Maritime Supply Chains [1 ed.] 0128184213, 9780128184219

Table of contents :
Cover
MARITIME SUPPLY
CHAINS
Copyright
Contributors
1
Integration of the maritime supply chain: Evolving from collaboration process to maritime supply chain network
Setting the scene
Aim of the book
Features of the book
Contents of the book
Acknowledgments
References
Part I: Shipping
2
Assessment of transportation demand on alternative short-sea shipping services considering external costs
Introduction
Literature review
Short-sea shipping policy studies
Factors affecting the competitiveness of short-sea shipping
External costs studies
Methodology and numerical methods
General
Transit time model
Internal costs model
External costs model
Decision-making model
Case study
Characterization and data
Results
Conclusions
Acknowledgments
References
Further reading
3
Identifying cost performance indicators for a logistics model for vessel trains
Introduction
Vessel train concepts
Current situation of waterborne transport and competitive transport modes
The inland navigation, short-sea shipping, and sea-river transport system
Competing transport modes
Literature review of supply-chain performance measures
Identifying cost performance indicators for the vessel train concept
Conclusions
Acknowledgments
References
Further reading
Part II: Port and terminals
4
Analysis of port waiting time due to congestion by applying Markov chain analysis
Introduction
Literature review
Method
Predicting port waiting time
Data source
Clustering the data into states
Transition probability matrix
State distribution vector
Generating output
MCA example
Results
Lack of convergence analysis
Progression behavior
Regular state convergence types
The MCA result
Discussion
Conclusion
References
5
A generic understanding of the economic changes of major port regions with shift-share analysis applied, South Kor
Introduction
Literature review
Port system in South Korea
Methodology
Model specification
Data collection
Results
Conclusion
Acknowledgments
References
Appendix: Compositions of freight types in four major ports in 2015
6
Policing flows of drugs in the harbor of Antwerp: A nodal-network analysis
Introduction
Conceptual framework
Belgian port research using the concept of ``nodal governance´´
Drug trafficking through the port of Antwerp a glocalized phenomenon
The Stroomplan, a policy-based and operational approach to a global phenomenon
Conclusion and reflections
Acknowledgment
References
Further reading
7
Economic, social, and environmental impacts of autonomous shipping strategies
Introduction
Framework
Results
General remarks economic analysis
Employment
Environment and reliability
Safety
Conclusions
References
Further reading
Part III: Hinterland
8
Identifying policies for intermodal logistics chains based on domestic Ro-Ro services
Introduction
Methodology
Selection of potential demand for the combined road-sea transport
Feasibility analysis
Methodology application
Scenarios and policies development
Feasibility analysis
Sensitivity analysis
Conclusion and final remarks
References
Further reading
9
Automated SME cargo bundling as a tool to reduce transaction costs while limiting the platform’s liability exposure
Introduction
Limiting service costs platform through liability management
Optimizing strategic matching
Facilitating operation bundling
Reducing contract costs
Interaction operational and legal designs
Liability management for SME automated cargo bundling tools
Obligations which could be imposed on the platform
Liability following from the different obligations
Liability in case of obligations to distribute information
Liability in case of an obligation to organize the transport properly
Liability in case of obligation to perform the transport successfully
Impact in hypothetical cases
Legal exoneration or limitation ground available to the platform
Contractual exoneration and limitation clauses
Impact on the platform's design
Verification mechanisms
Optimizing strategic matching
Algorithm layout
Phase I: Match on date
Phase II: Routing
Cargo bundling routing as an extended VRP
The loading and unloading routing problem
An optimization heuristic
The objective function
The capacity and product combination constraints
Phase III: Cost and benefits of the bundle
Periodic model
Facilitating operational matching
Closed auction
Distribution models in case of collective tendering
Selection process in case of individual tendering
Contracting in case of incomplete matches
Liability impact platform in case of auction system
Legal design allowing for risk management and reduced contract cost
Precontractual acceptance terms of cooperation
Design of contract clauses
Daily management clauses
Liability daily manager
Consolidation point clauses
Liability consolidation point
Liability clauses dealing with damage during transport
Attributable damage from delay
Attributable damage from damage or loss to the cargo/truck
Binding prospective partners to an effective cooperation
Incentivizing conclusion of the contract
Excluding early termination
Maximizing the chances of a long-term cooperation
Summary/conclusion and preliminary results
References
Further reading
Part IV: Transversal issues
10
Sustainable blockchain technology in the maritime shipping industry
Introduction
Background
ERP
Blockchain development
Distributed
Trust
Decentralized
Cryptocurrencies
Blockchain and the maritime logistics industry
Public vs private
Governance
Energy considerations
Smart contracts
ACID, BASE, and SALT: An analysis of transaction attributes
Overview
ACID
BASE
SALT
Discussion
Additional considerations
Conclusion
References
Further reading
11
Blockchain technology as key contributor to the integration of maritime supply chain?
Introduction
Research approach
Literature review
Maritime supply-chain inefficiencies caused by poor data transfer practices
MarSC integration barriers
Blockchain definition and technical design choices
Definition of blockchain technology
Blockchain technology key characteristics
Empirical study: The presence of blockchain technology in MarSC applications
The presence of blockchain-based initiatives in the MarSC and the inefficiencies they address
In-depth case studies and results
Case studies description
Case A-Phytosanitary certificate
Case B-Container release PIN codes
Case C-Smart B/L
Technological choices, addressed inefficiencies, and barriers
Nontechnical barriers overcome by blockchain applications in the MarSC
Conclusions, recommendations, and further research
Annex A. Semistructured interview design identifying contemporary inefficiencies
Annex B. Semistructured interview design focusing on addressed inefficiencies, barriers, and conceptual choices
Annex C. Key elements that enable the use of blockchain technology
References
12
Future maritime supply networks: Key issues in and solutions
Introduction
Key issues and solutions for future maritime supply networks
Reshaping the maritime supply chain
Industry
Policy
Policy and regulation
Policy and data
Policy and legal aspects
Management
Evolving from collaboration processes to a maritime supply network
Minimum/maximum scenario
Maritime supply ecosystem
Skills
Acknowledgments
References
Further reading
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
V
Back Cover

Citation preview

MARITIME SUPPLY CHAINS

MARITIME SUPPLY CHAINS Edited by

THIERRY VANELSLANDER Research Professor, Department of Transport and Regional Economics, University of Antwerp, Belgium

CHRISTA SYS Department of Transport and Regional Economics, Faculty Applied Economics, University of Antwerp, Belgium

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818421-9 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Romer, Brian Editorial Project Manager: Packowska, Aleksandra Production Project Manager: Raviraj, Selvaraj Cover Designer: Hitchen, Miles Typeset by SPi Global, India

Contributors Benson Beidler Maritime Transportation, State University of New York Maritime College, Bronx, NY, United States

Robert Hekkenberg Delft University of Technology, Department of Maritime and Transport Technology, Delft, Netherlands

Philippe De Bruecker KU Leuven, Leuven; Odisee UC, Brussels, Belgium

Katja Hoyer Development Centre for Ship Technology and Transport Systems, Department of Hydrodynamics, Duisburg, Germany

Valentin Carlan Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

A.A. Kana Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands

Stefano Carrese Department of Engineering, Roma Tre University, Rome, Italy

Bart Kuipers Erasmus Center for Urban, Port and Transport Economics, Erasmus University Rotterdam, The Netherlands

Christopher Clott Global Business & Transportation Department, State University of New York Maritime College, Bronx, NY, United States Francois Coppens National Bank of Belgium, Brussels, Belgium

Hilde Meersman Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

Eddy Van de Voorde Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

Eleni Moschouli Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

Marleen Easton Ghent University, Gent, Belgium; Griffith University, Brisbane, QLD, Australia

Marialisa Nigro Department of Engineering, Roma Tre University, Rome, Italy Marco Petrelli Department of Engineering, Roma Tre University, Rome, Italy

Benjamin Friedhoff Development Centre for Ship Technology and Transport Systems, Department of Hydrodynamics, Duisburg, Germany George Van Gastel National Bank of Belgium, Brussels, Belgium

John Preston Transportation Research Group, The Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, United Kingdom

W.M. Groeneveld Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands

J.F.J. Pruyn Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands

Bruce Hartman Department of International Business & Logistics, California State University Maritime, Vallejo, CA, United States

Alessandra Renna Department of Engineering, Roma Tre University, Rome, Italy Tiago A. Santos Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Portugal

Edwin van Hassel Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

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Contributors

C. Guedes Soares Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Portugal Jongjoon Song Transportation Research Group, The Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, United Kingdom Martijn Streng Erasmus Center for Urban, Port and Transport Economics, Erasmus University Rotterdam, The Netherlands Christa Sys Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

Matthias Tenzer Development Centre for Ship Technology and Transport Systems, Department of Hydrodynamics, Duisburg, Germany Thierry Vanelslander Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium Wouter Verheyen Erasmus School of Law, Erasmus University Rotterdam, Rotterdam, The Netherlands; KU Leuven, Leuven; University of Antwerp, Antwerp; Odisee UC, Brussels, Belgium

C H A P T E R

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Integration of the maritime supply chain: Evolving from collaboration process to maritime supply chain network Christa Sys, Thierry Vanelslander Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium

1 Setting the scene The main theme of the book focuses on the integration of the maritime supply chain. The maritime supply chain (MarSC) is understood as the “connected series of activities pertaining to shipping services which is concerned with planning, coordinating and controlling (containerized) cargoes from the point of origin to the point of destination” (Lam and Van De Voorde, 2011, pp. 366–367). Next to cargo and financial flows, liner carriers need to coordinate information and communication flows along the maritime supply chain, interfacing with stakeholders such as port operators, terminal operators, shipping agents, logistic service providers, freight forwarders, customs authorities, and shippers. Based on La Londe and Masters (1994), here, the integration of the maritime supply chain refers to all stakeholders along the maritime supply chain entering into a long-term agreement and connecting, including the sharing of relevant data. As opposed to general supply chain integration (a.o. Bowersox et al., 1999; Christopher, 1992; Mentzer et al., 2001; Rai et al., 2006), the literature regarding maritime supply chain integration is rather limited. Globalization, deregulation, liberalization, and increased competition highlighted that supply chain integration is an important research topic in the maritime and port economies. First, leading logistics companies understood the importance of supply chain orientation, driven by cost reduction, customer value, and competitive advantage (Christopher, 1992; Mentzer et al.,

Maritime Supply Chains https://doi.org/10.1016/B978-0-12-818421-9.00001-X

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# 2020 Elsevier Inc. All rights reserved.

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2001). Second, in the maritime business, the ship management and shipbuilding industries soon recognized supply chain partnerships in their corporate strategy (Kumar and Hoffmann, 2002). Last, regarding the maritime supply chain, Heaver (2001) and De Souza et al. (2003) explored managing maritime transport and logistics as an integrated chain. These authors reviewed the strategy of shipping lines in relation to terminal operating companies, intermodal services, and logistics services. Robinson (2002) and Mangan et al. (2008) focused on the effective integration of ports and terminals when delivering value to shippers and third-party logistics service providers. Carbone and De Martino (2003) employed an analytical model to study the actors’ behavior in the supply-chain relationship between a port (Le Havre) and a shipper (Renault automotive). Panayides (2006) discussed the evolution of the maritime logistics concept. Song and Panayides (2008) focused on the impact of the integration of ports in the supply chain on port competitiveness. Their model shows a “positive relationship between parameters of supply chain integration such as use of technology, value added, and user’s relationships and parameters of port competitiveness such as cost, quality, reliability, responsiveness, and customization.” Since the global financial-economic crisis of 2009, particularly in the container shipping industry, overcapacity resulting in low freight rates has put pressure on market players elsewhere along the maritime supply chain. The literature shifts its focus from (backward or forward) integration between two stakeholders (e.g., liner carriers and terminal operators, liner operators and shippers, etc.), which might lead to suboptimization (Lin et al., 2014) toward covering an entire supply chain. In general, there are different levels of supply chain integration; the highest level is vertical integration.a Here, vertical integration refers to collaboration agreements between subsequent stakeholders of the same MarSC. In this way, the competition changes from an individual firm-to-firm level toward competition between entire supply chains. Vertical integration is governed by stakeholders who aim to have control over a supply chain by increasing their market power (Lipczynski et al., 2005; Besanko and Braeutigam, 2010). In shipping, Meersman et al. (2010) analyzed the role of the various chain actors toward ports applying a regional input-output approach. The authors conclude that “all parties belonging to a given maritime logistics chain have one interest in common: to ensure that their chain is the most attractive, i.e. that it is the most efficient and the cheapest.” Lam and Van De Voorde (2011) presented a scenario analysis for examining the nature and level of supply chain integration in container shipping. Woo et al. (2012) assessed the integration of ports into the supply chain using a structural equation model. Lam (2013) found that individualism is a major obstacle to supply chain integration. Van de Voorde and Vanelslander (2014) stated that cooperation is a trend in the development of future maritime supply chains. Horizontal but especially vertical integration happens to a large extent, originating from shipping companies, but more recently also by terminal operators. Attention

a

Horizontal integration is beyond the scope of the present chapter.

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thereby shifts to the hinterland, as connections to the hinterland have become the crucial cost determinant in maritime supply chains and determine the strength of a port. The role of the latter port authority evolves from being a landlord to being a reliable actor in the supply chain, whose only remaining trump cards are concessions and the potential to lobby with governments for more capacity investment, also in the hinterland (Verhoeven, 2015). After this, the focus of recent research has shifted to topics such as the security (Banomyong, 2005; Yang, 2011), sustainability (Lam, 2015), and resilience (Christopher and Holweg, 2011; Christopher, 2016; McKinnon, 2018) of the maritime supply chain. These developments contribute to increased complexity of the maritime supply chain. In order to manage the end-to-end integration of maritime supply chain processes, each stakeholder needs to control its maritime supply chain (reduce uncertainty) and create value for every actor involved in the sustainable ecosystem with the aim of serving their customers in a better way.

2 Aim of the book This book provides an analysis of the most frequently encountered problems in maritime supply chains hampering the move toward network. It furthermore provides solutions for handling those problems. Those are of interest to scientists as the chapters are at the forefront of methodological developments in their respective fields. Also, the chapters are of immediate relevance to business practitioners at the managerial level as well as policy makers, as they provide answers to key operational issues. Finally, the book is useful for any supply chain course all over the world. Students, minimally at a master’s level or higher, can take useful lessons from the book. The book tackles problems and challenges throughout maritime supply chains. The parts of the book are also built up in that way. The first part deals with the maritime section. Those chapters are of interest to shipping companies, market analysts, and shipbuilders, but indirectly also to port authorities and terminal operators. The second part deals with the port. Again, market analysts can draw useful insights from the provided chapters, just like port authorities and terminal operators. Indirectly, shipping companies, as customers of the port, can also benefit. The third part touches upon the hinterland. Land transport (road, rail, and inland waterway) operators can directly profit from the findings of the research that has been carried out. But due to the increased involvement from other chain players (shipping companies, (inland) terminals, shippers) in land haulage, those can also enjoy useful lessons and recommendations. The final part consists of a transversal chain analysis. Here, the focus is on digital innovation gradually changing the maritime chain. More specifically, the potential of blockchain—as a technology that has the potential to fundamentally alter the way supply chains are operating and who is active in them—is addressed. The last chapter is an overview of the key issues and how to handle them. The outcome concerns a general discussion covering the further integration of the maritime supply chains and puts forward some scenarios as to how the industry should evolve from bilateral partner collaboration to a maritime supply chain network.

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The issues dealt with generically concern the operator’s strategic roles in chains and markets, pricing features, infrastructure investment, and regulatory developments and needs as well as technological items, including IT. The reader will find for each of those topics the latest developments as well as strategies that can be followed to cope with emerging challenges.

3 Features of the book This book has a number of particular features. First of all, it is the first book in the wider field of transport and logistics that takes a transversal look throughout the maritime supply chain, not staying at the higher level of chain analysis, but breaking the chain down into its components but always keeping the wider integrated chain perspective. Second, the book groups the key issues in current maritime supply chains. Hence, whatever the role of the reader in the maritime supply chain, or even outside as a scientist or market analyst, the reader will have a very complete overview of the decision items that matter. Third, the book serves scientists, who have a rather methodological perspective, as well as business practitioners. Methodologically, the chapters apply cutting-edge scientific techniques. For practitioners, the latest state of developments in business practice as well as recommendations can be found in the book.

4 Contents of the book The book contains 12 chapters and is structured around four themes: shipping, ports and terminals, the hinterland, and transversal issues. The introductory chapter to the book addresses the red line that runs through the book, so as to identify where the key issues are in the chain. The last chapter comes to overarching conclusions and reflections from the integrated chain perspective by combining the findings from the preceding chapters. Under the shipping section (Part I), the contribution by Santos and Guedes-Soares in Chapter 2 provides an integrated methodology for the economic assessment of possible alternative short-sea shipping (SSS) routes using roll-on/roll-off (Ro-Ro) ships. This methodology has been applied in a case study, which considered different destination ports in Northern Europe, namely Antwerp, Rotterdam, Le Havre, and Hamburg, to characterize the maritime transportation demand for intermodal transportation between Portugal (port of Leixo˜es) and these ports. The analysis takes the perspective of the users of transportation, their decisions being based on cost and transit time considerations, without and with external costs included. The practical use of these results is twofold. On the one hand, they may support a second step of the evaluation process, this time taking the perspective of the shipping company, which will determine if each pair of maritime freight rate and ship speed is technically and

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economically feasible. On the other hand, these results may assist European or national authorities in determining if a novel service should be granted public funding (considering also the intermodal attractiveness potential) and then defining the required level of such funding. Still in shipping, innovation in maritime supply chains is an important element of strategy. In Chapter 2, Meersman et al. introduce the innovative concept of vessel trains (VT) that resembles the concept of truck platooning. The VT is a waterborne platooning concept featuring a manned lead ship and a number of follower ships that follow at close distance by automatic control. In particular, the authors focus on the performance indicators (PIs), which such a concept should comply with from a supply chain point of view, so as to attract traffic and be successful. The triple bottom line performance of the concept is assessed and compared to the performance of the current transport system. This chapter focuses on the economic performance and sets the foundations for its measurement by (1) describing the current situation of inland navigation, short-sea shipping, and sea-river transport; (2) analyzing the railway and road operations as the main competitors so as to take lessons; (3) presenting a review of the data sources available with respect to cargo flows and collecting the current cargo flows that will be used to set up the origin-destination (OD) matrix of the Antwerp case, (4) determining the geographical scope of a second case study area; (5) conducting a literature review of supply chain PIs, and (6) identifying the key PIs. The final chapter in Part I, by Kuipers and Streng, analyzes the macroeconomic effects of the trend toward autonomous and unmanned shipping. Within a framework of technological development scenarios, the general directions of the impacts are indicated and the effects of autonomous ships are analyzed by means of a literature analysis and expert interviews. There seem to be economic effects of autonomous shipping such as a decrease in offshore employment, an increase in onshore employment, and a positive effect on sailing safety. This research focuses on the direction and main types of economic effects but does not aim to provide an exhaustive analysis of the economic and social effects of autonomous shipping. In the port segment (Part II), a key issue is the occurrence of congestion, as it risks spreading its impact further into the supply chain. With needs for port handling capacity ever increasing, and the means for investment more restrained, especially on the public side, congestion gets increasing attention. Pruyn and Groeneveld investigate the suitability and potential of a number of Markov processes in relation to port waiting times. An analysis is done on the applicability of a variety of Markov processes, an elaboration on the best usable Markov process, and the potential of the results. To limit the complexity of the problem and to make sure sufficient and relevant data are available, the focus is on the dry bulk shipping market and relevant ports/areas. The idea is that port waiting times are related to the area, economic situation, and current waiting time. By studying these relations, insight into triggers and potential long-term equilibria is obtained. Finally, the potential to predict future waiting times is evaluated. A further port study deals with the economic impact of ports. The functional role of a port has been diversified due to various factors in relation to its surroundings. In addition, the relational changes in port governance have caused various debates with regard to both the port hierarchy and port impact studies. Particularly, port impact studies face the criticism that

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they do not reflect the relational changes of port-related activities. This is mainly due to the methodological limitations of the cross-sectional approach rooted in input-output analyses. Soon and Preston develop an approach to understand the economic impacts of four major ports in South Korea indirectly through analyzing the gross value added per worker in transportation for 25 years using shift-share analysis as an exploratory method. The approach of this study clearly shows a number of limitations. It is expected that meaningful research results can be derived if some of the limitations would be removed through additional studies. First, as mentioned in the main text, the analysis of subsectors in transportation can be used to better understand the changes in port-related industries. It is possible to suggest more clearly how the port contributes to the growth of its regional economy by explicitly looking at the changes in more disaggregated subsectors. In addition, this study analyzes four major ports in South Korea, but it may be useful to study port systems in other countries or regions that have a port governance system where the port authority makes major decisions related to investment among several ports. Finally, a researcher can obtain information on the statistical significance that cannot be provided in this study if the data used in this study are analyzed by econometric analysis. A last port contribution, by Easton, deals with a flow of goods that passes through ports and attaches itself to regular flows of goods, namely that of illegal goods, and drugs in particular. The flow of cocaine through the port of Antwerp is a “glocal” phenomenon as cocaine trafficking is a global phenomenon with local impact on crime in the port and its surroundings. This chapter addresses the announced Stream Plan (2018), which was set up to tackle this phenomenon. This plan is studied using the conceptual framework of nodal and networked policing. It puts a focus on the diversity in views and approaches by the public and private actors involved. Findings from previous port-related research on these views and approaches are used as a source of inspiration to reflect upon expected dynamics and possible pitfalls in the implementation process of the Stream Plan. Further research into this issue (particularly with regard to ports) can be useful to make wellfounded statements about the nature of plural policing in Belgium and to gain further insight into the development of this concept in practice. Is the private sector considered a full partner in the pursuit of greater public safety? This type of analysis must form the basis of any comparison made between sectors or even countries so that possible differences may come to light and be explained. In terms of practice, it can be interesting to see how (and if ) the various policy domains address security as a horizontal theme. The question that arises is whether the efforts in all policy domains can be linked and whether this will generate a greater impact. Does cooperation work? Does this type of cooperation contribute to increased safety in the port and in the city (global phenomenon with local impact)? These are questions of great importance for the public and private sector, and they form a possible agenda for future research. At the intersection with the hinterland (Part III), starting from the enhancement of the Motorways of the Sea (MoS) in the White Paper 2001 on European Transport Policy, short-sea shipping became one of the key aspects in the development of the Trans-European Transport Network (TEN-T). Indeed, short-sea shipping is considered one of the possible strategies to find competitive and sustainable transport alternatives with respect to the road-only transport, if a length of over 300 km characterizes the connections. The chapter by Petrelli et al.

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proposes and investigates several policies to increase the use of short-sea shipping, moving freight from the road-only alternative to intermodal logistics chains based on domestic cabotage through Ro-Ro services. The objective is to quantify the potential demand of the combined road-sea transport as a function of different actions in terms of new Ro-Ro services/routes and subsidies to the road haulers or to the shipping companies. The case study refers to domestic services involving the Italian seaports. A typical problem in hinterland transport is suboptimal loading. Full container load (FCL) shipping allows for cost savings compared to less than container load (LCL) shipping. However, many small to medium companies (SMEs) lack sufficient volume (on specific routes) to allow for FCL shipping. Hence, efficiency gains can be obtained through cooperation between different LCL shippers (cargo bundling). Such cooperation has been researched extensively in logistics research. However, the problem with such cooperation is that the cooperation itself causes significant transaction costs. These costs can, especially for SMEs, strongly reduce or even eliminate the efficiency gains generated by FCL shipping. Therefore, Verheyen and Debruecker focus on making cargo bundling accessible for SMEs. In order to boost the competitiveness of the SMEs, they build a model for an online tool to consolidate small individual shipments of several SMEs. The goal is to match SMEs with compatible shipments and compatible destinations. Designing an automated cargo bundling tool requires research in two fields. First, the authors develop a matching algorithm based on mathematical optimization techniques. The result is a set of possible bundles with their respective costs, timing, and route, allowing for the strategic matching of the partners. Second, the design of the tool requires a legal design. This legal design takes place in two different relationships: (1) the relationship between the tool and the users (shippers), focusing specifically on the assessment of the liability exposure of the platform (as mentioned above); (2) the relationship between the different partners, focusing on the question as to how the legal design can maximize the chances for a successful contracting by (a) binding them at an early stage of the cooperation and (b) by providing contract terms that enjoy general acceptance from those parties. The legal design is built upon a literature study of case law and doctrine, supported by a survey on preferences for a design of a horizontal cooperation model. The transversal section, Part IV, features two contributions dealing with blockchain. The first one, by Clott et al., states that with the advent of blockchain-facilitated transactions, it is anticipated that major structural changes will occur in the maritime field. The impact of these changes is global; however, major transaction issues and standards still must be agreed upon and enacted. This paper investigates the use of blockchain technology, its limitations, and the potential impact of blockchain on the maritime industry. The blockchain technology itself is evolving so quickly that it is hard to track for any firm to detect which features and trends matter and which are irrelevant. Even the technical parameters are changing; this paper had to talk about changes that have not yet materialized, as though they were in place (PoS). The second chapter of Part IV, by Coppens et al., states that maritime supply chain thinking is an important trend. In practice, this thinking clashes with a few challenges, not the least with achieving collaboration with supply chain actors. Distributed ledger technology (DLT), or blockchain, having proven its merits in digital currencies (e.g., bitcoin), suggests large

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benefits for the actors in the maritime chain (where decentralized consensus is present as well). As the technology is not regarded as an issue anymore, however, questions with regard to the implications on the maritime supply chain, operational efficiency, ownership traceability, or financial streams remain unanswered. While DLT might theoretically seem an interesting fit, it was initially not intended for maritime supply chain applications. Therefore, there are several challenges that still need to be addressed in order to allow for a successful deployment of DLT in the maritime supply chain. And this is exactly the objective of the present paper. By taking an empirical approach, this scientific research investigates whether DLT can overcome barriers such as lack of trust and unwillingness to collaborate by the supply chain partners to provide integrative solutions. To do so, it conducts an in-depth literature review and a market overview with regard to emerging blockchain initiatives in the maritime supply chain as well as collects in-depth insights over an implemented blockchain solution. The literature review puts forward the fundamental conceptual choices that DLT developers make. In parallel, an overview is given with regard to the integration barriers that maritime supply chain stakeholders face. This overview brings, under a comprehensive framework, the integration barriers that are associated with economic, legal-political, technological, and culturalmanagerial reasons. From a competitive perspective, the further integration of the maritime chain is needed to eliminate inefficiencies and meet increased customer demand. These inefficiencies are linked with up-to-date data-exchange practices in the maritime supply chain. Furthermore, the market research gives valuable insights with regard to the presence of blockchain initiatives in the supply chain. The later in-depth interviews confront already implemented solutions with the literature results and point out the inefficiencies addressed by contemporary DLT solutions, the integration barriers they face, and the conceptual choices made for their successful implementation. The book concludes with an overarching chapter discussing the further integration of the maritime supply chain. The aim of this chapter is to put forward some scenarios on how the industry should evolve from partner collaboration (one-to-one collaboration) to a maritime supply chain network (many-to-many collaboration). From a business perspective, this trend of maritime supply chain actors banding together enabled by technology can leverage their strengths and competitiveness.

Acknowledgments As editors, we would like to thank both the authors and the reviewers who contributed to the creation of this book. Next, we would want to thank Patsy Geurts for her organizational support in preparing and hosting the conference. Finally, we also would like to thank Aleksandra Packowska & Selvaraj Raviraj, Elsevier, for their much appreciated assistance.

References Banomyong, R., 2005. The impact of port and trade security initiatives on maritime supply-chain management. Marit. Policy Manage. 32 (1), 3–13. https://doi.org/10.1080/0308883042000326102. Besanko, D., Braeutigam, R., 2010. Microeconomics. John Wiley & Sons. Bowersox, D., Closs, D.J., Stank, T.P., 1999. 21st Century Logistics: Making Supply Chain Integration a Reality. Council of Logistics Management, Oak Brook, IL.

References

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Carbone, V., De Martino, M., 2003. The changing role of ports in supply-chain management: an empirical analysis. Marit. Policy Manage. 30 (4), 305–320. Christopher, M., 1992. Logistics and Supply Chain Management. Pitman Publishing. Christopher, M., 2016. Logistics & Supply Chain Management. Pearson, Harlow, UK. Geraadpleegd van. https:// books.google.be/books?hl¼en&lr¼&id¼NIfQCwAAQBAJ&oi¼fnd&pg¼PT7&dq¼christopher+martin+1998 +supply+chain&ots¼x1a3JqLqrB&sig¼9b7Yo4faDARTwbYXcYiDp6s8ee8. Christopher, M., Holweg, M., 2011. “Supply chain 2.0”: managing supply chains in the era of turbulence. Int. J. Phys. Distrib. Logist. Manage. 41 (1), 63–82. https://doi.org/10.1108/09600031111101439. De Souza Jr., G.A., Beresford, A., Pettit, S., 2003. Liner shipping companies and terminal operators: internationalisation or globalisation? Marit. Econ. Logist. 5 (4), 393–412. Heaver, T.D., 2001. The evolving roles of shipping lines in international logistics. Int. J. Marit. Econ. 4 (3), 210–230. Kumar, S., Hoffmann, J., 2002. Globalisation: the maritime nexus. In: Grammenos, C.T. (Ed.), The Handbook of Maritime Economics and Business. LPP Publications, London, pp. 35–66. La Londe, B.J., Masters, J.M., 1994. Emerging logistics strategies: blueprints for the next century. Int. J. Phys. Distr. Log. Manag. 24 (7), 35–47. Lam, J.S.L., 2013. Benefits and barriers of supply chain integration: empirical analysis of liner shipping. Int. J. Shipp. Transp. Logist. 5 (1), 13–30. Lam, J.S.L., 2015. Designing a sustainable maritime supply chain: a hybrid QFD–ANP approach. Transp. Res. Part E Logist. Transp. Rev. 78, 70–81. https://doi.org/10.1016/j.tre.2014.10.003. Lam, J.S.L., Van De Voorde, E., 2011. Scenario analysis for supply chain integration in container shipping. Marit. Policy Manage. 38 (7), 705–725. Lin, S.-M., Potter, A., Pettit, S., Nair, R., 2014. A systems view of supply network integration in maritime logistics. In: Gepresenteerd bij 19th Logistics Research Network Conference, Huddersfield. Geraadpleegd van. http:// orca.cf.ac.uk/68101/1/LRN%20conference%202014%20Port%20SC%20Integration.pdf. Lipczynski, J., Wilson, J.O., Goddard, J.A., Goddard, J., 2005. Industrial Organization: Competition, Strategy, Policy. Pearson Education. Geraadpleegd van. https://books.google.be/books?hl¼en&lr¼&id¼El_ksKFN_8EC&oi¼fnd&pg¼ PR23&dq¼lipczynski+industrial+organization&ots¼Ajolb9_K2S&sig¼Mp4NeE4vS2LMZKRb7ILmGuNZJGw. Mangan, J., Lalwani, C., Fynes, B., 2008. Port-centric logistics. Int. J. Logist. Manage. https://doi.org/ 10.1108/09574090810872587. McKinnon, A., 2018. Balancing Efficiency and Resilience in Multimodal Supply Chains (International Transport Forum Discussion Papers). OECD Publishing, Paris, p. 21. Geraadpleegd van https://www.itf-oecd.org/ balancing-efficiency-and-resilience-multimodal-supply-chains. Meersman, H., Van de Voorde, E., Vanelslander, T., 2010. Port competition revisited. J. Pediatr. Matern. Fam. Health Chiropr. 55 (2), 210. Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., 2001. Defining supply chain management. J. Bus. Logist. 22 (2), 1–25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x. Panayides, P.M., 2006. Maritime logistics and global supply chains: towards a research agenda. Marit. Econ. Logist. 8 (1), 3–18. https://doi.org/10.1057/palgrave.mel.9100147. Rai, A., Patnayakuni, R., Seth, N., 2006. Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quart, 225–246. Robinson, R., 2002. Ports as elements in value-driven chain systems: the new paradigm. Marit. Policy Manage. 29 (3). Geraadpleegd van https://trid.trb.org/view/722965. Song, D.W., Panayides, P.M., 2008. Global supply chain and port/terminal: integration and competitiveness. Marit. Policy Manage. 35 (1), 73–87. https://doi.org/10.1080/03088830701848953. Van de Voorde, E., Vanelslander, T., 2014. Trends in the maritime logistics chain: vertical port co-operation: strategies and relationships. In: Port business: market challenges and management actions. Academic and Scientific Publishers, Brussels, pp. 121–140. Verhoeven, P., 2015. Economic Assessment of Management Reform in European Seaports. Universiteit Antwerpen, Faculteit Toegepaste Economische Wetenschappen, Antwerpen. Woo, S.-H., Pettit, S., Beresford, A., 2012. Logistics performance of supply chain-oriented ports. In: Song, D.W., Panayides, P.M. (Eds.), Maritime Logistics: A Complete Guide to Effective Shipping and Port Management. Kogan Page, London, pp. 271–310. Yang, Y.-C., 2011. Risk management of Taiwan’s maritime supply chain security. Saf. Sci. 49 (3), 382–393. https://doi. org/10.1016/j.ssci.2010.09.019.

C H A P T E R

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Assessment of transportation demand on alternative short-sea shipping services considering external costs Tiago A. Santos, C. Guedes Soares Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior T
ecnico, Universidade de Lisboa, Lisboa, Portugal

1 Introduction A main policy guideline of European Union (EU) transport policy has been for many years the promotion of intermodality and the use of other transport modes instead of the road mode (see European Commission, 1992, 1995, 1999). More recently, a roadmap toward a single European transport area has been outlined by the European Commission (2011) with great emphasis on the competitiveness and sustainability of the transport system, namely, setting a 60% GHG emission reduction target. These objectives, generally, may be achieved using rail and waterborne transport modes, which include short-sea shipping (SSS) and inland waterways (IWW), as mentioned in European Commission (2011). In this chapter, the definition of SSS is that originally given in European Commission (1999): “Short-sea shipping” means the movement of cargo and passengers by sea between ports situated in geographical Europe or between those ports and ports situated in non-European countries having a coastline on the enclosed seas bordering Europe. Most periphery countries in the EU do not have rail and IWW networks as developed as those in Central Europe and their networks are not effectively connected to Central Europe. For such countries, SSS remains a viable alternative to road transportation, allowing an adequate and uniform implementation of EU policies. Furthermore, these countries are often located far away from the core regions of Europe and shipping is, in comparison with road, cost and time competitive over longer distances. Thus, SSS could have a more prominent position in the achievement of the overall objectives of EU policies: promoting a European Single Market, reducing GHG emissions, and promoting intermodality. Maritime Supply Chains https://doi.org/10.1016/B978-0-12-818421-9.00002-1

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# 2020 Elsevier Inc. All rights reserved.

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The key objective of promoting intermodality in the EU implies that transportation units should be used with seamless transitions between transport modes and under single transportation contracts. Various technical options, in the field of shipping, are available for the purpose of achieving this, namely, the use of containers, carried in container ships or roll-on/roll-off (ro-ro) cargo ships. Another option is to have the ro-ro ships carrying trailers or complete trucks, accompanied or unaccompanied. In Northern Europe and certain parts of the Mediterranean, such services, organized as liner services (regular, scheduled, and advertised), have been in existence for many years now, often in combination with the carriage of passengers. However, such services have been mostly (not entirely) absent in the Western Coast of Europe (southwards of Brittany), along significant stretches of what has been defined by the EU as the Motorway of the Sea of Western Europe (see European Commission, 2001, 2004). It is well known that significant measures are being taken by countries in Central Europe aiming at restricting road transportation, especially the use of trucks (heavy goods vehicles). The broad rationale is that trucks are considered to contribute significantly to road degradation and air pollution, leading to increased maintenance costs and health-related costs, including a significant number of fatalities arising from accidents and health problems. Measures to restrict road transportation include bans on driving on weekends, at night, or on holidays, drivers cannot sleep in trucks, driving hours of truck drivers are being closely monitored, truck speeds are electronically registered and thoroughly checked, mounting toll and tariff systems have been put in place, and certain roads cannot be used by trucks or circulation is somewhat restricted. A recent issue is the requirement (in place or under discussion) for payment of national minimum wages when the truck is in a given country. These are just some examples of regulatory pressure on road transportation of cargo in Europe and of the economic implications of these pressures. In addition to this, in the EU, a broad approach to the assessment of the external costs of transportation has been in place for many years now (see Maibach et al., 2008; Korzhenevych et al., 2014). These references define external costs as the difference between social costs and private costs in a given activity, in this case transport, and provide a framework for the quantification in monetary terms of external costs. Section 2 presents a literature review that includes external cost calculation methods. In face of these restrictions, considering the EU goal of significantly reducing GHG emissions and also that the internalization of external costs is on the EU agenda (see European Commission, 2018), it is clear that countries like Spain and Portugal will experience increasing pressure to use rail services or ro-ro–based SSS services, since they have significant volumes of trade in goods with Central European countries, which are currently taken mainly through the road networks of these countries. Considering this background, the research objective of this chapter is to develop a methodology suitable for studying conditions under which ro-ro SSS services may be used to support EU policies for promotion of intermodality, especially in the context of growing pressure for the internalization of external costs, and foster the competitiveness and integration of peripheral economies in the European Single Market. The methodology should be able to assess which ports are more suitable for ro-ro SSS services, in terms of capacity to attract transportation demand, and also the potential for enlarging such demand when external costs are taken into account. The methodology should also be able to assess demand for transportation using SSS through considering costs and I. Shipping

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transit times involved in intermodal (SSS) and unimodal solutions, for supply chains running between a comprehensive set of origins and destinations. In this line, previous studies by Santos and Guedes Soares (2017a,b) have presented a methodology allowing the prediction of transportation demand for intermodal solutions in the corridor between Portugal and Northern Europe and the assessment of the technical and economic feasibility of services in the same corridor, but the present study compares different ports in the Northern Europe range and expands the methodology to cover also the quantification of external costs. This chapter presents further developments and results in this research line and is organized in the following manner. Section 2 presents a literature review dedicated to SSS and methods for the assessment of external costs. Building upon this review, the methodology and numerical models applied in this chapter are described in Section 3. In order to illustrate the application of these methods, a case study containing an assessment of the transportation demand between a Portuguese port and four different ports in Northern Europe is presented in Section 4. Finally, Section 5 presents the main conclusions, focusing especially on the case study.

2 Literature review 2.1 Short-sea shipping policy studies European authorities European Commission (1992, 1995, 1999, 2001, 2011) have devoted considerable attention to the promotion of SSS since the last quarter of the 20th century. Promotion has relied on numerous financing programs since at least 1992 (PACT, Marco Polo I and II). A substantial body of academic literature has reviewed and analyzed the reasons for the successes and failures of SSS in the EU, namely, Baird (2007), Styhre (2009), Medda and Trujillo (2010), Douet and Cappuccilli (2011), Baindur and Viegas (2011), Aperte and Baird (2013), Ng et al. (2013), and Sua´rez-Alema´n et al. (2014). In addition, the European Court of Auditors (2013) has concluded that the Marco Polo programs had little impact on shifting traffic off the road and there is little data to assess the benefits of the reduced environmental impact, reduced congestion, and improved road safety. Rather than subsidizing SSS through the above-mentioned programs, Medda and Trujillo (2010) concluded that reducing implicit subsidies or introducing explicit taxes to road haulers (internalization of external costs) is likely to encourage the shift from road to SSS. This is in line also with the conclusion by Blauwens et al. (2016) that modal shift is promoted when transport users are confronted with the full social costs of transportation. Psaraftis and Kontovas (2010) have also agreed that there is a need to balance environmental and economic considerations. Finally, public investments made in road infrastructures need to be transferred to the users and this has not been done uniformly across the EU. Another aspect hindering the development of SSS is the fact that there are numerous inconsistencies between EU sectorial policies for transport, environment, and security, as reported by Baindur and Viegas (2011). These authors have pointed out that while SSS is encouraged by the EU, environmental policies, such as the bird’s habitat and water directives, severely I. Shipping

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constrain seaport activities; shipping inspection regimes are in fact more stringent than inspections in the road sector; technical standards for loading units and/or vehicles for the carriage of intra-European freight on an intermodal platform are incompatible; and the harmonization of driver wages and fuel taxes is incomplete. On the side of ports, it is necessary to introduce improvements in terminals and procedures for dealing with the specific requirements of SSS, leading to cost efficiency, optimal departure/arrival schedules, decrease in total transit times, and increased reliability and efficiency. Without an integrated work on these and other regulatory and technical issues, SSS is likely to continue to face unfair competition from road haulage.

2.2 Factors affecting the competitiveness of short-sea shipping Paixa˜o Casaca and Marlow (2009) have analyzed the factors that have an impact on the use of SSS in multimodal logistics supply chains. Factors found to be important include selection of ports, service speed, cost of service, frequency, quality, reliability, commercial relationship including after sales attitude, corporate image, and involvement of forwarders. Lo´pez-Navarro (2014) indicated that cost, transit time, frequency, and reliability are the main factors in decision-maker’s options regarding transport mode(s). Finally, the importance of port selection has also been highlighted by Ng (2009), who has carried out a study of SSS services between Belgium and various ports in the Eastern Baltic Sea, aiming at evaluating which port presents a better potential for SSS in terms of hinterland capture and modal shift from road transport. Among the factors mentioned in the literature, the fact that ship speeds are typically relatively low in comparison with those allowed on the road is generally listed as one of the main drawbacks of SSS. This has indeed been confirmed, for example, by Santos and Guedes Soares (2017a). In this respect, ro-ro ships are generally identified as the most suitable ship type for SSS, as it allows shorter cargo handling and dwell time in ports, but suffers from the much higher investment cost (see Santos and Guedes Soares, 2017b; Maersk Brokers, 2016; Bartlett, 2012) in comparison with containerships (as ro-ro ships are, technically, more complex ships than containerships). In fact, some other authors, such as Becker et al. (2004), had previously claimed that the use of high-speed vessels in SSS (an extreme measure) is not really necessary, except in some roll-on/roll-off and passenger (ro-pax) services. This is even more true in current times of high fuel costs. Styhre (2009) found that appropriate vessel type and size (including cargo hold design), as well as appropriate schedules, are even more important for reaching an adequate level of capacity utilization (shipping companies mention between 75% and 88% as adequate values) than speed. Zis and Psaraftis (2017) have studied yet another factor of importance in SSS, namely, the impact of new sulfur limits on the European ro-ro sector, which is related to the relative cost levels of different fuel types, and has a large influence on the economic performance of SSS services. Some of the research mentioned earlier is empirical or qualitative in nature and aims mainly at understanding the determining factors of SSS and its limitations, often in support of policy

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development. There is much less research on calculation methods to assist shipping companies in assessing the potential of different SSS services to attract cargo transportation demand and determining the required fleet size, ship size, service speed, and freight rate. Exceptions to this are the studies by Morales-Fusco et al. (2018) and Santos and Guedes Soares (2017a). This chapter demonstrates how the methodology developed in this last paper may be applied in the selection of the most suitable service.

2.3 External costs studies External costs are those occurring during a certain activity and imposed on a third party without due compensation, as mentioned by, for example, Blauwens et al. (2016). The fact is that until now, these costs are seldom passed to the organization or company performing the activity. External costs may be calculated for many different human activities, including also transport and, naturally, shipping activities. It must be noted that these costs comprise only estimates of the economic consequences of different externalities (expressed in monetary units per t-km or vehicle-km). The externalities considered under the framework of the Marco Polo program (Brons and Christidis, 2013) included only air pollution, climate change, noise, accidents, and congestion. However, in the handbook on estimation of external costs in transport (Maibach et al., 2008), a number of additional external costs were mentioned, namely, costs for nature and landscape, soil and water pollution, costs in urban areas, up- and downstream processes, costs in sensitive areas, and costs of energy dependence. This version of the handbook did not provide detailed calculation methods for assessing these external costs. In the 2014 update to the handbook, by Korzhenevych et al. (2014), calculation methods for additional external costs associated with up- and downstream processes and marginal infrastructure are also included, but nothing is mentioned about other additional external costs listed above. It is also important to note that certain European countries have their own versions of external cost calculation methods (see Trafikverket, 2018). In this study, the external costs considered in the Marco Polo program framework will be considered as SSS is the main focus of the study. Furthermore, for SSS, external costs are generally restricted to the air pollution and climate change components; while the other components are neglected as its importance when compared with land-based transportation is felt to be of a lower order of magnitude. Air pollution external costs are related to damages caused to the human health and biosphere, with the most important pollutants being particulate matter (PM10, PM2.5), nitrogen oxides (NOx), sulfur oxide (SO2), ozone (O3), volatile organic compounds (VOC), ammonia (NH3), and polycyclic aromatic hydrocarbons (PAH). Climate change or global warming impacts of transportation is mainly related to the emission of greenhouse gases such as carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). Friedrich and Bickel (2001) and Bickel and Friedrich (2005) have indicated that there are two approaches for estimating external costs: bottom-up and top-down. The first approach starts at the microlevel, where basic elements are specified in detail and then linked together to provide a complete assessment. Hence, as explained by Friedrich and Bickel (2001) and Miola et al. (2008), this approach is more precise and has the potential for differentiation of marginal

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external costs. These advantages need however to be weighed against the complexity and completeness of this approach, which implies that it may be costly and difficult to implement. The top-down approach works at the macrolevel, with external costs of a country being calculated and divided by the national transport volume, leading to average external costs. Bickel and Friedrich (2005) have pointed out that this method is easier to use but fails to incorporate specific details. Studies on external costs related to shipping activities are in some cases of the bottom-up type but this typically requires detailed calculations of, for example, ship emissions and, secondly, calculation of external costs making use of the estimated cost factors. As this chapter presents a methodology applicable to service selection, it is considered that a less data-demanding approach is more suitable and thus marginal external cost factors from the latest version of the Marco Polo calculator are used (see Brons and Christidis, 2013). Throughout the years, many studies devoted to the calculation of external costs brought about by different modes of transportation have been funded in the EU. Mostert and Limbourg (2016) have provided a comprehensive review of such studies, although focusing primarily on external costs from land and IWW modes of transportation. The results of these studies, and in particular of the IMPACT study, have allowed for the development of a handbook on estimation of external costs in the transport sector, which has been applied in the Marco Polo program. This calculator has had different versions, with the most recent one being detailed by Brons and Christidis (2013). The Marco Polo calculator allows comparisons between different transport solutions on a certain route and considers only the air pollution and climate change dimensions of SSS external costs. SSS air pollution emission estimates are extracted from the database developed within the EXTREMIS study (see TRT Trasporti e Territorio, 2008). External cost factors, in turn, have been derived from results of studies such as HEATCO (see Bickel et al., 2006) and CAFE (see Holland et al., 2005), with differentiation between three ship types: ro-ro/ro-pax, general cargo ships and bulk carriers, and containerships. Regarding climate change, data on CO2 emissions are also extracted from EXTREMIS database. External costs per tonne of CO2 follow the values recommended by the IMPACT study (see Maibach et al., 2008), with the same ship type differentiation. In addition, the Marco Polo calculator accounts also for effects of speed on emissions (in particular for ro-ro ships, which are capable of very different speeds), effects of low sulfur fuel utilization, and effects of emission abatement technologies or alternative fuels. These effects are accounted for using correction factors which increase (or decrease) the emissions as necessary. The external costs calculated using the methodologies and models discussed earlier may in practice be applied for various purposes. The main ones are cost comparisons between different transport modes and multimodal voyages and cost-benefit analysis of emission reduction technologies. However, a significant number of studies are primarily interested in ship emissions for the purpose of assessing air pollution and climate change effects in the concerned areas, mainly coastal and port areas. Kalli and Tapaninen (2008) have studied emissions in the Gulf of Finland, while Jalkanen et al. (2009) have considered emissions across the entire Baltic Sea. Tzannatos (2010a,b) have evaluated external costs in a major Greek port, Piraeus. Another example is Kaohsiung, Taiwan, which has been studied by Berechman and Tseng (2012). I. Shipping

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Besides the calculations carried out using the Marco Polo calculator, a substantial number of studies containing cost comparisons of transport modes, including external costs, have been published. Lee et al. (2010) have provided a case study in Taiwan dedicated to the comparison of road and SSS. However, most studies relate to the European continent situation, reporting, for example, the relative performance of intermodal (rail based) and unimodal (road freight) transport solutions, considering also external costs (see Janic, 2007). A significant contribution has been presented by Lo´pez-Navarro (2014), who has calculated external costs using the Marco Polo approach for several routes from Spain to Northern Europe and Italy, comparing the use of road haulage with the intermodal option and concluding that intermodal solutions are not always the best option. This occurs when high-speed ships are used (typical of ro-pax services) or larger distances have to be covered in case the intermodal solution is used. A similar study by Vierth et al. (2018) presents an external cost calculation method for trailer transport by sea or road, between Stockholm and Travemund, comparing two methods for calculating externalities (Swedish guidelines and EU handbook). It was found that the two methods predict different relative levels of external costs and thus different preferable transport solutions (considering only external costs). This is because the two methods assume different marginal costs depending on compliance level (Tier 1 or 2) of engines. In this respect, it should be pointed out that new technologies have made road transportation more environment friendly, as mentioned by Ecorys (2004), and that the shipping industry needs to enhance its energy efficiency. The same type of studies has been carried out in Greece by Sambracos and Maniati (2012) and Tzannatos et al. (2014), including both internal and external costs associated with the SSS and road transport. The study concluded that SSS was not used since short transit time and just-intime delivery are preferred, and terminal infrastructure and superstructure, together with port access, need to be improved. Tzannatos et al. (2014) have also indicated that SSS is not any more environmentally superior to road transport due to the superior quality of the truck diesel engines. In this respect, ultralow sulfur fuels may actually benefit SSS competitiveness if environmental effects are taken into consideration. Overall, the experience with the Marco Polo program has indicated that SSS services have started and disappeared shortly after subsidies are over. Some authors such as Aperte and Baird (2013), who have expressed the view that a revision of EU policies is required and argued that the internalization of external costs for road transport, using user charges, still appears to be a more promising way to promote the competitiveness of SSS, proposed a way around this problem.

3 Methodology and numerical methods 3.1 General The model used in this chapter considers the transportation of cargo between origindestination pairs of regions in Europe, falling thus in a class of transportation problems generally named as “many-to-many.” In this model, these regions are defined at the NUTS2 level I. Shipping

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FIG. 1 General formulation of transport problem between origin and destination NUTS2 regions.

and statistics on cargos sent by road between these regions has been collected from Portuguese Statistics Bureau, see INE (2013). Fig. 1 shows the problem under consideration more in detail, namely, indicating that an intermodal voyage is considered which includes an SSS link between Northern Portugal (Leixo˜es) and a port in Northern Europe. Different ports in Northern Europe are considered in this chapter, while keeping the origin port constant. Cargo flows in both directions are considered in the model. In parallel to this intermodal transport solution, a fully unimodal solution is also considered, as shown in Fig. 1, linking each pair of regions. The model carries out comparisons between these two transport options, at the level of each pair of regions, and decides whether cargo flowing between each pair of regions will likely choose the intermodal or unimodal transport solution. The accumulation of cargos between various pairs of regions leads to an estimate of total transportation demand for the SSS link (and the cargo using only road). I. Shipping

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The decision on which transport solution to choose for each pair of regions is made based on two variables in this type of transport problems: transit time and cost of transportation (with or without the external costs). Numerical models, following the principles indicated by Santos and Guedes Soares (2017b), for the calculation of each of these variables are presented in the following. Furthermore, external costs are now also estimated by the numerical model and a more detailed model of the maritime route has also been included, allowing further refinements on navigation time and a realistic modeling of external costs.

3.2 Transit time model In the intermodal solution, transit time has several different components. In the initial road segment from the region of origin of cargos to the port, the voyage will take time, and this may be calculated using the following expression: TRegOriPort ¼

DRegOriPort SRegOriPort

(1)

where SRegOriPort represents the average road speed and DRegOriPort represents the distance between the region of origin of cargo and the loading port. A similar expression allows the calculation of TPortRegDes, which represents the time taken to travel from unloading port to region of destination of the cargo. Both these components include the rest period of truck drivers. Another component of transit time arises in the origin and destination ports, due to cargo handling and, eventually, early arrival of cargo to the port (implying a dwell time). In ro-ro terminals, these times are much smaller than that in container terminals (in the order of hours rather than days), making this type of transportation attractive in what concerns these components of transit time. The cargo handling times and dwell times, indicated as TOriPort, TDesPort, TDwlOri, and TDwlDes, are inputs of the current model and, therefore, are user specified under the form of average constant values. The most significant component of transit time in the intermodal solution is the navigation time (TNav). This is the time necessary to complete the voyage between ports, where the total distance is DPorts. This distance has been categorized into a distance traveled outside the emission control area (ECA), DPorts-nonECA; a distance within the ECA, DPorts-ECA; and a distance in the vicinity of the destination port (DinPort). The first two are covered with a constant speed SShp and the last part at a reduced speed Sport (user specified and, in the case study below, assumed to be 10 knots). This last part is necessary in order to model adequately the approaches to river ports such as Hamburg and Antwerp, where navigation conditions over significant distances require less speed than usual. Therefore, the total navigation time is given by TNav ¼

DPortsnonECA DPortsECA DinPort + + SShp SShp SPort

(2)

Consequently, the total time taken by a certain cargo element in the intermodal solution between region i and region j is given by TTotInterij ¼ TRegOriPorti + TDwlOri + TOriPort + TNav + TDesPort + TDwlDes + TPortRegDesj

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(3)

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For the unimodal solution, the total time between region i and region j is given by TTotUniij ¼ TRegsij

(4)

In the unimodal solution, the voyage time is, if taken in a simple form, a function of the distance between origin and destination regions. In practice, the nature of the roads, the maximum allowed number of driving hours, the speed of the vehicle, the congestion, and weather conditions all affect the voyage time. This variability in the voyage times TRegs has been taken into account by dividing the distance DRdRegs by an average road speed SRd: TRegs ¼

DRdRegs SRd

(5)

3.3 Internal costs model The cost and time models adopted in this study reflect the perspective of the user of the transport solution. The user (decision-maker) might be a freight forwarder, a third-party logistics service provider (3PL), or a cargo owner directly, as discussed by Lo´pez-Navarro (2014). His decision will be based mainly on the cost and time factors. Costs, in general, include prehaulage and posthaulage costs (intermodal solution), port and ship costs, and inventory costs. A relatively new cost, related to the rising costs of fuel, is the bunker adjustment factor (BAF), which is also included in the model for intermodal transport (as an additional maritime freight). From the perspective of the user of transport services, the reality he perceives is that in the unimodal solution he will be charged by the road haulage company a fare for taking the cargo from A to B, subject to a certain delivery time. In the intermodal solution, a fare will be charged by the road haulage company to take the cargo to the port. The same will occur in the road segment from the destination port to the final destination region. In addition to this, the shipping company will charge a freight rate (FR) per cargo unit that under liner terms will cover all the costs including port costs. It is useful, for the purpose of this model, to uncouple from the freight rate the terminal operator costs (basically cargo handling) and any eventual bunker adjustment factor (BAF), related to either an increase in fuel costs or simply the need to change to another fuel type. It is usual that the freight rate includes the fuel costs, but the BAF is charged separately. Another cost that is frequently taken into account in logistics is the inventory cost (see Blauwens et al., 2006), which is related to the cost of having cargo (merchandise) in stock and maintaining it in the inventory for the time necessary to carry out the transportation. The model is prepared to include these costs but depends on user specification of the value of time for the cargo, which varies significantly. In the case study below, these costs have not been used (are set to 0) as time is considered an independent decision parameter (Section 3.5). Santos and Guedes Soares (2019) have considered the contribution of transit time to a “generalized transportation cost” when studying the road transportation of containers to port terminals.

I. Shipping

3 Methodology and numerical methods

23

Taking into consideration the above, the general expression for the total cost of the intermodal solution for the parcel of cargo moving from region i to region j is CTotInterij ¼ CTotInvij + CRdOriPorti + COriPortCH + COriPortDW + CFR + CBAF + CDesPortCH + CDesPortDW + CRdPortDesj

(6)

where CTotInvij is the total inventory cost, CRdOriPorti, CRdPortDesj are the road pre- and posthaulage costs, COriPortCH, CDesPortCH are the cargo handling costs in the origin and destination ports, COriPortDW, CDesPortDW are the dwell time costs in the origin and destination ports, CFR is the freight rate cost, CBAF is the bunker adjustement factor. The total cost of the unimodal solution is CTotUniij ¼ CTotInvij + CRdRegsij

(7)

where CRdRegs  ij are the road haulage costs, which in this case relate to the full road transport between Portugal and Northern Europe.

3.4 External costs model External costs are also included in the numerical model. These costs are currently not internalized, at least not in totality, implying that transport users do not have the full perception of the existence of these costs. In the current model, it is possible to decide whether external costs should be added to other transportation costs or not. The case study below presents results of a comparison of the attractiveness of intermodal and unimodal transport solutions when external costs are, firstly, not included, and then when these costs are fully included (internalized). The calculation method for external costs follows the Marco Polo calculator, as reported by Brons and Christidis (2013). The external costs for the intermodal transport solution are calculated using the following expression: CExtInter ¼ Wt  DRegOriPort  cExtRd + Wt  DPortsnonECA  cExtnonECA + Wt  DPortsECA  cExtECA (8) + Wt  DinPort  cExtECA + Wt  DPortRegDes  cExtRd where Wt indicates the total cargo quantity, which is to be transported from region i of origin to region j of destination. The coefficient cExt  Rd is the marginal cost coefficient for the road and rail modes at the EU27 level in Euro per 1000 t km. This coefficient, taken from Brons and Christidis (2013), represents the total value, including air pollution, climate change, noise, accidents, and congestion components. It is assumed that most of the distance traveled on land corresponds to motorways.

I. Shipping

24

2. Assessment of transportation demand

Coefficients cExt  nonECA and cExt  ECA represent the marginal coefficients for SSS at EU27 level, arising because of air pollution and climate change effects. The first coefficient is applied when high-sulfur fuel, a cheaper fuel grade, is used and this occurs when the ship travels outside the ECA zone. Upon arrival in the ECA zone, 5° W (approximately at the western edge of Brittany), the ship must switch to low-sulfur fuel and the second marginal coefficient is applied. The case study below assumes that the ship operates in this way, but the model could easily consider that the ship is fitted with a scrubber or uses LNG/methanol for propulsion, implying that other marginal coefficients would be used. The numerical model also considers that different marginal coefficients for SSS should be used for different ship speeds (see Brons and Christidis, 2013). If the ship speed under study is below 17 knots a lower coefficient is applied, and if the ship’s speed is above or equal to 17 knots a higher coefficient is applied. This is related to the fact that higher speed implies higher propulsion power and therefore higher external costs. The external costs for the unimodal transport solution are more simple to calculate. The model uses coefficient cExt  Rd, the marginal cost coefficient for the road and rail modes at the EU27 level in Euro per 1000 t km, and assumes that the fully road-based transportation uses mainly motorways across Europe. The following expression for external costs is used: CExtUni ¼ Wt  DRdRegs  cExtRd

(9)

3.5 Decision-making model The methodology used to evaluate the intermodal demand includes systematic calculations over various combinations of freight rate and speed (both in the maritime mode). The freight rate, CFR, expressed in monetary units per cargo unit, is systematically varied, the same occurring with speed. The costs of both the unimodal and the intermodal solution and the transit times are evaluated for each cargo with origin in region i and destination in region j. For each pair of regions, it is evaluated if the cargo has the potential for being carried using an intermodal solution or should use the conventional unimodal transport solution. This process is repeated over all pairs of regions (cargo elements). The identification of cargo elements with potential for intermodal transportation is carried out through the comparison of the cost and transit time implied in each solution. A number of different situations may occur as shown in Fig. 2. When cost and time in the intermodal solution are smaller than that in the unimodal solution, that is, area A in Fig. 2, the intermodal solution is to be used and the cargo element is added to the total amount of cargo, QInterTot, that represents the demand for SSS. In area D, the unimodal solution is preferred, as both the cost and the time are inferior to that of the intermodal solution, and the cargo element is added to the total amount of cargo that will use the unimodal solution, QUniTot. In cases B and C, uncertainty arises on the decisions that will be taken by decision-makers. Furthermore, the model allows tolerances on time (hours) and cost (%) to be specified, leading to the shaded areas in Fig. 2. In this study, when specified

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4 Case study

25

FIG. 2 Comparison of unimodal and intermodal costs and times.

tolerances are used, it has been assumed that for cargos inside the shaded areas, the option taken by decision-makers is to use the intermodal solution. Different values for these tolerances can be found in the literature, so in this model these tolerances are kept as parameters that can be set by the model user.

4 Case study 4.1 Characterization and data In order to test the model described earlier, a case study is conducted dealing with imports/ exports of cargo between Portugal and Northern Europe. The main Portuguese trading partners in this geographical region are France and Germany. Portugal currently uses predominantly the road mode of transportation to carry cargo from/to Northern Europe, using heavy goods vehicles (trucks). In addition, ships are also used to carry containerized cargo to the main hub ports in the Le Havre, to Hamburg range, and to different ports in the United Kingdom and containerships are the main type of ship used in this traffic. Currently, only one cargo ro-ro regular service is in operation, as detailed by Santos et al. (2018). However, the ro-ro cargo handling method offers significant advantages in terms of time taken in port terminals over the lift-on/lift-off (lo-lo) cargo handling method (containers). Also, dwell time in such ro-ro terminals is typically much smaller than that in container terminals. These aspects, in conjunction with the fact that road transportation is experiencing increasing difficulties in various countries crossed by trucks, mean that a competitive intermodal solution is on high demand, motivating this case study.

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26

2. Assessment of transportation demand

Santos and Guedes Soares (2017a,b) have presented a methodology for estimating the potential demand for intermodal cargo transportation solutions and for determining the optimal cargo ro-ro ship for such demand. These methods were applied in a case study focused on one specific route, between Leixo˜es (Portugal) and Rotterdam (the Netherlands). However, given the dispersion of Portuguese trade between the main continental nations, France and Germany, other ports might also prove suitable as destinations of regular cargo ro-ro liner services. That is, the question to be answered through this case study is whether there are other ports that would attract potentially higher volumes of intermodal cargo between Portugal and Northern Europe. To answer this question, this case study evaluates the transportation demand attracted by four different ports: Le Havre, Antwerp, Rotterdam, and Hamburg. The case study takes as a starting point data on imports/exports between Portuguese NUTS2 regions and NUTS2 regions spread across Northern European countries in the range between Northern France and Denmark. The characterization of imports/exports was done based on the data from the statistics of the Portuguese Board of Statistics (INE, 2013) for external trade. However, the statistics available from INE relate to the external trade in number of tons of cargo between NUTS2 regions in Portugal and countries in Europe. There is no splitting of the data on imports and exports by European NUTS2 regions, only by country. It is also important to have in mind that in the relevant statistics, values are given for different transportation modes, namely, road and maritime, and also airplane, rail, and others which are not considered in this study. The cargo currently sent by ship from Portuguese ports to the same countries (France, Belgium, the Netherlands, Germany) is considered as captive of containerships. Therefore, the model basically assesses the potential for modal shift from road to SSS. For reasons of convenience, all cargo (in tons) has been converted to TEUs (at an average of 14 tons) as this is the average load per TEU considered in ship design and should constitute a good approximation for the number of boxes requiring transportation. Cargo bound to a certain EU country has been split by different NUTS2 regions of the concerned country. This is done considering the population and gross domestic product (GDP) per capita of each region, as given in the EUROSTAT statistics, allowing for a geographical decomposition of Portuguese imports and exports. Fig. 3 shows the resulting geographical distribution of import cargos from NUTS2 regions spread across Northern European countries in the range between Northern France and Denmark. Densely populated areas in countries with which Portuguese trade is significant, typically present large volumes of cargo. Exports are similarly distributed, but present slightly different overall values. The four ports considered in this study are also indicated in Fig. 3, and the port in Portugal is always Leixo˜es, which is located near the main manufacturing areas in Portugal. Table 1 illustrates the nautical distances (in nautical miles) between Leixo˜es and these ports. Distances are split between non-ECA areas and ECA areas. The last column in Table 1 shows the average navigation speed assumed to be used in coastal and inland waters, which is typically restricted due to practical navigational safety reasons. As could be noted in the table, access to both Antwerp and Hamburg implies navigation over significant distances in coastal or inland waters, which in conjunction with the reduced speed leads to higher transit times. Along I. Shipping

27

4 Case study

FIG. 3 Geographical distribution of import cargos (expressed in TEUs) through NUTS2 regions. TABLE 1

Comparison of nautical distances. ECA distance (mi)

Coastal or inland distance (mi)

Total distance (mi)

Speed in coastal and inland waters (kn)

Origin

Destination

Non-ECA distance (mi)

Leixo˜es

Le Havre

510

213

2

725

5

Leixo˜es

Antwerp

510

360

60

930

10

Leixo˜es

Rotterdam

510

400

10

920

10

Leixo˜es

Hamburg

510

630

40

1180

10

the ECA and non-ECA open waters, the navigation speed is assumed to be unconstrained and transportation demand is calculated systematically over a range of such speeds. The distinction between ECA and non-ECA waters is required because the external cost model depends on distances traveled in such zones. Overall, total nautical distances range from 725 miles for Le Havre to 1180 miles for Hamburg. Antwerp and Rotterdam are located approximately in the middle of these extremes. In general, these nautical distances allow a regular round voyage to be completed in less than a week, except for the port of Hamburg, which requires speeds of at least 17 knots. Such speeds are however well within the current possibilities of cargo ro-ro ships. I. Shipping

28

2. Assessment of transportation demand

For the unimodal transportation solution and for the road-based (pre- and postroad haulage) parts of the intermodal solution, trucks speeds of 65 km/h (Delhaye et al., 2010) have been assumed both for Portuguese and European roads. This is clearly below the regulatory speed limits and represents average values.

4.2 Results Table 2 presents distances, transit times, external costs, internal costs, and total costs (including external costs) for a sample of origins and destinations, using the four mentioned ports in Northern Europe. PT North represents the NUTS2 region in North Portugal and the cities are the most populous centers in the respective NUTS2 regions. In all cases the maritime freight is 350 € (per TEU) and the speed of the ship is 15 knots. Tolerances on extra cost implied by intermodal solution have been set to 0 €/TEU and on extra time to 24 h. The final column indicates the preferred transport solution, considering both decision parameters. That is, this column only shows the intermodal solution as the preferred choice if, simultaneously, the intermodal cost (without external cost) is lower than that for the unimodal solution and the intermodal transit time does not exceed the transit time of the unimodal solution by 24 h. The tool developed for this study aggregates individual results for pairs of regions to obtain estimates of the total amount of intermodal cargo which may be attracted by a regular cargo ro-ro service between Leixo˜es and each port in Northern Europe. Fig. 4 (left) shows a surface representing the intermodal cargo potentially attracted by the service as a function of ship speed and maritime freight rate for the port of Antwerp. It could be seen that intermodal cargo decreases with an increase (more costs) in freight rate and with a decrease in ship speed (more transit time). Fig. 5 (left) shows similar results for Rotterdam. The results for Antwerp and Rotterdam are similar in shape and magnitude of cargos, with a steep increase in intermodal cargo when ship speeds are increased above 13–14 knots. As explained by Santos and Guedes Soares (2017a), this is linked with the specified tolerance of shippers to extra transit time when using the intermodal transport solution (which includes a short-sea link). That tolerance is user-specified and could be set to 0 if necessary. A lower tolerance to transit time causes a sharp increase to occur at higher ship speeds. Fig. 6 (left) shows results for Le Havre, while Fig. 7 (left) corresponds to the port of Hamburg. Results evidence also the decrease in intermodal cargo as freight rates increase and the steep increase in cargo volumes as the ship’s speed increases above the 13–14 knots threshold. However, in general, the cargo volumes are much below the volumes attracted by Antwerp or Rotterdam. For Le Havre and Hamburg, cargo volumes are only significant if low or very low freight rates and high ship speeds are used, but this combination may easily lead to substantial financial losses for the shipping company. It is interesting to note that, for Le Havre, for a maritime freight rate of above 450 €, cargo practically disappears, certainly because the distance to Portugal is the smallest (in comparison with other ports) and in such case the unimodal solution remains cost competitive. For Hamburg, even at very high freight rates, there is always some cargo which is attracted by the intermodal solution (around 25,000TEU), probably because of the longer time required by the road voyage.

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TABLE 2 Comparison of distances, transit times and costs for selected pairs origin-destination. Option Port/ NUTS2 Intermodal Unimodal Intermodal Unimodal Total Total (without NUTS2 (main city) distance distance Intermodal Unimodal external external Intermodal Unimodal intermodal unimodal external origin destination (km) (km) time (h) time (h) cost (€) cost (€) cost (€) cost (€) cost (€) cost (€) costs) Hamburg PT North

Hamburg

79

2409

102

97

170

624

436

1205

606

1828

Int

PT North

Dusseldorf

462

2029

108

76

270

526

1011

1015

1281

1541

Uni

PT North

Paris

947

1533

131

54

395

397

1738

767

2133

1164

Uni

PT North

Hamburg

620

2409

95

97

281

624

1248

1205

1529

1829

Uni

PT North

Dusseldorf

283

2029

89

76

194

526

742

1015

936

1540

Int

PT North

Paris

448

1533

92

54

237

397

990

767

1226

1164

Uni

PT North

Hamburg

551

2409

91

97

262

624

1144

1205

1406

1828

Int

PT North

Dusseldorf

295

2029

87

76

196

526

760

1015

956

1540

Int

PT North

Paris

509

1533

91

54

252

397

1081

767

1333

1164

Uni

PT North

Hamburg

1036

2409

101

97

365

624

1872

1205

2237

1828

Uni

PT North

Dusseldorf

664

2029

95

76

269

526

1314

1015

1582

1540

Uni

PT North

Paris

267

1533

74

54

166

397

718

767

884

1164

Int

Antwerp

Rotterdam

Le Havre

Bold indicates the lower transit time and cost.

FIG. 4

Intermodal import cargos (expressed in TEUs) through the port of Antwerp (without and with external costs).

FIG. 5

Intermodal import cargos (expressed in TEUs) through the port of Rotterdam (without and with external costs).

FIG. 6

Intermodal import cargos (expressed in TEUs) through the port of Le Havre (without and with external costs).

FIG. 7

Intermodal import cargos (expressed in TEUs) through the port of Hamburg (without and with external costs).

34

2. Assessment of transportation demand

Figs. 4–7 (right) show the consequences of a full internalization of external costs. Substantial increases in intermodal cargo occur for the full range of maritime freight rates and speeds under consideration. Considering first Antwerp and Rotterdam, taking a common speed in this type of service, 15 knots, and only freight rates between 200 €/TEU and 500 €/TEU (considered as potentially more viable), increases in intermodal cargo range from 22% to 45% with an average of 32%. Considering a constant freight rate of 350 €/TEU and a speed range between 13 and 20 knots, increases in intermodal cargo would be between 22% and 32%, with an average increase of 27%. Regarding the effects of external cost internalization in intermodal cargo attracted by Le Havre and Hamburg, it could be seen that the increases in intermodal cargo are very significant, especially in the case of Le Havre. Gains in Hamburg, probably because of proportionally smaller external costs in intermodal transportation, are limited due to cost and time constraints for NUTS2 regions further south (see Fig. 14). Fig. 8 shows a summary of results for four specific pairs of maritime freight rate and ship speed. Each of the two consecutive vertical bars correspond to one port, the first bar represents intermodal cargo attracted without considering external costs and the second bar with external costs fully internalized. It could be seen that, in every case and port, the intermodal cargos increase when external costs are internalized. Increases are less evident in the case of Hamburg, but still significant for the speed of 18 knots, which is required to attract more cargo since the port is the one more distant from Leixo˜es. Le Havre, for lower freight rates (350 €/ TEU), shows considerable gains in intermodal cargo when external costs are accounted for. Overall, Le Havre presents more potential to attract intermodal cargo, if external costs are

FIG. 8

Increase in intermodal import cargos attracted in various ports when external costs are fully internalized.

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4 Case study

35

internalized than Hamburg. This last port requires higher speeds (to keep the round voyage time under 1 week) and most likely higher freight rates (to be profitable) so a scenario of 600 €/18 knots is the most realistic and, in this scenario, even with external costs accounted for, intermodal cargo is still lower than that for Le Havre. Fig. 9 shows the increases in intermodal cargo (import) when external costs are fully internalized for the ports of Antwerp and Rotterdam. Very low ship speeds and very high freight rates are excluded as in these cases the intermodal cargos, without external costs included, are nonexistent or very small. When considering external costs, there are very large increases in the volume of intermodal cargo but starting from very low values, implying that such increases are not meaningful, in practice. Fig. 9 thus considers speeds above 13 knots and freight rates up to 500 €/TEU. It may be seen that increases vary between 12% and 46% with an average of about 30%. It is also noticeable that the higher is the freight rate, the larger the increase in percentage of intermodal cargo. Another point worth mentioning is the fact that when the considered speed goes from 16 to 17 knots, there is a generalized drop in intermodal cargo. This is due to the fact that the specific external cost for 17 knots speed is much larger than that for 16 knots (as specified in the Marco Polo calculation method, it grows from 0.00543 to 0.00885 €/t km) and this causes a large increase in external costs, causing a relative loss of competitiveness for maritime transport (part of the intermodal transport solution) in comparison with the unimodal transport solution. In the case of Rotterdam, see Fig. 9 (right), the trends are basically the same but increases in intermodal cargo are generally slightly larger (about 5%) than that for Antwerp. Again, the drop in intermodal cargo is noticeable when ship speed is greater than 16 knots. Fig. 10 shows the same type of results for Le Havre (left) and Hamburg (right). For Le Havre, no curves appear for maritime freight rates 400 €–500 € because the increases in intermodal cargo are, in relative terms, extremely high. In any case, it may be seen that very significant relative increases occur, but as mentioned previously the absolute magnitude of intermodal cargos will remain low, as the starting point is very low. The model presented in this chapter also allows identifying the NUTS2 regions for which intermodal transport is competitive. Fig. 11 shows the NUTS2 regions for which the intermodal transport solution is competitive, when considering the port of Antwerp without (left) and with (right) external costs internalized. A noticeable expansion of the geographic scope of NUTS2 regions for which intermodal solutions are feasible is visible, namely because Eastern Germany and some Northern France regions are gained for intermodal solutions running through Antwerp. Fig. 12 shows the same type of results for the port of Rotterdam. The expansion of the geographic scope of NUTS2 for which the intermodal solution is feasible is identical to Antwerp. Fig. 13 shows the same results for Le Havre, indicating that this port is adequate for a limited number of regions in Northwestern France. When external costs are internalized, intermodal solutions become competitive for most of Belgium and the Pas-de-Calais (in France). Regarding Hamburg, as shown in Fig. 14, the intermodal transport solution is competitive for a significant number of NUTS2 regions in Northeast Germany and Denmark. The internalization

I. Shipping

FIG. 9 Increase in intermodal import cargos (percentage) through the port of Antwerp (left) and Rotterdam (right) when external costs are internalized.

FIG. 10

Increase in intermodal import cargos (percentage) through the port of Le Havre (left) and Hamburg (right) when external costs are internalized.

FIG. 11 NUTS2 regions attracted to intermodal transport solution using the port of Antwerp (maritime freight rate of 350 € and ship speed of 15 knots), without and with external costs fully internalized.

FIG. 12 NUTS2 regions attracted to intermodal transport solution using the port of Rotterdam (maritime freight rate of 350 € and ship speed of 15 knots), without (left) and with (right) external costs fully internalized.

FIG. 13 NUTS2 regions attracted to intermodal transport solution using the port of Le Havre (maritime freight rate of 350 € and ship speed of 15 knots), without and with external costs fully internalized.

FIG. 14

NUTS2 regions attracted to intermodal transport solution using the port of Hamburg (maritime freight rate of 350 € and ship speed of 15 knots), without and with external costs fully internalized.

42

2. Assessment of transportation demand

of external costs, for this maritime freight rate and speed, produces almost no gains (one NUTS2 region in East Germany). For other pairs of these parameters, namely higher ship speeds (18 knots or more, as in Fig. 8), increases in the scope of NUTS2 gained for intermodal transport are more significant.

5 Conclusions A methodology has been presented which allows the assessment of intermodal transportation demand through considering costs, including also external costs, and transit times involved in intermodal and unimodal solutions, for supply chains running between a comprehensive set of origins and destinations. This methodology has been applied in a case study which considered different destination ports in Northern Europe, namely Antwerp, Rotterdam, Le Havre, and Hamburg, in order to characterize maritime transportation demand for intermodal transportation between Portugal (port of Leixo˜es) and these ports. The analysis takes the perspective of the users of transportation (freight forwarders, 3PLS, cargo owners), their decisions being based on cost and transit time considerations, without and with external costs included. When external costs are not taken into consideration (no internalization), it has been found that Antwerp and Rotterdam present similar potentials for attracting intermodal cargo. When using Le Havre and Hamburg as destination ports, intermodal cargo is much less significant. With external costs added, a substantial increase in intermodal cargo is noticeable. In Antwerp and Rotterdam, cargo grows between 25% and 30%. In Le Havre, gains can reach 100%–200% and in Hamburg the intermodal cargo may increase by up to 60%, but in these two ports the total amounts of intermodal cargo remain low. The results also show that ship speeds higher than 17 knots limit the gains in intermodal cargo, as specific external costs are higher when more speed is used, and the resulting external costs penalize the competitiveness of the intermodal solution. Overall, Antwerp and Rotterdam are competitive gateways for intermodal transport for NUTS2 regions in the Benelux and Western Germany. Hamburg can only attract cargos for the intermodal solution bound for Northern Germany and Denmark, and Le Havre serves a limited number of regions in Northwest France. The same occurs in case external costs are internalized, but in this case, both Antwerp and Rotterdam show a large increase in geographic scope of NUTS2 attracted to intermodal transport. The practical use of these results is twofold. On the one hand, they may support a second step of the evaluation process, this time taking the perspective of the shipping company, which will determine if each pair maritime freight rate and ship speed are technically and economically feasible. On the other hand, these results may assist European or national authorities in determining if a novel service should be granted public funding (considering also the intermodal attractiveness potential) and then defining the required level of such funding.

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Acknowledgments The research presented in this chapter received support from the research project PTDC/ECI-TRA/28754/2017, financed by the Portuguese Foundation for Science and Technology (Fundac¸a˜o para a Ci^encia e Tecnologia—FCT). The authors also acknowledge the comments of an anonymous reviewer, which greatly improved this manuscript.

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Holland, M., Pye, S., Watkiss, S., Droste-Franke, B., Bickel, P., 2005. Damages per Tonne of PM2.5, NH3, SO2, NOx, and VOC’s of EU25 Member State (Excluding Cyprus) and Surrounding Seas. AEA Technology Environment, Didcot. INE, 2013. Portuguese Transport Statistics (in Portuguese). INE, Lisbon, Portugal. Jalkanen, J.-P., Brink, A., Kalli, J., Pettersson, H., Kukkonen, J., Stipa, T., 2009. A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area. Atmos. Chem. Phys. 9, 9209–9223. Janic, M., 2007. Modelling the full costs of an intermodal and road freight transport network. Transp. Res. Part D Transp. Environ. 12, 33–44. Kalli, J., Tapaninen, U., 2008. Externalities of Shipping in the Gulf of Finland Until 2015. Center for Maritime Studies, University of Turku, Turku. Korzhenevych, A., Dehnen, N., Br€ ocker, J., Holtkamp, M., Meier, H., Gibson, G., Varma, A., Cox, V., 2014. Update of the Handbook on External Costs of Transport. European Commission–DG Mobility and Transport, London, UK. Lee, P.T.W., Hu, K.C., Chen, T., 2010. External costs of domestic container transportation: short-sea shipping versus trucking in Taiwan. Transp. Rev. 30 (3), 315–335. Lo´pez-Navarro, M.A., 2014. Environmental factors and intermodal freight transportation: analysis of the decision bases in the case of Spanish motorways of the Seas. Sustainability 6, 1544–1566. Maersk Brokers, 2016. Container Market – Weekly Report. Maibach, M., Schreyer, C., Sutter, D., Van Essen, H.P., Boon, B.H., Smokers, R., Schroten, A., Doll, C., Pawlowska, B., Bak, M., 2008. Handbook on Estimation of External Cost in the Transport Sector, Version 1.1. For the European Commission DG TREN, Delft, The Netherlands. Medda, F., Trujillo, L., 2010. Short-sea shipping: an analysis of its determinants. Marit. Policy Manage. 37, 285–303. Miola, A., Paccagnan, V., Turvani, M., Andreoni, V., Massarutto, A., Perujo, A., 2008. Review of the measurement of external costs of transportation in theory and practice. JRC Scientific and Technical Reports, Institute for Environment and Sustainability, Joint Research Center, European Commission, Ispra, Italy. Morales-Fusco, P., Grau, M., Saurı´, S., 2018. Effects of RoPax shipping line strategies on freight price and transporter’s choice. Policy implications for promoting MoS. Transp. Policy 67, 67–76. Mostert, M., Limbourg, S., 2016. External costs as competitiveness factors for freight transport – a state of the art. Transp. Rev. 36 (6), 692–712. Ng, A.K.Y., 2009. Competitiveness of short sea shipping and the role of port: the case of North Europe. Marit. Policy Manag. 36 (4), 337–352. Ng, A.K.Y., Sauri, S., Turro´, M., 2013. Short sea shipping in Europe: issues, policies and challenges. In: Finger, M., Holvad, T. (Eds.), Regulating Transport in Europe. Edward Elgar, Cheltenham, pp. 196–217. Paixa˜o Casaca, A.C., Marlow, P.B., 2009. Logistics strategies for short sea shipping operating as part of multimodal transport chains. Marit. Policy Manag. 36 (1), 1–19. Psaraftis, H.N., Kontovas, C.A., 2010. Balancing the economic and environmental performance of maritime transportation. Transport. Res. D 15, 458–462. Sambracos, E., Maniati, M., 2012. Competitiveness between short sea shipping and road freight transport in mainland port connections; the case of two Greek ports. Marit. Policy Manag. 39 (3), 321–337. Santos, T.A., Guedes Soares, C., 2017a. Modelling of transportation demand in short sea shipping. Marit. Econ. Logist. 19 (4), 695–722. Santos, T.A., Guedes Soares, C., 2017b. Ship and fleet sizing in short sea shipping. Marit. Policy Manage. 47 (7), 859–881. Santos, T.A., Guedes Soares, C., 2019. Methodology for container terminal potential hinterland characterization in a multi-port system subject to a regionalization process. J. Transp. Geogr. 75, 132–146. Santos, T.A., Botter, R.C., Guedes Soares, C., 2018. Characterizing the operation of a roll-on roll-off short sea shipping service. In: Guedes Soares, C., Santos, T.A. (Eds.), Progress in Maritime Technology and Engineering. Taylor & Francis Group, London, UK, pp. 77–88. Styhre, L., 2009. Strategies for capacity utilisation in short sea shipping. Marit. Econ. Logist. 11 (4), 418–437. Sua´rez-Alema´n, A., Trujillo, L., Cullinane, K.P.B., 2014. Time at ports in short sea shipping: when timing is crucial. Marit. Econ. Logist. 16, 399–417. Trafikverket, 2018. Kapitel 20 English Summary of ASEK Guidelines. Trafikverket, B€ orlange, Sweden.

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TRT Trasporti e Territorio, 2008. Development of a reference system on emissions factors for rail, maritime and air transport (EXTREMIS). Study for European Commission, Joint Research Centre. Contract 150653-2006 F1ED-IT. Final Report. Tzannatos, E., 2010a. Ship emissions and their externalities for the port of Piraeus – Greece. Atmos. Environ. 44 (3), 400–407. Tzannatos, E., 2010b. Ship emissions and their externalities for Greece. Atmos. Environ. 44 (18), 2194–2202. Tzannatos, E., Papadimitriou, S., Katsouli, A., 2014. The cost of modal shift: a short sea shipping service compared to its road alternative in Greece. European Transport \ Transporti Europei. Issue 56, Paper nº2. Vierth, I., Sowa, V., Cullinane, K., 2018. Evaluating the external costs of trailer transport: a comparison of sea and road. Marit. Econ. Logist. https://doi.org/10.1057/s41278-018-0099-7. Zis, T., Psaraftis, H.N., 2017. The implications of the new sulphur limits on the European Ro-Ro sector. Transp. Res. Part D Transp. Environ. 55, 185–201.

Further reading European Commission, 2014. Mid-Term Review of the EU’s Maritime Transport Policy Until 2018 and Outlook to 2020. Hellenic Presidency of the Council of the European Union, Athens, Greece.

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Identifying cost performance indicators for a logistics model for vessel trains Hilde Meersmana, Eleni Moschoulia, Christa Sysa, Eddy Van de Voordea, Thierry Vanelslandera, Edwin van Hassela, Benjamin Friedhoffb, Katja Hoyerb, Matthias Tenzerb, Robert Hekkenbergc a

Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium b Development Centre for Ship Technology and Transport Systems, Department of Hydrodynamics, Duisburg, Germany cDelft University of Technology, Department of Maritime and Transport Technology, Delft, Netherlands

1 Introduction A modal shift toward more environment-friendly modes including rail and waterborne transport is among the ten main key goals of the European Commission to be achieved by 2030 and 2050 (European Commission, 2011, March 28, p. 11). Specifically, the goal that is related to a shift toward waterborne transport is the following: Thirty percent of road freight over 300 km should shift to other modes such as rail or waterborne transport by 2030, and more than 50% by 2050, facilitated by efficient and green freight corridors. To meet this goal will also require appropriate infrastructure to be developed.

However, the evolution of the modal split of land transport of goods in the European Union (EU) from 1999 to 2014 shows no significant change among road, rail, and inland waterways transport (IWT) modes (European Commission, 2016). There is also no sign of any significant shift of transport from truck to rail or inland ship since 2014. In addition, the increased port

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volumes during the last four decades in the ports in the Hamburg-Le Havre range indicate the increased need for hinterland transport (van Hassel et al., in press; De Langhe et al., 2016). Thus, solutions that can contribute to the modal shift from road to waterborne transport are needed. Furthermore, economies of scale are a dominant factor in all transport modes. In waterborne transport, the increased vessel sizes aim at reducing costs, due to economies of scale (van Hassel et al., 2016). However, the big vessel sizes that are economically competitive cannot access smaller inland waterways and smaller short-sea ports or terminals. In the H2020 NOVIMAR project (NOVel Iwt and MARitime transport concept) (2017), the problem statement is the following: vessels that can access these infrastructures are, due to their limited (economies of ) scale, less or not economically viable. This has reduced waterborne transport competitiveness and modal share. An approach providing economies of scale effects on the one hand but at the same time enabling waterborne transport to access smaller waterways, terminals and urban areas is presently missing (NOVIMAR, 2017, p. 2). NOVIMAR aims at developing such an approach.

The innovative concept of the vessel train (VT) that is introduced by the H2020 NOVIMAR project comes to fill this gap and contribute to the modal shift in favor of waterborne transport. In this project, the term “waterborne transport” refers to inland waterway (IW), sea-river and short-sea shipping (SSS) transport. The VT is a waterborne platooning concept featuring a manned lead ship (either dedicated or not) and a number of follower ships that follow at close distance by automatic control (see Fig. 1). A dedicated lead vessel is a vessel with no cargo carrying capacity and only functions as a waterborne “locomotive” for a VT; meaning that in a nontangible way it is connected with the rest of the vessels and its role is to show the path to

FIG. 1 Schematic presentation of the VT concept. Source: Reproduced with permission from the Novimar project consortium, Novimar website, https://novimar.eu/concept/.

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the following vessels to monitor and communicate with them. This is the reason why the follower vessels can be operated with reduced crew levels. Particularly, the VT concept aims: to adjust the waterborne transport such that it can make optimal use of the existing shortsea, sea-river and inland waterways and vessels, while benefitting from a new system of waterborne transport operations that will expand the entire waterborne transport chain up and into the urban environment (NOVIMAR, 2017, p. 2).

A VT can become a new waterborne transport system, which can fit into the current and welldeveloped system. For a successful introduction and integration of the VT concept into the existing transport system, one has to start from an analysis of the current situation in IWT, SSS and sea-river transport, and of the competing modes. A project is deemed successful when it meets its goals (Edkins et al., 2013). The project success can be assessed based on the achievement or failure of the expected project goals. In this project, success is defined in the following way: A minor success will be achieved, when the VT concept is able to fill gaps in the existing transport concept of inland navigation, shortsea shipping or sea river transport or when it is able to perform better in economic and environmental aspects. For example by penetrating into urban areas due to the use of smaller vessels or improving the linking within waterborne transport modes. Further improvements within the waterborne transport sector might be a reduction of cost due to potential crew reduction. A full success will be achieved when the VT concept improves the total waterborne transport (economically or environmentally) in such a way, that it will lead to a modal shift to waterborne transport. (Hoyer et al., 2017, p. 18).

The aim of this chapter is to identify the main cost performance indicators (PIs) for measuring the economic performance of the transport system with and without the VT and also for the different VT constellations. The current situation will be used as a benchmark to evaluate the VT concept. The basic work of this chapter consists of desk research. Based on the selected PIs, conclusions can be drawn firstly whether the VT can become a new logistics system in the SSS, sea-river and IWT markets and on which the specific VT constellations with the best scoring (positive values) PIs are. Using PIs for performance measurement ensures that one always evaluates business activity based on a basic benchmark. PIs provide visibility of the business performance and allow for objective quantitative and qualitative evaluation (Logistics Bureau, 2013). Therefore, a very good definition of the main objective of the PIs needs to be developed. Not only is the cost performance of the VT examined in NOVIMAR but also the social and environmental performance. Since the focus of this chapter is on the identification of business-economic PIs, the welfare-economic indicators are not presented in this chapter. The economic performance of the VT concept is evaluated at different levels and from different perspectives, from the operational cost level for the ship owners to the total logistics costs for the shipper/owner of the goods. Initially, the VT concept will be analyzed for containers and Roll on Roll off (Ro-Ro) cargo because it is expected that they have the highest potential for generating a modal shift (J.-C. Maass, personal communication, October 27, 2017).

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The chapter is structured in the following way. Section 2 develops different VT concepts. Section 3 describes the current situation of the waterborne transport along with its main competitors. Section 4 provides a literature review with respect to the supply-chain performance measures (SCPMs). Section 5 presents the selected PIs. Section 6 formulates the conclusions.

2 Vessel train concepts It is possible to create different constellations of VTs; because within the concept, it is possible to link different types of vessels to one VT. However, not all vessel types can be linked to a single VT due to different technical characteristics of the vessels, which come from the different markets in which these vessels operate. First of all, there is the geographical distinction between the VTs for SSS and the ones on the inland waterways. Here, the sea-river vessels are in between and can be linked to both. Besides these geographical distinctions, the different vessels operate in different markets. For instance, Ro-Ro and feeder vessels, which are bound to a fixed sailing schedule, will have higher design speed than a coaster, which is sailing in a tramp market. This is the case because they are bound to schedules and they need to be on time to their destination and this is the reason why their design speed is higher. This is important because we compose the VT out of vessels of similar operating speed because the speed of the whole VT is the speed of the slowest vessel in the VT. Therefore, the following types of VT will be considered: – Short-sea VT:
Ro-Ro and feeder VTs (high speed/displacement ratio)
Multipurpose coasters and sea-river VTs (low speed/displacement ratio) – Inland VT:
IWT (large vessels) and sea-river VTs
IWT (large and small vessels) VTs The VTs in which a small inland vessel can be linked to a VT is an important option to consider. The reason for this is that this type of VT can link these small inland vessels to the VT and bring them to the entrance of the small waterway, from where it can sail to urban areas. Therefore, the VT can offer economies of scale effects and access to smaller waterways at the same time; something that would not be possible with big inland vessels. Thus, when small waterways need to be accessed, the respective small inland vessel(s) will be detached from the whole VT at the entrance of the small waterway and will sail independently for the last few kilometers. On top of this, a VT can be formed with existing vessels equipped with VT technology, but also newly built vessels, which have been dedicatedly designed for a VT. The VT concept aims to minimize, to the extent possible, the investment costs in the vessels and infrastructures. The former can be achieved using existing vessels. With respect to the latter, major infrastructure investment costs are not expected. An infrastructure cost that might be needed is for the development of an onshore control centre at points that the VT is (de)composed or/and if an automated mooring system at the port terminal is needed (depending on the crew levels). I. Shipping

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These potential infrastructure costs are not expected to affect either the vessel owner’s costs, or the total logistics cost (TLC) of the cargo owner because they are assumed to be public investments. Therefore, these investments will be taken into account when the full social cost-benefit Analysis (SCBA) will be conducted at a later stage of the NOVIMAR project. Another important element to take into account is the business concept of VT. Conceptually, there are three main actors to consider: the vessel owner/operator, the VT organizer, and the cargo owner. First, the vessel owner/operator is the actor who performs the actual transport in the waterborne transport system. Second, the VT organizer is either a third party who acts as an intermediate operator or a shipping company, which brings together the leader and follower vessels so as to compose the VTs. Third, the cargo owner is the actor who wants to ship cargo and sets the boundary conditions related to the transport (cost, reliability, etc.). This actor is the one that takes the ultimate mode choice. For structuring the main business concepts of the VT, four elements are taken into account: 1. the three aforementioned main actors, 2. the current structure of the waterborne transport market with a lot of tramp shipping (especially in IWT and the short-sea general cargo market) and also the distinction between tramp on the one hand and liner shipping (Ro-Ro shipping and feeder vessels) on the other hand, thus examining the VT operating in both tramp and liner shipping market, 3. the possibility to use an existing cargo vessel as a lead vessel or a dedicated lead vessel, and 3. the possibility to compose the VT using the fleet of only one shipping company (applicable to large shipping companies) or the fleet of different shipping companies (applicable to small shipping companies). Based on these elements, three different business concepts can be distinguished, which will be further researched. These are: 1. Dedicated shipping company forms VTs with its own vessels. An own vessel (existing cargo vessel with lead vessel technology) or a dedicated lead vessel can be used as a lead vessel. This could be a viable option for container feeder vessels, inland container vessels, and Ro-Ro operators, where many large shipping companies are active. 2. Third-party shipping company owns and operates dedicated lead vessels or existing vessels with lead vessel technology, which will sail in fixed lines between different ports. This company sells slots in a VT. The follower vessels can use this service so that the vessel can be operated with less crew or sail more hours with the same crew. This option could be viable for the operations on the inland waterways where many small operators (one owner, one vessel) are active. 3. “Tramp/Uber service” is an opportunity-driven business concept. For this business concept, a new entrant could step into the waterborne market via a cloud-based system. Based on a cloud system, vessels can “hook on” a vessel that is a lead vessel, which can be any vessel with the right lead vessel technology. The follower vessel can use the time in the VT to have the crew rest so as to reduce the number of crew members. Clearly, these different business concepts set different requirements for the determination of the cost PIs. I. Shipping

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Since the VT is a partially autonomous concept, this allows reducing the number of crew members on board of the follower vessels. In that case, the crew on the leader vessel will control/supervise/operate the VT and in the follower vessels there will be at least one crew member that will be in charge not of the navigation, which will be automated, but of other tasks that will keep the vessel functioning (such as maintenance tasks) and also of emergency situations. Thus, in the VT, thanks to the reduced number of crew members on board, the fleet capacity will increase; thanks to the automated navigation, the operating hours can increase, and also thanks to the concept itself, smaller vessels will be used instead of large to allow access to small inland waterways, while achieving at the same time economies of scale. All these characteristics are identified by Hendrickx and Breemersch (2012) as adaptation strategies to low water levels and in general climate change effects, hence enhancing the potential of modal shift into IWT when using the VT, thanks to the use of small vessels composing a VT (assuming that small vessels are available). Although crew cost savings are essential; they are not the most important benefits. Most often, the main benefits come from increasing the reliability and reduction of waiting time in ports. If one goes to a port to many terminals, this is where the VT makes a difference in its advantage because one does not need to wait at each terminal (van Hassel et al., 2018). This advantage of less waiting time due to not waiting at each terminal is thanks to the VT capability of cargo consolidation. This capability will allow consolidating the cargo; that is, loading the vessels with cargo that goes to one single discharge port.

3 Current situation of waterborne transport and competitive transport modes This section describes the application of the VT in the current waterborne transport market and other competing transport modes, namely road and rail.

3.1 The inland navigation, short-sea shipping, and sea-river transport system Since the main goal of this chapter is to measure the economic performance of the transport system with and without the VT (current situation) through the selected PIs, firstly, the current situation of the inland navigation, SSS, and sea-river transport system is examined. The evolution of the modal split of land transport of goods in the European Union (EU) from 1999 to 2014 shows no significant change among road, rail, and IWT modes. There is no sign of any shift of transports from truck to rail or inland ship since 2014. The modal share for inland navigation varies within the EU, mainly due to the inland waterway infrastructure. The Netherlands, Belgium, and Germany have a very dense inland waterway network and thus they have a higher modal share in comparison with the overall EU share (European Commission, 2016). With respect to the cargo, “the share of transport of containers on total IWT” in the total EU (in tkm) is quite low but is constantly rising from around 7.8% in 2009 to 10.2% in 2015 (Eurostat, 2016, November 30). The above figures give us an overview of the current situation of the total transport performance of IWT in Europe. However, considering the scope of the NOVIMAR project that is also enabling accessibility to small inland waterways, smaller shortsea ports or terminals, it is of value to examine the current situation of the smaller waterways.

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The transported tonnages per year for 2015 on the Flemish small waterways were equal to almost 3.7 million compared to 35 million of the total transported tonnage via all the Flemish waterways in 2015 (Vlaamsewaterweg, 2015). More specifically, the tonnages transported per year in 2015 on the Dessel-Turnhout-Schoten Canal were 748,138, on the Bocholt-Herentals Canal 1,930,073, and on the Zuid Willemsvaart 975,328 (Vlaamsewaterweg, 2015). Also, it is observed that the transported tonnages on the small waterways are decreasing from 2005 onward. This stresses the claim that something should be done to revive the small waterways. With respect to the containers transported, “there are not a lot of containers transported via the small waterways.” This is due to the fact that there are no container terminals at the small waterways (van Hassel, 2011). These figures give an indication of a need for innovation suited to small waterways, which will increase the tonnage transported on the small waterways. In 2016, with respect to the fleet, the three countries with the highest performance in transport are the Netherlands, followed by Germany and Belgium and the majority of the cargo is transported via either self-propelled barges or push barges (Eurostat, 2017, November 10). The current situation of the labor market in the inland navigation sector is also examined because labor costs, which depend on different factors such as the type of vessel, and therefore the number of crew members and salary levels per country, etc., contribute to the total costs quite significantly, varying around at least 30% (Al Enezy et al., 2017). However, to sail unmanned or with a reduced crew, additional equipment will be needed among others to guarantee the same safety levels as with fully manned vessels. Currently, the minimum required crew for motor vessels and pushers differentiates based on the length of the vessels being (1) L  70 m; (2) 70 m < L  86 m, and (3) L > 86 m. Also based on the operating mode, being A1: navigation for a maximum of 14 h; A2: up to 18 and B: up to 24 h a day and also based on having or not bow thruster. Thus, for the smallest size vessel (L  70 m), without bow thruster, of operating mode A1, minimum two crew members are required on board. While for the biggest vessels when sailing up to 24 h a day, five crew members are required on board. Also, when describing the current situation of inland navigation, it is critical to mention the impact of climate change, mostly on low water levels. Low water levels were empirically established as the most influential for the inland navigation sector (Hendrickx and Breemersch, 2012). They reduce the cargo-carrying capacity in a ship and also increase the costs of transportation (Schweighofer, 2013). Although this is not of high relevance for smaller waterways, which are canals with controlled water levels, it is of relevance for the bigger inland waterways, for example, the Rhine, which will be also examined for the VT concept. The available data for SSSa are not as extensive as for inland navigation. In 2012, the SSS in the EU-28 represented 60% of the total maritime transport of goods within Europe (CENIT, VITO, a

According to the European Commission, the term shortsea shipping means “the movement of cargo and passengers by sea between ports situated in geographical Europe or between those ports and ports situated in non-European countries having a coastline on the enclosed seas bordering Europe. Short sea shipping includes domestic and international maritime transport, including feeder services, along the coast and to and from the islands, rivers and lakes. The concept of short sea shipping also extends to maritime transport between the Member States of the Union and Norway and Iceland and other States on the Baltic Sea, the Black Sea and the Mediterranean.” (European Commission, 1999, June 29, p. 2).

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COWI, 2015, June). The distribution among countries or sea areas differs strongly (Eurostat, 2019, May 27). According to Eurostat, most freight transports took place in the Mediterranean Sea (29%), followed by the North Sea (26%) and the Baltic Sea (21%) (Eurostat, 2019a, May 23). Freight transport in thousand tonnes by shortsea increased between 2005 and 2017 (EU-level) by 3.15% (Eurostat, 2019b, May 22), although a decrease was observed between 2009 and 2014. The most common cargo in SSS is liquid bulk and dry bulk cargo (Eurostat, 2019c, May 23). Containers and Ro-Ro cargo make up 35% of the total cargo transport with regional variations (Eurostat, 2019d, May 22). With respect to the SSS fleet, Russia holds the highest number of SSS vessels, followed by Germany and Norway (Wijnolst and Waals, 2005, May). In Europe, the vessels used for SSS are quite old, around 20 years, and a big percentage of them (between 57% and 72%) are not built for one purpose. Therefore, the fleet needs to be replaced by faster and better designed vessels (OECD, 2001). The vessels composing the EU short-sea fleet are mixed-purpose, ferries, car carriers, etc. The sizes and prices for newly built vessels can significantly vary, from large vessels of 100 million euro to small sea-river vessels of 15 million euro (European Community Shipowners’ Associations (ECSA), 2016, February 1). Sea-river transport is possible on inland waterways of sufficient size with access to the sea. The most important bottleneck is the allowable air draught under bridges and overhead cables, followed by the water depth. According to market analysis of the CCNR, about 90–100 million tonnes of cargo, in 2016, are transported annually by means of sea-river transport (SPC, 2017, May 4; ERSTU Navigator, 2017, June). Unfortunately, more detailed and recent data sources are not known. Boxed cargo is rare in sea-river cargo flows (ERSTU Navigator, 2017, June).

3.2 Competing transport modes The two main alternative land transport modes, road and rail, compared to waterborne transport, have completely different operating conditions and, thus, completely different strengths and weaknesses. However, the VT concept brings up some similarities to rail and road transport. Therefore, potential lessons learned from the road and rail transport sector are researched on their value for the VT concept. With respect to railway freight transport, it can be concluded that it has certain similarities to the desired VT concept, considering a locomotive with its wagons. However, there is a very important difference: the ships in the VT can sail independently, which is not the case for the wagons of a freight train. This may be an advantage for the VT. Hence, a detailed analysis of European rail freight transport is carried out with a focus on similarities and differences to the VT concept. A well-founded summary of important aspects in the analysis of logistics systems is given by Janic (2014). In general, the transport velocity for cargo by rail depends strongly on the distribution network and traffic volume. The transport velocity can be quite fast for a direct connection but is reduced by cargo handling for other distribution modes. Further, rail transport does not suffer from driving bans on Sundays or Holidays, as road transport does for certain countries for vehicles over a specified weight. However, freight and passenger transport share an extensive amount of railways, which restricts the freedom of rail freight transport, since passenger I. Shipping

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trains usually get priority. Additionally, repair and maintenance works on the rail network often take place at night, when freight transport is operating. A further advantage of rail transport is predictability since transport is organized in plannable timetables. On the other hand, these timetables restrict flexibility (Balfaqih et al., 2016). Freight transport by road is the dominant mode for the transport of cargo. The transport velocity on the road is usually higher than for rail and waterborne transport. In some European countries, it is only limited by driving bans on Sundays or Holidays, or by traffic jams. Road transport is highly flexible and suitable for door-to-door delivery. On the other hand, the transport capacity is limited and, thus, transport costs are high. From the ecological point of view, road transport performs the worst (Gibson et al., 2014; Balfaqih et al., 2016; Schroten et al., 2017). In the road transport sector, a lot of research has been done regarding collaborative transport. Here, the road sector is pretty far ahead of the waterborne sector. The so-called platooning could become quite attractive. Platooning describes a way of traveling in a convoy of several trucks, which are electronically connected, which is exactly the same idea for the VT (International Transport Forum-ITF, 2017). Since few years, several studies and projects started to investigate the concept of truck platooning and develop according to the technology. In 2015, the “White Paper: Automated Driving and Platooning—Issues and Opportunities” (ATA Technology and Maintenance Council Future Truck Program, 2015) and the report of “Automated and Autonomous Driving: Regulation under uncertainty” of Smith et al. (2015) have been published. In these reports, a wide range of aspects for automated driving and platooning are examined and presented. One of the latest and quite successful projects in Europe is the “European Truck Platooning Challenge 2016." The concept of freight platooning on the road and the VT concept seem to be quite similar at first glance, but different on second thought. Truck platooning can bring a number of advantages over single driving trucks. The most prominent advantages are: – – – –

Fuel saving and reduced emission Road space reduction Labor cost reduction Improvement of safety

However, truck platooning can also bring challenges, mostly regulatory (Smith et al., 2015). With respect to the VT, one of the main advantages of platooning on the road, mainly saving fuel and thus reducing emissions, is not valid based on model tests performed. Further, the reduction of needed space due to the reduction of the inter-vehicle distance will not be of much added value because waterborne transport does not suffer from a lack of available space. From a technical point of view, the coupling of trucks is currently feasible, whereas, the technology for a VT has to be developed or transferred to vessels in the first place. I. Shipping

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Challenges for the implementation of the new VT into the normal traffic will show several similarities to the implementation of truck platooning into normal road traffic. Suitable solutions for road traffic might be usable or at least partly transferable to the VT. Further, new logistics concepts developed for road platooning can be considered for a new logistics concept for the VT. Therefore, road platooning and the VT have also aspects in common. The reduction of labor costs due to a reduction of personnel/crew will be one of the advantages, which are expected for the VT as well and will range from small to high depending on the different scenarios. Also, road transport is becoming cleaner due to the more energy-efficient engines and cleaner fuels that are being developed. The EU finances R&D programs for the development of electric transport (European Commission, 2012), thus making the road transport more competitive also in terms of its external costs.

4 Literature review of supply-chain performance measures The starting point for the identification of business-economic PIs for the VT concept is a literature review with respect to the SCPMs because the VT will be a part of the supply chain (SC) but with its own characteristics. This review has been conducted to take main lessons with respect to the existing SCPMs and with respect to how to select the most appropriate indicators for the own SC. The latter is very important because there are no standard sets of PIs or SCPMs that can be equally well applied for all organizations and SCs (Balfaqih et al., 2016). Therefore, decision makers need to select performance measurement approaches and indicators that suit their SC (Balfaqih et al., 2016). This is the reason why the focus of this chapter is on the design phase of the supply-chain performance measurement system (SCPMS) life cycle. The majority of the relevant papers focus on this phase (Maestrini et al., 2017). However, a good design of the SCPMS does not guarantee a successful adoption, since failures might occur in the other three phases, being “implement, use and review.” The papers that are reviewed with respect to the SCPMS show that the focus is on: (1) clearly defining the key terms used, such as performance measurement system (PMS), SCPMS, metrics, etc., (2) providing recommendations on how to develop own key performance measures, (3) categorizing the SCPMs that are most widely used, and (4) presenting also the existing SCPMS frameworks and the techniques that are used (Gunasekaran and Kobu, 2007; Krauth et al., 2005; European Shortsea Network, 2017, July 13; Lai et al., 2002; Maestrini et al., 2017; Stank et al., 2001; van Hassel et al., 2016; Al Enezy et al., 2017). Therefore, attention is not only paid to the specific existing SCPMs as such. The main lessons learned from the literature review are: – Less is better with respect to the number of indicators to be used (Bongsug, 2009). – Categorize key PIs (KPIs) in primary and secondary (Bongsug, 2009). – Develop KPIs for each of the critical operations of the SC (see four supply-chain operations reference (SCOR) processes) (Bongsug, 2009).

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– Keep in mind the changing environment: the level at which SC management takes place shifted from an internal business level to the enterprise management level of the SC. Thus, it is significant to consider the SC as a whole when designing an SCPMS (Balfaqih et al., 2016). – It is critical that PIs are based on a clear definition of scope and on suitable data and calculation methods (Balfaqih et al., 2016). Consider developing PIs based on the perspectives of the different stakeholders (Krauth et al., 2005, June 19–22): – From a logistics service provider’s point of view, performance is measured through timeliness and accuracy, delivery performance, personnel scheduling, safety measures, customer satisfaction, and loyalty (Krauth et al., 2005, June 19–22). – From a management point of view, effectiveness, efficiency, satisfaction, IT and innovation are key performance overall categories for measuring performance (Krauth et al., 2005, June 19–22). – From the perspective of the customer, mainly three PIs matter: costs, reliability, and flexibility. Costs are measured as costs per stored unit; performance is measured as On-Time and In-Full (OTIF); and flexibility measures the ability to accommodate decreases and increases in the flow of goods (Krauth et al., 2005, June 19–22). Some of these lessons provide the foundations for selecting the most appropriate PIs for the VT concept. First of all, it is clear that a small number of PIs should be developed and used (Bongsug, 2009). Second, the PIs need to be classified based on the perspective of the different stakeholders (Krauth et al., 2005, June 19–22). In the VT concept, the main different actors under consideration are the vessel owner/operator, the VT organizer, the cargo owner, and the society (the latter is not examined in this chapter) and thus each indicator proposed is linked to one of these actors. Third, PIs should be based on a clear description of the scope. The ultimate goal of performance measures is to minimize the gap between what is planned and what is finally executed and to correct potential problems (Bongsug, 2009). In this chapter, however, it is the intention to evaluate the different constellations of VTs and thus select these constellations that perform better in terms of costs than other ones and also measure the economic performance of the transport system with and without the VT. As mentioned before, the aforementioned evaluations will be made from the perspective of the private actors. The VT will be evaluated taking into account the SC. However, indirectly through the results that will be received by the evaluations made for the private actors, input will be provided also for the society actor, showing the potential of modal shift to waterborne transportation (see in Section 5: Total Logistics Cost (TLC) Indicator). This is important since modal shift in favor of waterborne freight transport is the core objective of the NOVIMAR project and the VT concept. Keeping in mind the above main lessons from the literature review, the main elements that are expected to be influenced by the realization of the VT are taken into account for the selection of the appropriate PIs. Therefore, at least the following six elements are included:

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Transport cost Total transport time (including delays and waiting times at deep sea and inland terminals) Transport reliability Inventory cost (both in-transit and in a warehouse) Flexibility External cost (Welfare assessment of the VT) (this element will be assessed at a later stage and thus it is not included in this chapter)

5 Identifying cost performance indicators for the vessel train concept In order to assess the viability of the VT concept, a limited set of PIs needs to be selected. The successful implementation of a VT depends on the net returns it gives to the vessel owners/ operators, the shippers and/or society. This net return depends on the costs and the benefits, private and/or social of the VT concept. Therefore, the PIs need to include at least the six elements listed in Section 4. In the present chapter, the business-economic cost aspect of the VT for a cargo flow between origin i and destination j is examined at three levels. The first level compares the cost of sailing in a VT and the cost of sailing in a traditional way for the shipowner/operator in the business concept of a dedicated shipping company. The second level compares again the cost of sailing in a VT with the cost of sailing without the VT for the shipowner/operator in the business concept of a third-party VT service in operation. Finally, in the third level, the VT concept is introduced in the logistics chain in order to consider the attitude of the shipper/owner of the goods toward this new transport concept. It is the shipper/owner of the goods who decides which mode finally will be chosen. From the perspective of the vessel owner, the generalized cost (GC) indicator is used (van Hassel et al., 2016). The first PI simply measures whether there is a cost advantage for the shipowner/operator when using the VT concept instead of traditional individual sailing: ΔCi, j ðNVT, VTÞ ¼ Ci, j ðNVTÞ  Ci, j ðVTÞ

(1)

in which – Ci,j(NVT) ¼ the current cost per unit of goods (e.g., tonnes, TEUs, and lane meters) for IWT, SSS or sea-river transport between origin i and destination j without using the VT concept. – Ci,j(VT) ¼ the cost when using a VT. The cost elements for the shipowner/operator that need to be considered are (van Hassel et al., 2016; Al Enezy et al., 2017): – Running cost (crew cost, insurance, consumables, repair and maintenance, and overhead) – Voyage cost (fuel, lubrication oil, and port dues) – Fixed cost (depreciation and repayment of a loan)

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The main cost elements that will be affected by the introduction of the VT are: – Reduction in crew cost for the follower vessels. – An increase in fixed cost for both the leader vessel and the follower vessels for the installation of the necessary equipment to be part of the VT. The first PI is linked to the business concept of the dedicated shipping company because here only the internal cost of the company is considered. If the PI turns out to be positive, then the VT concept leads to a lower cost per transported unit and the VT concept will be successful from a business-economic point of view. This will indicate that it is worthwhile for the shipowner/operator to shift from the traditional waterborne transport to a system with VTs. If the PI is negative, then the VT concept will lead to a higher cost per unit, which will not make it worthwhile for the shipowner/operator to shift to a VT system. Therefore, the threshold value for this PI is zero. In the second PI, a third-party operating the VT concept is assessed, thus an additional cost element needs to be introduced. This cost element consists of the cost to operate the lead vessel and the cost of managing and combining the different vessels into a VT. The third-party operating the VT will recover these costs by asking a price for providing the service. The owners/operators of follower vessels will pay this price. Therefore, these costs need to be incorporated in the cost structure of the vessel owner/operator when using the third party to form the VTs. These costs can be calculated with the following formula:
 ΔCi, j ðNVT, VTÞTPS ¼ Ci, j ðNVTÞ  Ci, j ðVTÞTPS + Ci, j ðTPSÞ

(2)

In this formula of the second PI: – Δ Ci, j(NVT,VT)TPS ¼ the cost savings per transport unit of transport for a vessel owner/ operator, if he/she is a third party operating the VT (VT organizer) and does not belong to the same shipping company with the follower vessels. – Ci, j(VT)TPS ¼ the unit cost for the vessel owner/operator to make use of a VT through a third-party operating the VT. – Ci, j(TPS) ¼ the unit cost, between origin i and destination j, of the third-party service, which is determined by (a) the cost of operating a dedicated lead vessel, (b) the cost of organizing and forming a train, and (c) a profit margin for the VT organizer. Here, a positive PI shows that the VT concept leads to a lower cost per transported unit and the VT concept will be successful from a business-economic point of view. This will indicate that it is worthwhile for both the shipowner/operator to shift from the traditional waterborne transport to a system with VTs and for a VT organizer to set up a business case. Otherwise, there is no incentive to opt for the VT system. The third PI that is used is the indicator of TLC (Blauwens et al., 2006), which examines the perspective of the cargo owner (customer). This indicator includes the main elements that are

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expected to be influenced by the materialization of the VT (see Section 4) and expresses the potential for a modal shift in freight transport. This PI is related to the size of the waterborne freight market, which is determined by the modal share of waterborne transport. The modal choice is made based on the TLC of the shipper/owner of the goods. The TLC is calculated with the following formula: ΔTLCi, j ¼ TLCi, j ðNVTÞ  TLCi, j ðVTÞ

(3)

in which: – ΔTLCi, j is the difference in total logistics cost (Blauwens et al., 2006, 2016) from origin i to destination j of a situation without VT (TLCi,j(NVT) (this is the lowest value of each transport chain which can either be a uni- or a multimodal situation with traditional waterborne transport, road, and/or rail transport) and a situation with VT (TLCi, j(VT)). The TLC can be calculated based on the following formula (Blauwens et al., 2006):  TLC ¼ TC +

    qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Q h 1  vh + Lv +  v  h  k  ðL  dÞ + ðD2  lÞ R 2 365 R

(4)

in which the parameters are the following: – – – – – – – – – –

TC: transport costs (euro/unit), R: annual volume (units), Q: loading capacity (units), v: value of the goods (euro/unit), h: holding cost (% per year), L: average lead-time (days), k: safety factor (goods flow parameters), d: variance of daily demand (units2/day), D: average daily demand (units/day), l: variance of lead time (days2).

TC is the price the shipper has to pay for the transport and which will be based on the cost of the transport operators (Ci,j) who provide the transport service (this can be unimodal or multimodal) and the market situation. The first part between brackets in Eq. (4) relates to the annual costs of cycle stock. This part of the formula shows that the higher the cargo value and the more cargo is shipped in high volumes, the higher the TLC will be. The second block between brackets relates to the inventory costs during transport. If the value of the cargo transported is high and the transport speed is low, a higher TLC occurs. The last block between brackets in Eq. (4) gives the safety stock cost of a shipment. This part of the formula shows that the higher the variation in lead-time (l),

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which is a measure for reliability,b the higher the TLC will be. Also, the variance in daily demand for cargo (d) will have the same impact.c The main advantage of this PI is that a change in the transport system is reflected at a company level (both in-transit and warehouse inventory cost). The cargo owners set the boundary conditions to determine the modal choice. So if a VT option leads to lower transport cost (TC), while all the other elements are not changed, there might only be a limited modal shift toward waterborne transport. A disadvantage is that company-specific data are needed for each considered cargo. Since the VT brings different cargo flows together, data are needed from multiple companies, which makes this indicator very data consuming. Therefore, this indicator finally determines whether the concept of the VT will be used for the specific transport from i to j and whether it will result in a modal shift to waterborne transport. If the PI is positive, then there is a gain for the cargo owner. If the PI is negative, then there is a cost increase for the cargo owner. Therefore, if this PI is negative, there is no incentive to shift. For a positive value, even very small, there is an incentive for a modal shift for the cargo owner. Table 1 gives an overview of the proposed PIs. The above main PIs are used to assess different constellations of the VT (see Section 2). Based on a sensitivity analysis, the main impact of changes to the VT transport system can be assessed and those VT solutions that have the best scores on the developed KPIs can be developed into concrete business cases. Also, different scenarios will be used for the calculation of the PIs: among others, a scenario of wet and dry periods, thus taking into consideration the low water levels and the impact of climate change.

b

The higher the variance, the more unreliable the transport will be. Reliability is critical for the VT concept and is taken into consideration by the TLCs indicator ( PI3) via the parameter “variance of the lead time.” The key factor that is expected to affect reliability in the VT concept is waiting time. The leader vessel or the whole VT cannot wait for the follower vessel to depart. Thus, one of the “responsibilities” of the follower vessels is to be ready on time for departure to avoid causing waiting times. The following two scenarios are examined for reducing waiting time and increasing reliability: (1) prioritisation of the VT at the locks and (2) cargo consolidation that will allow the VT to call at one or few terminals, less than the ones that would be needed without the cargo consolidation concept. c

The element of flexibility is indirectly included in the TLC via the frequency of the VT. If the frequency increases, the flexibility increases as well.

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TABLE 1 Overview of proposed performance indicators.

Vessel owner benefit

Cargo owner benefit

PI

Calculation

Meaning

Impact

Cost advantage in a dedicated shipping company

ΔCi, j(NVT, VT) ¼ Ci, j(NVT)  Ci, j(VT)

Difference in cost per load unit for vessel owners/operators between i and j

Attractiveness of the new transport system for vessel owners/ operators of large shipping companies

Cost advantage in a thirdparty operation

ΔCi, j(NVT, VT)TPS ¼ Ci, j(NVT)  [Ci, j(VT)TPS + Ci, j(TPS)]

Difference in cost per load unit for vessel owners/operators using a third-party VT service between i and j

Attractiveness of the new transport system for vessel owners/ operators of small shipping companies

Difference in total logistics cost for cargo owners between i and j

Attractiveness of the VT for a cargo owner. If the TLC for the VT option is lower, then also a mode shift can be expected

Total logistics cost

    1 Q h  vh + Lv TLC ¼ TC + R 2 365  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2  v  h  k  ðL  dÞ + ðD  lÞ + R

Source: Authors’ composition.

6 Conclusions To achieve a modal shift, the present chapter introduces the innovative concept of the vessel train (VT). The VT is a waterborne platooning concept featuring a manned lead ship and a number of follower ships that follow by automatic control. The aim of this chapter is to identify the main performance indicators (PIs) for measuring the business-economic performance of different VT constellations and of the transport system with and without the VT (current situation); the latter will be used as a benchmark to evaluate the VT concept. The analysis of the current situation shows that a lot of data on waterway structures, fleets, and transported cargo can be found for inland waterway navigation, but hardly any updated data for SSS and sea-river transport. The analysis of the competing transport modes of road and rail and of their platooning configurations shows that rail transport is quite different from the VT concept and the transfer of relevant information is difficult. In the road transport sector, truck platooning is currently investigated and already highly developed, thus knowledge might be gained from a technical and logistics point of view. The first two proposed business-economic PIs measure the cost advantages for vessel owners/operators who use either an own dedicated lead vessel or an own existing vessel with lead vessel technology (mainly applicable for large shipping companies) and the cost advantage for vessel owners who make use of a third party VT operation (mainly applicable for small shipping companies). If there are net savings, then there is an incentive for the vessel owners to invest in the required VT technology.

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The third PI measures the cost advantage for the cargo owner. Thus, it reflects the potential for a modal shift in favor of the waterborne transport and for the growth of the waterborne freight market. The third PI depends on all relevant attributes that determine the modal choice. This means that not only the transport costs are assessed but also the reliability of the transport, the shipment size, the in-transit, cyclical, and safety stock costs. This implies that if a possible reduction in transport cost for waterborne transport is too small (if all other attributes do not change), a modal shift might not take place, which implies that the waterborne freight market will not grow. The selected PIs will be also used to assess different constellations of the VT. Based on a sensitivity analysis, the main impact of changes to the VT transport system can also be assessed and those VT solutions that have the best (positive) scores on the developed KPIs can be developed into concrete business cases. Further research will focus on (a) the calculation of the economic performance of the transport system with and without the VT using the three PIs presented in this book chapter, (b) the assessment of the economic performance of the different VT constellations, and (c) the enlargement of the set of PIs including also environmental and safety indicators.

Acknowledgments The research leading to these results has been conducted within the NOVIMAR project (NOVel Iwt and MARitime transport concept) and received funding from the European Union Horizon 2020 Program under grant agreement no. 723009.

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Hoyer, K., Tenzer, M., Friedhoff, B., van Hassel, E., Sys, C., Vanelslander, T., Moschouli, E., Van de Voorde, E., Meersman, H., and Hekkenberg, R. (2017). Determining Detailed Requirements for the Baseline Logistic Model. Deliverable 2.1, H2020 Novimar Project. International Transport Forum-ITF, 2017. Managing the Transition to Driverless Road Freight Transport, CaseSpecific Policy Analysis Reports. Retrieved from: https://www.itf-oecd.org/sites/default/files/docs/ managing-transition-driverless-road-freight-transport.pdf. Janic, M., 2014. Advanced Transport Systems—Analysis, Modeling, and Evaluation of Performances. SpringerVerlag, London. Krauth, E., Moonen, H., Popova, V., Schut, M., 2005. In: Performance Indicators in Logistics Service Provision and Warehouse Management. A Literature Review and Framework.Paper presented at Euroma International Conference, Budapest, Hungary. Lai, H., Ngai, E.W.T., Cheng, T.C.E., 2002. Measures for evaluating supply chain performance in transport logistics. Transp. Res. E: Logist. Transp. Rev. 38 (6), 439–456. https://doi.org/10.1016/S1366-5545(02)00019-4. Logistics Bureau, 2013. KPI Key Performance Indicators in Supply Chain & Logistics. Retrieved from: http://www. logisticsbureau.com/kpi-key-performance-indicator/. Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., 2017. Supply chain performance measurement systems: a systematic review and research agenda. Int. J. Prod. Econ. 183 (Part A), 299–315. https://doi.org/10.1016/j. ijpe.2016.11.005. NOVIMAR, 2017. H2020 NOVIMAR Project Proposal. OECD, 2001. Short Sea Shipping in Europe. OECD Publications Service, Paris, France. Retrieved from: https://www. itf-oecd.org/sites/default/files/docs/01shortsea.pdf. Schroten, A., van Essen, H., Scholten, P., van Wijngaarden, L., ‘t Hoen, M., Breemersch, T., Vanderlinden, S., Sedlacek, N., Seelmann, H., Burg, R., Eszterga´r, D., 2017. Case Study Analysis of the Burden of Taxation and Charges on Transport. Final Report, Directorate-General for Mobility and Transport. Publications Office of the European Union, Luxembourg. Retrieved from: https://ec.europa.eu/transport/sites/transport/files/ studies/2017-01-24-study-taxation-charges_final_report.pdf. Schweighofer, J., 2013. The impact of extreme weather and climate change on inland waterway transport. Nat. Hazards 72 (1), 23–40. https://doi.org/10.1007/s11069-012-0541-6. Smith, B.W., Svensson, J., Humanes, P., Konzett, G., Paier, A., Mizuno, T., Lykotrafiti, A., 2015. Automated and autonomous driving: regulation under uncertainty. organization for economic cooperation and development/ international transport. Forum. Retrieved from: https://www.itf-oecd.org/sites/default/files/docs/15cpb_ autonomousdriving.pdf. SPC, 2017. News From the Water. Retrieved from: www.shortseashipping.de. Stank, T.P., Keller, B., Daugherty, P.J., 2001. Supply chain collaboration and logistical service performance. J. Bus. Logist. 22 (1), 29–48. https://doi.org/10.1002/j.2158-1592.2001.tb00158.x. van Hassel, E., 2011. Developing a Small Barge Convoy System to Reactivate the Use of the Small Inland Waterway Network. (Doctoral Dissertation). Retrieved from: https://repository.uantwerpen.be/desktop/irua/core/index. phtml?language¼E&euser¼&session¼&service¼opacirua&robot¼&deskservice¼desktop&desktop¼irua& workstation¼&extra¼loi¼c:irua:93046. van Hassel, E., Meersman, H., Van de Voorde, E., Vanelslander, T., 2016. Impact of scale increase of container ships on the generalised chain cost. Marit. Policy Manag. 43 (2), 192–208. https://doi.org/10.1080/03088839.2015.1132342. van Hassel, E., Colling, A., Loghman, N.B., Moschouli, E., Frindik, R., Thury, M., Vanelslander, T., 2018. Transport System Model, Deliverable 2.2, WP2, H2020 Novimar Project. van Hassel, E., Meersman, H., Van de Voorde E. and Vanelslander T. (in press). Investing in New Port Capacity From a Shipper and a Shipowner Perspective: The Case of Maasvlakte II Vlaamsewaterweg, 2015. nv de scheepvaart, jaarverslag 2015, Ontwikkeling per waterweg van de vervoerde tonnage jaren 1977, 2005, 2014 en 2015. https://www.vlaamsewaterweg.be/sites/default/files/jaarverslag_2015_-_nv_ de_scheepvaart.pdf. (Accessed 02 September 2019). Wijnolst, N., Waals, F., 2005, May. European Short Sea Fleet Renewal: Opportunities for Shipowners and Shipyards. Paper Presented at Society of Naval Architects and Marine Engineers (SNAME) Greek Section International Symposium on Ship Operations, Management and Economics, Athens, Greece.

Further reading Hekkenberg, R. (2017). Attuning of requirements and PIs. Deliverable 1.1, H2020 Novimar project.

I. Shipping

C H A P T E R

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Analysis of port waiting time due to congestion by applying Markov chain analysis J.F.J. Pruyn, A.A. Kana, W.M. Groeneveld Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands

1 Introduction If maritime supply chains meet the demand of the same infrastructure at the same time, supplied infrastructure may be insufficient to handle all demand at once. The interference of port users (carriers, shippers, and passengers) is called port congestion. Port congestion may cause delays for the port users to make use of the supplied infrastructure. In many ports, these delays vary significantly between a couple of hours to more than a month (Global Ports, 2018). Some owners even claim delays of 80days (Poulsen and Sampson, 2019). Moreover, these variations can change rapidly, causing disturbances to the planning of the shipper and the shipping company. Port congestion, in general, appear in various forms such as it can be more or less hidden, featuring congestion costs, or it can be visually present, featuring queues which are building up (Meersman et al., 2012). Visually present port congestion comes in several forms such as ship berth congestion, ship work congestion, and vehicle gate congestion. Ship berth congestion arises when a ship has to wait for a berth that is currently occupied by another ship. Ship work congestion arises when a berthed ship has to wait to have its cargo loaded/unloaded until another ship has had its cargo loaded/unloaded. This may be due to the port’s limited ship loading/unloading resources. Vehicle gate congestion may arise at the entrance and departure gates of container ports. Lastly, vehicle work congestion may arise within the port during the loading/unloading of containers on to and from trucks

Maritime Supply Chains https://doi.org/10.1016/B978-0-12-818421-9.00005-7

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4. Analysis of port waiting time due to congestion by applying Markov chain analysis

and trains (Talley, 2017; Cooper et al., 1997). All of these congestion types result in port queuing or waiting time. Port queuing causes delays usually expressed in days of delay. It is the goal of all trading parties to trade with the most profitable prices possible. Transportation of goods is an essential part of trading. Since 80% of all global trade by goods (Berle et al., 2011; UNCTAD, 2017) is carried by ship, maritime transport has an impact on transport costs and trading prices. Within maritime transportation, the seaports are key elements among vessels, suppliers, users, and land transportation by shifting cargo between transportation providers. Many logistics activities have influence on transportation efficiency at seaports with each of their own variables that cause a disturbance. The effect of these disturbances can be simulated, which may help predict some future expected congestion. This chapter investigates the prediction of waiting time for a large group of coal and iron ore terminals worldwide, for which an extensive data set was collected. Coal and iron ore are dry-bulk cargos. These two are the largest dry-bulk trades by weight (UNCTAD, 2017). The larger dry-bulk ships (Capesize vessels of 100,000 DWT and above), almost solely transport coal and iron ore. These vessels usually have long-term (time charter) contracts and work on long hauls of 2–4 weeks for a single trip. Typical trips are Australia to Asia/ China and South America to Europe. Besides these larger vessels, coal and iron ore are also carried in smaller amounts by smaller vessels, such as Handysize (