang and Peyton Heinze
332
Hub Status of Primary Cities in Asia from the Perspective of International Air Transportation Hidenobu Matsumoto and Koji Domae
346
Dynamic Interdependence and Spillovers of Market Volatility over Distance: Evidence from Bunker Fuel Markets
Xiaoxia Liand Tsz Leung Yip
361
New Developments of Seaworthiness —— In Aspect of the Rotterdam Rules Shengyun Ding
378
System Dynamic Modelling of the Intermodal Transportation System in Guangdong-Hong Kong-Macao Greater Bay Area
Huiling Zhong, Dan Cheng and Yimiao Gu
392
Landing Charges with Ramsey Pricing Mechanism: An Application at TPE
Jin-Ru Yen, Chia-Yi Shih, Chia-Ping Tsou and Man-Hsuan Chen
403
Economic Analysis of Liner Company's Collection of Fuel Surcharge JinYang
414
Structural Breaks and Fuel Surcharge Policy: Evidence from the U.S. Less-than-Truckload Motor Carrier Industry
Angela Yan Du and Chi Keung Marco Lau
419
Liability Implications of the Rotterdam Rules for Indian Dry Ports Girish Gujar, Sik Kwan Tai and Adolf K.Y. Ng
427
Analysis of the Matching Relationship between Airport Passenger Transport Scale and Home City Scale in China Based on Relative Concentration Index
Zhongzhen Yang, Dan Wang, Rui Ding, and Chao Wang
A Study on the Impact of Cruise Liner Service Quality on Customer Loyalty – Using Trust as a Moderator
Tan-Shou Shee, Shih-Liang Chao, Hsuan-Shih Leeand Hsian-Ning Hsu
447
Research on the Ship Exhaust Emissions Monitoring Techniques in Port Water Xin Peng, Yuanqiao Wen, Changshi Xiao, Liang Huang, Chunhui Zhou, Fan Zhang
and Haiwen Yuan
456
A Study on the Implementation of Ocean Freight Forwarders’ Corporate Social Responsibility in Vietnam
Tai-Cherng Lirn and Doan Thi Ngoc Thao
468
The Effects of the Employee’s Perceived Safety Behavior in Ferry Services Rhaptyalyani Herno Della, Taih-Cherng Lirn and Kuo-Chung Shang
478
The Impact of Mergers and Acquisitions on Profitability and Marketability of Major Airlines Young-Tae Changand Suhyung Lee
490
Global Shipping in Asia in the 21st Century Dr., Professor Mariner Wang
510
An Integrated Fuzzy ANP-QFD Approach for Port Selection Considering Intermodal Transportation: A Case Study about China-Africa line under Belt and Road Initiative Huiling Zhong, Xinyi Zou and Yimiao Gu
186
From Full Freighters to Belly Cargo Aircraft: Stochastic Analysis of Alternatives
for Freight in Schiphol Airport
Miguel Mujica Mota
1, Abdel El Makhloufi
2, and Rodrigo Romero
31Aviation Academy, Amsterdam University of Applied Sciences, The Netherlands 2Urban Technology, Amsterdam University of applied Sciences, Amsterdam, The Netherlands
3VU Amsterdam University, Amsterdam, The Netherlands
Abstract
KLM has revealed the plan to downsize the full-freight cargo fleet in Schiphol Airport, for that reason the company requires to explore the consequences of moving the cargo transported by the full freighters into the bellies of the passenger flights. In this study, the authors analyze the implications of this decision by considering the variability of the load factors and the impact that replacing old aircraft might have. The study addresses how the transition towards the belly operation should impact the current operation of KLM at Schiphol. Our study shows that the replacement of old aircraft with new 787s and 777s will have significant effect on the cargo capacity of the company. The results rise the discussion on future problems to be faced and how to make the transition from full freighter to belly operation.
Keywords: Air freight operations, optimum capacity, air-freight performance, Full Freighters, Belly capacity, scenario, multi-modal transport
1. Introduction
On a worldwide level, the transport of commercial cargo is a key economic indicator of international trade as well as a thermometer for the state of the global economy. As people become more productive, they become richer and they demand more goods. The supply chain and logistics industry exist to connect manufacturers with suppliers and middlemen shippers with the end customer (Feng, 2015).
In terms of the business model between cargo operations and passenger operations, there exist many similarities as well as differences. Air cargo transport is more complex than passenger transport because the former involves more players, more sophisticated processes, a combination of weight and volume, varied priority services, integration and consolidation strategies, and multiple itineraries of a network than the latter.
The key differences between cargo and passenger operations have been reported as uncertainty, complexity, and flexibility (e.g., Bartodziej et al., 2009; Leung et al., 2009; Li et al., 2009; Wang and Kao, 2008):
• Uncertainty: Air cargo transport has higher uncertainty than passenger transport in terms of capacity availability. In passenger transport, passengers may cancel reservations, and a small number of passengers may not show up. In contrast, capacity booking for air cargo, freight forwarders must pledge the use of the cargo capacity on specific flights twelve (or six) months ahead (Amaruchkul et al., 2011). Because freight forwarders do not need to pay for unused capacity, they may book more than the actual needed capacity to reduce risks or to compete with other forwarders. Many bookings in air cargo can be cancelled and/or rebooked because airlines typically do not charge for changing reservations. For this reason, the booking process is subject to considerable volatility (Petersen, 2007).
• Complexity: forecasting cargo capacity is more complex than forecasting passenger capacity. While the capacity of a passenger aircraft is fixed by its number of seats, cargo capacity depends on the type and dimensions of the container used (called unit load devices, ULDs), and specified according to pivot weight, pivot volume, type, and centre of gravity (Leung et al., 2009). Multiple dimensions are a key feature of freight,
187
which render both complexity and uncertainty to air cargo capacity management.
• Flexibility: Transshipment itineraries between an origin and destination (OD) pair for cargo transport benefit the airline more than passenger transport. In general, all major airlines operate within so-called hub-and-spoke networks. Both, passengers, and cargo are transported from many different origins to a small number of hubs, where passengers and cargo are consolidated and then transported further to other hubs by wide-body aircrafts. The total number of transits are limited for passenger transport, and much larger for air cargo transport i.e. air cargo can be transshipped via several intermediate airports from the origin to the destination to meet the delivery time (Amaruchkul et al., 2011). The airline only needs to declare the origin, stopover (transit) airports, and destination to the forwarders and can decide on the optimal use of transhipment itineraries of air networks.
Air freight is the fastest form of transportation in terms of distance divided by time travelled. However, given extra costs related to delivery time such as customs processing, security considerations, consolidation of loads at a hub, warehousing, etc. air freight is less suitable for short distances transport (less than 700 km, rail is a more efficient transport mode, less costly and less complex than transport by air). Since the key value-added by air transport is time, the quick delivery of goods results in a demand and high willingness to pay for specific types of items. Common items shipped by air include perishables, pharmaceutical products, high-tech and electronics, clothing, animals, and high-value products such as diamond, art, cars among others.
Because of these differences, air freight accounts for less than 1% of total freight carried by all transport modalities (air, sea, water, and road) in terms of both volume and weight. However, air freight accounts for about 40% of its value (Damme et al, 2014) and almost 1% of global GDP is spent on air transport (IATA 2016). 1.1. The Development of World Cargo and its Impact in Schiphol
According to BOEING (BOEING 2016), the growth trend estimates that cargo will be doubled the next 20 years (in Revenue Tons Kilometers) as Figure 1 illustrates.
Figure 1: Cargo Trend Worldwide
Purchases of new high-capacity cargo aircraft saw a gradual slow-down between 2012 and 2015, moving from 42 to 25 annual deliveries. This steady downward trend looks set to continue for the next years with 19 deliveries in 2017.
Belly capacity in the passenger aircraft fleet continued to see strong growth thanks to the expansion in the fleet, the latter expected to see annual growth of 5% between 2011 and 2019. For the years to come, around 400 new aircraft (for the most part B777s, B787s and A350s) will be introduced each year with the phasing out of some 100 to 200 aircraft per year (mostly B747s, B767s and A340s). The introduction of new aircraft will have an important impact since the bellies of new aircrafts have more capacity than the older aircrafts. For instance, the belly of the B787-9 can handle 20 tons of cargo in comparison with the 747 used for passenger which is able to handle only 12.5 tons of cargo, or the A340-300 which has a capacity of 14 Tons. In view of this dynamic,
188
several air freight carriers are following the Air France –KLM Group’s example by gradually decreasing their full freighter fleets in favour of bellies.
1.2. Schiphol as Important Node in Air-Cargo Networks
The cargo operations at Schiphol faces major challenges from macro- developments such as rapid changes in aviation sector and cargo market, technology/ICT revolution, transitions to green and circular economy, e-commerce, increasing volumes, volatility and uncertainty of airfreight and logistics. Last but not least, changes in freight strategy of the hub carrier KLM cargo. The last one consists in reducing its full-freighter and increase cargo transport in the bellies of the passenger’s aircrafts as the recent phasing out of some freighters indicate. However, this transition has several challenges such as synchronization of cargo operations with passenger ones, capacity of ground handlers for increasing the cargo operation in the passenger bellies, time slot coordination, schedule and punctuality among others. Schiphol is important for the logistics sector and economic growth of the Amsterdam metropolitan region economy as well as for the Dutch one. Airfreight operations are fully concentrated at Schiphol airport, with minimal cargo activities in other regional airports such as Maastricht or Eindhoven airports.
Schiphol is ranked third in Europe in term of airfreight aggregated volumes (2 million tons in 2015), behind Charles-de-Gaulle and Frankfurt airports.
There exists strong competition between the three European airports, which all of them function as gateway to the European market. Also, there is increase in the number of European regional airports that focus on airfreight activities/operations, which attract mainly full-freighters (and almost no belly aircrafts). The top four regional airports have home carrier as the main airfreight carrier, such as Cargolux/Panalpina in Luxemburg, UPS in Köln, DHL and TNT in Liège/Belgium.
The main carrier in Schiphol is KLM whose passenger operation accounts for more than 80% of the revenues of KLM-Air-France group, however, an important part of airfreight volumes is transported in combined (belly) aircrafts. In this way, revenues generated from airfreight operations are complementary to passenger operations, especially on intercontinental networks that are difficult to maintain financially.
During the financial year of 2015, the Group transported nine billion Revenue Ton-Kilometers of which 75% in the bellies of passenger aircraft and 25% in the dedicated full-freighter fleet, to a network of 316 destinations in 115 countries. However, starting from 2010 and confronted with the crisis of the sector, the group adopted a new “priority to bellies and combis” strategy, aimed at optimizing the economics of passenger aircraft bellies. The full- freighter fleet then is used as a complement to cover the routes not served with passenger flights, products that cannot be carried in bellies and markets in which belly capacity is not adapted to demand.
Over the past four years, Air France – KLM Cargo implemented a transformation and adaptation program focused on revenues, costs, hub productivity and the quality of bellies and combis, to optimize the payload on its full-freighter fleet.
Roughly speaking, 30% of the total cargo capacity of Schiphol is handled by KLM and Martinair (a cargo subsidiary of KLM). At December 31, 2015, the KLM fleet comprised 113 aircraft (111 at December 31, 2014), of which 65 long-haul aircraft and 48 medium-haul aircraft. KLM reduced Martinair cargo fleet from 10 Full Freighters (FF) to only 4 (KLM 2016)
The current study approximates the consequence for KLM fleet at Schiphol and the knock-on effects that this decision might have. In a previous work (Mujica et al. 2017) we have analysed the impact of the phasing-out of the full freighters under deterministic scenarios. The current work analyses the impact of the variability of the load factors and the effect of replacing old aircraft from the fleet with new ones that have bigger capacity than the current fleet. As it has been mentioned, it is expected that the downsize of the fleet will continue, thus, for KLM and Schiphol group the understanding of the consequences of the fading-out of the remaining full
189
freighters is key for improving the operational management of the airport system. Besides all the restrictions and limitations, the demand will continue with the collateral consequences in congestion, delays, and capacity. In the next sections we present the study where some implications of phasing out the FF and replacing the old fleet are discussed.
2. Methodology: Stochastic Modelling of Cargo OPS
For the evaluation of the transition from Full freighter to the transport of cargo in the bellies of the aircraft (A/C) we took the public information from KLM and Martinair together with the traffic numbers from OAG database for developing and analyzing different scenarios. We performed some assumptions based on different studies and reports and we came up with the numbers and the initial implications of the transition.
2.1. Base Case of Cargo Operation
The model considers different elements which interact with each other: layout, traffic, infrastructure, vehicles, and cargo. The ratio between input/output is calculated from the reports of cargo from Schiphol Group in 2016 (Schiphol 2016). According to those reports, the number of tons transported to and from Schiphol are approximately 823 tons per year. Figure 2 illustrates the amounts that are imports, exports, and road imports or transshipments.
Figure 2: Flow Model of Cargo at Schiphol
The inbound flux (Figure 2) is a combination of aircraft with cargo that arrive at Schiphol and ground cargo transported by trucks. The outbound flux is the combination of cargo that goes as export products and goes to a destination. The outbound flux – the inbound flux represents the accumulation of goods or the amount of cargo that is either non-reported (not found in the consulted reports) or stays in the warehouses in the surroundings of Schiphol. As it can be appreciated, the airport is just a stopover for the products. We considered these values for estimating the cargo load between the arrivals and departures. Furthermore, the load factors vary during the year, as Figure 3 illustrates. In our approach, we considered this variation for accounting the maximum values of cargo that are transported with the use of the A/C fleet of KLM+ Martinair.
In the model, we considered one day as the representative of the operation, the information of March 16th of 2015 was considered as the input for the model.
Figure 3 presents the variability of load factors of passenger traffic and cargo from the year 2000 until 2013 (CAPA, 2014).
190
Figure 3: Evolution of World Load Factors (% 2000 to 2013)
It can be noticed that for the FF the load factor has a variability between 65% to 70% maximum, while for the belly operation the variability is between 35% to 40% maximum. In the variability values considered, we assumed that it was not possible to surpass the higher bounds of 70% for the cargo load factors. Therefore, for the different scenarios, we assumed that 70% load factor is a hard restriction or upper bound in the operation. 2.2. Stochastic Analysis
As initial analysis, the load factors for passenger flights and for the full freighters were modelled using variations of Beta Distribution as Table 2 illustrates. In addition, the table also presents the modifications of load factors of the different scenarios.
Table 1: Values for the Uncertainty in A/C Cargo Belly Load Factor
Scenario Mean Min Max alpha1 = alpha2 Distribution 1 0.375 0.375 0.375 1 0.375 2 0.375 0.35 0.4 24 0.35+0.05*random.beta(24,24) 3 0.375 0.35 0.4 4 0.35+0.05*random.beta(4,4) 4 0.375 0.375 0.375 1 0.375 5 0.375 0.35 0.4 24 0.35+0.05*random.beta(24,24) 6 0.375 0.35 0.4 4 0.35+0.05*random.beta(4,4) 7 0.375 0.375 0.375 1 0.375 8 0.375 0.35 0.4 24 0.35+0.05*random.beta(24,24) 9 0.375 0.35 0.4 4 0.35+0.05*random.beta(4,4)
191
(Continued) Table 1: Values for the Uncertainty in A/C Cargo Full Freighter Load Factor
Scenario Mean Min Max alpha1 = alpha2 Distribution 1 0.675 0.675 0.675 1 0.675 2 0.675 0.675 0.675 1 0.675 3 0.675 0.675 0.675 1 0.675 4 0.675 0.65 0.7 24 0.65+0.05*random.beta(24,24) 5 0.675 0.65 0.7 24 0.65+0.05*random.beta(24,24) 6 0.675 0.65 0.7 24 0.65+0.05*random.beta(24,24) 7 0.675 0.65 0.7 4 0.65+0.05*random.beta(4,4) 8 0.675 0.65 0.7 4 0.65+0.05*random.beta(4,4) 9 0.675 0.65 0.7 4 0.65+0.05*random.beta(4,4)
A total of 9 scenarios were built to assess the impact that different levels of variability in the load factor will have on overall performance. Thus, we considered 3 levels of variability for both the “Belly Load Factor” and the “Full Freighter Load Factor”: zero variability, moderate variability, and high variability. Zero variability was modelled by a constant load factor for each flight. Medium variability was modelled using a Beta distribution with alpha1 and alpha2 parameters equal to 24. High variability was modelled using a Beta distribution with alpha 1 and alpha2 parameters equal to 4.
All the distributions were centered around 0.375 and 0.675 load factors for Belly cargo and Full freighters, respectively. With the use of load factors, we multiplied the capacity of each type of aircraft for the load factors to get the estimation of the amount of cargo transported by the flights and by following this approach we could estimate the impact of the variability in the total cargo transported via Schiphol. Table 3 provides the cargo capacities of the different types of aircraft within the fleet under study.
Table 2: Cargo Capacities of different Aircraft
A/C Type Cargo Capacity (Kg)
B744 12500 B772 12700 B738 2000 A332 10900 E190 1000 B737 2000 A333 17400 F70 1000 B739 2000 RJ85 1000 B787-9 20000 B777-3 23700
Making the previous assumptions we were able to construct different scenarios where we could give light to the impact of variability in the operation. Once we identified the impact of it, by performing simulations with different scenarios, we could propose solutions for rebalancing the input flow if, for example, one or more full freighters are phased out.
192
3. Results
For the following scenarios, we included the variability of the load factors in the scenario that has one full freighter less. The purpose of this was for addressing what is the maximum cargo that can be transported assuming a natural variation of the load factors in the aircraft.
For the different scenarios developed, we assumed the traffic mix is kept constant. Thus, the variation in the cargo numbers of the simulations is due only to the different parameters: phasing out of FF and replacement of old 747s and 772s with B787s and B777s.
Regarding the FF, Martinair currently have only 4 aircraft in operation. Some scenarios will evaluate the situation that Martinair will fade out the fleet from 4 to 3 full freighters. If we calculate the implication of that action, we end up with a deficit of 28 k tons annually. We assumed the following fleet flies every day:
• 3 McDonnell Douglas MD11F with capacity of 95 tons of cargo
• 1 Boeing 747 ERF with capacity of 112 tons of cargo 3.1. Base Scenario: Variability in Place
Table 3 shows the average flow of cargo based on flight arrivals separated by inbound (input) and outbound (output) flows from and to the airport for a total of 100 simulation replications for each scenario. Results show that Scenario 9, which considers the highest amount of variability in the load factor of both bellies and FF, is the one that produced the highest total average input and output flows of all the scenarios. These results show that, even though all scenarios had the same mean load factor and a non-skewed distribution, a higher variability in the load factor tended to produce more flow of cargo. Because of Scenario 9 presented the highest values in terms of total flow, it was used as a comparison the Scenarios 10 and 11, where FF capacity was reduced.
Table 3: Results from the Stochastic Analysis (Average Annual kg.)
Scenario Input Belly Input FF Input Total Output Belly Output FF Output Total 1 104,187,334 97,810,875 201,998,209 100,639,427 94,583,116 195,222,543 2 102,754,139 97,810,875 200,565,014 99,177,335 94,583,116 193,760,451 3 103,177,257 97,810,875 200,988,132 99,599,022 94,583,116 194,182,138 4 102,690,195 97,822,396 200,512,591 99,260,388 94,594,256 193,854,644 5 105,120,173 97,822,396 202,942,569 101,536,972 94,594,256 196,131,228 6 105,830,683 97,822,396 203,653,078 102,258,821 94,594,256 196,853,077 7 102,442,588 97,842,173 200,284,761 99,001,098 94,613,381 193,614,479 8 105,060,903 97,842,173 202,903,075 101,479,658 94,613,381 196,093,039 9 105,946,896 97,842,173 203,789,069 102,408,936 94,613,381 197,022,317 3.2. Scenarios 10 and 11
In these scenarios, we removed one FF and assumed that part of the fleet was replaced with new models of aircraft. Table 4Error! Reference source not found. presents the new cargo values once the fleet is reduced to 3 full freighters.
193
Table 4: Capacity Numbers for Reduction of 25% FF Capacity
(ARR) 25% Less FF
Cargo Capacity Annually bellies 114,084,400
Cargo Capacity Full Freighters 72,817,500
Total Capacity (Annually) 186,901,900
Missing Capacity (Daily) -78,400 Missing Capacity (Annually) -28,616,000 3.2.1. Scenario 10: Replacing all 747s for B777s and 787s Aircraft
For the first additional scenario we assumed that all Boeing 747s were replaced from the fleet to evaluate if it is possible to absorb the cargo capacity of the removed full freighter. This fleet was chosen because it is one of the oldest aircrafts in the fleet of KLM, according to Planespotters (www.planespotters.net). Thus, to compensate for the full capacity of FF, a total of thirteen 747s would be replaced by six 787-9 and seven 777-3. This change of fleet from B747s to aircraft with more capacity will resulted in an added annual capacity of 18k tons, which is still short for absorbing the missing capacity of 28k tons as Error! Reference source not found. shows. 3.2.2. Scenario 11: Replacing all 777-2 with 777-3
Owing to the fact that the proposal of Scenario10 could probably come short of capacity, as it only adds 18k tons of capacity in comparison with the 28k tons that are needed, the second additional scenario considers a change of all 777-2 in the fleet (nine aircraft) to 777-3 aircraft as it has more cargo capacity and 777-2 are the second oldest aircraft in the fleet (www.planespotters.net). This will result in an additional annual capacity of 14k tons, which added to the previous 18k tons, will result in a total of 32k additional capacity.
Table 5: Cargo Flow for Base Scenario Compared with Scenarios where one FF was Phased Out (Annual kg.)
Scenario Measure Input Belly Input FF Input Total Output Belly Output FF Output Total
Scenario 9 Average 105,946,896 97,842,173 203,789,069 102,408,936 94,613,381 197,022,317 Scenario 10 Average 120,292,263 74,414,857 194,707,120 116,279,296 71,959,167 188,238,463 Scenario 11 Average 133,485,761 74,414,857 207,900,618 129,037,408 71,959,167 200,996,575 Scenario 9 Percentile 95 107,276,044 98,628,231 205,904,275 103,735,934 95,373,499 199,109,434
To assess the impact of phasing out one FF, Error! Reference source not found. compares the average inbound and outbound flows for Scenario 9 (baseline with high variability but full fleet of FF) with Scenarios 10 and 11 (where one FF has been removed).
Results from Table 5 show that removing one FF and substituting all 747s with B777s and 787s aircraft (Scenario 10) results in a reduction in capacity when compared with the baseline scenario (Scenario 9). It is only when all 777-2 fleet is replaced by the bigger 777-3 that the lost average capacity from phasing out one FF is recovered by the additional belly capacity.
In addition, as simulation results vary among replications because of the stochasticity modelled in the load factors. Table 5 presents the results of the 95th percentile of the total inbound and outbound flow for the baseline scenario to perform a risk assessment. In this manner, the 95th percentile of Scenario 9 shows that at most 95% of the times the total inbound cargo needed to be transported is 206k tons per year, instead of an average of 204k tons per year. If we compare this 95th percentile of Scenario 9 with the results from Scenario 11, we can see that, on average, removing one FF and replacing its capacity with bigger belly capacity results in a higher total
194
capacity than what would be needed to be carried the 95% of times in the baseline scenario. 3.3. Discussion
Results from this simulation study show that the total cargo capacity lost, irrespective of origin and destination, by removing even one FF is very difficult to overcome by increasing the capacity in passenger flight’s bellies by just replacing old aircraft with new model versions, as a total of 22 aircraft would need to be replaced. Furthermore, results also show that by incorporating variability into the load factor of both the bellies and FF, the fleet would need to transport a bigger quantity of cargo and more cargo capacity will be needed.
Further analysis is needed to investigate the impact in financial terms that removing one FF and replacing it with bigger capacity passenger aircraft will produce in the operations of the airline. Moreover, future research should also consider the actual flows from and to different destinations to Schiphol to assess the real impact of phasing out an FF flight and the impact of increasing the capacity of a passenger flight.
4. Conclusions and Future Work
In the paper we analysed the implications of replacing old aircraft with new models (with bigger cargo capacity) of KLM and Martinair. The objective of this study was to assess whether, in the presence of variability, the replacement of aircraft is enough for absorbing the missing capacity if one of the four full freighters is retired from the cargo fleet. The simulations show that by replacing the old models with new ones the increase in capacity is just barely enough to absorb the capacity missed. Therefore, the results suggest that in addition to the renovation of fleet it is necessary that the load factors (in the bellies) should be increased so that it is possible to absorb the missing capacity if more than one full freighter is planned to be retired. This action implies a better coordination than the current one for ground handlers and the fleet; since due to the punctuality policies of commercial airlines, it is not possible to absorb delay due to the increase of cargo loading operations. Furthermore, if the airline is not willing to quit some routes, because of the lack of capacity, the airline should face the challenge making use of novel techniques mainly from the IT and OR realm to improve the coordination of the cargo operation. As a future work, the authors will investigate what the optimal balance between fleet replacement and increase of load factor will be assuming it is possible to increase the load factors in the bellies by improving the coordination of the cargo operations.
References
IATA, 2016,”Another Strong Year for Airline Profits in 2017”, press release 76
Amaruchkul, K., Cooper,W.L., & Gupta, D. (2011). A note on air-cargo capacity contracts, Production and Operations Management, 20(1), 152-162
Bartodziej, P., Derigs, U., Malcherek, D., & Vogel, U. (2009). Models and algorithms for solving combined vehicle and crew scheduling problems with rest constraints: an application to road feeder service planning in air cargo transportation. Operation research-spektrum, 31(2), 405-429
BOEING, (2016), “World Air Cargo Forecast 2016-2017”, Technical Report
Bronsing, M. (2013). KLM Cargo Flow Allocation Optimization at Schiphol. <http://repository.tudelft.nl/islandora/object/uuid:7bff00c1-895a-47be-9a4a-
cd65650c7652/datastream/OBJ/download)>
CAPA. (2014). Air Cargo: few other industries would tolerate its structural overcapacity
<http://centreforaviation.com/analysis/air-cargo-few-other-industries-would-tolerate-its-structural- overcapacity-192139>
Damme, van. D., Radstaak, B., & Santbulte, W. (2014). Luchtvrachtlogistriek een dynamische keten in perspectief. SDU, Den Haag.
Feng, B. et al. (2015). Air cargo operations: Literature review and comparison with practices KLM. (2016). Our Fleet <https://afklcargo.com/NL/en/common/about_us/fleet.jsp>
Leung, L.C., van Hui, Y., Wang, Y., Chen, G., 2009. A 0–1 LP model for the integration and consolidation of air cargo shipments. Operational Research. 57 (2), 402–412
195
capacitated air cargo loading planning. European Journal of Operational Research, 199(2), 533-560
Mujica Mota M., Makhloufi A., Boosten, G., Van Damme, D., Hunt, E., (2017). Is your belly empty? the analysis of the transition from full freighters to belly cargo of KLM. In Proc. of ATRS 2017, Antwerp, Belgium
Planespotters (2018). https://www.planespotters.net/airline/KLM-Royal-Dutch-Airlines. Updated March 30th 2018. Accessed May 1st 2018.
Petersen, J. (2007). Air Freight Industry—White Paper. Georgia Institute of Technology, Supply Chain and Logistics Institute, H. Milton Stewart School of Industrial and Systems Engineering. April 1, 2007. www.scl.gatech.edu/industry/industry- studies/AirFreight.pdf. Accessed December 12, 2016. Schiphol Group. (2016). Transport and Traffic Statistics. https://www.schiphol.nl/en/schiphol- group/page/transport-and-traffic-statistics/
Wang, Y.J., & Kao, C.S. (2008). An Application of a fuzzy knowledge system for air cargo overbooking under uncertain capacity. Computer & Mathematics with Applications, 56(10), 2666-2675.
