Dynamic Port Planning under Competition

Transcription

1 Dynamic Port Planning under Competition Development of a tool for strategic investment planning W.A. Gerrits BSc June 2007 Delft University of Technology Faculty of Civil Engineering and Geosciences Section of Hydraulic Engineering Ports and Waterways Significance Joint Venture with NEA Transport Research and Training

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3 Dynamic Port Planning under Competition Development of a tool for strategic investment planning W.A. Gerrits BSc. Student number June 2007 A dissertation submitted in fulfillment of the requirements for the degree of Master of Science in Civil Engineering, specialization Hydraulic Engineering and Ports and Waterways. Graduation Committee: Prof. Ir. H. Ligteringen Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Hydraulic Engineering, Ports and Waterways Dr. Ir. R.J. Verhaeghe Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning Dr. Ir. B. Zondag Significance Joint Venture with NEA Transport Research and Training Prof. Dr. E. Van de Voorde University of Antwerp, Faculty of Applied Economics, Department of Transport and Regional Economics Guest professor at Delft University of Technology, Faculty of 3ME, Department of Marine and Transport Technology

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5 Dynamic Port Planning under Competition Preface This report is the final result of the research project undertaken in order to obtain the degree of Master of Science at the Civil Engineering faculty of Delft University of Technology. The curriculum of this course includes a graduation thesis to be carried out by the student in the final year, with a duration of approximately seven months. The subject for the thesis was offered by the section Ports and Waterways, part of the section Hydraulic Engineering, and the section of Transport & Planning and the work has been carried out at Significance, Joint Venture with NEA Transport Research and Training, in Leiden. The aim of this research is to provide port authorities with a tool to support their strategic investment planning in a competitive market. The tool should provide port authorities with information on the impacts of, structural and non-structural, capacity improvement measures on the future demand for container service. First of all my thanks go to my graduation committee: Prof. Ir. H. Ligteringen who provided overall guidance of my work on this thesis, Dr. Ir. B. Zondag who helped me on a day to day basis, Dr. Ir. R.J. Verhaeghe and Prof. Dr. E. Van de Voorde for their kind cooperation, enthusiasm and feedback on this research project. Next I would like to thank all the staff members of the section Hydraulic Engineering for facilitating my research project. My thanks also go to Jan Kiel and Marco Duijnisveld of NEA for providing me all the input information I needed for my model. I thank my colleagues of Significance for providing a very welcome and supportive working environment during my stay at the office in Leiden. In particular Ismira, Michiel and Jelte, thank you for the great support and all the nice chats at the coffee and ice-cream breaks. The last words of gratitude go out to my friends and family for their endless interest in my graduation project, the mental support and all the efforts to cheer me up now and then. My special thanks go to Gemma, who really supported me in the course of this project with good dinners, exhaustive but relaxing hours of spinning, unexpected coffee breaks and quite a few hours of photo shopping. Wendy Gerrits June 2007 Delft University of Technology June 2007 i

6 Dynamic Port Planning under Competition Abstract Since the container revolution in the mid-sixties and the scale increase it caused, seaports face changing technological developments and economic circumstances. Terminal operators and port authorities have to deal with an increasing number of uncertainties. Especially in container transport competition plays a major role. Containers have a relative large mobility and container transportation routes are less bounded to modalities, ports and logistic chains. As a consequence the substitutability of ports is considerable. Port selection within the logistic chain is part of the selection of the logistic chain which is the primary choice. The market power of shipping companies has been enhanced to the extent that they have gained control of logistic chains. The capacity of container carriers is still growing and as long as there is enough freight to transport they tend to call at less and less ports. There are huge challenges for container ports and their terminals to remain competitive and to handle the anticipated growth with an increase of their productivity, a reduction of port congestion (e.g. Maasvlakte II) and improvement of their hinterland accessibility (e.g. the Betuweline). The overall objective of this research is to provide port authorities with a tool to support their strategic investment planning in a competitive market. The tool should provide port authorities with information on the impacts of, structural and non-structural, capacity improvement measures on the future demand for container service. The present thesis contributes to this objective by the development of the Port Competition Model, a supporting planning tool. The port can be considered a node in a transport network with competition, which faces a dynamic situation concerning the timing and sizing of capacity expansions of other ports and changes in the transport network. The challenge is to incorporate the aspects of the full dynamics of port competition over the network in the Port Competition Model by simulation of the competitive strategies of other ports and the sensitivity of decisions on port investment for such strategies. To this end the Port Competition Model needs to represent varying market shares over the years in order to reflect strategies of other ports. When port planning is based on varying market shares of competitive ports it can be defined as dynamic port planning. The focus of this research is the reaction of a particular port on a change in the transportation network. A scenario for such change is the entry of new routes via a competing port. This leads to decreased demands and benefits for the particular port. Potential reactions of the ports on this change include investment in port expansion and improvement of hinterland connections. The expansion of a port s surface area takes a central place in this research. The density of ports in Northwest Europe is the highest in the world. This implies severe competition. Four of the five biggest European ports are situated within the Hamburg Le Havre range: Rotterdam, Antwerp, Hamburg and Bremen. Within this range the ports compete mainly on transit containers. The Port Competition Model has been developed for these four ports within the Hamburg Le Havre range. The main findings of this study are: The challenge has been to incorporate the aspects of the full dynamics of port competition over the network in the Port Competition Model by simulation of the competitive strategies of other ports and the sensitivity of decisions on port investment for such strategies. For inter-port competition at the level of authorities ports operate as nodes in global transport-logistic chains connecting origins and destinations for freight flows. Sea transport is beyond the scope of this study as the largest part of the sea transport costs are independent of which port in Northern Europe is chosen; the rest of the logistical chain (i.e. port and hinterland transport) is incorporated in the modeling. In this research the discrete choice model is used to model the traffic assignment: the shipping companies choose the logistic chain and the associated port based on the utility for each chain. A main variable in this utility is the generalized transport cost for the different logistical chains. This cost includes all monetary costs of using a route but also monetary values for other factors, such as the time required for the transport. In this research the generalized transport cost is quantified to a very detailed level including call costs, container handling costs and hinterland transportation costs (both monetary costs and monetary values for other factors). ii June 2007

7 Dynamic Port Planning under Competition The dynamics of port competition are incorporated in the Port Competition Model to the extent that varying market shares are represented for every year of the modeling period, using the discrete choice model. In this research a What-If approach is applied. Gaming or strategic behavior of other ports is not included in the Port Competition Model. This means that the investments in one port are not affecting the behavior of other port authorities. A first attempt has been made to incorporate costs associated with port congestion in the Port Competition Model. However, there is a lack of data on the behavior of congestion within a port and additional research is needed to derive an accurate port congestion function. The Port Competition Model has been developed for four ports within the Hamburg - Le Havre range: Rotterdam, Antwerp, Hamburg and Bremen. In this research the contestable hinterland of these ports is assumed to consists of six countries: Germany, Switzerland, Austria, Poland, the Czech Republic and Italy. An origin-destination matrix has been used to model the hinterland transportation costs for the container flows. The Port Competition Model has been calibrated on a dataset (provided by NEA) for the base year The following remarks can be made concerning the performance of the calibrated model: For the ports of Rotterdam, Hamburg and Bremen the Port Competition Model predicts plausible results. The model needs a rather high correction for the port of Antwerp for all hinterland countries (except for Poland). There are different plausible hypotheses to explain why Antwerp has a big disadvantage for shipping companies relative to the other ports. It is beyond the scope of this study to carry out a thorough analysis on the port of Antwerp to explain the disadvantage relative to the other ports. Some hinterland countries need small additional cost corrections to the generalized transportation costs to get good results from the model for the market shares of the ports per country. Because 86% of all cargo is transported to Germany the market shares for the parts of Germany have the strongest influence on the result of the total market shares of the ports. After calibration the results that are obtained with the model come very close to the data from the OD-matrix, especially for the market shares of the four parts of Germany. Therefore it can be concluded that the performance of the calibrated model is fairly good. An analysis of a range of possible scenarios and their impacts is performed using the Port Competition Model. To determine what an effective strategy is for a port to deal with competition the results of the different scenarios have been compared. The Port Competition Model provides information on the impacts of, structural and non-structural, capacity improvement measures on future container volumes. Insight is created in the possible impacts of different scenarios that may appear. The conclusions from the Case Study display the importance of the development of the Port Competition Model and the contribution of this research. Topics recommended for further research in the field of planning of port capacity comprise 1) widen the scope of the research, 2) further analysis of port congestion, 3) further analysis of the port of Antwerp, and 4) modeling the full dynamics of port competition. June 2007 iii

8 Dynamic Port Planning under Competition Table of Content PREFACE...I ABSTRACT...II LIST OF FIGURES...VI LIST OF TABLES... VII GLOSSARY OF ECONOMIC TERMS...VIII DEFINITIONS...IX NOTATION... X 1 INTRODUCTION STUDY SCOPE INTRODUCTION PROBLEM ANALYSIS PROBLEM DEFINITION THESIS OBJECTIVE STAKEHOLDERS INTEREST RESEARCH FRAMEWORK DESCRIPTION OF SYSTEM INTRODUCTION BACKGROUND OF DYNAMIC PORT PLANNING Globalization Impact of containerization on port competition Privatization & capacity planning Port competition & cooperation Criteria for port selection Dynamic port planning ISSUES IN PLANNING OF SEAPORT CAPACITY Seaport capacity Port-commercial and public interests Competition Economies of scale Capacity problems EXISTING APPROACHES FOR PLANNING Capacity Expansion Transportation demand modeling COMPETITION HAMBURG LE HAVRE The port of Rotterdam Hinterland connections Call pattern PORT COMPETITION MODEL INTRODUCTION RECENTLY DEVELOPED METHODOLOGY Brief summary Main findings Limitations MODELING CONCEPTS - METHODOLOGY Port demand modeling Port supply modeling Supply-demand interaction PORT COMPETITION MODEL DESCRIPTION OF MODEL COMPONENTS Market share Total unit cost Port investment cost Port residence cost & congestion cost Hinterland transportation cost & congestion cost Utilization rate iv June 2007

9 Dynamic Port Planning under Competition Capacity expansion strategy Upgrade hinterland infrastructure EFFECTIVE STRATEGY DATA REQUIREMENTS FOR THE MODEL DATA FOR THE MODEL INTRODUCTION GENERAL INFORMATION ABOUT PORTS IN NORTH EUROPE GENERALIZED COST CARGO FLOW ANALYSIS: PORT RELATED COSTS Port investment cost Port residence cost CARGO FLOW ANALYSIS: HINTERLAND TRANSPORTATION COSTS OD-matrix: Hinterland transportation cost at disaggregate level General throughput conclusions OD-matrix Summary throughput data per hinterland country Hinterland transportation cost at aggregate level MODEL CALIBRATION INTRODUCTION MODEL DIMENSIONS CALIBRATION Calibration approach Starting point Calibration factors Performance calibrated model BASE CASE SCENARIO INTRODUCTION ASSUMPTIONS BASE CASE SCENARIO RESULTS BASE CASE SCENARIO SENSITIVITY TO CONGESTION CONCLUSIONS BASE CASE SCENARIO CASE STUDY INTRODUCTION DESCRIPTION SCENARIOS SCENARIO 1: CHANGING EXPANSION PLANS Delay Tweede Maasvlakte Phasing Tweede Maasvlakte Additional expansions other ports Conclusions SCENARIO 2: HINTERLAND CONGESTION SCENARIO 3: SENSITIVITY TO PORT DUES CONCLUSIONS CASE STUDY CONCLUSIONS AND RECOMMENDATIONS INTRODUCTION CONCLUSIONS Conclusions Model Conclusions Base Case Scenario Conclusions Case Study RECOMMENDATIONS REFERENCES LIST OF APPENDICES June 2007 v

10 Dynamic Port Planning under Competition List of Figures Figure 2-1 Framework research...5 Figure 3-1 International sea borne trade. Source: UNCTAD, Review of Maritime Transport, Figure 3-2 Major transport routes. Source: Sanders, Verhaeghe, Dekker, Figure 3-3 Relative dependency of demand, capacity expansion, cost and price. Source: Op het Veld 2003, based on Freidenfelds, Figure 3-4 Levels of port competition. Source: Op het Veld, Figure 3-5 Ports as nodes in a transportation network. Source: Dekker, Figure 3-6 Investment cost function characterized by economies of scale. Source: Dekker, Figure 3-7 Capacity expansion pattern to meet growing demand. Source: Dekker, Figure 3-8 Hamburg - Le Havre range. Source : CRA, Figure 3-9 The Rotterdam port area. Source: Figure 3-10 Hinterland transportation modes. Source: Figure 3-11 Example for a call pattern of a container line. Source: CRA 2004, based on HbR...18 Figure 4-1 Capacity design approach. Source: Dekker, Figure 4-2 Clustering of hinterland transportation routes. Source: Dekker, Figure 4-3 Supply and demand curves. Source: Dekker, Figure 4-4 Increase of consumers surplus due to capacity expansion or demand shift. Source: Dekker, Figure 4-5 Conceptual systems diagram for the Port Competition Model...27 Figure 5-1 Total container throughput of ports in HLH range. Source: websites ports...33 Figure 5-2 Market shares of ports in HLH range...34 Figure 5-3 Capacity and Utilization rate of ports in HLH range. Source: CRA and websites ports...34 Figure 5-4 Modal split Source: websites ports...35 Figure 5-5 Port dues per container within HLH range in Source: CRA, Figure 5-6 Tidal windows ports...40 Figure 5-7 Representation of a queue delay system. Source: adapted from Groenveld, Figure 5-8 Port congestion functions...42 Figure 5-9 Schematization of the OD-matrix Figure 5-10 Schematization: from disaggregate level to aggregate level...48 Figure 5-11 Forecast demand HLH range Source: NEA...49 Figure 5-12 Regions in Germany. Source: Figure 6-1 Conceptual systems diagram for four ports...55 Figure 6-2 Determination of market shares and demand of ports per hinterland country...56 Figure 7-1 Growth rate demand for hinterland countries...64 Figure 7-2 Growth rate demand for zones in Germany...65 Figure 7-3 Capacity expansions Figure 7-4 Weighed mean of total unit costs Figure 7-5 Total volume Figure 7-6 Total market share Figure 7-7 Utilization rate Figure 7-8 Port development Figure 7-9 Increase of service cost due to congestion (f2)...70 Figure 8-1 Weighed mean of total unit costs for delay Maasvlakte II...74 Figure 8-2 Total market share for delay Maasvlakte II...74 Figure 8-3 Port development for delay Maasvlakte II...75 Figure 8-4 Weighed mean of total unit costs for phasing Maasvlakte II...76 Figure 8-5 Total market share for phasing Maasvlakte II...76 Figure 8-6 Port development for phasing Maasvlakte II...77 Figure 8-7 Weighed mean of total unit costs for additional expansions of other ports...78 Figure 8-8 Total market share for additional expansions of other ports...78 Figure 8-9 Port development for additional expansions of other ports...79 Figure 8-10 Weighed mean of total unit costs for congestion A Figure 8-11 Total market share for congestion A Figure 8-12 Port development for congestion A Figure 8-13 Weighed mean of total unit costs for pricing of Rotterdam...83 Figure 8-14 Total market share for pricing of Rotterdam...83 Figure 8-15 Port development for pricing of Rotterdam...84 vi June 2007

11 Dynamic Port Planning under Competition List of Tables Table 2-1 Operational and strategic port capacity measures. Source: Dekker, Table 2-2 Design levels and main design variables for port expansion. Source: Dekker, Table 3-1 Co-operation in maritime sector. Source: Heaver et al., Table 5-1 Port dues per container within HLH range in Source: CRA, Table 5-2 Terminal charges per container, Far East trade Source: CRA, Table 5-3 Terminal charges per container, Transatlantic trade Source: CRA, Table 5-4 Total port investment cost per TEU, Far East trade, Table 5-5 Total port investment cost per TEU, Transatlantic trade, Table 5-6 Total VOT for containers on a ship. Source: Dekker, 2005, Rand Europe, Table 5-7 Additional shipping costs per container relative to Le Havre. Source: CRA, Table 5-8 Tidal windows for selected ports (largest window available in 2001). Source: CRA, Table 5-9 Time costs of seaside access Table 5-10 Productivity, cranes, port service characteristics for a 5000 TEU vessel. Source: Dekker, Table 5-11 Costs of service time including congestion costs Table 5-12 Total port residence cost per TEU, Far East trade Table 5-13 Total port residence cost per TEU, Transatlantic trade...43 Table 5-14 Codes for transport modes. Source: NEAC...45 Table 5-15 Codes commodity groups. Source NEAC Table 5-16 VOT s for specific commodity groups. Source: Dekker, Table 5-17 Transportation costs in Source: Dekker, Table 5-18 Dwell time in the port. Source: Dekker, Table 5-19 Average transportation speed. Source: Dekker, 2005 and NEA...47 Table 5-20 Market shares of ports for all cargo...48 Table 5-21 Modal split per port based on OD-matrix...49 Table 5-22 Share of the market represented in OD-matrix Table 5-23 Balance Import-Export. Source: NEA, Table 5-24 Hinterland transportation cost at aggregate level...54 Table 6-1 OD-matrix 2002: Market shares per hinterland country...57 Table 6-2 Overview of composition of the transportation cost per country...58 Table 6-3 Model output 2002: Market shares per hinterland country...59 Table 6-4 Corrections applied on the generalized cost, with cost coefficient (1/(Euro/TEU))...60 Table 6-5 Comparison market shares OD-matrix and market shares calibrated model...60 Table 7-1 Overview capacities Table 8-1 Scenarios...73 June 2007 vii

12 Dynamic Port Planning under Competition Glossary of Economic Terms Consumers surplus: The difference between the amount consumers are willing to pay for a good or service and the amount they actually pay. Economic efficiency: Adjust project/production until no individual can be made better off without making any other individual worse off (also referred to as Pareto-efficiency). Economic development scenarios: This research presents four economic development scenarios for Europe until 2040 developed by the CPB. The scenarios are around two key uncertainties: international cooperation and institutional reforms. In the scenarios Strong Europe (SE) and Global Economy (GE), international cooperation is prominent, while the other scenarios, Regional Communities (RC) and Transatlantic Market (TM), feature limited international cooperation. Public institutions are important in Strong Europe and Regional Communities. In Global Economy and Transatlantic Market the role of the public sector is limited. There is more room for private initiatives in these scenarios Economies of scale: Characterizes a production process in which an increase in the number of units produced causes a decrease in the average cost of each unit. Efficiency: Optimum use of scarce resources. Fixed costs: Costs that do not vary with the level of activity. Generalized cost: This cost includes all monetary costs of using a route but also monetary values for other factors, such as the time required for the transport. Marginal cost: The change in total cost that arises when one more unit of production is added. Marginal private cost: The marginal cost incurred by the producer of a good. It represents the cost associated to the firm in question. The marginal private cost is used by business decision makers in their profit maximization goals, and by individuals in their purchasing and consumption choices. Marginal social cost: The marginal cost incurred by the producer of a good (marginal private cost) plus the marginal cost imposed on other members of society (external cost). It incorporates all negative and positive externalities, of both production and consumption (e.g. congestion). Opportunity cost: The cost associated with opportunities (benefits) that are foregone by choosing not to put resources to those opportunities. Opportunity cost of time: The cost associated with opportunities that are foregone during service times, waiting times, delays, etc. Partial market model: A model that concentrates on a single market or industry and ignores effects on other markets. Slot auctioning: Selling the right to use facilities at a certain time during the day (slot) to the highest bidders. The market forces determine the cost, which are simply what users are willing to pay for using a scarce resource such as capacity at a certain time. Social costs: Costs (in terms of direct out-of-pocket costs or negative external effects) imposed on society. Variable costs: Costs that vary directly with the level of activity; usually attached to a particular time period (e.g. short or long term). viii June 2007

13 Dynamic Port Planning under Competition Definitions Actual capacity: May be higher than the design capacity, since 1) the actual level of service and design level of service may differ, and 2) process improvements may have occurred before reaching the design year. The design year is some future year when available capacity meets expected demand. Carrier: Any person or entity who, in a contract of a carriage, undertakes to perform or to procure the performance of a carriage by sea, inland waterway, rail, road, or a combination of such modes. Also used as a synonym for vessel (e.g. bulk carrier) Congestion: The accumulation of transported items at a certain point in time and space resulting in queuing. A transportation system can be described as congested if the (time-averaged) flow of transported items approaches design capacity, which leads to higher service times than ideally can be performed by the system. Demurrage charges: Resulting payment to the owner of a ship for the delay of a vessel or detention of a shipment beyond a stipulated time allowed for loading or unloading. Design capacity: A single value representing the highest volume or flow a transportation system can handle at a certain minimum level of service quality. It is determined by the system s design features (sizes and numbers), service characteristics (e.g. productivity), demand conditions (e.g. demand patterns) and external factors (e.g. weather conditions). Dynamic port planning: When port planning is based on varying market shares of competitive ports it is defined as dynamic port planning. Line haul: The movement of freight over tracks of a transportation line from one city to another. Liner: A vessel sailing between specified ports on a regular basis. Modal split: Distribution of cargo over transport modes (truck, train, barge). Mooring / Unmooring: To attach or detach a ship to the shore using ropes. Pilotage: The act of assisting the master of a ship in navigation when entering or leaving a port. The fee payable for the services of a pilot is normally based on the ship s registered tonnage. Port dues: Charges levied against a ship owner or a ship operator by a port authority for the use of a port. Terminal charge: A charge made for a service performed in a carrier s terminal area. Towage: Charge for the services of tugs assisting a ship in ports. Transshipment: A distribution method whereby containers are moved between large mother ships and small feeder vessels, or between equally large ships sailing north-south (Europe-Africa) and east-west (Asia-Europe) routes. Transshipment can also mean the transfer of cargo from one vessel to another, e.g. from sea-going bulk vessels to inland barges or vice versa. Twenty Feet Equivalent Unit (TEU): Container size standard of twenty feet. Two twenty-feet containers (TEUs) equal one Forty Feet Equivalent Unit (FEU). Container vessel capacity and port throughput capacity are frequently referred to in TEUs or FEUs. June 2007 ix

14 Dynamic Port Planning under Competition Notation a parameter in investment cost function b scale factor in investment cost function c parameter in PBR-formula g growth rate of local port demand i commodity group j mode type k scale factor in PBR-formula l an maritime distance between region a and port n l r hinterland distance of route r pd n port dues at port n p t charge at an hinterland terminal t t time t n travel time for using port n t ff,n ideal service time without port congestion tc n terminal charges at port n x expansion size C an costs for maritime transportation between region a and port n C nmr costs for hinterland transportation between port n and hinterland region m via route r D(Q) demand D an number of days spent in maritime transit D nmr number of days spent in hinterland transit via route r GC generalized transportation cost per unit H n average time for container discharge in port n H nd dwell time in port n H td dwell time at an inland terminal t K n capacity of port n MC marginal cost for a port user MPC marginal private cost MSC marginal social cost OC opportunity cost of time P n probability for choosing a specific chain (port) Q n throughput or flow through port n Q * equilibrium demand S j hinterland transportation speed S s maritime transportation speed T j investment recovery period for expansion alternative j TEU Twenty Feat Equivalent Unit V i value per TEU of commodity group i VOT value-of-time α unit cost of maritime transportation β unit cost of hinterland transportation µ cost coefficient ρ daily unit cost of capital τ relief interval x June 2007

15 1 Introduction 1 Introduction The last three decades container transport has grown very rapidly worldwide. Globalization, economic growth and the rising Chinese economy have tremendously increased flows of goods between the continents and this has significantly affected the development of container transport. From 1985 to 2005 global container transport grew on average by 10% per year, while the growth of general cargo was just 3.8% annually. The growth rate of container flows from now to 2020 is still expected to be 7.5% per year. The increasing numbers of containers, the rise of global shipping alliances and the trend to post-panamax container vessels are putting intense pressure on seaports. In addition, container transportation is a particular competitive sector. It is characterized by a consolidation of container flows at a relative small number of ports. Due to the relative large mobility of containers the transportation routes are less bounded to modalities, ports and logistic chains and, as a consequence, the substitutability of ports is considerable. The increasing market power of the shipping companies (united in the global shipping alliances) puts even more pressure on the seaports. Lately port congestion has become a major issue for shippers and terminal operators, particularly in Europe and the United States. The congestion problem seems to be of a structural nature as terminal operators continue to struggle with ever-increasing container flows. Many actors in the logistic chain are facing the negative effects of congestion. Deep-sea carriers may be charged additionally for missing berthing slots in subsequent ports and are confronted with higher fuel costs to make up or readjust schedules as a consequence. Because of delayed arrival of deep-sea vessels container stevedores have difficulties to perform according to plan and finally hinterland operators are affected as well because intermodal transfers are also delayed (i.e. containers miss their feeder, truck, train or barge connection). Such delays result in huge costs for shippers and receivers and make the supply chain unreliable. As a consequence shipping companies are always searching for ports supplying free capacity. The rising Chinese economy and the resulting flows of cargo have been identified as the main reasons for current congestion problems. However, it is widely believed that a lack of investment, planning and outdated practices in terminals have significant contributed to the problem (Van der Jagt, 2005). Furthermore, it is recognized that part of the congestion problem lies with the policy of terminals to offer a long free storage of containers, as a way to attract and keep customers. In the US the average duration a container remains on a marine terminal is about 6 to 8 days (Garcia, 2006); in Europe the dwell time is about 3 to 5 days (Günther and Kim, 2006). Many ports are trying to cope with the increasing container volumes either by upgrading existing terminals or developing ambitious capacity expansion plans. However, this covers only part of the problem. For many seaports space limitations and environmental regulations are likely to restrict future expansion. Furthermore, improvement of capacity supply does nothing to reduce road freight flows and congestion around terminals, they merely add to an already serious problem (Slack, 1999). Increasing container throughput in the ports has also led to increasing transport volumes to their hinterlands. The geographical market coverage of seaports has increased substantially due to containerization and as a result the hinterlands of seaports have transformed from captive regions to contestable regions (see e.g. De Langen and Chouly, 2004; Notteboom, 1997). In other words, ports are more and more in competition to serve the same inland areas. This is especially the case for the major West European seaports (Rotterdam, Antwerp, Hamburg and Bremen) as the distance of these ports to the hinterland countries is not a very distinguishing factor. These circumstances have made hinterland transportation costs increasingly important for the competitiveness of a seaport. Concluding from the above observations, there are huge challenges for container ports and their terminals to remain competitive and to handle the anticipated growth with an increase of their productivity, a reduction of port congestion and improvement of their hinterland accessibility. These challenges support the idea to develop an investment planning tool for ports in a competitive environment (in this research the Hamburg Le Havre range) June

16 1 Introduction Port authorities are challenged with strategic investment decisions in the face of a strongly growing market and a volatile demand. Often these decisions concern huge investments, e.g. for capacity expansions. Because of these huge investments port authorities try to reduce the risks by means of port planning. Port planning enables port authorities to develop a program to reach their goals as efficient as possible and to adopt and anticipate on the demands of the container transport market. Port planning is a very complex process because the priorities of port users are subject to fast changes, stimulated by technological developments. The position of port authorities in this transport market should be strengthened by providing them with a tool to support the port planning and to improve their strategic decision making. But why has port planning to be dynamic as the title of this research project reveals? The port can be considered a node in a transport network with competition, which faces a dynamic situation concerning the timing and sizing of capacity expansions of other ports and changes in the transport network. Modeling of this dynamic system is indicated to map out the many options and interactions. However, the majority of the port planning models (e.g. Rotterdam) uses trend extrapolation and a constant market share. This means that the dynamics of competition are underexposed. To incorporate the aspects of the full dynamics of port competition over the network in a model the competitive strategies of other ports and the sensitivity of decisions on port investment for such strategies need to be simulated. The planning tool needs to be able to represent varying market shares over the years in order to reflect strategies of other ports. When port planning is based on varying market shares of competitive ports it can be defined as dynamic port planning. In this research a planning tool is developed for ports in the Hamburg Le Havre range, first on a conceptual level and than on an operating level. After calibration the application of the Port Competition Model (as the tool is called) is shown on the basis of a number of scenarios that is worked out for competition within the Hamburg Le Havre range in order to create a good view on how the model can be used in the strategic investment decision process. The setup of this report is as follows: in chapter 2 the problem setting of the present research is described and the thesis objective is defined, including a framework to clearly define the scope of the research. Chapter 3 describes the system of dynamic port planning. Attention is paid to the backgrounds of dynamic port planning and to existing approaches. Chapter 4 explains the methodology for the Port Competition Model which is developed and used in this research. It further presents the functional design of the model, a description of the components and the data requirements. Chapter 5 presents and discusses all data used in the Port Competition Model. In chapter 6 the calibration of the model is discussed. In chapter 7 a base case scenario is worked out for the modeling period and the sensitivity of the model is analyzed. The results from the base case scenario serve as a frame of reference to explain the results that are obtained with the model when it is run for different scenarios. Chapter 8 handles a case study to illustrate the use of the model in the strategic investment decision making process. Different scenarios are simulated with the Port Competition Model followed by interpretation of the results. Finally chapter 9 presents the conclusions and recommendations of this research. 2 June 2007

17 2 Study Scope 2 Study Scope 2.1 Introduction This chapter defines the study scope of the research to be carried out. First the problem is analyzed in short and the problem definitions are formulated. Based on this analysis the objective of this thesis is set. Considering the reach of the subject the scope of interest is defined clearly. For a more extensive description of the backgrounds of dynamic port planning reference is made to chapter Problem Analysis Since the container revolution in the mid-sixties and the scale increase it caused, seaports face fast changing technological developments and economic circumstances. Terminal operators and port authorities have to deal with an increasing number of uncertainties. Especially in container transport, competition plays a major role. Containers have a relative large mobility: container transportation routes are less bounded to modalities, ports and logistic chains. As a consequence the substitutability of ports is considerable. Port selection within the logistic chain is part of the selection of the logistic chain which is the primary choice. A large part of the generalized chain costs of container transport is associated with port service. This explains the major role that container terminals, especially for transshipment containers, play in the competition between ports. Despite the severe competition this market provides growth possibilities for ports, even in times of recession. The market power of shipping companies has been enhanced to the extent that they have gained control of logistic chains. The capacity of container carriers is still growing and as long as there is enough freight to transport they tend to call at less and less ports. Ports on the other hand try to maintain their international position as a container port by means of large investments. The profit margins in container transport are small and the competition is severe. The strong negotiation position of shipping companies puts pressure on port authorities to grant them concessions. The density of ports in Northwest Europe is the highest in the world. This implies severe competition. Four of the five biggest European ports are situated within the Hamburg Le Havre range: Rotterdam, Antwerp, Hamburg and Bremen. Within this range the ports compete mainly on transit containers. The main competitor of Rotterdam is the port of Antwerp. EU policy and a withdrawing government cause an increasing privatization of ports. Government subsidies are scaled back and, in the future, ports will more and more have to take care of them selves. As a consequence investments have to pay off and will be passed on to the user, influencing the attractiveness of the port, determining its market share, and in turn the viability of investments. Port investments are further characterized by large economies of scale 1, and need to be made in the face of a strong growth of the transport market (double over the next years). Congestion in ports as well as other links in logistic chains form another important factor determining the attractiveness of the particular chains. Planning for a port is thus faced with an increasing number of uncertainties, and needs to consider developments in the transport network (e.g. hinterland connections and port congestion) and investments in other ports as well. For capacity planning this means that simple fixed market share models no longer suffice. The port can be considered a node in a transport network with competition, which faces a dynamic situation concerning the timing and sizing of capacity expansions. Modeling of this dynamic system is indicated to map out the many options and interactions. Existing models address the port expansion problem and the freight movement in the network separately. The majority of the port planning models (e.g. Rotterdam) use trend extrapolation and a constant market share. Freight transportation models make an allocation of demand for freight over a network without considering the investment dynamics of the (port) nodes. The aspect of port competition over the network is thus being underexposed in this approach. 1 See paragraph and the Glossary of Economic Terms June

18 2 Study Scope 2.3 Problem Definition The problem analysis above can be summarized as follows: 1) Intensified competition between North-European seaports requires a more accurate investment planning, taking strategies of other ports into account. 2) Up to now the effect of the generalized unit cost of the logistical chain of a port on the demand for that specific port has been underexposed. The impact of several key elements on the demand for a specific port need to be investigated: The influence of timing and sizing in the planning of capacity expansions on the demand needs to be established. The impact of port congestion and hinterland congestion on the demand for a specific port needs to be quantified and incorporated in decision making. The effect of pricing on the demand for a specific port needs to be established. 2.4 Thesis Objective Based on the problems described above the following thesis objective is set: The aim of this research is to provide port authorities with a tool to support their strategic investment planning in a competitive market. The tool should provide port authorities with information on the impacts of, structural and non-structural, capacity improvement measures on the future demand for container service. Elaborating on the above objective some more detailed research questions are presented below: 1) What role does port competition play within the Hamburg Le Havre range? 2) What are existing approaches for strategic investment planning? 3) What is the importance of an improved dynamic port planning? 4) Which different capacity improvement measures exist? 5) How can a dynamic port planning model support the determination of an effective strategy to deal with competition? These research questions are answered throughout the report. 2.5 Stakeholders Interest This thesis is not carried out in relation to a particular party with specific interest. The subject is defined under supervision of Significance, Joint Venture with NEA Transport Research and Training, and Delft University of Technology. They both have interest in the research on dynamic port planning under competition. An overall approach towards the development of a tool for the strategic decision making of port authorities is sought. Many parties can have an interest in this research. 2.6 Research Framework The present investment dynamics model will be extended with the element of port competition. Considering the scope of the concept of port competition the project has to be defined clearly. In Figure 2-1 the framework of this research is shown. It represents the total set of factors involved in port planning (see Coppens et al., 2007). The shaded blocks point out the scope of interest. 4 June 2007

19 2 Study Scope Port competition Competition Intra-port competition Inter-port competition Market Bulk Containers Mixed cargo Actors Shipping companies Terminal operators Port authorities Forwarders Competition factors Demand conditions Factor conditions (Capacity) Strategy Supporting industry Government Chance Port component Maritime transport Nautical entrance Transfer ship - quay Transport quay - storage Storage Hinterland transport Weigh factors Generalized unit cost Quality & Reliability (Free) Capacity Figure 2-1 Framework research As is explained in the next chapter different levels of port competition exist. One can distinguish intraport competition and inter-port competition. The focus of this research will be on inter-port competition i.e. competition between the port authorities of different ports at international level within the Hamburg-Le Havre range. The market which is considered is the container transport market. This is the fastest growing market and the market where the most severe competition occurs. No attention will be paid to bulk and mixed cargo. In Appendix I the container transfer process is schematized. Inefficiencies in this process lead to higher service times than ideally can be performed by the port with port congestion as a result. Port authorities are charged with the strategic investment decision concerning structural and nonstructural measures in relation to capacity improvements. Often these decisions concern huge investments, e.g. for capacity expansions. The tool developed in this research serves to strengthen the position of port authorities by improving their strategic decision making process. In his Competitive Advantage of Nations Porter discerns four factors which can determine the competition power of a region or country: demand conditions, factor conditions, supporting industry and (capacity) strategy. The latter can be used to influence the other competition factors. Capacity problems in the container transfer process can be solved by (a combination of) structural measures leading to facility expansion and non-structural measures leading to an improved utilization of existing facilities. An inventory of the different alternatives for port capacity improvement is given in Appendix II. Below the differences between structural an non-structural measures are explained in short before further defining the scope of interest. Structural vs. non-structural measures Structural capacity measures aim at more or bigger. Different types of measures can be applied in the different links of the container transfer process, such as dredging works making entrance channels and basins deeper to be able to receive bigger vessels, more cranes per berth increasing berth productivity, and expansion of hinterland connections shortening travel times. An often applied structural measure is seaward expansion of the port by land reclamation. The surface area of the port is expanded and bigger ships can be handled, because channels and basins become deeper in seaward direction. Examples of such expansion projects are Maasvlakte I and the planned Maasvlakte II in the port of Rotterdam. Non-structural alternatives relate to technological, managerial, economic and regulatory measures that improve the handling capability of the port or affect the behavior of port users. The former is referred to as supply management measures and the latter as demand management measures. Congestion pricing is a good example of a non-structural measure (demand-management measure). Typically a surcharge June

20 2 Study Scope according to the level of congestion is added to the port dues that have to be paid by a shipper for the use of facilities. Operational vs. strategic measures The scope of this research is rather strategic than operational planning. Therefore port capacity measures are further divided into operational measures and strategic measures. Operational measures particularly deal with short-run demand fluctuations and strategic measures focus on long-term continuation of port operation. Table 2-1 presents an overview of operational and strategic measures based on the division of structural and non-structural measures as discussed above. Structural measures Non-structural measures Supply management Demand management Operational measures Improved berth capacity Improved yard capacity Improved gate capacity Peak pricing Demurrage charges Slot auctioning Strategic measures Dredging works Removal of obstacles Additional berths Application of locks More cranes / Container Drawer Additional road and rail connections Land reclamation Exchange of information Loading/unloading without berth interference Better terminal design Improved port-land interface Spreading of activities to other regions Reallocation of activities Privatization Private funding of investments Congestion pricing Redirection of cargo flows Table 2-1 Operational and strategic port capacity measures. Source: Dekker, 2005 The components of the logistic chain that are included in the development of the Port Competition Model are the nautical entrance of the ports, transfer from ship to quay, storage and hinterland transport. As the determination of the demand for each port will be based on the generalized unit cost of the logistical chain of a port, the sea transport is left out from the modeling as the largest part of the sea transport costs are independent of which port in the Hamburg Le Havre range is chosen. The following two types of port investment in order to minimize the generalized cost of a transportlogistic chain can be distinguished: 1) Investment in port expansion, aiming to reduce generalized cost of cargo handling in the port 2) Improvement of hinterland connections, aiming to reduce generalized cost of hinterland transport Both types of port investments are dealt with in this research but port expansions play the leading part. Finally in the design of port expansion, three levels can be distinguished: strategic, tactical and operational design. The different design levels and associated main design variables are presented in Table 2-2. As is said above this research focuses on the strategic level. Design level Strategic Tactical Operational Main design variables size, timing, phasing, location, investment recovery period layout, berth lengths, cargo-handling technology structural strength, selection construction material Table 2-2 Design levels and main design variables for port expansion. Source: Dekker, 2005 At the strategic level decisions are made on the size, timing, phasing, location (landward using existing areas or seaward by land reclamation) and the investment recovery period. At the tactical level decisions are made concerning layout, berth lengths and cargo-handling technology. Design variables at the operational level include determination of structural strength and the construction material of, for instance, a quay wall. 6 June 2007

21 3 Description of System 3 Description of System 3.1 Introduction This chapter comprises an exploration of the context for dynamic port planning. Globalization set the container revolution in the mid-sixties going and caused increasing competition between ports. The impact of containerization will be described in short and the importance of dynamic port planning will be explained on the basis of present capacity planning and port competition. With reference to this, some issues in planning of seaport capacity are described and an overview is given of the existing approaches for planning. 2 The chapter finishes with a description of the position of the port of Rotterdam within the Hamburg-Le Havre range. 3.2 Background of Dynamic Port Planning Globalization Globalization implies international mobility of goods and services, as well as persons and capital. The importance of seaports and airports as throughput, storage and processing nodes may be clear. The changed world economy, characterized by an international redistribution of labour and capital, and an integration and globalization of the markets, has generated enormous growth in international trade. As we look at the maritime goods flows, sea borne trade has more than doubled between 1970 and 2000, see Figure 3-1. In 2005, 7.1 billion tons were transported by sea. The tanker market accounts for slightly over a third of that figure, while the principal dry cargo markets (iron ore, grain, coal, bauxite/aluminium and phosphate) account for just under a quarter. Remarkably, however, the share of other dry cargo, which is shipped primarily in containers, has grown very strongly over the years (Meersman and Van de Voorde, 2006). Figure 3-1 International sea borne trade. Source: UNCTAD, Review of Maritime Transport, 2006 The growth in sea borne trade, port throughput and port capacity has formed the basis for a sharp increase in competition between ports. (see among others Huybrechts et al., 2002). Greater competition always induces strategic behaviour, including in the port and maritime sector. In the next paragraphs the grounds for this strategic behaviour will be further elaborated Impact of containerization on port competition Since the container revolution in the mid-sixties and the scale increase it caused, seaports face changing technological developments and economic circumstances. Terminal operators and port authorities have to deal with an increasing number of uncertainties. Especially in container transport competition plays a major role. 2 Paragraph 3.3 and 3.4 are largely based on Dekker, June

22 3 Description of System Containers have a relative large mobility and container transportation routes are less bounded to modalities, ports and logistic chains. As a consequence the substitutability of ports is considerable. Port selection within the logistic chain is no longer a primary choice but a secondary choice. Figure 3-2 Major transport routes. Source: Sanders, Verhaeghe, Dekker, 2006 The market power of shipping companies has been enhanced to the extent that they have gained control of logistic chains. The capacity of container carriers is still growing and as long as there is enough freight to transport they tend to call at less and less ports. Ports on the other hand try to maintain their international position as a container port by means of large investments. The profit margins in container transport are small and the competition is severe. The strong negotiation position of shipping companies puts pressure on port authorities to grant them concessions. The main focus of smaller ports is to serve their own hinterland. Bigger ports (hubs), on the contrary, are ports located on or nearby the main lines of global maritime networks playing an important role in maritime container transport. The main function of these ports for the hinterland is a transshipment function. The number of chain possibilities is relative large and with that the mobility. Competition on the transshipment market is therefore stronger than for throughput containers. To optimize the unit cost of each transported container the costs of the total transport chain have to be taken into account. A large part of the generalized chain costs of container transport is associated with port service. This explains the major role that container terminals, especially for transshipment containers, play in the competition between ports. Despite the severe competition this market provides growth possibilities for ports. The high level of homogeneity of containers has led to competition with a focus on scale and cost advantages Privatization & capacity planning EU policy and a withdrawing government cause an increasing privatization of ports. Government subsidies are scaled back and in the future ports will more and more have to take care of them selves. As a consequence investments have to pay off and will be passed on to the user. To maintain an acceptable port price under these circumstances a realistic port planning is essential. To that end it is important to consider the developments and investments of other ports as well. For capacity planning this means that simple hinterland models no longer suffice. After all a port is a part of a dynamic system. Assessing the economic feasibility of investments involves getting insight in price and demand effects of capacity expansions, taking competing ports into account. On the other hand the throughput is an important aspect to assess as well to be able to estimate possible economic effects which capacity expansions directly or indirectly bring about. Figure 3-3 shows the relative dependency of demand, capacity expansion, cost and price (Freidenfelds, 1981). 8 June 2007

23 3 Description of System Demand Capacity Cost User Cost Subsidy Figure 3-3 Relative dependency of demand, capacity expansion, cost and price. Source: Op het Veld 2003, based on Freidenfelds, 1981 Capacity expansions are accomplished to satisfy the growing demand. Cost follow from new equipment or facilities, keeping scale advantages in mind. In addition time plays a major role. Especially, when there is a continuous demand for equipment or facilities with a certain life span. The decision on capacity expansion involves size, number and timing of the added equipment or facilities. To which extend expansion investments are necessary depends on the way how capacity is used. On her turn the use of capacity determines in a strong way the quality that a port offers. Besides physical capacity and waiting time, the frequency of shipping services and the wideness of the served network play a role as well. A deficiency of capacity in a port has far-reaching consequences. Congestion stands for higher costs and a lower reliability. This will make shipping companies choose for other ports involving a decrease in shipping services and possibly also a decrease in the extent of the network. These effects make the particular port even less attractive and the port should guard against a downward spiral of higher costs, lower demand and thus lower quality. To protect themselves against this negative spiral, ports are more or less forced to provide a certain overcapacity. In general the next principle applies: the fiercer the competition, the greater the necessity for overcapacity. The only way to maintain a competing position is to assure always sharper prices. Therefore ports have to keep up with the continuously increase in scale. A certain amount of overcapacity is always necessary for flexibility and to meet with peak rates Port competition & cooperation In the past ports used to operate relatively independent of each other. Each port served its own natural hinterland which guaranteed the port more or less a monopoly position in that region. Especially trade barriers, national borders and bad infrastructure contributed to that. Many of these public ports operated inefficiently and were expensive. Shipping companies did not have many alternatives. Increase in scale and improvement of hinterland connections has led to greater cross-elasticity of port service. The demand for ports and their services is interrelated. When one port enforces price changes, this will immediately influence the demand for services of her competitors. Competing ports will, if possible, rapidly react with changes in their services and prices. In practice competition takes place between logistic chains rather than between ports. Port competition refers to competition between port enterprises. Within a port range one can distinguish three levels of port competition (Haezendonck and Notteboom, 2002). First there is competition between companies within the same port, e.g. between different terminals. This is called intra-port competition at the level of enterprises. Then there is competition between companies of different ports: inter-port competition. Finally there is competition between the port authorities of different ports at (inter)national, regional or local level: inter-port competition at the level of authorities. June

24 3 Description of System Port Port Port Port Inter Range Port Port Range Range Figure 3-4 Levels of port competition. Source: Op het Veld, 2003 Besides these three competition levels within a range, also known as intra-range competition, we can also discern competition between ranges, viz. inter-range competition, both at the level of enterprises and authorities. At international scale inter-range competition plays a role but this competition is not by far as severe as intra-range competition at the level of authorities. An example of inter-range competition is the competition between the Hamburg-Le Havre range and the Mediterranean range. Practically different parties are competitors as well as allies. It depends on the considered scale level if competition or co-operation is the issue. Two companies which are competitors at intra-port level could possibly be working together at inter-port level. Competition and integration takes place horizontally as well as vertically within a chain. It appears in different forms e.g. mergers, co-operations, capital participation or capacity sharing. Horizontal integration is frequently used to benefit from increase in scale and to be able to provide a higher quality. However extreme concentration can be a disadvantage. Vertical integration can provide terminal operators with a stronger negotiation position (Cariou and Corrail, 2001). Heaver et al. (2001) have studied the various forms of co-operation in the maritime sector. The configuration outlined still holds today, though some companies are now very actively and even aggressively seeking partnerships. The table below provides an overview of the various types of co-operation within the sector. It is restricted to shipping companies, terminal operators and port authorities. Market actors Shipping company Terminal operator Port authorities Shipping company vessel-sharing agreements joint ventures consortia alliances mergers/acquisitions conferences joint ventures mergers/acquisitions Terminal operator dedicated terminals joint ventures capital participation consortia Port authorities concessions concerning dedicated terminals ECT (period) HHLA (Hamburg) Table 3-1 Co-operation in maritime sector. Source: Heaver et al., Criteria for port selection alliances Port selection criteria can be divided into qualitative and quantitative criteria. Quantitative criteria are measurable and can be compared objectively. One can think of route factors, cost factors and service factors. Qualitative criteria consist of subjective factors like flexibility, users comfort, marketing, tradition, personal contacts and the level of co-operation between port and shipping company. The last decennium a shift of the priorities took place from price to quality. Shipping companies are by far the most influential actors for port selection. For them, a good service level and minimizing the waiting times in ports is of great importance. To restrict waiting times and 10 June 2007

25 3 Description of System transfer times they value productivity of terminals, quay length and nautical entrance. Forwarders and carriers on the contrary will be less interested in the seaside of the container handling process but all the more in storage, transfer to hinterland modalities with capacities to match, (time) cost, quality etc. In reality there will be always a trade-off between strategic, financial and practical considerations Dynamic port planning In the middle of the competitive struggle between shipping companies and terminal operators, a decrease of the market power of port authorities can be noticed. Port authorities have to make strategic decisions in the face of a strongly growing market and volatile demand. The investment decision making has to incorporate scale effects, congestion, competition and a financing and pricing which has to account for an increasing privatization of port operations. To strengthen the position of port authorities they should be provided with a planning tool to support the decision making. In general port expansions require huge investments. Because of these huge investments port authorities try to reduce the risks by means of port planning. Port planning enables port authorities to develop a program to reach their goals as efficient as possible. This port planning is a very complex process because ports are part of a dynamic system. The priorities of port users are subject to fast changes, stimulated by technological developments. Port planning helps port authorities to adopt and anticipate on the demands of the container transport market. To incorporate the aspects of the full dynamics of port competition over the network in the model the competitive strategies of other ports and the sensitivity of decisions on port investment for such strategies need to be simulated. The planning tool needs to be able to represent varying market shares over the years in order to reflect strategies of other ports. When port planning is based on varying market shares of competitive ports it can be defined as dynamic port planning. 3.3 Issues in Planning of Seaport Capacity Seaport capacity Basically a seaport is an area of land and water where ocean vessels can be loaded and unloaded, where cargo can be stored, and where hinterland transportation modes can collect and deliver cargo (see Van de Voorde and Winkelmans, 2002). In addition to that a seaport can be considered as a link in global tranport-logistic chains connecting origins and destinations for freight flows (Suykens and Van de Voorde, 1998). Here port capacity is defined as a seaport s maximum cargo handling capacity, consisting of a port s facilities and associated services. Port facilities comprise land, infrastructure, superstructure, and maritime and hinterland access infrastructure. Port services include mainly cargo handling services, which are provided with the help of port facilities. The following issues complicate the planning of a port s capacity: 1) port-commercial versus public interests, 2) competition, 3) economies of scale, and 4) capacity problems. Interactions between these issues make planning for ports even more complicated. These issues are discussed in short below Port-commercial and public interests Three port actors need to be distinguished for the planning of a port s capacity. First, there is the port owner 3 who provides port capacity. His interest can be considered from the port-commercial perspective. The interests associated with the port-commercial perspective include: maximization of profit, maximization of throughput and recovery of the investment cost. Second, there are port users who demand efficient (i.e. cheap and fast) port services. They represent the freight carriers, in particular ocean carriers, who select a specific port. The third actor is society, which desires the presence of ports because of their contribution to quality of life and economic development. Besides, society sets limits for negative effects of port usage such as environmental pollution. The interests of port users and society are considered from the public perspective. The port planner s task is to determine the optimal port capacity to deal with competition and to facilitate further growth of demand. His aim is overall viability of the port expansion project by integrating public interests and the port-commercial interest. 3 Here it is assumed that the port owner is a private owner, which is not necessarily true, because there are also public port owners. June

26 3 Description of System From the public perspective, port capacity can be determined by finding a balance between improved service quality for the port users and welfare effects on society on one hand, and the associated investment cost of capacity improvement on the other hand. In addition a realistic planning has to consider the commercial interests of the port owner Competition As explained in paragraph there are different levels of port competition. With a view on the overall objective of this research - to provide port authorities with a tool to improve their strategic decision making - this research focuses on inter-port competition at the level of authorities. At this level ports operate as nodes in global transport-logistic chains connecting origins and destinations for freight flows as conceptually shown in Figure 3-5. Figure 3-5 Ports as nodes in a transportation network. Source: Dekker, 2005 Competition between alternative routes is the main factor on which determination of the demand for a port s services is based. For transporting containers between, for instance, origins in the US and destinations in Europe many different routes are possible. However, the geographically closest port is not always the one in favor. Some routes may use a longer maritime section but a shorter land section, so the transportation cost is low, but transport may take a longer time to the destination. Other routes use less maritime transportation but more land transportation. It is assumed that a carrier selects the route that minimizes the sum of transportation and time costs. The total transportation cost and duration, using a particular route, are influenced by the service characteristics (i.e. service time, container-handling costs and port dues) of the selected port. A particular port can affect the route selection decision with different competition strategies. For example, physical expansion of port capacity leads to a higher service quality by reducing the portcongestion costs. This makes the port more attractive for carriers and at the same time it allows autonomous growth of demand Economies of scale Economies of scale can be used in different ways. For the general economic definition reference is made to the Glossary of Economic Terms. Within the context of this research one can distinguish two types of economies of scale: Economies of scale in investment cost Economies of scale in port operation 12 June 2007

27 3 Description of System Economies of scale in investment cost means that expansion of capacity increases the investment cost at a decreasing rate, which occurs due to the existence of fixed cost components. For example, costs for mobilization of construction equipment are left unchanged when an extra hectare in surface area is added to an existing port expansion plan. This phenomenon is conceptually shown in Figure 3-6. Figure 3-6 Investment cost function characterized by economies of scale. Source: Dekker, 2005 This graph can be described by the next function: C ( x ) = ax b with C : total investment cost (mln euro) a : parameter x : expansion size (hectares) b : scale factor 4 (0<b<1) Economies of scale in port operation may occur if the throughput of a port increases. For higher throughputs the investment cost is distributed over a larger number of handled items resulting in lower unit costs and (if passed-on to the users) lower port dues and terminal charges Capacity problems In determining the optimal port capacity to deal with competition the port planner faces two capacity problems: shortage in capacity and over-capacity. The port planner s task is to find a balance between both situations. Shortage in capacity indicates scarcity in the port market, which will lead to higher prices, port congestion and associated delays for port users. Ports that are cheaper and less congested than others become more attractive which may lead to a decrease in demand for the congested ports. In the short run port demand may fluctuate causing temporary shortages in capacity due to peak loads. Over-capacity indicates the presence of too much supply in the port market, which will lead to more competition between ports and lower prices making investment recovery difficult. On the other hand, ports with over-capacity are more attractive for potential users because the level of congestion is low. This may result in an increase in the demand for ports with over-capacity. With an eye on the competitiveness of a port, it is sensible to supply a certain amount of free capacity. 4 See Appendix III June

28 3 Description of System 3.4 Existing Approaches for Planning The focus of this research is to improve the strategic investment decision concerning expansion of a port s capacity. This is studied from the viewpoint of the port planner whose aim is overall viability of the port investment project. Existing approaches in infrastructure planning and transportation planning are briefly discussed below Capacity Expansion In infrastructure planning an engineering approach to deal with capacity expansion is based on minimizing the net present value of the investment cost of an expansion strategy. This comprises the expansion of capacity with regular time intervals. The growth rate of the forecasted demand together with the scale characteristics of the investment cost function as described in paragraph determine the size of the capacity expansion, x, and the length of the time intervals, τ. In Figure 3-7 the resulting capacity expansion pattern to meet linearly growing demand is shown. The basics of this approach and extension to non-linearly growing demand are described by amongst others Manne (1967) and Freidenfelds (1981). See Appendix IV for a short review. Figure 3-7 Capacity expansion pattern to meet growing demand. Source: Dekker, Transportation demand modeling Planning of port capacity requires schematization of each port as a node in a transportation network. A port can react on developments elsewhere in the network (e.g. the entering of a new route via a competing port or port expansion elsewhere) with an expansion of its capacity. The effect of the expansion on the port s competitiveness can then be analyzed by transportation demand modeling. Concerning the port expansion problem, roughly two approaches for modeling transportation demand can be distinguished in transportation planning: 1) Simulation of traffic assignment in a network. An example is the SMILE model (Strategic Model for Integrated Logistics and Evaluation) as developed by the Dutch institute TNO, which simulates the assignment of freight flows for different commodity types and transportation modes (see, e.g., Tavasszy, 2003) 2) Forecast of port demand based on macro-economic relationships with a more or less fixed market share for the particular port. An example is the GSM model (Goederen Stromen Model) as used by the Port of Rotterdam for long-term demand predictions, particularly for container flows. The first approach does not account for port investment characteristics (economies of scale). The second approach accounts for port development, but does not incorporate potential changes in a port s market share due to, for instance, competition between transportation routes. A combination of both approaches can be used to simulate the effect of competition and to incorporate autonomous demand growth (Dekker, 2005). 14 June 2007

29 3 Description of System 3.5 Competition Hamburg Le Havre Nowhere in the world is the density of ports as high as in Northwest Europe. This implies severe competition. Within Europe several ranges can be distinguished: the Atlantic, the Irish-British, Scandinavian, Baltic, Mediterranean and the Hamburg Le Havre range. All these ranges compete for a hinterland that includes the industrial heart of Europe. Four of the five biggest European ports are situated within the Hamburg Le Havre range: Rotterdam, Antwerp, Hamburg and Bremen. Within this range the ports compete mainly on transit containers. The main competitor of Rotterdam is Antwerp. This research focuses on the ports of Rotterdam, Antwerp, Hamburg and Bremen. A short description of the port of Rotterdam is included below, key information on the other ports can be found in Appendix V and Appendix IX. In addition attention is paid to the hinterland connections of the port of Rotterdam and the call pattern for the Hamburg-Le Havre range. Figure 3-8 Hamburg - Le Havre range. Source : CRA, The port of Rotterdam Rotterdam is the largest container port in Europe. In 2005 container throughput was 9.3 million TEU 5, closely followed by its direct competitors Hamburg (8.1 million TEU), Antwerp (6.5 million TEU) and Bremen (3.7 million TEU). In the period 1995 to 2005 growth of throughput in Rotterdam increased by 95%. The total container volume is expected to increase another 70% to 15.9 million TEU in 2020 (Municipality of Rotterdam and Port Authority Rotterdam, 2004). The western part of the seaport (Maasvlakte area) is directly located near the sea, which provides a good accessibility for the largest contemporary container vessels ( TEU). Even larger vessels (vessels of TEU which are envisaged for the future) can be handled. This increasing throughput volume and vessel size resulting in also larger call sizes - will increasingly put pressure on the terminal and hinterland performance. Container activities in the port of Rotterdam are spread over the port area, but in a clustered way. There are three clusters: Eem/Waalhaven, Botlek and Maasvlakte. The maximum distance between these clusters is about 40 km. Deep-sea container handling is concentrated in Eem/Waalhaven (35%) and Maasvlakte (65%). In the future the Maasvlakte will play an even more important role, because port expansion is planned at the Maasvlakte area. The container handling capacity of the port will increase from currently 10.3 million TEU to 16 million TEU in 2013, when the expansion of Maasvlakte II should be completed. 5 TEU = Twenty Feet Equivalent Unit; i.e. a standard measure for containers. See also List of Definitions. June

30 3 Description of System Figure 3-9 The Rotterdam port area. Source: The deep-sea stevedore activities are dominated by two large companies (ECT and APM terminals). In addition, some small stevedore companies are involved in container handling (Hanno, Uniport and Rotterdam Short Sea Terminals). As the Maasvlakte II expansion is completed, some other companies most likely shipping lines are also expected to perform container handling. According to the port authorities of Rotterdam the port owes its position as the main European container port to factors such as (CRA, 2004): Excellent accessibility, also for the most recent generations of container ships Nautical safety Dedicated terminal facilities, both on the landside and the waterside European transport hub function Excellent hinterland connections, especially via inland vessel, short sea/feeder and rail Possibilities for expansion and setting up new operations Fast turnaround times Attractive location for bunkering, among other things as a result of competitive tariffs Hinterland connections In hinterland container traffic road transport plays a dominant role. Its current share in the modal split of the port of Rotterdam is about 60%, while barge and rail have a share of 31% and 9% respectively. Although the share of road transport has been more or less stable during the last five years the number of containers transported by road still increased from 1.87 million units in 2001 to 2.45 million containers in 2005 (a growth of 31%) in this relative short space of time. The only hinterland route by road consists of the A15 highway, connecting the port of Rotterdam with the hinterland in eastern direction. The capacity expansion of the A15 did not keep up with the persistent growth of road transport and the highway is increasingly faced with congestion problems both inside and outside the port area. The fact that the A15 is the only available major road that provides access to the port not only endangers future accessibility, but also makes it very vulnerable (IJsselstijn et al., 2006). Until now rail transport has played a modest role in container hinterland traffic for several reasons, including a lack of rail capacity. Currently a new dedicated freight rail line, the Betuweline, connecting the port of Rotterdam with the German hinterland has been constructed and is operational as from June This offers opportunities for substantial growth of rail transport if rail operators can offer services at competitive freight tariffs and quality. Rail container traffic is predominantly international traffic at distances ranging from 150 km (to Antwerp, Belgium) to 1100 km (North Italy) and more. Barge transport has dramatically gained importance as a hinterland transport mode. The ability to offer cheap and reliable services has attracted the interest of shippers and carriers in barge transport and explains the significant growth of container barge transport since the mid eighties. In the period from 16 June 2007

31 3 Description of System 1985 to 1995 barge traffic in the hinterland of Rotterdam grew from 200,000 TEU to about 1 million TEU. In 2005 more than 2 million TEU were transported by barge. About 40% of the total volume consists of Rhine river traffic and hence has its origin or destination in Germany over a distance of 200 up to 900 km from the port of Rotterdam. About 35% is container barge traffic between the port of Rotterdam and Antwerp over a distance varying from 125 to 180 km. The remaining volume consists of national traffic with a rather dispersed pattern of flows and at distances ranging from 50 to 250 km. All these container movements are still a huge burden on the rail and road infrastructure in and around the port. Assuming a constant modal split in 2020, 7 million(!) TEU has to be transported on the A15 and 1 million TEU over the Betuweline, and taking the aimed modal split of the port authority of Rotterdam still 4 million TEU would be transported over the road and 2.3 million TEU over the Betuweline. This means that within the port authorities ambition in 2020 about 3 million TEU must be shifted from road to rail and other modes (adapted from Visser, Konings, Pielage, Wiegmans, 2007). Figure 3-10 Hinterland transportation modes. Source: In Appendix VI three maps are included with a comparison of network costs per mode for the ports within the Hamburg-Le Havre range. The maps give an overview of the relative position of the Dutch seaports (Rotterdam and Amsterdam) with regard to the ports of Hamburg, Bremen and Antwerp for transportation between the ports and the hinterland regions in For each hinterland region the results are marked by means of a color. The different colors should be interpreted as follows: Regions with a red color Concerning transportation costs the Dutch seaports are more than 50% cheaper than the best scoring foreign seaport. Regions with a pink color Concerning transportation costs the Dutch seaports are at most 25% cheaper than the best scoring foreign seaport. Regions with a grey color Concerning transportation costs the Dutch seaports are just as costly as the best scoring foreign seaport. Regions with a light blue color Concerning transportation costs the Dutch seaports are at most 25% more expensive than the best scoring foreign seaport. June

32 3 Description of System Regions with a dark blue color Concerning transportation costs the Dutch seaports are more than 50% more expensive than the best scoring foreign seaport. In short, in all red areas the network costs between ports and hinterland regions are lower for the Dutch ports than for the foreign ports, in the blue areas the network costs between ports and hinterland regions are higher for the Dutch ports than for the foreign ports Call pattern In one loop, container liners usually have three to four, and a maximum of five calls in Northern Europe. This can be concluded from the typical call patterns of deep-sea vessels which can be found in Appendix VII. The call patterns are differentiated for the Far East trade and the Transatlantic trade. Figure 3-11 shows an example for a call pattern of a container line. Only the seven main container ports are shown. On this route Rotterdam is the first port of call, Hamburg is the second and Southampton the last. The map clearly shows that for optimal geographical coverage with three to four calls in Northern Europe, liners would call at one of the two UK ports, one of the three Benelux ports, one of the two German ports and at Le Havre. Figure 3-11 Example for a call pattern of a container line. Source: CRA 2004, based on HbR A first observation from Appendix VII is that for the majority of carriers and strings German ports are not a substitute for Benelux ports. Only very few strings do not call at both a German and a Benelux port. This accounts for the Transatlantic trade as well as for the Far-East trade. A second observation is that the majority of lines for a specific trade route chooses either Antwerp or Rotterdam. However, the number of strings calling at either Antwerp or Rotterdam and Hamburg or Bremen is slightly smaller. Although the general pattern is that typically one of the Benelux ports is chosen for a call, some liners call at both Antwerp and Rotterdam in one string. One reason for having relationships with both ports is to keep up competition between the two. Another reason for multi-porting is to ensure that there is a potential substitute destination if something goes wrong in one of the ports (e.g. vessel collision or explosion of an oil tanker that blocks a port for some time). 6 6 Paragraph is largely based on CRA, June 2007

33 4 Port Competition Model 4 Port Competition Model 4.1 Introduction In 2005 a PhD study was completed concerning the development of a methodology for planning of port capacity anticipating on the increasing competition between European ports with relation to the container market (Dekker, 2005). The present research elaborates on that PhD study and will make use of the methodology for the development of the Port Competition Model; a brief summary and the main findings and limitations are summarized in paragraph 4.2. The modeling concepts of the methodology are explained in paragraphs In paragraph 4.4 the functional design of the Port Competition Model, as developed in this research, is explained. The remainder of this chapter contains a description of the model components, in paragraph 4.5, and the data requirements for the model, in paragraph Recently Developed Methodology Brief summary The overall objective of the PhD study has been to support strategic planning of a node in a (transportation) service network, which is characterized by competition. The study contributed to that objective by the development of a methodology for planning of port capacity in which modeling of the system and (pragmatic) application of economic concepts are major components. The challenge was to integrate port-commercial and public interests in such methodology, and to incorporate competition, autonomous growth of demand, economies of scale and technological development. competition public interests increase demand supply-demand interaction; congestion; optimization; efficiency overall viability structural & nonstructural measures investment commercial interests Figure 4-1 Capacity design approach. Source: Dekker, 2005 The focus of the PhD study has been the reaction of a particular port on a change in the transportation network. A scenario for such disturbance is the entry of a new route via a competing port or port expansion elsewhere in the network. This leads to decreased demands and benefits for the particular port and the nation in which the port is located. Potential reactions of the port to such change include investment in port expansion and improvement of hinterland connections. The reaction that has been worked out in the PhD study is expansion of the port s surface area, which allows also for autonomous growth of port demand due to, for instance, economic growth. The PhD study addressed in particular the following two research questions: 1) What is the optimal expansion strategy for a single port to deal with route competition and to facilitate further growth of the port s demand? 2) Can the expansion strategy be self-financing? The methodology as developed in the PhD study is based on competition analysis in a partial model. This modeling approach building on partial approaches for network design, capacity expansion, transportation modeling, investment financing and congestion-based design addresses the simultaneous solution of determining the optimal set of 1) proposed capacity, and 2) investment recovery period. This efficiency problem has been treated as an optimization problem, which was decomposed in two parts. The first main part, optimization of the port expansion size, is followed by the part that accounts for the improvement of hinterland connections. An approach has been used to trace 7 Paragraph 4.2 and 4.3 are largely based on Dekker, June

34 4 Port Competition Model the response surface (increase of consumers surplus) in order to identify the optimal set among a large number of alternative sets. Further elaboration of the methodology for planning of port capacity has been focused on 1) modeling for demand and supply, and 2) incorporating developments in container transportation technology Ports constitute nodes in transportation networks connecting origins and destinations for freight flows. Determination of port demand has therefore essentially been based on competition between transportation routes. This involved simulation of port demand with a traffic assignment model. Port supply has been schematized with a marginal cost curve that is often used for research on passenger transport. The assumption was that a curve with similar characteristics can be used to simulate port congestion. A particularly important development in container transportation technology is the trend of increasing mode sizes. This leads to transportation cost reductions due to economies of scale. This has been incorporated in the methodology for planning of port capacity by inclusion of relationships between mode sizes and transportation costs in the traffic assignment model. To demonstrate the methodology, an application with an explorative character has been carried out to the port of Rotterdam. This study focused on a hypothetical port expansion for non-domestic container flows by means of expansion of the port surface area by land reclamation. It was assumed that only the ports surface area is relevant for capacity expansion; the capacities of the hinterland connections were assumed to automatically follow port capacity Main findings The main findings of this PhD study were: Planning of port capacity can be based on an assumed match between supply of port capacity, characterized by economies of scale, and demand for port services, which is obtained in competition between alternative routes and characterized by further growth. Developments in transportation technology can also be incorporated in such planning. Physical port expansion leads to a reduction of port-congestion costs and thus to an increase in consumers surplus. This makes a port more attractive for freight flows, which can be used to recover a preceding loss of demand to some extent. The response surface for the increase of consumers surplus has been established in function of proposed capacity and recovery period. Tracing this response surface in a spreadsheet, based on maximizing the increase of consumers surplus, appears to be an appropriate approach to identify the optimum set of proposed capacity and recovery period. It can be observed from the results of the application to the port of Rotterdam that port expansion by land-reclamation can be self-financing. It should however be noted that some influential aspects, e.g. the effect of reactions by competing ports during the investment recovery period of Rotterdam, are not included in the self-financing principle as applied to Rotterdam. In the application to the port of Rotterdam, there was almost no congestion in the reference situation; this highlights the question if port expansion is then the most obvious strategy to deal with a loss of demand. A tariff strategy, for instance, would then be more obvious. This should be traded off against the potential of port expansion to facilitate future demand growth due to exogenous factors such as trade growth (which is however very uncertain) Limitations Some limitations in the focus and content of this PhD study are: A port has been considered as a point entity with an overall capacity instead of as a set of interdependent stages or links, which need to be optimally tuned to each other. Any inefficiencies in these links and their joint functioning lead to higher service times than ideally can be performed by the port. These higher service times are interpreted in this study as port congestion. However analysis of port congestion and its impact on competitiveness was not carried out. 20 June 2007

35 4 Port Competition Model The focus has been the reaction of a single port on a change in the transportation network. The full dynamics of port competition due to, for instance, reactions of competing ports during the investment recovery period of the particular port, has not been captured by the methodology. Only expansion of a port s surface area has been considered to deal with port competition. Other, less capital-intensive strategies that can improve port competitiveness (especially for relative low levels of port congestion) have not been considered. Improvement of hinterland connections has been addressed at the conceptual level, but not further elaborated in the application. All these limitations require further research to improve the completeness and applicability of the developed methodology. In the development of the Port Competition Model these limitations are taken into account and are incorporated in the model. 4.3 Modeling Concepts - Methodology In this paragraph the different modeling concepts as applied in the partial market model are described. For the development of the Port Competition Model the port demand modeling is particularly important. For the sake of completeness and good understanding of the methodology the concepts of port supply modeling and maximizing consumers surplus are also included Port demand modeling Various studies (e.g. Huybrechts et al., 2002) make clear that analysis of port demand is a difficult task. Some of the reasons are the uncertain global-economic developments, the dynamics of port competition, strategic behavior of ports, commercial decisions of shipping companies, and traditional relationships between ports and shipping companies as explained in chapter 3. The basis of port demand modeling is formed by traffic assignment by simulating route choice. First the way of assigning traffic to different ports has to be determined before simulating the local demand for a particular port. Traffic assignment The basic premise in traffic assignment is the choice of a route, which offers the least anticipated costs. This means a rational choice for the lowest transportation costs and shortest duration of transportation. These transportation costs and travel time costs associated with a particular route in a network can be expressed in generalized cost, involving a weighed sum of different cost components. The generalized cost is just one factor in the selection of a particular route. Other factors which should be incorporated are reliability of the route (e.g. chance of strikes), the risk of accidents and losses by container-handling activities, and the quality of auxiliary services in the port. Incorporating all of the quantitative and qualitative selection factors in a generalized cost expression is a difficult task and using an approximation is inevitable. The simplest assignment modeling is the all-or-nothing assignment, which assumes that the total demand is assigned to the route of the lowest generalized cost. However this way of traffic assignment is not preferable because its bad representation of reality. The qualitative selection factors are fully left out in this approximation. In this research the discrete choice model will be used to model the traffic assignment. The shipping companies choose the logistic chain and the associated port based on the utility for each chain. A main variable in this utility is the generalized transport cost for the different logistical chains. Following this approach the utility for the shipping companies to choose logistical chain (port) n can be written as: U = µ GC + ε n anm n with GC anm : generalized transport cost between region a and hinterland region m via port n µ : cost coefficient ε n : error term representing measurement errors and choice attributes not modelled Using a logit-type assignment modeling for traffic assignment incorporates uncertainties about the factors that determine route choice by shipping companies. The probability of choosing a specific chain (port) can then be expressed as: June

36 4 Port Competition Model exp( U n ) Pn = or as exp( U ) n n P = n i exp( µ GC anm ) i exp( µ GC ) n anm in which µ represents the cost coefficient. Data to estimate such model consist of revealed choices by the companies in the past or/and stated preference data collected using a survey. However data on revealed choices is very hard to get. Basically for each origin-destination pair such choice problem can be formulated. The demand through a particular port is then the sum of the flows of the logistic chains using the port. Demand simulation i Assume there are Q am containers (in TEU s) of commodity type i (i [1,I]) that are to be transported from region a (continent) to a destination m in Europe. The unit cost for maritime transportation is α euros per kilometer per TEU. There are N coastal ports to choose from; the maritime distance to the n th port (n [1,N]) is l an. The port dues and terminal charges 8 at the n th port are pd n and tc n, respectively, per TEU. The costs for maritime transportation and port usage, C an, are then: C = α l + pd + tc an an n n For hinterland transportation, various route and mode combinations are possible. The hinterland transportation cost from the n th port to destination m via route r is the sum of the costs over all modes used for that route. Assume for mode j (j [truck, train, barge]) that the unit cost is β j euros per TEU per kilometer, with transportation distance l rj. The transfer between two modes is performed at an inland terminal with a charge p t per TEU. The costs for hinterland transportation via route r, C nmr, can then be expressed with: C = p + β l nmr t j rj j 1 j The maritime transportation speed is S s kilometer per hour; the time spent on the maritime leg is then l an days. If the average time for container discharge in port n is H n days then the total number of 24 S s days spent in maritime transit is: D an l an = + H 24 S s n Hinterland transportation speed is S j kilometres per hour; the time spent per hinterland transportation l rj mode is then days. The dwell time in the port, H ndj, and the dwell time at an hinterland 24 S s terminal, H tdj, are mode-dependent. The total number of days spent in hinterland transit for route r is: l D = H + + H rj nmr ndj tdj j 24 S j j 1 Further assume the value per TEU is V i, and the daily unit cost of capital is ρ (see Appendix VIII). For commodity group i, the time cost of transportation is approximated by the opportunity cost of time, OC i. This represents the loss on capital for the receiver of the container in transit. The opportunity cost of time can be approximated with: OC i ( D ) = V ρ D app i The generalized cost for maritime transportation and port usage for commodity group i, GC, can be expressed with: i an 8 See List of Definitions 22 June 2007

37 4 Port Competition Model i i GC = C + OC ( D ) an an an The generalized cost for commodity group i using hinterland transportation route r, expressed with: GC i nmr, can be i i GC = C + OC ( D ) nmr nmr nmr To cancel out biased results due to the choice process between different routes of hinterland transportation, the hinterland transportation routes are aggregated into a single link. hinterland destination m hinterland destination m r port n port n Figure 4-2 Clustering of hinterland transportation routes. Source: Dekker, 2005 Again a logit-type assignment modeling is used to incorporate uncertainty on the route choice, because route choice is only partly explained by transportation cost and duration. The generalized hinterland transportation cost for port n can then be formulated as: GC = i µ GC i exp( µ GC ) i GC exp( ) i nmr nmr nm r nmr r The total generalized cost for transporting commodity group i by using the n th port is: GC = GC + GC i i i anm an nm i Let Q anm be the number of containers for commodity group i that moves from a to m and uses port n, and i Q anm can be calculated with the following formulation: Q = Q P and thus with i i anm am n Q i Q = i exp( µ GC ) i exp( µ GC ) i am anm anm anm n The local demand for port n, Q n, is then: n = i Q Q a m i anm As can be observed from the above equations, changes in transportation speed, costs and duration will affect the demand for port services. This model can be used to examine the effect of changes in these factors due to, for instance, technological development or congestion. The demand curve for port n can now be established from a set of traffic assignment simulations with varying port tariffs. June

38 4.3.2 Port supply modeling 4 Port Competition Model The supply of capacity for port n can be schematized by the marginal social cost, MSC n. The marginal social cost is a function of the port s throughput, Q n, and capacity, K n. The MSC n is derived from the expression for the marginal private cost, MPC n. The establishment of the supply curve is described below. For an explanation of the difference between the marginal social cost and marginal private cost reference is made to the Glossary of Economic Terms. The marginal private cost, MPC n, is determined by the sum of port dues, terminal charges, and the product of the VOT (value-of-time: the monetary cost of one unit of travel time, see Appendix VIII) and the travel time t n for using port n. The travel time t n depends on the port throughput/capacity ratio (i.e. the utilization ratio). The following relationship is adopted to incorporate congestion in the port: k = Q t +, 1 n n tff n c K n The free-flow-travel-time is expressed by the factor t ff,n, representing the ideal service time without port congestion. This factor is set equal to the vessel discharge time. The vessel discharge time depends on the vessel size; the larger the vessel, the more cargo it transports, thus the longer it takes to discharge the vessel. For particular values of parameters c and k (0.15 and 4 respectively) the Bureau of Public Roads (BPR) formula is represented. This formula is often use for research on passenger transport. It is assumed here that a curve with similar characteristics can be used to simulate port congestion. The expression for the marginal private cost, MPC n, is then: k = + + Q MPC +, 1 n n pdn tcn VOT tff n c K n The last part of the equation (ratio of throughput and capacity) represents the private congestion cost. Observe that MPC n increases unlimited for increasing flows, suggesting that the throughput (Q n ) can become higher than capacity (K n ) and that travel time (t n ) can grow endless (i.e. capacity does not restrict throughput increase). To restrict throughput increase a maximum allowed utilization rate is set in the present modeling approach (see also paragraph 6.2). The marginal social cost, MSC n, can be expressed as: δq MPC δmpc MSC n = = MPC n + Qn δq δq n n n n k Q MSC = + n n MPC n VOT tff, n c k K n The curves for demand and supply are shown in Figure 4-3. n 24 June 2007

39 4 Port Competition Model Figure 4-3 Supply and demand curves. Source: Dekker, Supply-demand interaction Now the supply and demand curve are established, the effect of changes in these curves can be investigated. The picture below shows the effects for a capacity expansion and a shift in demand (autonomous growth of demand). Figure 4-4 Increase of consumers surplus due to capacity expansion or demand shift. Source: Dekker, 2005 Before explaining this graph it is important to mention that one of the main assumptions that Dekker made in his PhD study is that terminal operators do not exhibit market behavior. Physical port expansion leads to a reduction of port-congestion costs (less steep MSC curve) and thus to an increase in consumers surplus (shaded area). This makes a port more attractive for freight flows, June

40 4 Port Competition Model which can be used to recover a preceding loss of demand to some extent. It is important that this recovered demand is not being cancelled out due to increasing port tariffs for investment recovery. Increased local demand due to expansion may however also affect port dues and terminal charges via economies of scale in port operation, because due to increased demand the investment cost can be distributed over larger number of handled items resulting in lower port tariffs for higher throughputs. A shift of the demand curve to the right due to autonomous growth of demand involves a decrease in consumers surplus (not indicated in figure), because higher throughputs bring along higher congestion cost. The response surface for the increase of consumers surplus has been established in function of proposed capacity and recovery period. Tracing this response surface in a spreadsheet, based on maximizing the increase of consumers surplus, appears to be an appropriate approach to identify the optimum set of proposed capacity and recovery period. 4.4 Port Competition Model The emphasis of the PhD study as described above was on developing a methodology to address trade offs in a port s investment planning rather than on the choice of the most effective strategy to deal with competition. Tracing the maximum consumers surplus appeared to be an appropriate approach to identify the optimum set of proposed capacity and investment recovery period. The full dynamics of port competition due to, for instance, reactions of competing ports during the investment recovery period of the particular port, were left out in this methodology. The objective of this research is to provide port authorities with a tool to support their strategic investment planning in a competitive market. The tool should provide port authorities with information on the impacts of, structural and non-structural, capacity improvement measures on the future demand for container service. Therefore, the focus in the development of this tool is to support the determination of an effective strategy to deal with competition; the dynamics of port competition takes a central place in the development of the Port Competition Model. A recent analysis (Sanders, Verhaeghe, Dekker, 2006) proposes a modeling approach which integrates the development of the port node with the competition over the network. The analysis focuses on the interactive investments of two ports. The modeling approach as proposed in this paper is used in this research as a starting point for the development of the Port Competition Model. The setup of the Port Competition Model is such that the interactive investments of four ports, viz. Rotterdam, Antwerp, Hamburg and Bremen, can be analyzed. In this approach the modeling concepts from Dekker s methodology as described in paragraph 4.3 are integrated. Figure 4-5 shows the conceptual systems diagram for the modeling of port competition: The diagram is divided in four parts representing the ports of Rotterdam, Antwerp, Hamburg and Bremen. The ports are marked with a color. These colors are used throughout the remainder of this report to indicate the ports. In each part the three cost components that compose the generalized unit cost can be determined. These three cost components take a central place in the development of the Port Competition Model. 26 June 2007

41 4 Port Competition Model Figure 4-5 Conceptual systems diagram for the Port Competition Model June

42 4 Port Competition Model 4.5 Description of Model Components In the next subparagraphs the model components from the conceptual systems diagram for the modeling of port competition are described Market share In this research the assumption is made that the market share of a port is mainly determined by the generalized unit cost of the logistical chain where it makes part of and the generalized unit cost of logistical chains with other port nodes, serving the same hinterland. The total demand is assigned to the different ports (logistical chains) using the discrete choice model: Q i Q = i exp( µ GC ) i exp( µ GC ) i am anm anm anm n with i Q anm : number of containers for commodity group i that moves from a to m and uses port n GC i anm : total generalized cost for transporting commodity group i by using the n th port µ : cost coefficient Total unit cost The discrete choice model is based on the total generalized cost per TEU to assign the total demand to the different ports. This unit cost is composed of: a cost for recovery of port investments a cost associated with the time spent in the port, including congestion a cost associated with hinterland transport, including congestion Port investment cost Maritime transportation is beyond the scope of this research and the costs for maritime transportation are therefore left out of consideration. The costs for port usage, C n, are: C n = pd n + tc n It is assumed here that port expansion (by land-reclamation) can be self-financing and that the port investment cost can be fully recovered by port dues and terminal charges. For the sake of simplicity, port dues pd n and terminal charges tc n are assumed to be independent from vessel size and dwell time Port residence cost & congestion cost The total number of days spent in the port: D = H = t n nd ff, n The average time for container discharge H n is here assumed to be independent from the throughput and capacity, suggesting there is no port congestion. The time cost of port usage is approximated by the opportunity cost of time, OC i : i OC ( Dn ) = Vi ρ Dn with Vi ρ = VOT (see Appendix VIII) i OC ( D ) = VOT D n Including private congestion cost: n 28 June 2007

43 4 Port Competition Model k = Q t +, 1 n n tff n c K n k i Q ( ), 1 n OC Dn = VOT tff n + c K n Including external cost: k k i Q ( ), 1 n Qn n = ff n + + ff, n K n K n OC D VOT t c VOT t c k Hinterland transportation cost & congestion cost The costs for hinterland transportation via route r, C nmr, can be expressed with: C = p + β l nmr t j rj j 1 j The total number of days spent in hinterland transit for route r is: l D = H + + H = t rj nmr ndj tdj ff, nmr j 24 S j j 1 The dwell time in a port H nd and the dwell time at an inland terminal H tdj are here assumed to be independent from the throughput and capacity, suggesting there is no port congestion. The time cost of hinterland transportation is approximated by the opportunity cost of time, OC i : i OC ( Dnmr ) = Vi ρ Dnmr with Vi ρ = VOT (see Appendix VIII) i OC ( D ) = VOT D nmr nmr Including private congestion cost: k Q, 1 nmr nmr = ff nmr + K nmr t t c k i Q ( ), 1 nmr OC Dnmr = VOT tff nmr + c K nmr Including external cost: k k i Q ( ), 1 nmr Qnmr nmr = ff nmr + + ff, nmr K nmr K nmr OC D VOT t c VOT t c k An envisaged further detailing of the model includes a specific modeling of the transportation network, including different transport modes using a joint modeling with a specific freight transportation model Utilization rate An important variable is the utilization rate, defined as the ratio of actual flow through the port over capacity. The utilization rate forms the main input to determine port congestion. A new capacity expansion step is triggered when the utilization rate reaches a particular maximum threshold value. A certain amount of reserve is however necessary for peak load handling. A maximum utilization rate of June

44 4 Port Competition Model about 90 % at a port component is a frequently used value 9. The utilization rate is a control variable: it may be decided to lower congestion levels in order to attract a larger market share Capacity expansion strategy The capacity expansion strategy forms a main input to the modeling, one of the possibilities is to use the expanded Manne method, to determine the optimal expansion size and optimal recovery period, taking into account a progressive scale effect in combination with price-demand interaction. For a detailed description of this method see Appendix IV. As a starting point the capacity expansion strategy consists of fixed capacity expansions Upgrade hinterland infrastructure The hinterland connection is mainly represented by the distance form the port to the main hinterland centre and the cost for transport. A congested hinterland connection will have a strong effect on the competitiveness of the logistical chain. Therefore a gradual expansion of this hinterland capacity is incorporated in the simulation. It is assumed that the development of the hinterland infrastructure keeps up with the increase of cargo throughput to the hinterland countries, implying there is no additional hinterland congestion, in other words the situation stays as it is. 4.6 Effective Strategy As stated above the focus in the development of the Port Competition Model is to support the determination of an effective strategy to deal with competition. To determine what an effective strategy could be for a port to deal with competition the results of different scenarios, carried out with the Port competition Model, have to be compared allowing to make a more informed decision. In chapter 8 an analysis of a range of possible scenarios and their impacts is performed using the Port Competition Model. In this paragraph it is explained in short how to approach this search for an effective strategy. As presented in paragraph in the model the market share of a port is mainly determined by the generalized unit cost of the logistical chain where she makes part of and the generalized unit cost of logistical chains with other port nodes, serving the same hinterland. The total demand is assigned to the different ports (logistical chains) using the discrete choice model. A very logical question to pose is then what effects do changes in the generalized unit cost of the logistical chain of a specific port have on the local demand for this port? This effect has to be studied keeping in mind that a reduction of (port-congestion) costs results in an increase in consumers surplus, making a port more attractive for freight flows, which can be used to recover a preceding loss of demand to some extent. The investigation of the following two effects take a central place in the determination of an effective strategy to deal with competition: 1) The effect of a general change in the generalized unit cost of the logistical chain of a specific port on the local demand for this port. 2) The effect of a capacity strategy or a change in hinterland transportation and port congestion costs on the generalized unit cost of a logistical chain of a specific port and thus on the local demand for this port. Port capacity problems can be solved by (a combination of) structural measures leading to facility expansion, and non-structural measures leading to a more efficient utilization of existing facilities. What the capacity strategy mentioned in the second part holds, depends on the congestion level in the port. When the level of congestion is high (i.e. a utilization rate of about 90%) the capacity strategy refers to a capacity expansion. This can be an expansion of the port by land reclamation or it can be an improvement of the hinterland connections. When the level of congestion is low (say about 50%) the capacity strategy refers to (congestion) pricing. 9 The maximum allowed utilization rate of 90% at a port component, based on figures from the ECT terminal in Rotterdam, is rather high. Particularly berth occupancy rates are generally considered to be bottlenecks in port capacity. If competition between ports exists, the berth occupancy rate usually does not exceed 50-60% (Fourgeaud, 2000). This is an optimum utilization rate until the cost function increases proportionally. 30 June 2007

45 4 Port Competition Model Comparing the results of the different capacity strategies and determining what strategy leads to the largest (growth in) market share, results in the determination of the strategy to be followed by the port authorities. 4.7 Data Requirements for the Model This paragraph describes the data required for the model. All required data concerns container transport. In this research the model serves to investigate the competition between the ports in the Hamburg Le Havre range. In further research the model can be expanded to more ports in other ranges (e.g. the Italian ports). Growing overall demand Actual (2006) total demand Hamburg - Le Havre range. Data on actual (2006) throughput, capacity and utilisation rate of different ports: Rotterdam, Antwerp, Hamburg and Bremen. Forecast throughput volume Hamburg - Le Havre range for the coming 20 to 30 years (up to 2030/2040). Demand specific port Port dues and terminal charges (and composition of these amounts) for Rotterdam, Antwerp, Hamburg and Bremen. Service and dwell times for Rotterdam, Antwerp, Hamburg and Bremen. Specific modeling of the transportation network for different transport modes, with data on transportation cost, distance and duration including dwell times at inland terminals. Data on route choice (i.e. performance total chain for transport via specific port: value of time, congestion, reliability, out-of-pocket costs, scope possibilities). Data on shippers choice for a specific port (i.e. qualitative and quantitative e.g. generalized transport cost, reliability). Data on route choice and shippers choice is very difficult to acquire. Because the availability is poor the assignment of traffic will be based purely on the generalized unit cost of the logistical chain and allocation of freight flows will be determined using the discrete choice model. Upgrading hinterland infrastructure Forecast growth hinterland infrastructure Hamburg - Le Havre range for the coming 20 to 30 years (up to 2030/2040): improvements or construction of major transport lines to the contestable hinterland of the Hamburg - Le Havre range (e.g. Betuweline). June

46

47 5 Data for the Model 5 Data for the Model 5.1 Introduction In this chapter all data that is necessary to build the model is described. First some general information about the ports in the Hamburg - Le Havre range (HLH range) is given. In paragraph 5.3 the concept of generalized cost is explained. Then, in paragraph 5.4 and 5.5, the cargo flow is analyzed and the port investment cost, the port residence cost and the hinterland transportation cost are constructed for the Port Competition Model. The chapter ends with an overview of the capacity expansion plans of the ports in the HLH range in paragraph 5.6 and a short note on the different trends within the container market in paragraph 5.7. Some cost data differs for the Far East trade and the Transatlantic trade. Data for both trades is included in this chapter. Which data has to be used in the model depends on which economic development scenario is applied. For the sake of uniformity the Dutch government agreed on distinguishing four long-term economic development scenarios viz. Global Economy, Transatlantic Market, Strong Europe and Regional Communities set up by the CPB. 10 It is beyond the scope of this study to dwell on the implications of these scenarios. Here it suffices to say that the Global Economy scenario is characterized by a very active Far East trade and for the Transatlantic Market scenario there is a strong Europe-US (or Transatlantic) trade. The cost data is not always given in the same units. It can differ between euros per ton, euros per TEU and euros per container. To get workable data for the model all data is expressed in euros per TEU. It is assumed that 1 TEU equals 10 metric tons. This assumption is founded in paragraph To convert the expression Euro/container in Euro/TEU the TEU-factor is used. The TEU-factor gives the ratio of 20 ft containers (TEUs) and 40 ft containers (FEUs). The TEU-factor can differ from port to port. A TEUfactor of 1.5 indicates an equal number of TEUs and FEUs. Modern ports (like the ports in the HLH range) handle more FEUs than TEUs and therefore the TEU-factor is set at General Information about Ports in North Europe Figure 5-1 shows the total container throughput in the Hamburg-Le Havre range between 1999 and Figure 5-2 shows the corresponding market shares. The tables building these figures can be found in Appendix IX. 12 Total throughput Throughput (mln TEU) Rotterdam A ntwerpen Hamburg Bremen Figure 5-1 Total container throughput of ports in HLH range. Source: websites ports 10 See the Glossary for Economic Terms for a short description of the different economic development scenarios. June

48 5 Data for the Model From Figure 5-1 it can be concluded that all ports have benefited from the strong growth of the container transport market in the past few years. However, from Figure 5-2 it is clear that the relative positions of the ports have changed somewhat. The market shares of Antwerp and Bremen did not change significantly. Rotterdam on the contrary has lost market share on behalf of Hamburg. 45% Market share Market share (%) 40% 35% 30% 25% 20% 15% 10% 5% 0% Rotterdam A ntwerpen Hamburg Bremen Figure 5-2 Market shares of ports in HLH range Figure 5-3 shows the throughput capacity of the ports in the HLH range in 2003 and in With the throughput data from Figure 5-1 the utilization rates in these years are calculated and shown in Figure 5-3 as well. 11 In 2003 all ports were dealing with a shortage of capacity. Especially the port of Antwerp was facing tight capacity constraints. In the period between 2003 and 2006 several capacity expansion projects were carried out by the ports, but only the ports of Antwerp and Bremen increased their capacity enough to keep up with the strong demand growth and their utilization rates decreased to a better operational level. The ports of Rotterdam and Hamburg did also increase their capacity, but obviously the demand was growing at a higher rate than the ports increased their capacity. 20,0 Capacity 120 Utilization rate Capacity (mln TEU) 15,0 10,0 5,0 0, Rotterdam Antwerp Hamburg Bremen Utilization rate (%) Rotterdam Antwerp Hamburg Bremen Figure 5-3 Capacity and Utilization rate of ports in HLH range. Source: CRA and websites ports In Figure 5-4 the modal split of the four ports is shown. When the transshipment percentages are compared it can be seen that the ports of Rotterdam and Hamburg have a stronger hub-function than the ports of Antwerp and Bremen. Comparing the percentages of the modes of hinterland transport 11 The utilization rates are calculated by dividing the throughput volumes by the design capacities of the ports. A difference between design capacity and actual capacity can explain the high values of the utilization rates, see also the List of Definitions. 34 June 2007

49 5 Data for the Model there are two important observations. First, the German ports have almost no barge transport. This can easily be explained by the fact that they do not have an inland waterway network as strong as that of the Benelux ports (i.e. the River Rhine). The second observation is that to and from Hamburg more cargo is transported by truck than by train whereas for Bremen the transport by truck and train have an equal share. 23% 27% 27% 18% 7% 6% Rotterdam 43% Transshipment Truck Train Barge Antwerp 49% Transshipment Truck Train Barge 22% 2% 26% 2% 20% 40% Hamburg 50% Transshipment Truck Train Barge Bremen 38% Transshipment Truck Train Barge Figure 5-4 Modal split Source: websites ports 5.3 Generalized Cost As explained in paragraph the shipping companies choose the logistic chain and the associated port based on the utility for each chain. A main variable in this utility is the generalized transport cost for the different logistical chains. This cost includes all monetary costs of using a route but also monetary values for other factors, such as the time required for the transport. The generalized cost for a transport chain consists of the following items: Transport costs Sea transport costs Call costs o o o o o o o o Port dues Buoy dues Quay dues Towage Pilotage Mooring, unmooring Vessel Traffic Service (VTS) Other dues (e.g. waste disposal dues) June

50 5 Data for the Model Container handling costs o Sea move, land move Hinterland transportation costs o Road, rail, inland waterways Non-monetary factors that affect the generalized transport cost Transport time Availability of connections, frequency regarding the hinterland Quality and speed of container handling Reference is made to the list with definitions for an explanation of some of the items. In the next paragraphs the cargo flows through different logistic chains are analyzed and the generalized transport cost (consisting of quantitative and qualitative factors) is determined as detailed as the available data allows. Sea transport is beyond the scope of this study as the largest part of the sea transport costs are independent of which port in Northern Europe is chosen. Therefore sea transport costs will not be quantified. 5.4 Cargo Flow Analysis: Port Related Costs This paragraph describes the port related costs of the cargo flows through the different ports of the Hamburg-Le Havre range. In the previous paragraph port related costs are split up in call costs and container handling costs. However, in paragraph 4.5 where the components of the Port Competition Model are described, the port related part of the total unit cost (i.e. generalized cost) is divided in the port investment cost and the port residence cost. In order to obtain workable input for the Port Competition Model this division is used in this paragraph as well. The items of the call costs and container handling costs as described in the previous paragraph are both allocated to the port investment cost and the associated time costs (i.e. the non-monetary factors) are allocated to the port residence cost Port investment cost The port investment cost consists of port dues and terminal charges. It is assumed here that terminal charges consist of all other call costs except port dues and of container handling costs. This is a quite rough assumption because the items of the call costs are paid to different parties and not all to the terminal owner. However, it would lead too far to investigate the allocation structure of port tariffs. Besides, this assumption does not change the total charge to be paid per TEU and therefore does not influence the choice of a shipping company for a logistic chain and the associated port. Port dues It is generally known that for containers port dues account only for a small part of the total transport costs. This results from the fact that port dues are just a small portion of the total call costs. If, for example, the percentage of a shipper s cost for port services through a specific port is 10% of the total shipping costs, an increase of port dues of 10% by the port authorities of this port will result in a change in total shipping costs of about 1%. Given that the pricing of the port authority affects only a small share of total cargo shipment costs that a shipper must pay, shippers may be not likely to switch to another port in response to a price increase. If, for a given port, other costs make up for most of the call costs this implies fairly high pricing power for the port authority, due to the limited pass-on of its price increase that generates substitution effects (CRA, 2004). In the short run port authorities do not have the means to influence the quality of the basic service of providing port infrastructure such as quay walls, jetties and roads. 12 Thus, a higher price does not reflect a better service provided by the port authority. This means that higher pricing of a specific port would then be an indication of pricing power relative to rival ports. In Figure 5-5 the port dues per container of the ports in the HLH range are presented both for the Far East trade as the Transatlantic trade. Because the unity in this graph is Euro/container, these numbers have to be converted to Euro/TEU by using the TEU-factor (i.e. dividing by 1.6). At the end of this paragraph a summarizing table is included with all costs in Euro/TEU. 12 However, port authorities do maintain the existing infrastructure, for example by dredging. 36 June 2007

51 5 Data for the Model 16 Port dues Euro Port dues per container (Far-East trade) Port dues per container (Transatlantic trade) Rotterdam A ntwerp Hamburg Bremen Figure 5-5 Port dues per container within HLH range in Source: CRA, 2004 The graph clearly reveals that port dues in Rotterdam are significantly higher than in the other three ports. Table 5-1 also shows the port dues but now the price differences of the other ports compared to Rotterdam are included. Port dues (Euro) Far East Trade Difference to Rotterdam (%) Port dues (Euro) Transatlantic Trade Difference to Rotterdam (%) Rotterdam 14 0% 14 0% Antwerp 7-50% 9-38% Hamburg 7-50% 7-51% Bremen 5-65% 6-53% Table 5-1 Port dues per container within HLH range in Source: CRA, 2004 It can be noticed that Antwerp charges higher port dues per container on a typical vessel on the Transatlantic trade than on the Far East trade. Antwerp is much stronger on the Transatlantic trade than on the Far East trade. Nevertheless, the over-all price level at the port of Antwerp is considerably lower than in the port of Rotterdam. If for a given origin-destination pair the different cost elements in the transport chain for shipping cargo through alternative ports are known, the maximum difference of port dues that is possible before the shipper would be indifferent in the selection of ports (assuming constant quality between ports) can be determined. If, for example, for a given type of cargo a large quantity of cargo flows are captive in the sense that the flows would not be moved to another port if port dues were increased by 5%, this would suggest that a 5% price increase would be profitable. If it appears that it would not be possible for a port to profitably raise prices, this would suggest that it should be analyzed whether such a price increase would be profitable if competing ports would raise prices jointly. (It is assumed here that existing prices are fairly competitive). In chapter 8, where different scenarios are investigated using the Port Competition Model, also a scenario is discussed that concerns pricing. Terminal charges As assumed above terminal charges consist of all other call costs but port dues and container handling costs. For containers, the share of other call costs in terminal charges is relatively low. The main part of these charges are container handling costs. The costs of container handling can be up to 10 times higher than all other call costs together. In the tables below the terminal charges are presented. June

52 5 Data for the Model Rotterdam Antwerp Hamburg Bremen Other call costs per container Costs of container land move Total Table 5-2 Terminal charges per container, Far East trade Source: CRA, 2004 Rotterdam Antwerp Hamburg Bremen Other call costs per container Costs of container land move Total Table 5-3 Terminal charges per container, Transatlantic trade Source: CRA, 2004 The most important observation is that container handling costs are much higher for Hamburg and Bremen than for Rotterdam and Antwerp. This can be explained by differences in quay productivity, see paragraph Total port investment cost The total port investment cost for the Far-East trade and the Transatlantic trade are summarized in Table 5-4 and Table 5-5. In these tables the costs are converted to euro/teu. Rotterdam Antwerp Hamburg Bremen Port dues per container Terminal charges per container Total Table 5-4 Total port investment cost per TEU, Far East trade, Rotterdam Antwerp Hamburg Bremen Port dues per container Terminal charges per container Total Table 5-5 Total port investment cost per TEU, Transatlantic trade, Port residence cost The port residence cost consists of different components. Most of these components are time costs, e.g. costs for the duration of transportation or container handling. To assign a monetary value to a time unit, the time unit has to be multiplied by the VOT, which expresses the willingness to pay of a port user for a unit reduction of transportation time. As explained in Appendix VIII the VOT can adopt many different values. In this study, the VOT is approximated by the daily loss on capital for the receiver of the container in transit. 13 If the time cost is calculated for containers on a ship the VOT has to be increased with an additional VOT for the usage of the ship. It is self-evident that the time cost of a container using storage space is lower than the time cost of a container on a ship. In Table 5-6 the total VOT for containers on a ship is calculated, by multiplying the values per TEU with the daily unit cost of capital ρ, which is set at /day (based on 15% interest per year). In the first column the values per TEU are given for different types of commodities. The numbers between brackets refer to the commodity groups. For the classification and values of the commodities reference is made to paragraph Next to the values per TEU the shares of the commodities 13 Note: The daily loss on capital varies among different goods, e.g. laptops have a higher daily loss of capital than clothes. 38 June 2007

53 5 Data for the Model transported through a specific port are given. The VOT of a commodity is the weighed mean of the values per TEU. For the shares of the commodities transported through a specific port reference is made to paragraph The additional VOT for the usage of the ship is adopted from Rand Europe. Value (euro/teu) Rotterdam Antwerp Hamburg Bremen (1) (6) (9) (0,2,4,5,7,8,10) VOT Commodity (euro/teu/day) VOT Ship (euro/teu/day) Total VOT (euro/teu/day) Table 5-6 Total VOT for containers on a ship. Source: Dekker, 2005, Rand Europe, 2002 Additional shipping costs relative to Le Havre For both the Transatlantic trade and the Far East trade, Le Havre is the closest port to call at coming from the sea. Although the sea transport costs are not considered in this study, the additional transport costs relative to Le Havre are included in the computations of the port residence costs because they can influence shipper s choice for a port within the HLH range. Table 5-7 presents these costs. The time cost of the additional transportation duration is included in the values. Rotterdam Antwerp Hamburg Bremen Additional costs of sea transport per container (compared to Le Havre) Far East trade Additional costs of sea transport per container (compared to Le Havre) Transatlantic trade Table 5-7 Additional shipping costs per container relative to Le Havre. Source: CRA, 2004 Seaside access The seaside access is not equal for the ports in the HLH range. Table 5-8 shows the tidal windows for the selected ports, referring to the terminal with the best accessibility in the port. For information on vessel sizes see Appendix X. Tidal window (%) at draught of 14 m Tidal window (%) at draught of 15 m Rotterdam Antwerp Hamburg Bremen Table 5-8 Tidal windows for selected ports (largest window available in 2001). Source: CRA, 2004 Table 5-8 clearly reveals the substantial disadvantage of Antwerp with respect to nautical accessibility. Not only is the tidal window considerably smaller than one third of that of Rotterdam, the additional planning constraint over time, with a semi-diurnal tide, gives the port of Antwerp a considerable disadvantage in terms of attractiveness for shippers. The river Scheldt, which links Antwerp to the sea, has been further deepened since 2001 (viz. maximum draught 15.6 meters sailing upriver and 14 meters sailing downriver) implying that the tidal window has increased. However, Antwerp is receiving June

54 5 Data for the Model container ships with over 8000 TEUs capacity at a regular basis and tidal restrictions and the associated planning problems are significant for ships of this size. Therefore the right column of Table 5-8 is used to determine the time cost incurred by the tidal window. In this way an upper-limit is calculated for the time cost. Figure 5-6 Tidal windows ports Rotterdam A ntwerpen Hamburg Bremen In Figure 5-6 the tidal windows of the four ports are shown graphically. All ports have a semi-diurnal tide which is represented by the sinus-shaped line. The dotted lines represent the tidal windows of the specific ports. A ship can only sail through the nautical entrance when the sinus-shaped line is above the dotted line. A higher dotted line means a smaller tidal window and thus higher time costs. The situation of a ship entering a port is schematically drawn in Figure 5-7. To calculate the mean waiting time of an arriving ship before it can enter the nautical entrance a uniform distribution of one hour is assumed for the inter arrival times of the ships. In other words, the arrival time between two ships is one hour. For the ports of Hamburg and Bremen the mean waiting time before entrance (i.e. until the tide is above the dotted line) can be calculated by adding the waiting times of all ships arriving within 12 hours (i.e. 12 ships) and then divide this sum by the number of ships. For the port of Antwerp the sailing time through the river Scheldt has to be considered in determining the waiting time of each arriving ship. The tide may not drop below the dotted line before a ship has left the river Scheldt, meaning that a ship can only enter the river Scheldt if there are at least five hours left to reach the port. Again the mean waiting time before entrance then be calculated by adding the waiting times of all ships arriving within 12 hours (i.e. 12 ships) and then divide this sum by the number of ships. Berths Genera tor Nautical e ntrance Anchorage Queue Figure 5-7 Representation of a queue delay system. Source: adapted from Groenveld, 2002 Multiplying the mean waiting time with the total VOT for containers on a ship this gives the time costs caused by the tidal windows. These are shown in Table 5-9 for the four ports. 40 June 2007

55 5 Data for the Model Rotterdam Antwerp Hamburg Bremen Mean waiting time (hour) Time cost tidal window (Euro/TEU) Table 5-9 Time costs of seaside access. River Scheldt The river Scheldt is about 80 km long. Sailing the river Scheldt with an ocean vessel takes roughly 5 hours. The cost associated with this additional transport (relative to the other ports) consists of a monetary transportation cost and a time cost for transportation duration. Transportation cost: Euro/(TEU*km) * 80 km = 7.4 Euro/TEU Time cost: 5/24 day * 43.2 Euro/TEU/day = 9 Euro/TEU Total cost for sailing the river Scheldt = = 16.4 Euro/TEU For the value of transport per TEU* km reference is made to paragraph Container discharge time For the duration of container handling a time cost has to be included in the port residence cost. It depends on the speed of unloading how big this time cost is. In Table 5-10 some port characteristics are shown for the port within the HLH range. The mean discharge time is calculated taking the TEUfactor of 1.6 in account. To calculate the time cost of container handling the mean discharge times have to be multiplied by the total VOT of containers on a ship. Rotterdam Antwerp Hamburg Bremen Stack productivity (TEU/ha/yr) Quay productivity (TEU/m/yr) Crane productivity (moves/hour) Cranes per vessel (Un)loading vessel (moves/hour) Mean discharge time (hours) Time cost container handling (euro/teu) Table 5-10 Productivity, cranes, port service characteristics for a 5000 TEU vessel. Source: Dekker, 2005 The table reveals that the time cost of container handling is much higher for the German ports than for the Benelux ports. This can be explained by the low crane productivity and the lower number of cranes per vessel. Congestion As can be seen from Figure 5-7 the service time (i.e. discharge time) of a ship depends on the queue discipline. The queue discipline sets the rules of the order in which ships are going to be served, e.g. first in first out or first come first served (Groenveld, 2002). Determining the queuing system of each port and calculating the associated waiting times and time costs is beyond the scope of this study. Therefore it is assumed here that waiting times at an anchorage are included in the mean discharge times as calculated above. However, it can be easily understood that if the level of congestion within a port increases these waiting times and time costs will increase as well. Because it is generally assumed that the time cost of congestion can have a considerable share in the port related costs they are approximated and included in the port residence cost. In paragraph service time including congestion was represented by the following expression: June

56 5 Data for the Model k = Q t +, 1 n n tff n c K n The cost for service time is the product of service time and the VOT of containers on a ship. The cost is then: k i Q ( ), 1 n n = ff n + K n OC D VOT t c Considerable information on congestion behavior is available for highways and the parameters c and k are usually set at 0.15 and 4, respectively, representing the Bureau of Public Roads formula which is often used in passenger transport. However, the congestion behavior within a port is more complicated. As was assumed in paragraph a curve with similar characteristics can be used to simulate port congestion. In Figure 5-8 three plausible congestion functions are drawn. The functions represent the last part of the expression above: c k Q n K with c=0.15, c=0.45 and c=0.90 for f1, f2 and f3 respectively. In all functions k is set at 4. n 100% Congestion Increase of service time (%) 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Utilization rate (%) Figure 5-8 Port congestion functions f1 f2 f3 The graph reveals a highly non-linear effect of congestion. This means that as the demand is reaching the design capacity of a port, the increase of service time (i.e. congestion time) and thus the congestion costs grow more rapidly. Up to an utilization rate of 50% hardly no increase in service time occurs. In this study the blue congestion function (with c=0.45 and k=4) is chosen. In the chapter 7 attention is paid to the sensitivity of the model to this choice. With the utilization rates of 2001, estimated from Figure 5-1 and 5-3, the congestion costs are calculated for the different ports in See the table below. Unit is Euro/TEU. 14 The congestion cost depends on the utilization rate of the port of the year before and therefore changes every year. 42 June 2007

57 5 Data for the Model Rotterdam Antwerp Hamburg Bremen Container discharge time Congestion costs Total costs service time with congestion Table 5-11 Costs of service time including congestion costs. Total port residence costs The total port residence cost for the Far East trade and the Transatlantic trade are summarized in Table 5-12 and Table In these tables all costs are in Euro/TEU. Additional costs of sea transport per container (compared to Le Havre) far East trade Rotterdam Antwerp Hamburg Bremen Sea side access River Scheldt Container discharge time Total port residence costs Congestion costs Total port residence costs with congestion Table 5-12 Total port residence cost per TEU, Far East trade. Additional costs of sea transport per container (compared to Le Havre) Transatlantic trade Rotterdam Antwerp Hamburg Bremen Sea side access River Scheldt Container discharge time Total port residence costs Congestion costs Total port residence costs with congestion Table 5-13 Total port residence cost per TEU, Transatlantic trade 5.5 Cargo Flow Analysis: Hinterland Transportation Costs A further detailing of the model includes a specific modeling of the hinterland transportation network. In this paragraph the hinterland transportation cost is determined for specific combinations of the ports in the HLH range and hinterland countries. First, in paragraph 5.5.1, the hinterland transportation cost of a cargo flow is calculated at a very disaggregate level using an origin-destination matrix. At the end of this paragraph it is explained how to go from hinterland transportation costs at a disaggregate level to hinterland transportation costs at an aggregate and workable level for the Port Competition Model. Then, before calculating the hinterland transportation cost at an aggregate level, some general conclusions from the OD-matrix are discussed in paragraph In paragraph and the hinterland transportation costs are constructed at an aggregate level. From here no longer distinction is made between cargo flows of the Far East trade and cargo flows of the Transatlantic trade. Hinterland transport is independent of overseas origins and therefore hinterland transportation costs are equal. June

58 5 Data for the Model OD-matrix: Hinterland transportation cost at disaggregate level NEA Transport Research and Training has provided the data on hinterland transportation for this study consisting of an origin-destination matrix (OD-matrix) and some additional information about the costs of hinterland transport. The OD-matrix gives all cargo flows (in tons) with destinations in Switzerland, Austria, Poland, the Czech Republic, Italy and Germany with origins in the ports of Rotterdam, Antwerp, Hamburg and Bremen in the year All hinterland countries are divided in several destination regions (85 in total), meaning a total number of origin-destination pairs of 4 * 85 = 340. In addition all cargo flows are differentiated to transport mode (3 types) and commodity (10 types) increasing the total number of cargo flows to 340 * 3 * 10 = A schematization of the OD-matrix and its cargo flows is presented in Figure 5-9. In the OD-matrix also the forecasted cargo flows in 2020 and 2040 are given according to the different economic development scenarios which were explained in the introduction of this chapter. NEA has based the data for the base year 2002 as much as possible on available statistics. To acquire the missing information models are used to fill these gaps in the data. In this way a complete description of the cargo flows is obtained at a high level of detail. Figure 5-9 Schematization of the OD-matrix. To classify origins, destinations, modes and commodity groups NEA has used the NEAC codes. For the origins (ports) and destinations (regions) the codes are given in Figure 5-7. For the modes and commodity groups the codes are given in Table 5-14 and Table These codes are used at several places in the remainder of this report. 15 From here it is assumed that for each cargo flow there is only one route. See also paragraph June 2007

59 5 Data for the Model Code Mode 0 Rest/Unknown 1 Road 2 Rail 3 Inland Waterways 4 Sea Table 5-14 Codes for transport modes. Source: NEAC Code 4 obviously does not appear in the OD-matrix. The cargo flows with code 0 for the transport mode are removed from the OD-matrix. This can be justified by the fact that those flows only account for less than 0.1% of the cargo flows. It is common practice of ports to distinguish three cargo categories and a number of cargo types per category: General cargo (or packed goods): container, roll-on/roll-off, other general cargo Dry bulk: iron ore and scrap, coal, agribulk, other dry bulk Wet bulk: crude oil, mineral oil products, other liquid bulk (mainly chemicals, oils and fast) Each commodity group has a different distribution for the form of appearance (i.e. packed goods, dry bulk and wet bulk). The percentage of packed goods of a commodity group is a fairly good approximation of container transport. The NEAC codes for the commodity groups and the percentages of packed goods are given in the table below. Code Commodity group Packed Goods (%) 0 Agricultural products 52 1 Foodstuffs 6 2 Solid mineral fuels 0 3 Crude oil 0 4 Ores, metal waste 1 5 Metal products 82 6 Building minerals / material 10 7 Fertilizers 8 8 Chemicals 9 9 Machinery & other manufacturing Petroleum products 0 Table 5-15 Codes commodity groups. Source NEAC. The cargo flows with code 3 for the commodity group are removed from the OD-matrix because crude oil never appears in containers. Although the percentages for group 2 and 10 seem to be zero as well, these cargo flows are not removed because they do appear in containers is it in very small amounts. (The percentages are zero due to rounding). Different commodity groups have different values per container. These values are already given in Euro/TEU in paragraph 5.4.2, Table 5-6. Because the cargo flows in the OD-matrix have unity metric tons, the values are here converted to Euro/ton to calculate the hinterland transportation costs at a disaggregate level as accurate as possible. For that purpose the values per TEU are divided by the different masses per TEU. The result is shown in column 4 of Table In the last column of the table the VOT is calculated for each specific commodity group, by multiplying the values per ton with the daily unit cost of capital ρ, which is set at /day. June

60 5 Data for the Model Commodity group Value (euro/teu) Mass (ton/teu) Value (euro/ton) VOT Commodity (euro/ton/day) Consumer food (1) Conditioned food (1) Cement/manufactured builing materials (6) Small machinery (9) Miscellaneous (0,2,4,5,7,8,10) Table 5-16 VOT s for specific commodity groups. Source: Dekker, 2005 To calculate the hinterland transportation cost at a disaggregate level the monetary transportation costs, C nmr, and the opportunity costs of time, OC(D nmr ), are calculated for each cargo flow and added afterwards. Cnmr As explained in paragraph the monetary transportation costs can be calculated with the following equation: C = p + β l nmr t j rj j 1 j It is assumed here that the cargo flows in the OD-matrix do not change of transport mode. Therefore the first term disappears from the equation and the only parameters left to determine are the unit cost per ton per km, β j, and the transportation distance, l rj, both depending on the transport mode (see Appendix XI for transportation distances by mode). The unit cost per ton per km is given in Table 5-17 for different transport modes. Size (TEU) Transportation cost ( /ton*km) Ocean vessel Short sea vessel Truck Train Barge Table 5-17 Transportation costs in Source: Dekker, 2005 Hinterland transportation costs can consist of the following items: Transport costs o Labour costs o Vehicle costs o Loading, unloading o Transfer costs o Waiting time o Repose time o Insurance o Interest o Depreciation vehicle o Administrative costs Emission costs Noise pollution costs Safety costs Spatial costs Infrastructure costs 46 June 2007

61 5 Data for the Model In the transportation costs as presented in Table 5-17 all items of transport costs listed above are included. Thus, the unit cost per ton per km consists of both monetary and time costs (but not congestion costs!). For this moment it is assumed that the capacity of the hinterland keeps up with the growth of cargo throughput in the ports. In chapter 8 a scenario is worked out where the effect of a congested hinterland is investigated. NEA provided tables with the distances for all 340 origin-destination pairs for transport by truck, train and barge. These tables are too big to show in an appendix. OC(Dnmr) As explained in paragraph the total number of days spent in hinterland transit can be calculated with: l D = H + + H rj nmr ndj tdj j 24 S j j 1 Because it is assumed here that cargo flows do not change modes at an inland terminal the last term disappears from the equation. The remaining parameters to determine are the dwell time in the port, H ndj, and the speed of hinterland transportation, S j, both depending on the transport mode. In Table 5-18 the dwell times are given for the different modes for the ports in the HLH range. Rotterdam Antwerp Hamburg Bremen Import dwell vessel to truck (days) Import dwell vessel to train (days) Import dwell vessel to barge (days) Table 5-18 Dwell time in the port. Source: Dekker, 2005 In Table 5-19 the average speed of hinterland transportation is given per mode. Size (TEU) Average speed (km/h) Ocean vessel Short sea vessel Truck Train Barge Table 5-19 Average transportation speed. Source: Dekker, 2005 and NEA. The opportunity cost of time can now be calculated with: i OC ( D ) = VOT D nmr nmr This is done for each cargo flow in the OD-matrix. Finally the monetary transportation costs, C nmr, and the opportunity costs of time, OC(D nmr ), are added to obtain the hinterland transportation cost for each cargo flow at a disaggregate level. As it was said in the introduction of paragraph 5.5 hinterland transportation costs at a disaggregate level are not workable for the Port Competition Model because of the high number of connections. Now it will be explained how to go from hinterland transportation costs at a disaggregate level (port-region) to hinterland transportation costs at an aggregate and workable level (port-country) for the Port Competition Model with the help of Figure June

62 5 Data for the Model Figure 5-10 Schematization: from disaggregate level to aggregate level The key assumption is that the amount of transported tons of a specific cargo flow (region, mode and commodity) decide what the share is of this hinterland transportation cost at a disaggregate level in the hinterland transportation cost of a specific port to a specific country. In this way the number of hinterland transportation costs is reduced from (see introduction) to 4 * 6 = 24 (number of ports times number of countries). First the hinterland transportation costs are weighed for the tons of commodities transported 16, then these costs are weighed for the amount of tons transported per mode and finally these costs are weighed for the amount of tons transported to each region. The different types of lines in Figure 5-8 represent the distribution of the total transported tons of cargo over the different regions General throughput conclusions OD-matrix In this paragraph some general conclusions are drawn from the OD-matrix. These conclusions concern the amounts of cargo transported to the different hinterland countries through each port, the modal split of these cargo flows and the commodities transported. The tables on which these conclusions are based are constructed from the OD-matrix at the level of countries and are presented in Appendix XII. In addition it is computed for each port what portion of their total container throughput is covered by the OD-matrix and thus what part of the container market is being modeled. Throughput (6.7 mln TEU) 86% of all cargo has a destination in Germany 30% of all cargo has Rotterdam as origin, 7% Antwerp, 38% Hamburg and 25% Bremen 72% of all cargo is transported by truck, 18% by train and 11% by barge 70% of all cargo is from commodity group 9 (i.e. machinery and other manufacturing) Market share (%) Rotterdam Antwerp Hamburg Bremen All countries Table 5-20 Market shares of ports for all cargo 16 For Cnmr the normal average is calculated because this part of the hinterland transportation cost is independent of the commodity group, for OC(D nmr) the weighed mean is calculated. 48 June 2007

63 5 Data for the Model The modal split per port can be found in Table 5-21: Modal split (%) Truck Train Barge Rotterdam Antwerp Hamburg Bremen Weighed mean Table 5-21 Modal split per port based on OD-matrix From this table it can be seen that from each port most cargo is transported by truck, that almost one third of all cargo via Rotterdam is transported by barge and that from the German ports hardly no cargo is transported by barge. These results are fairly consistent to the conclusions from Figure 5-4. Differences can be explained by the fact that only a part of the market is covered by the OD-matrix. The value of 10 ton/teu, which is assumed in the introduction of this chapter is proven right, based on the 70% of commodity group 9 and on Table Forecast Demand In the OD-matrix the forecasted cargo flows in 2020 and 2040 are given according to the different economic development scenarios which were explained in the introduction of this chapter. These forecasts are linearly interpolated 17 for the periods an for the Transatlantic Market scenario and the Global economy scenario. The growth of demand for the HLH range as a whole is presented in Figure ,0 Forecast Demand 30,0 25,0 20,0 15,0 10,0 5,0 0, Demand (mln TEU) Transatlantic Market Global Economy Figure 5-11 Forecast demand HLH range Source: NEA 17 In reality, the Transatlantic Market scenario predicts an annual growth of 3.2% and the Global Economy scenario an annual growth of 4.5% for the period TM predicts an annual growth of 2.4% and GE an annual growth of 4.2% for the period June

64 Share of the container market in OD-matrix 5 Data for the Model The share of the container market which is covered in the OD-matrix is calculated in Table Rotterdam Antwerp Hamburg Bremen Throughput data OD-matrix (mln TEU) Throughput numbers Figure 5-1 (mln TEU) Share of the market in OD-matrix (%) Table 5-22 Share of the market represented in OD-matrix. This table clearly reveals that the shares covered in the OD-matrix are smaller for the Benelux ports than for the German ports. This can logically be explained by the fact that the Netherlands, Belgium and France are not included in the hinterland destinations. These countries are not interesting to include in the Port Competition Model because the German ports hardly compete for this hinterland. The shares covered for Hamburg and Bremen seem reasonable because only import flows are included in the ODmatrix, export is left out of consideration. However, it is striking that the share for Antwerp is only 10%. A factor that can explain why the throughput data for the port of Antwerp is such a small portion of the market, is the balance of import and export of hinterland transport. In Appendix XIII balance maps of hinterland freight flows are enclosed for Rotterdam, Antwerp, Hamburg and Bremen. The maps are created by NEA bases on data of 2002 and concern only maritime flows between seaports and hinterland regions. In the maps an overview is given of the balance of the hinterland transport of packed goods between the four ports and the hinterland regions in The balance shows the share of import by sea in the total hinterland flows (import and export over sea). For each hinterland region the balance of import and export handled in a specific port is marked by means of a color. The different colors should be interpreted as follows: Regions with a red color This means that the balance has a value between 0.7 and 1.0. For such regions the share of import over sea lies between 70% and 100%, the share of export over sea lies between 0% and 30%. These regions are characterized by a strong imbalance, where import over sea is much higher than export over sea. Regions with a light green color This means that the balance has a value between 0.45 and For such regions the share of import over sea lies between 45% and 55%, the share of export over sea lies as well between 45% and 55%. These regions are characterized by a balance of import and export, because the volumes are roughly the same size. Regions with a dark green color This means that the balance has a value between 0 and 0.3. For such regions the share of import over sea lies between 0% and 30%, the share of export over sea lies between 70% and 100%. These regions are characterized by a strong imbalance, where import over sea is much lower than export over sea. The other colors indicate a value of the balance lying between the values of the above explained colors. For regions with a limited volume (hinterland transport less than ton) the balance is not shown. The regions this accounts for are white in the maps. From an analysis of the balance map per seaport, the differences between these seaports become very clear. Of course only the hinterland regions from the OD-matrix are taken into account for this analysis. If we look at the overall geographical picture the results can be summarized as described in Table June 2007

65 5 Data for the Model Packed goods Import-Export (%) Rotterdam Balance Antwerp Export Hamburg Balance Bremen Export Table 5-23 Balance Import-Export. Source: NEA, 2005 Balance means that as many regions have a higher import as a higher export. Export means that most hinterland regions have a higher export than import. From this table it can be seen that especially Antwerp is an exporting port. Because in the OD-matrix only import freight flows are given, this can (partly) explain why the throughput data for the port of Antwerp is such a small portion of the market. Although the maps are forecasts of the balances in 2010, it is a solid assumption to say that the balance of a port does not change within a few years and that these balance maps are also valid for 2002 (especially because the forecasts are based on 2002 data). See also Appendix XIV. The following quick calculation shows that the share of the throughput in Antwerp covered by the OD-matrix is not odd. Quick calculation for Antwerp: total throughput 2002 is 53.0 mln ton, see Appendix IX 18% transshipment, 82% hinterland transport to all countries Figure mln ton assume 40% of hinterland transport is to/from Belgium, France and the Netherlands 26.1 mln ton 20% import 5.2 mln ton Thus with 4.7 mln ton from Table 5-19 about 90% of the imported cargo is covered by the OD-matrix Summary throughput data per hinterland country The same type of tables as in the previous paragraph are constructed from the OD-matrix for each separate hinterland country, but now at the level of regions within a country. These tables are necessary to obtain hinterland transportation costs at an aggregate level as explained in paragraph The conclusions which can be drawn from these tables for a country as a whole are presented in this paragraph. However, because the countries consist of a large number of regions the tables for each separate hinterland country are very big and are therefore not presented in this report. Switzerland 3% of all cargo has a destination in Switzerland (0.22 mln TEU) 61% of this cargo has Rotterdam as origin, 17% Antwerp, 14% Hamburg and 8% Bremen 18% of this cargo is transported by truck, 65% by train and 16% by barge 77% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 12% from group 5 (i.e. metal products), 11% other Market share (%) Rotterdam Antwerp Hamburg Bremen Switzerland Austria 2% of all cargo has a destination in Austria (0.15 mln TEU) 24% of this cargo has Rotterdam as origin, 13% Antwerp, 40% Hamburg and 23% Bremen 46% of this cargo is transported by truck, 48% by train and 5% by barge 70% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 19% from group 5 (i.e. metal products), 11% other Market share (%) Rotterdam Antwerp Hamburg Bremen Austria June

66 5 Data for the Model Poland 3% of all cargo has a destination in Poland (0.17 mln TEU) 11% of this cargo has Rotterdam as origin, 11% Antwerp, 60% Hamburg and 18% Bremen 67% of this cargo is transported by truck, 27% by train and 1% by barge 62% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 24% from group 0 (i.e. agricultural products), 14% other Market share (%) Rotterdam Antwerp Hamburg Bremen Poland Czech Republic 2% of all cargo has a destination in Czech Republic (0.12 mln TEU) 12% of this cargo has Rotterdam as origin, 12% Antwerp, 46% Hamburg and 30% Bremen 61% of this cargo is transported by truck, 30% by train and 6% by barge 64% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 22% from group 0 (i.e. agricultural products), 14% other Market share (%) Rotterdam Antwerp Hamburg Bremen Czech Republic Italy 4% of all cargo has a destination in Italy (0.26 mln TEU) 44% of this cargo has Rotterdam as origin, 16% Antwerp, 10% Hamburg and 30% Bremen 38% of this cargo is transported by truck, 62% by train and 0% by barge 82% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 9% from group 5 (i.e. metal products), 9% other Market share (%) Rotterdam Antwerp Hamburg Bremen Italy Germany 86% of all cargo has a destination in Germany (5.8 mln TEU) 30% of this cargo has Rotterdam as origin, 6% Antwerp, 39% Hamburg and 25% Bremen 76% of this cargo is transported by truck, 12% by train and 11% by barge 69% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 12% from group 5 (i.e. metal products), 9% from group 0 (i.e. agricultural products), 10% other Market share (%) Rotterdam Antwerp Hamburg Bremen Germany If the approximation of the hinterland transportation cost at an aggregate level as explained in paragraph is accurate depends on a couple of factors: The distance between a port and a hinterland country (the smaller, the less accurate) The number of regions (the more, the less accurate) The size of a country ( the bigger, the less accurate) The amount of cargo transported (the higher, the less accurate). All these factors together imply that the approximation of the hinterland transportation cost at an aggregate level is not very accurate for Germany. Therefore it is decided to divide Germany in four parts and calculate four hinterland transportation costs for each port. Regions with the same origin distribution constitute one of these parts. In Figure 5-13 the different regions in Germany are shown. 52 June 2007

67 5 Data for the Model Figure 5-12 Regions in Germany. Source: The four parts consist of the following regions: Strong Rotterdam (Germany 1): Nordrhein-Westfalen, Rheinland-Pfalz, Saarland, Baden- Wurttemberg Strong Hamburg (Germany 2): Hamburg, Schleswig-Holstein, Mecklenburg-Vorpommern, Berlin Strong Bremen (Germany 3): Bremen, Niedersachsen Equal shares (Germany 4): Sachsen-Anhalt, Hessen, Thüringen, Brandenburg, Sachsen, Bayern Germany 1 28% of all cargo transported to Germany has a destination in Germany 1 (1.6 mln TEU) 58% of this cargo has Rotterdam as origin, 12% Antwerp, 17% Hamburg and 13% Bremen 60% of this cargo is transported by truck, 12% by train and 28% by barge 66% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 20% from group 5 (i.e. metal products), 6% from group 0 (i.e. agricultural products), 8% other Market share (%) Rotterdam Antwerp Hamburg Bremen Germany June

68 5 Data for the Model Germany 2 28% of all cargo transported to Germany has a destination in Germany 2 (1.6 mln TEU) 8% of this cargo has Rotterdam as origin, 1% Antwerp, 80% Hamburg and 11% Bremen 83% of this cargo is transported by truck, 15% by train and 2% by barge 70% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 8% from group 6 (i.e. building minerals and material), 7% from group 5 (i.e. metal products), 9% from group 0 (i.e. agricultural products), 6% other Market share (%) Rotterdam Antwerp Hamburg Bremen Germany Germany 3 13% of all cargo transported to Germany has a destination in Germany 3 (0.76 mln TEU) 4% of this cargo has Rotterdam as origin, 2% Antwerp, 15% Hamburg and 79% Bremen 93% of this cargo is transported by truck, 3% by train and 5% by barge 65% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 10% from group 6 (i.e. building minerals and material), 14% from group 0 (i.e. agricultural products), 11% other Market share (%) Rotterdam Antwerp Hamburg Bremen Germany Germany 4 31% of all cargo transported to Germany has a destination in Germany 4 (1.8 mln TEU) 35% of this cargo has Rotterdam as origin, 7% Antwerp, 32% Hamburg and 26% Bremen 77% of this cargo is transported by truck, 15% by train and 8% by barge 74% of this cargo is from commodity group 9 (i.e. machinery and other manufacturing), 11% from group 5 (i.e. metal products), 9% from group 0 (i.e. agricultural products), 6% other Market share (%) Rotterdam Antwerp Hamburg Bremen Germany Hinterland transportation cost at aggregate level Now the hinterland transportation cost at a disaggregate level and the distributions over commodities, modes and regions are known for the cargo flows the hinterland transportation cost at an aggregate level can be calculated and is given in Table 5-24 for each combination of a port and a hinterland country. The unity of the costs is euro/teu. Country Rotterdam Antwerp Hamburg Bremen Switzerland Austria Poland Czech Republic Italy Germany Germany Germany Germany Table 5-24 Hinterland transportation cost at aggregate level 54 June 2007

69 6 Model Calibration 6 Model Calibration 6.1 Introduction This chapter deals with the setup and calibration of the Port Competition Model. First, in paragraph 6.2 the model dimensions are discussed. Paragraph 6.3 deals with the calibration of the model. In order to make the output for the base year 2002 corresponding to the data in the OD-matrix the model has to be calibrated. First, the calibration approach is described in short. Then, after presenting a summary of the input data as described in chapter 5, the output for the base year 2002 calculated with the model is presented and compared with the data from the OD-matrix and the differences are analyzed. Next, the calibration factors are determined and the paragraph ends with judgment on the calibrated model. 6.2 Model Dimensions In order to explain the model dimensions the conceptual systems diagram for the Port Competition Model as shown in Figure 4-5 is once more presented in Figure 6-1. Figure 6-1 Conceptual systems diagram for four ports. The total demand for the HLH range is assigned to the four ports using a logit-type assignment modeling as is explained in paragraph 4.3. The four ports of the HLH range constitute the first dimension of the model. The market shares of the ports (and thus the demand for each port) are calculated separately for each hinterland country (and not for each specific cargo flow). This is schematically shown in Figure 6-2. The capital letter (and also the color) represents the specific port and the number indicates the hinterland country concerned. The hinterland countries are the second dimension of the model. June

70 6 Model Calibration Figure 6-2 Determination of market shares and demand of ports per hinterland country The total demand of a specific port can be calculated with: P = n exp( µ GC nc ) exp( µ GC ) n Qnc = Qc Pn nc Q n = c Q nc and the total market share with: Q n Qn n With the model the total demand for each hinterland country is assigned to the four ports. Summation of these amounts of cargo for all countries gives the total demand for a specific port. To avoid that the model assigns unrealistic amounts of cargo to a port with a relative low generalized transportation cost but which is already highly congested, a congestion limit is build in the model. If the utilization rate of one (or more) of the ports is higher than 90% after the assignment of cargo for a specific year, a small penalty of 2 Euro/TEU is added to the generalized transportation cost of this port and the model calculates the assignment of cargo to the four ports over again for that year. This is iteratively repeated until none of the ports has a utilization rate exceeding 90% after assignment of the cargo. With this congestion limit it is assumed that, until capacity is expanded, carriers do not close new contracts with highly congested ports and the total demand for a congested port stays at the same level. 6.3 Calibration This paragraph deals with the calibration process that is carried out in order to make the output for the base year 2002 corresponding to the data in the OD-matrix. First the approach for the calibration is described. Then, the differences between the output for the base year 2002 calculated with the model and the data from the OD-matrix are analyzed. On the base of the analyzed differences the calibration factors are determined. Finally the performance of the calibrated model is discussed. 56 June 2007

71 6.3.1 Calibration approach 6 Model Calibration The model has to be calibrated on the model dimensions. Here the approach for the calibration of the model is described. The calibration process consists of the following steps: Determination of the correct cost coefficient Correction for Antwerp Corrections for other ports per country Starting point For a good overview of the inconsistencies between model output and OD-matrix data the market shares per hinterland country from the OD-matrix, as described in paragraph 5.5.3, are included here once more in Table 6-1. Market share (%) Rotterdam Antwerp Hamburg Bremen Switzerland Austria Poland Czech Republic Italy Germany Germany Germany Germany Total market share Table 6-1 OD-matrix 2002: Market shares per hinterland country The input data for the Port Competition Model as described in chapter 5 is summarized. In Table 6-2 an overview is given of all components of the transportation cost for each country. In this table the congestion cost for 2002 is included in the port residence cost. Note that because the congestion varies with the utilization rate of a port the port residence cost changes every year. The other costs in the table are all independent of time. June

72 6 Model Calibration Country Cost Rotterdam Antwerp Hamburg Bremen Switzerland Austria Poland Czech Republic Italy Germany 1 Germany 2 Germany 3 Germany 4 Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Hinterland transportation cost Port investment cost Port residence cost Total unit cost Table 6-2 Overview of composition of the transportation cost per country. As explained in paragraph using a logit-type assignment modeling for traffic assignment incorporates uncertainties about the factors that determine route choice by shipping companies. To run the model the cost coefficient for the logit function is in the first instance set at (1/(Euro/TEU)). This value is often found in literature (see for example Verhaeghe, 2006). In Table 6-3 the output for the year 2002 calculated with the model is presented. The results in this table can now be compared with the data from the OD-matrix as presented in Table June 2007

73 6 Model Calibration Market share (%) Rotterdam Antwerp Hamburg Bremen Switzerland Austria Poland Czech Republic Italy Germany Germany Germany Germany Total market share Table 6-3 Model output 2002: Market shares per hinterland country The most important observation is that the market share of Antwerp calculated with the model is way to high for almost every hinterland destination, implying that the port of Antwerp has a big disadvantage for shipping companies relative to the other ports which is not incorporated in the model. The surplus of market share for the port of Antwerp is mainly at the expense of market share for the port of Rotterdam and a little bit at the expense of the German ports. It is beyond the scope of this study to carry out a thorough analysis on the port of Antwerp to explain the disadvantage relative to the other ports. Taking the above into account, for the ports of Rotterdam, Hamburg and Bremen the Port Competition Model predicts plausible results Calibration factors The steps of the calibration process are explained in short below. The output from the model obtained with each step of the calibration process is included in Appendix XV. Determination of the correct cost coefficient The first step is to determine the cost coefficient of the logit function. To this end the total market shares of the ports are calculated for values between and 0.5 (1/(Euro/TEU)). No distinction is made yet for the market shares per country. For very small values (around ) of the cost coefficient the logit function hardly reflects differences in the generalized transportation costs and all ports are assigned equal market shares. For very large values of the cost coefficient the logit function is very sensitive to differences in the generalized transportation costs and the model assigns almost all cargo to the port with the lowest generalized transportation costs. From the different runs with an always varying cost coefficient it becomes clear that the cost coefficient should have a value somewhere in the range of (1/(Euro/TEU)). After several tests for combinations of cost coefficients and cost corrections for Antwerp (see below) the cost coefficient is set at (1/(Euro/TEU)). Correction for Antwerp For all these values of the cost coefficient the total market share of the port of Antwerp is much higher than it should be according to the data from the OD-matrix. As was already said above a cost correction has to be added to the generalized transportation costs of Antwerp to incorporate the disadvantage that the port appears to have for shipping companies relative to the other ports. Trial and error with various combinations of cost coefficients and cost corrections for Antwerp proves that, also taking the market shares per country in account, the best result for the total market shares of the four ports is achieved by applying a cost coefficient of (1/(Euro/TEU)) and a cost correction for Antwerp of 200 Euro/TEU. For other values of the cost coefficient higher cost corrections for Antwerp are necessary to get the same result. This is undesirable because cost corrections have to be restricted as much as possible. Corrections per country Now the cost coefficient for the logit function and the cost correction for Antwerp are known to get the best result for the total market shares, the model needs some fine-tuning per country as can be seen from Table 6-1 and 6-3. Especially Austria, the Czech Republic and Germany 3 need some cost June

74 6 Model Calibration corrections to the generalized transportation costs to get good results for the market shares of the ports per country. The determination of these cost corrections is a very comprehensive and time-consuming process and therefore not all steps are explained here and even not all interim results are included in Appendix XV. It suffices to say that the emphasis in determining the cost corrections per country needs to be on achieving good results for the market shares of the ports for the four parts of Germany. Because 86% of all cargo is transported to Germany the market shares for the parts of Germany have the strongest influence on the result of the total market shares of the ports. The corrections that are applied in the model are summarized in Table 6-4. Correction ( /TEU) Rotterdam Antwerp Hamburg Bremen Switzerland Austria Poland 0 Czech Republic Italy Germany Germany Germany Germany Table 6-4 Corrections applied on the generalized cost, with cost coefficient (1/(Euro/TEU)) Performance calibrated model In Table 6-5 the market shares according to the OD-matrix (left columns) and the market shares obtained with the calibrated model (right columns) are compared for all hinterland countries and for the total market shares of the ports. It can be concluded that applying the cost corrections from Table 6-4 fairly good results are obtained with the Port Competition Model. Market share (%) Rotterdam Antwerp Hamburg Bremen Data Model Data Model Data Model Data Model Switzerland Austria Poland Czech Republic Italy Germany Germany Germany Germany Total market share Table 6-5 Comparison market shares OD-matrix and market shares calibrated model. This chapter concludes with a summary of the main findings from the calibration process. For the ports of Rotterdam, Hamburg and Bremen the Port Competition Model predicts plausible results. The port of Antwerp needs a rather high correction for all hinterland countries (except for Poland). There are different plausible hypotheses to explain why Antwerp has a big disadvantage for 60 June 2007

75 6 Model Calibration shipping companies relative to the other ports. It is beyond the scope of this study to carry out a thorough analysis on the port of Antwerp to explain the disadvantage relative to the other ports. Particularly Austria, the Czech Republic and Germany 3 need some cost corrections to the generalized transportation costs to get good results for the market shares of the ports per country. It is striking that Poland does not need any correction at all. The results obtained with the model correspond very good with the data from the OD-matrix. Because 86% of all cargo is transported to Germany the market shares for the parts of Germany have the strongest influence on the result of the total market shares of the ports. After calibration the results that are obtained with the model come very close to the data from the OD-matrix, especially for the market shares of the four parts of Germany. Therefore it can be concluded that the performance of the calibrated model is fairly good. June

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77 7 Base Case Scenario 7 Base Case Scenario 7.1 Introduction This chapter presents the results obtained with the Port Competition Model when it is run for a base case scenario. The modeling period is from 2002 to In the next chapter the results from the base case scenario serve as a frame of reference to explain the results that are obtained with the model when it is run for different scenarios. In paragraph 7.2 the assumptions for the base case scenario are presented. Then, in paragraph 7.3, the results obtained with the model are presented and explained on the basis of various graphs. In paragraph 7.4 some attention is paid to the sensitivity of the model to the choice of the congestion function as described in paragraph The chapter ends with the conclusions concerning the base case scenario. 7.2 Assumptions Base Case Scenario Capacity expansion plans The Port Competition Model can either be run with capacity expansion plans that are fixed or with dynamic capacity expansions. For the remainder of this study and thus for the base case scenario the model is run with capacity expansions that are fixed. As presented in paragraph 5.2 the capacities of the ports are only known for the years 2003 and For the model also the capacities in 2002, 2004 and 2005 are required. It is assumed here that the capacities in 2002 are equal to the capacities in To determine the capacities for the years 2004 and 2005 the increase of the capacities between 2003 and 2006 is equally divided over the years. Together with the capacity expansion plans from paragraph 5.6 these capacities are fixed input in the model. The capacities in the years 2002 to 2006 are summarized in Table 7-1. Capacity 2002 Capacity 2003 Capacity 2004 Capacity 2005 Capacity 2006 Rotterdam Antwerp Hamburg Bremen Total Table 7-1 Overview capacities The capacity expansion plans as published on the websites of the ports as from 2007 are presented in Table For the base case scenario it is assumed that all expansion plans are realized according to plan and that no additional expansions are planned in the period Note 1: The Deurganckdock Terminals in the port of Antwerp have already been completed in 2006 and are therefore included in the capacity increase from 2003 to 2006, see also Table 7-1. Note 2: Because only a part of the market is modeled (see paragraph 5.5.2) also only a part of the size of each capacity expansion is implemented in the model, corresponding to the part of the market modeled for the associated port. It is assumed that the same growth pattern as discussed below applies for the other markets (i.e. the transshipment market). 18 The table includes only expansion plans that are officially signed at the moment of writing. In the future ports will definitely develop additional expansion plans. June

78 7 Base Case Scenario Rotterdam EUROMAX Terminal Tweede Maasvlakte Terminal Additional capacity (mln TEU) 3.0 up to 16.0 Planned introduction Antwerp Deurganckdock Terminals Hamburg Eurogate Container Terminal Hamburg CTH HHLA Container Terminal Burchardkai CTB HHLA Container Terminal Altenwerder CTA HHLA Container Terminal Tollefort CTT Bremen CT IV Table 7-2 Capacity expansion plans. Source: websites ports Forecast The total demand for the HLH range is an exogenous factor in the model. The forecast of the total demand for the HLH range based on the OD-matrix is given in paragraph both for the Transatlantic Market and the Global Economy scenario. For the base case it is assumed that economy will develop in accordance with the Transatlantic Market scenario. However, the economic development can differ from one country to another (within the limits of the Transatlantic Market scenario) and as a consequence the demands for the different hinterland countries do not necessarily grow at the same rate. To incorporate this in the results for the base case the total forecasted demand for the HLH range is unequally divided over the different hinterland countries, taking their differences in economic development in account. For most countries a growth of 2.5% per year is adopted. For Poland and the Czech Republic the growth rate is set at 4.5%, for Italy at 1.5% (also reflecting the competition from the Italian ports) and for Germany 4 at 3.5%. This is graphically shown in Figure 7-1 and 7-2 on the next page. 1,0 Forecast Transatlantic Market 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0, Demand (mln TEY) Switzerland Austria Poland Czech Republic Italy Figure 7-1 Growth rate demand for hinterland countries 64 June 2007

79 7 Base Case Scenario 7,0 Forecast Transatlantic Market 2 6,0 5,0 4,0 3,0 2,0 1,0 0, Demand (mln TEY) Germany 1 Germany 2 Germany 3 Germany 4 Figure 7-2 Growth rate demand for zones in Germany Hinterland As the conceptual systems diagram shows in Figure 6-1 the development of the hinterland infrastructure is also an exogenous factor in the model. For the base case scenario it is assumed that the development of the hinterland infrastructure keeps up with the increase of cargo throughput to the hinterland countries, implying there is no additional hinterland congestion, in other words the situation stays as it is. In chapter 8 a scenario is worked out to investigate the influence of hinterland congestion on the costs an thus on the market share and demand of a specific port. 7.3 Results Base Case Scenario In this paragraph the results for the base case scenario calculated with the Port Competition Model are presented. Several graphs are included to explain these results and to give a good view on how the model functions and what information can be obtained with it. The graphs that are included here comprehend the following information for the years 2002 to 2040: Capacity expansions Weighed mean of total unit costs Total market shares Total throughput volumes Utilization rate Port development On the basis of these graphs the results are explained step by step. For each graph first it is explained how it should be interpreted and then the main observations are discussed. Capacity expansions The first graph, presented in Figure 7-3, illustrates the capacity expansion plans of the four ports up to The input for the model concerning capacity expansions as described in paragraph 7.2 can be clearly recognized. No further comments are made on the interpretation of this graph because it is supposed to be fairly self-evident. From the graph is can be observed that from 2002 up to 2009 the capacities of the ports of Rotterdam and Bremen are increasing almost at the same rate and both ports provide almost the same amount of capacity. The port of Hamburg provides more capacity but its capacity is increasing at a somewhat June

80 7 Base Case Scenario lower rate, resulting in equal capacities of the German ports and Rotterdam in The capacity of Antwerp lags behind relative to the other ports. It is noted once again that because only a part of the market is modeled (see paragraph 5.5.2) also only a part of the size of each capacity expansion is implemented in the model, corresponding to the part of the market modeled for the associated port. As the part of the market modeled for Antwerp is very small, the part of the size of the port s capacity that is modeled is very small as well compared to the other ports. As from 2006 the capacity of the port of Antwerp remains constant and as from 2009 no expansions are added to the capacity of Bremen. In 2010 Hamburg almost doubles its capacity, but as Maasvlakte II is put into use in 2013 the capacity of the port of Rotterdam exceeds the capacities of the other ports abundantly. 10,0 Capacity expansions 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity (mln TEU) Rotterdam Antwerp Hamburg Bremen Figure 7-3 Capacity expansions Weighed mean of total unit costs Figure 7-4 presents the changes in the weighed mean of the total unit costs of each port over the years. This graph is constructed from the separate graphs of the total unit cost per hinterland country using a specific port. These graphs are included in Appendix XVI. For each port the total unit costs from the separate graphs are weighed in the graph in Figure 7-4 corresponding to the share of the total throughput volume of a port that is transported to a specific hinterland country. If, for instance, the highest percentage of cargo which is transported via Rotterdam has a destination in Germany 1, the total unit cost for transportation of cargo to Germany 1 has to preponderate in the weighed mean of the total unit costs of Rotterdam. From the separate graphs in Appendix XVI it can be observed that the total unit costs increase over de years for all ports. This is due to a rise in the level of congestion (i.e. an increase in service time, service costs, port residence costs and thus in total unit costs) which can be explained by the fact that the throughput volumes are increasing for all ports, according to the Transatlantic Market forecast, while the capacities of all ports remain constant as from As can be seen from the graphs in Appendix XVI, for all hinterland countries the total unit costs of Rotterdam increase at a lower rate than the total unit costs of the other ports implying that in the other ports higher utilization rates are reached at an earlier point in time. This can also be observed from the graph in Figure June 2007

81 7 Base Case Scenario 1100 Weighed mean total unit cost 1050 Total unit cost (euro/teu) Rotterdam Antwerp Hamburg Bremen Figure 7-4 Weighed mean of total unit costs Total volume & Total market share As explained in paragraph 6.2 the market shares of the ports are calculated separately for each hinterland country using a logit function on the total unit costs of the four ports. The market shares of the ports, calculated again for every year from 2002 to 2040, are included in Appendix XVI for each hinterland country. Multiplying these (changing!) market shares with the total forecasted amount of cargo to the specific country for a specific year gives the amount of cargo that is transported to that country via a specific port in that year. In Appendix XVI the graphs are included with the cargo volumes transported via the four ports to each hinterland country over the years. Adding these amounts for all hinterland countries gives the total volume that is transported via a specific port. Figure 7-5 presents the growth of the total volume of cargo transported through each port over the years. 9,0 Total volume 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Volume (mln TEU) Rotterdam Antwerp Hamburg Bremen Figure 7-5 Total volume June

82 7 Base Case Scenario If the market share of a port for a specific hinterland country is increasing over the years due to a decrease in the total unit cost caused by, for example, a capacity expansion in the particular port, this means that other ports are losing market share for that hinterland country. This is reflected in the distribution of the forecasted cargo to that country over the four ports. See for instance the graphs of market shares and transported volumes for Germany 4 in Appendix XVI. As from 2013, when Maasvlakte II is put into use, the market share of Rotterdam increases for cargo with a destination in Germany 4 and thus the growth of transported cargo to Germany 4 increases for Rotterdam at the expense of the growth of transported cargo to Germany 4 through the other ports. From Figure 7-5 it can be observed that all ports are benefiting from the growth in the container market. However, due to the construction of Maasvlakte II, the amount of cargo transported through Rotterdam grows at a higher rate than the amounts of cargo transported trough the other ports. Rotterdam benefits even more as from 2035 when all other ports are congested and Rotterdam is the only port with free capacity. As explained in paragraph 6.2 the total market share of a port can be calculated by dividing its total throughput by the total cargo transported through the HLH range to the hinterland. The change in the total market shares of the ports over the years is shown in Figure 7-6. The growth of the market share of Rotterdam, caused by Maasvlakte II, is mainly at the expense of the market share of Bremen. 45% Total market share 40% 35% 30% 25% 20% 15% 10% 5% 0% Market share (%) Rotterdam Antwerp Hamburg Bremen Figure 7-6 Total market share Utilization rate Figure 7-7 presents the changes in the utilization rates in the ports over the years. No further comments are made on the interpretation of this graph because it is supposed to be fairly self-evident. 68 June 2007

83 7 Base Case Scenario 100% Utilization rate 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Utilization rate (%) Rotterdam Antwerp Hamburg Bremen Figure 7-7 Utilization rate Port development The last graph to be explained is presented in Figure 7-8. It illustrates the developments of the ports over the years. In fact it is a summary of the results for the base case scenario. The capacity expansions within the ports and the total cargo volumes transported through the ports are united in one graph to get a good view on the situation in every year. Together with the graph of the weighed mean of the total unit costs and the graph of the total market shares of the ports this graph presents the most important results of the base case scenario. Therefore these three graphs form the frame of reference to explain the results that are obtained with the model when it is run for different scenarios. 10,0 Port development 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity & Volume (mln TEU) Capacity Rotterdam Capacity Antwerp Capacity Hamburg Capacity Bremen Volume Rotterdam Volume Antwerp Volume Hamburg Volume Bremen Figure 7-8 Port development June

84 7.4 Sensitivity to Congestion 7 Base Case Scenario In paragraph an assumption is made for the shape of the congestion function that is used to calculate the (varying) service cost as a part of the port residence cost. In comparison with the data that is available to determine the two other cost components of the total unit cost there is a lack of data on the behavior of congestion within a port to accurately calculate the port residence costs of the four ports. In this paragraph the influence of the choice of the congestion function on the service time and thus on the service cost of a port is considered and the sensitivity of the model to this choice is investigated. Figure 7-9 shows the influence of the level of congestion on the service cost when congestion function f2 is chosen. The output for the base case scenario as described in the previous paragraph is generated with the model using congestion function f2. In Appendix XVII the graphs are included that show the influence of the level of congestion on the service cost if congestion function f1 and f3 are chosen. From Figure 7-9 it can be seen that the service cost of a port increases with 30% for a utilization rate of 90% if congestion function f2 is chosen. From the similar graphs in Appendix XVII it can be seen that the service cost of a port increases with 10% for a utilization rate of 90% if congestion function f1 is chosen and that the service cost of a port increases with 60% for a utilization rate of 90% if congestion function f3 is chosen. 200 Service cost f2 180 Service cost (euro/teu) % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Utilization rate (%) Rotterdam Antwerp Hamburg Bremen Figure 7-9 Increase of service cost due to congestion (f2) Appendix XVII also includes the graphs of the port development generated with the model using congestion functions f1 and f3. Comparing these graphs with Figure 7-8 gives a good view on the sensitivity of the model for the choice of the congestion function. When congestion function f1 is chosen, i.e. a smaller increase in service time and service costs for increasing utilization rates than for congestion function f2, the output generated with the model is almost identical to that in Figure 7-8. This means that the model is not sensitive to a decrease of the influence of the level of congestion on the service time and the service cost. When congestion function f3 is chosen, i.e. a stronger increase in service time and service costs for increasing utilization rates than for congestion function f2, the output generated with the model is very different from that in Figure 7-8. High fluctuations can be noticed during the first years of the modeling period and in the last stage of the modeling period pointing at instability of the model. This means that the model is very sensitive to an increase of the influence of the level of congestion on the service time and the service cost. 70 June 2007

85 7 Base Case Scenario 7.5 Conclusions Base Case Scenario The following observations can be done on the base case scenario: The modeling period is rather long and therefore the results obtained for the period are the most important. It is most likely that the ports will develop additional expansion plans as from 2020, meaning that the pattern of the results obtained for the period will change. The model is sensitive to congestion. This can be concluded from the course of the weighed means of the total unit costs of the ports over the years. If the level of congestion increases the weighed means of the total unit costs raise accordingly. The modeled system is sensitive to capacity constraints. The utilization rates of the ports never exceed the 90% congestion limit and if this limit is reached within a port the total volume of cargo transported through the port stays at a constant level. The model is hardly sensitive to free capacity and thus to the service level. Immediately after the introduction of a capacity expansion one would expect a strong increase of the total market share of a port and an increase of the growth of the total volume of cargo transported through the port. However, in the results obtained with the model only a small increase of the total market share of a port and a small increase of the growth of the total volume of cargo transported through the port can be noticed. On the long term the model does react to free capacity but that is mainly caused by the fact that other ports are reaching the congestion limit. The model is not sensitive to a decrease of the influence of the level of congestion on the service time and the service cost. The model is very sensitive to an increase of the influence of the level of congestion on the service time and the service cost. It is suggested here that the behavior of congestion within a port could be simulated with Harboursim. June

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87 8 Case Study 8 Case Study 8.1 Introduction In this chapter an analysis of a range of possible scenarios and their impacts is performed using the Port Competition Model. To determine what an effective strategy is for a port to deal with competition the results of the different scenarios have to be compared allowing to make a more informed decision. Here the application of the Port Competition Model is shown on the basis of a number of scenarios that is worked out for competition within the Hamburg Le Havre range. The objective of this chapter is to create a good view on how the model can be used in the strategic investment decision process. In paragraph 7.2 the different scenarios that are worked out are described. The following paragraphs each deal with one scenario including the changes in input for the model, the output and a short explanation of the main observations from the results that are computed with the Port Competition Model. As was said in the introduction of the previous chapter the results from the base case serve as a frame of reference to explain the results that are obtained with the model when it is run for different scenarios. In this chapter the results of different scenarios are discussed and explained with the help of similar graphs. However, not all six output graphs that are discussed in chapter 7 are included for every scenario, only the graphs are included that are of interest to explain the main observations. 8.2 Description Scenarios The scenarios that are worked out using the Port Competition Model are described in Table 8-1. Scenario Situation Characteristics 1. Changing expansion plans a) Delay Maasvlakte II 7 years delay MV II b) Phasing Maasvlakte II 2 phases of MV II c) Additional expansion plans other ports 2. Hinterland congestion Hinterland congestion A15 3. Sensitivity to port dues Pricing Rotterdam Expansions other ports, 12 years delay MV II Cost increase of 10% for truck transport from Rotterdam Increase of 50% in port dues of Rotterdam Table 8-1 Scenarios 8.3 Scenario 1: Changing Expansion Plans The first scenario concerns changes in expansion plans and three situations are discussed: First, the consequences of a delay in the introduction of Maasvlakte II and the consequences of a phased introduction of Maasvlakte II are investigated. For these situations it is assumed that the other ports do not plan any capacity expansions as from 2010 to The third situation that is discussed deals with the consequences of a combination of a delay in the introduction of Maasvlakte II and additional capacity expansions of the other ports Delay Tweede Maasvlakte If a delay of 7 years is assumed the year of the fixed input of the introduction of Maasvlakte II has to be changed from 2013 into The output that is generated by the model is presented in Figure 8-1, Figure 8-2 and Figure 8-3. June

88 8 Case Study 1100 Weighed mean total unit cost Total unit cost (euro/teu) Rotterdam Antwerp Hamburg Bremen Figure 8-1 Weighed mean of total unit costs for delay Maasvlakte II When Figure 8-1 is compared with Figure 7-4 it can be concluded that the delay of Maasvlakte II only has an effect on the weighed mean of the total unit costs of the port of Rotterdam for the period For that period the level of the weighed mean of the total unit costs is higher than for the base case scenario. In the year 2020 the introduction of Maasvlakte II is slightly better noticeable than for the base case scenario. The decrease in the weighed mean of the total unit costs of Rotterdam, due to the sudden decrease of the congestion level, is accompanied with a small increase in the weighed mean of the total unit costs of Antwerp. Apparently the port of Antwerp incurs a small raise in the level of congestion as a result of the (delayed) introduction of Maasvlakte II. It is not unambiguously clear how this raise in the level of congestion is caused. 45% Total market share 40% 35% 30% 25% 20% 15% 10% 5% 0% Market share (%) Figure 8-2 Total market share for delay Maasvlakte II Rotterdam Antwerp Hamburg Bremen When Figure 8-2 is compared with Figure 7-6 it can be concluded that the German ports are benefiting from the delay of Maasvlakte II in the period The port of Bremen maintains its total market share of almost 28% until it starts decreasing in On the long term the effect of the delayed introduction of Maasvlakte II fades away. As from 2032 the total market share of Bremen decreases at 74 June 2007

89 8 Case Study a higher rate due to congestion, similar to the results from the base case scenario. The total market share of the port of Hamburg even increases from 35% in 2013 to 37% in After 2020 the total market share of Hamburg remains constant until it starts decreasing in 2036 due to congestion. The total market share of Antwerp of 7% remains almost constant over all years until it starts decreasing in 2034 also due to congestion. Besides these small changes the overall picture of the total market shares remains the same over the years for the delayed introduction of Maasvlakte II as for the base case scenario and the final distribution (i.e. in 2040) of the total market shares of the ports is equal to that of the base case scenario. In Figure 8-3 the effects of the delayed introduction of Maasvlakte II on the development of the ports is illustrated. From this figure it is clear that in the period the volume of cargo transported through Rotterdam is smaller than in the base case scenario but as soon as Maasvlakte II is put into use the volume of cargo transported through Rotterdam starts to grow at a higher rate at the expense of growth of the volumes transported through the other ports. This effect could also be noticed from the results of the base case scenario, but the increase in the growth of the volumes transported through Rotterdam caused by the delayed introduction of Maasvlakte II is stronger than for the base case scenario. This can be explained by the fact that the congestion levels of the other ports are already considerable at the moment of introduction. From this concise analysis it can be concluded that on the short term (up to 2020) a delay of the introduction of Maasvlakte II has negative consequences for the competitive position of the port of Rotterdam. On the long term (as from 2020) the sensitivity of the competitive position of Rotterdam to a delay of Maasvlakte II depends on additional capacity expansion plans of the other ports. 10,0 Port development Capacity & Volume (mln TEU) 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity Rotterdam Capacity Antwerp Capacity Hamburg Capacity Bremen Volume Rotterdam Volume Antwerp Volume Hamburg Volume Bremen Figure 8-3 Port development for delay Maasvlakte II Phasing Tweede Maasvlakte The capacity expansion of Maasvlakte II is divided in two equally sized parts and these parts are implemented in the model as fixed input. The first phase is put into use in 2013 and the second phase is put into use in The output that is generated by the model is presented in Figure 8-4, Figure 8-5 and Figure 8-6. June

90 8 Case Study 1100 Weighed mean total unit cost Total unit cost (euro/teu) Rotterdam Antwerp Hamburg Bremen Figure 8-4 Weighed mean of total unit costs for phasing Maasvlakte II When Figure 8-4 is compared with Figure 7-4 it can be concluded that phasing of the construction of Maasvlakte II has almost no effect on the weighed mean of the total unit costs of the ports. 45% Total market share 40% 35% 30% 25% 20% 15% 10% 5% 0% Market share (%) Figure 8-5 Total market share for phasing Maasvlakte II Rotterdam Antwerp Hamburg Bremen When Figure 8-5 is compared with Figure 7-6 it can be concluded that phasing of the construction of Maasvlakte II has almost no effect on the distribution of the total market shares of the ports over the years. In Figure 8-6 the effects of phasing of the construction of Maasvlakte II on the development of the ports is illustrated. It can be concluded that the growth of the volume transported through the ports does not change due to a phased construction of the Tweede Maavlakte in comparison with the base case scenario. In the perspective of the port authority of Rotterdam this means that phasing of Maasvlakte II could be a good strategy for the development of the port in order to spread the investment costs over the two phases without losing market share with a decrease in the growth of 76 June 2007

91 8 Case Study throughput as a consequence. In the perspective of a hydraulic engineer, for instance, it could be preferable to construct Maasvlakte II all at once in order to reduce the construction costs. 10,0 Port development Capacity & Volume (mln TEU) 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity Rotterdam Capacity Antwerp Capacity Hamburg Capacity Bremen Volume Rotterdam Volume Antwerp Volume Hamburg Volume Bremen Figure 8-6 Port development for phasing Maasvlakte II Additional expansions other ports The third situation that is discussed deals with the consequences of a combination of a delay in the introduction of Maasvlakte II and additional capacity expansions of the other ports. This is a interesting situation to have a look at because it is more likely that the other ports do plan other capacity expansions as from 2010 in contrast to the assumption that is made for the preceding situations. To investigate the effects of a combination of a delay in the introduction of Maasvlakte II in combination with additional capacity expansions of the other ports a few changes in the input for the model have to be made. If a delay of 12 years is assumed the year of the fixed input of the introduction of Maasvlakte II has to be changed from 2013 into For the ports of Antwerp and Bremen it is assumed that capacity expansions are put into use in 2020 and for the port of Hamburg a capacity expansion is assumed to be completed in the year These capacity expansions are all of an arbitrary size and are implemented in the model as fixed input. The output that is generated by the model is presented in Figure 8-7, Figure 8-8 and Figure 8-9. When Figure 8-7 is compared with Figure 7-4 the main observation is that for this situation the weighed means of the total unit costs of the four ports stay almost at a constant level and are only increasing slightly over the years. This can easily be explained by the fact that due to the capacity expansions the utilization rates in the ports are lower than the utilization rates in the base case scenario. This results in a lower level of congestion which is reflected in the port residence costs and thus in the total unit costs of the ports. For each port a sudden drop in the weighed mean of the total unit costs can be discerned right after the introduction of a capacity expansion. This is caused by the sudden decrease of the congestion level. Just as in the situation described in paragraph the sudden decrease in the weighed mean of the total unit costs of Rotterdam is accompanied with a small increase in the weighed mean of the total unit costs of Antwerp. Again it is not clear what causes this small increase. June

92 8 Case Study 1100 Weighed mean total unit cost Figure 8-7 Weighed mean of total unit costs for additional expansions of other ports From Figure 8-8 it can be seen that the almost constant level of the weighed means of the total unit costs of the four ports results in a more constant distribution of the total market shares of the ports over the years as well. For the base case scenario the introduction of Maasvlakte II causes a strong increase in the market share of Rotterdam even if the introduction is delayed. For this situation, where the other ports expand their capacity as well, on the long term the port of Rotterdam hardly benefits from its enormous investment if the introduction of Maasvlakte II is delayed. From Figure 8-8 it can also be concluded that for this situation the strongest competition takes place between the port of Rotterdam and the port of Bremen. 40% Total market share 35% 30% 25% 20% 15% 10% 5% 0% Total unit cost (euro/teu) Rotterdam Antwerp Hamburg Bremen Market share (%) Rotterdam Antwerp Hamburg Bremen Figure 8-8 Total market share for additional expansions of other ports In Figure 8-9 the effects of a combination of a delay in the introduction of Maasvlakte II and additional capacity expansions of the other ports on the development of the ports is illustrated. The constant level of the total market shares of the ports is reflected by an almost linear increase in the total volumes of cargo transported through the ports. When Figure 8-9 is compared with Figure 7-8 it can be concluded that a situation as described in this paragraph is highly undesirable for the port of Rotterdam, as the volume of cargo transported through Rotterdam in 2040 is substantially lower than for the base case 78 June 2007

93 8 Case Study scenario whereas all other ports handle higher amounts of cargo in 2040 in comparison with the base case scenario. 10,0 Port development 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity & Volume (mln TEU) Capacity Rotterdam Capacity Antwerp Capacity Hamburg Capacity Bremen Volume Rotterdam Volume Antwerp Volume Hamburg Volume Bremen Figure 8-9 Port development for additional expansions of other ports Conclusions In this paragraph the conclusion from the three situations that are discussed for Scenario 1 are summarized. From the concise analysis of the effects of a delayed introduction of Maasvlakte II on the development of the port of Rotterdam it can be concluded that on the short term (up to 2020) a delay of the introduction of Maasvlakte II has negative consequences for the competitive position of the port of Rotterdam. On the long term (as from 2020) the sensitivity of the competitive position of Rotterdam to a delay of Maasvlakte II depends on additional capacity expansion plans of the other ports. It can be concluded that the growth of the volume transported through the four ports does not change due to a phased construction of Maasvlakte II in comparison with the base case scenario. In the perspective of the port authority of Rotterdam this means that phasing of Maasvlakte II could be a good strategy for the development of the port in order to spread the investment costs over the two phases without losing market share with a decrease in the growth of throughput as a consequence. In the perspective of a hydraulic engineer, for instance, it could be preferable to construct Maasvlakte II all at once in order to reduce the construction costs. From the investigation of the effects of a combination of a delay in the introduction of Maasvlakte II in combination with additional capacity expansions of the other ports it can be concluded that a situation as described in paragraph is highly undesirable for the port of Rotterdam, as the volume of cargo transported through Rotterdam in 2040 is substantially lower than for the base case scenario whereas all other ports handle higher amounts of cargo in Scenario 2: Hinterland Congestion The second scenario concerns hinterland congestion and one situation is discussed. In paragraph attention is paid to the hinterland connections of the port of Rotterdam and a closer look at the consequences of congestion of the A15 on the development of the port of Rotterdam seems interesting. June

94 8 Case Study Recapitulating, the A15 connects the port of Rotterdam with the hinterland in eastern direction. The capacity expansion of the A15 does not keep up with the persistent growth of road transport and the highway is increasingly faced with congestion problems. To investigate the effects of congestion of the A15 on the competitive position of the port of Rotterdam the changes that have to be made in the input for the model are a little more complicated than for Scenario 1 because the transportation costs at a disaggregate level have to be calculated again for truck transport from the port of Rotterdam. If a cost increase of 10% for truck transport is assumed caused by congestion of the A15, this 10% increase has to be applied on the transportation costs at a disaggregate level for all truck transport from Rotterdam. 19 After that the transportation costs at an aggregate level can be calculated again for the port of Rotterdam in the same way as is explained in paragraph These transportation costs at an aggregate level are implemented in the model. The output that is generated by the model is presented in Figure 8-10, Figure 8-11 and Figure Weighed mean total unit cost Total unit cost (euro/teu) Rotterdam Antwerp Hamburg Bremen Figure 8-10 Weighed mean of total unit costs for congestion A15 When Figure 8-10 is compared with Figure 7-4 it can be concluded that congestion of the A15 has a large impact on the overall picture of the weighed mean of the total unit costs of the ports. The weighed mean of the total unit costs of the port of Rotterdam is higher than for the base case scenario due to the increase in the hinterland transportation costs but stays at a constant level over the years until it finally increases rapidly as from In the first years the weighed means of the total unit costs of the other ports are roughly the same as for the base case scenario but they start increasing at a very early stage in comparison with the base case scenario, in particular for the port of Antwerp. These results may seem contrary to expectations but they can be understood by comparing Figure 8-11 with Figure 7-5 and comparing Figure 8-12 with Figure Note that no distinction is made between hinterland countries. 20 Note that the recalculated transportation costs are fixed input for the model and do not change over the years, whereas they should actually increase over the years due to ongoing congestion of the A15 if no measures are taken. The model has to be further improved to make the transportation costs variable input. 21 In the final years of the modeling period a disturbance in the graph can be noticed. This is caused by the fact that none has free capacity. 80 June 2007

95 8 Case Study 50% Total market share 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Market share (%) Rotterdam Antwerp Hamburg Bremen Figure 8-11 Total market share for congestion A15 Figure 8-11 shows that, for the first years of the modeling period (i.e. before 2010), the total market share of the port of Rotterdam drops drastically from about 30% to merely 15% due to congestion of the A15. This drop of the total market share of Rotterdam is mainly absorbed by the ports of Bremen and Antwerp. In comparison with the base case scenario the total market share of Bremen increases from 30% to 35% in the first years of the modeling period and the total market share of Antwerp increases from 7% to 15%. The low total market share of Rotterdam causes the volume of cargo transported through Rotterdam to be much lower than for the base case scenario. As a consequence the of level port congestion is also very low for the first years of the modeling period and even decreases when Maasvlakte II is put into use. This explains the constant level of the weighed mean of the total unit costs of Rotterdam. The other ports on the other hand receive much more cargo in comparison with the first modeling period of the base case scenario and therefore have to deal with higher levels of congestion in an early stage. This explains why the weighed means of the total unit costs of these ports start to increase in an early stage, particularly for Antwerp. As from 2006 the port of Antwerp is completely congested instead of as from 2034 according to the base case scenario. This results in a decrease of the total market share of Antwerp and a stagnation of the growth of the total transported volume through the port. The ports of Rotterdam and Hamburg benefit from this decrease as can be seen from Figure As from 2020 the port of Bremen is completely congested, instead of as from 2032 according to the base case scenario again resulting in a decrease of the total market share and no further growth of the total transported volume. Again the ports of Rotterdam and Hamburg benefit from this decrease. As from 2028 also the port of Hamburg is completely congested, instead of as from 2036 according to the base case scenario, with a decrease of the total market share and no further growth of the total transported volume as a consequence. Because Rotterdam is then the only port with free capacity the very strong growth of the total market share and of the total volume transported through Rotterdam are logical results. Further growth of the container market is completely absorbed by the port of Rotterdam which leads to a very rapid congestion of this port as well. This explains the very rapid increase of the weighed mean of the total unit costs of Rotterdam as from Although many differences can be noticed over the entire modeling period the total market shares of the ports and the total transported volumes of cargo through the ports in 2040 are equal to those from the base case scenario. June

96 8 Case Study 10,0 Port development 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity & Volume (mln TEU) Capacity Rotterdam Capacity Antwerp Capacity Hamburg Capacity Bremen Volume Rotterdam Volume Antwerp Volume Hamburg Volume Bremen Figure 8-12 Port development for congestion A15 Although many differences can be noticed over the entire modeling period, the total market shares of the ports and the total transported volumes of cargo through the ports in 2040 are equal to those from the base case scenario, implying that congestion of the A15 appears to have no significant impact on the market position of Rotterdam on the long term. However, as was already concluded in paragraph 8.3, on the long term (as from 2020) the competitive position of Rotterdam depends on additional capacity expansion plans of the other ports. From the graphs of the total market shares it can be conclude that congestion of the A15 does have significant impact on the market position of Rotterdam in the period before In addition, it is most likely that the other ports will supply additional capacity in the period meaning that the port of Rotterdam will most likely not be able to increase its total market share and the total volume of cargo transported through the port to a level as in the situation described above, despite of the introduction of Maasvlakte II. Note that in Figure 8-10, Figure 8-11 and Figure 8-12 some fluctuations can be noticed in the early stage of the modeling period that are not present for the base case scenario. The model obviously needs some time to adapt to the changes of the input. 8.5 Scenario 3: Sensitivity to Port Dues The third scenario concerns the sensitivity of the model to port dues and one situation is discussed. In paragraph the concept of pricing is explained and the consequences of a pricing strategy of the port of Rotterdam on the development of the port is discussed in this paragraph. Shippers may be not likely to switch to another port in response to a price increase, given that the pricing of a port authority affects only a small share of total cargo shipment costs that a shipper must pay, as is demonstrated in paragraph If, for a given port, other costs make up for most of the call costs this implies fairly high pricing power for the port authority, due to the limited pass-on of its price increase that generates substitution effects. Here an increase of the port dues of Rotterdam of 50% is assumed. For this situation the input for the model can be altered easily. The fixed input of the port dues of Rotterdam has to be changed from 9 euro/teu into 13.5 euro/teu. The output that is generated by the model is presented in Figure 8-13, Figure 8-14 and Figure June 2007

97 8 Case Study 1100 Weighed mean total unit cost 1050 Total unit cost (euro/teu) Rotterdam Antwerp Hamburg Bremen Figure 8-13 Weighed mean of total unit costs for pricing of Rotterdam When Figure 8-13 is compared with Figure 7-4 it can be concluded that the pricing strategy of the port of Rotterdam has almost no effect on the weighed mean of the total unit costs of the ports. 45% Total market share 40% 35% 30% 25% 20% 15% 10% 5% 0% Market share (%) Rotterdam Antwerp Hamburg Bremen Figure 8-14 Total market share for pricing of Rotterdam When Figure 8-14 is compared with Figure 7-6 it can be concluded that the pricing strategy of the port of Rotterdam neither has any effect on the distribution of the total market shares of the ports over the years, which is logical as the weighed means of the total unit costs of the ports are the same as for the base case scenario. June

98 8 Case Study 10,0 Port development 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0 1,0 0, Capacity & Volume (mln TEU) Capacity Rotterdam Capacity Antwerp Capacity Hamburg Capacity Bremen Volume Rotterdam Volume Antwerp Volume Hamburg Volume Bremen Figure 8-15 Port development for pricing of Rotterdam The overall picture of the effect of pricing on the development of the ports is equal to that of the base case scenario, implying that the port of Rotterdam seems to have fairly high pricing power considering that the port dues are raised with 50%. 8.6 Conclusions Case Study The Port Competition Model is a planning tool that can be used by port authorities to examine the efficiency of strategic measures under competition. In this chapter the model is used to investigate three scenarios: changing expansion plans, hinterland congestion and sensitivity to port dues. In this paragraph the conclusion from these scenarios are summarized. Scenario 1: Changing expansion plans From the concise analysis of the effects of a delayed introduction of Maasvlakte II on the development of the port of Rotterdam it can be concluded that on the short term (up to 2020) a delay of the introduction of Maasvlakte II has negative consequences for the competitive position of the port of Rotterdam. On the long term (as from 2020) the sensitivity of the competitive position of Rotterdam to a delay of Maasvlakte II depends on additional capacity expansion plans of the other ports. It can be concluded that the growth of the volume transported through the four ports does not change due to a phased construction of the Tweede Maavlakte in comparison with the base case scenario. In the perspective of the port authority of Rotterdam this means that phasing of Maasvlakte II could be a good strategy for the development of the port in order to spread the investment costs over the two phases without losing market share with a decrease in the growth of throughput as a consequence. In the perspective of a hydraulic engineer, for instance, it could be preferable to construct Maasvlakte II all at once in order to reduce the construction costs. From the investigation of the effects of a combination of a delay in the introduction of Maasvlakte II in combination with additional capacity expansions of the other ports it can be concluded that a situation as described in paragraph is highly undesirable for the port of Rotterdam, as the 84 June 2007

99 8 Case Study volume of cargo transported through Rotterdam in 2040 is substantially lower than for the base case scenario whereas all other ports handle higher amounts of cargo in Scenario 2: Hinterland congestion Although many differences can be noticed over the entire modeling period, the total market shares of the ports and the total transported volumes of cargo through the ports in 2040 are equal to those from the base case scenario, implying that congestion of the A15 appears to have no significant impact on the market position of Rotterdam on the long term. However, as was already concluded in paragraph 8.3, on the long term (as from 2020) the competitive position of Rotterdam depends on additional capacity expansion plans of the other ports. From the graphs of the total market shares it can be concluded that congestion of the A15 does have significant impact on the market position of Rotterdam in the period before In addition, it is most likely that the other ports will supply additional capacity in the period meaning that the port of Rotterdam will most likely not be able to increase its total market share and the total volume of cargo transported through the port to a level as in the situation described above, despite of the introduction of Maasvlakte II. Scenario 3: Sensitivity to port dues The overall picture of the effect of a pricing strategy of the port of Rotterdam on the development of the ports is equal to that of the base case scenario, implying that the port of Rotterdam seems to have fairly high pricing power considering that the port dues are raised with 50%. General conclusions from the Case Study At last this chapter ends with three important general conclusions that can be drawn from the scenarios: The model is sensitive to capacity constraints. The utilization rates of the ports never exceed the 90% congestion limit for none of the scenarios and if this limit is reached within a port the total volume of cargo transported through the port stays at a constant level. The model is very sensitive to hinterland congestion. This can be concluded from the course of the total market shares over the years. If the congestion level of the A15 increases the total market share of the port of Rotterdam drops drastically from about 30% to merely 15%. The model gives plausible results on the short term ( ). On the long term (as from 2020) the competitive position of Rotterdam depends on additional capacity expansion plans of the other ports. As a consequence there are many uncertainties about the future market share and throughput volumes of Rotterdam as from 2020, which is crucial for further development of the port. These conclusions display the importance of the development of the Port Competition Model and the contribution of this research. June

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101 9 Conclusions and Recommendations 9 Conclusions and Recommendations 9.1 Introduction This chapter reflects on the main findings of this graduation project and gives some recommendations. Paragraph 9.2 summarizes the conclusions of this study after recapitulating the thesis objective. In the development of the Port Competition Model many assumptions had to be made because of the data and time constraints. In paragraph 9.3 some recommendations and suggestions for further studies and improvement of the model are formulated. 9.2 Conclusions The aim of this research has been to provide port authorities with a tool to support their strategic investment planning in a competitive market. The tool should provide port authorities with information on the impacts of, structural and non-structural, capacity improvement measures on the future demand for container service. A tool has been developed that can support port authorities in their strategic decision making process in a competitive market: The Port Competition Model. In order to summarize the conclusions in a clear way, they are organized according to the phase of the development of the Port Competition Model. First some general conclusions that answer the thesis objective are given. These conclusions concern the performance of the Port Competition Model. Next the conclusions concerning the base case scenario and the sensitivity of the model for the choice of the congestion function are given. Finally the conclusions derived from the case study are described Conclusions Model The challenge has been to incorporate the aspects of the full dynamics of port competition over the network in the Port Competition Model by simulation of the competitive strategies of other ports and the sensitivity of decisions on port investment for such strategies. For inter-port competition at the level of authorities ports operate as nodes in global transport-logistic chains connecting origins and destinations for freight flows. Sea transport is beyond the scope of this study as the largest part of the sea transport costs are independent of which port in Northern Europe is chosen; the rest of the logistical chain (i.e. port and hinterland transport) is incorporated in the modeling. In this research the discrete choice model is used to model the traffic assignment: the shipping companies choose the logistic chain and the associated port based on the utility for each chain. A main variable in this utility is the generalized transport cost for the different logistical chains. This cost includes all monetary costs of using a route but also monetary values for other factors, such as the time required for the transport. In this research the generalized transport cost is quantified to a very detailed level including call costs, container handling costs and hinterland transportation costs (both monetary costs and monetary values for other factors). The dynamics of port competition are incorporated in the Port Competition Model to the extent that varying market shares are represented for every year of the modeling period, using the discrete choice model. In this research a What-If approach is applied. Gaming or strategic behavior of other ports is not included in the Port Competition Model. This means that the investments in one port are not affecting the behavior of other port authorities. A first attempt has been made to incorporate costs associated with port congestion in the Port Competition Model. However, there is a lack of data on the behavior of congestion within a port and additional research is needed to derive an accurate port congestion function. The Port Competition Model has been developed for four ports within the Hamburg - Le Havre range: Rotterdam, Antwerp, Hamburg and Bremen. In this research the contestable hinterland of these ports is assumed to consists of six countries: Germany, Switzerland, Austria, Poland, the Czech Republic and Italy. An origin-destination matrix has been used to model the hinterland transportation costs for the container flows. June

102 9 Conclusions and Recommendations The Port Competition Model has been calibrated on a dataset (provided by NEA) for the base year The following remarks can be made concerning the performance of the calibrated model: For the ports of Rotterdam, Hamburg and Bremen the Port Competition Model predicts plausible results. The model needs a rather high correction for the port of Antwerp for all hinterland countries (except for Poland). There are different plausible hypotheses to explain why Antwerp has a big disadvantage for shipping companies relative to the other ports. It is beyond the scope of this study to carry out a thorough analysis on the port of Antwerp to explain the disadvantage relative to the other ports. Some hinterland countries need small additional cost corrections to the generalized transportation costs to get good results from the model for the market shares of the ports per country. Because 86% of all cargo is transported to Germany the market shares for the parts of Germany have the strongest influence on the result of the total market shares of the ports. After calibration the results that are obtained with the model come very close to the data from the OD-matrix, especially for the market shares of the four parts of Germany. Therefore it can be concluded that the performance of the calibrated model is fairly good. An analysis of a range of possible scenarios and their impacts is performed using the Port Competition Model. To determine what an effective strategy is for a port to deal with competition the results of the different scenarios have been compared. The Port Competition Model provides information on the impacts of, structural and non-structural, capacity improvement measures on future container volumes. Insight is created in the possible impacts of different scenarios that may appear. The conclusions from the Case Study (see below) display the importance of the development of the Port Competition Model and the contribution of this research Conclusions Base Case Scenario The following conclusions can be drawn from the base case scenario: The modeling period is rather long and therefore the results obtained for the period are the most important. It is most likely that the ports will develop additional expansion plans as from 2020, meaning that the pattern of the results obtained for the period will change. The model is sensitive to congestion. This can be concluded from the course of the weighed means of the total unit costs of the ports over the years. If the level of congestion increases the weighed means of the total unit costs raise accordingly. The modeled system is sensitive to capacity constraints. The utilization rates of the ports never exceed the 90% congestion limit and if this limit is reached within a port the total volume of cargo transported through the port stays at a constant level. The model is hardly sensitive to free capacity and thus to the service level.. Immediately after the introduction of a capacity expansion one would expect a strong increase of the total market share of a port and an increase of the growth of the total volume of cargo transported through the port. However, in the results obtained with the model only a small increase of the total market share of a port and a small increase of the growth of the total volume of cargo transported through the port can be noticed. On the long term the model does react to free capacity but that is mainly caused by the fact that other ports are reaching the congestion limit. The model is not sensitive to a decrease of the influence of the level of congestion on the service time and the service cost. The model is very sensitive to an increase of the influence of the level of congestion on the service time and the service cost. It is suggested here that the behavior of congestion within a port could be simulated with Harboursim. 88 June 2007

103 9.2.3 Conclusions Case Study 9 Conclusions and Recommendations The Port Competition Model is a planning tool that can be used by port authorities to examine the efficiency of strategic measures under competition. The model is used to investigate three scenarios: changing expansion plans, hinterland congestion and sensitivity to port dues. The conclusion from these scenarios are summarized below. Scenario 1: Changing expansion plans From the concise analysis of the effects of a delayed introduction of Maasvlakte II on the development of the port of Rotterdam it can be concluded that on the short term (up to 2020) a delay of the introduction of Maasvlakte II has negative consequences for the competitive position of the port of Rotterdam. On the long term (as from 2020) the sensitivity of the competitive position of Rotterdam to a delay of Maasvlakte II depends on additional capacity expansion plans of the other ports. It can be concluded that the growth of the volume transported through the four ports does not change due to a phased construction of the Tweede Maavlakte in comparison with the base case scenario. In the perspective of the port authority of Rotterdam this means that phasing of Maasvlakte II could be a good strategy for the development of the port in order to spread the investment costs over the two phases without losing market share with a decrease in the growth of throughput as a consequence. In the perspective of a hydraulic engineer, for instance, it could be preferable to construct Maasvlakte II all at once in order to reduce the construction costs. From the investigation of the effects of a combination of a delay in the introduction of Maasvlakte II in combination with additional capacity expansions of the other ports it can be concluded that a situation as described in paragraph is highly undesirable for the port of Rotterdam, as the volume of cargo transported through Rotterdam in 2040 is substantially lower than for the base case scenario whereas all other ports handle higher amounts of cargo in Scenario 2: Hinterland congestion Although many differences can be noticed over the entire modeling period, the total market shares of the ports and the total transported volumes of cargo through the ports in 2040 are equal to those from the base case scenario, implying that congestion of the A15 appears to have no significant impact on the market position of Rotterdam on the long term. However, as was already concluded in paragraph 8.3, on the long term (as from 2020) the competitive position of Rotterdam depends on additional capacity expansion plans of the other ports. From the graphs of the total market shares it can be concluded that congestion of the A15 does have significant impact on the market position of Rotterdam in the period before In addition, it is most likely that the other ports will supply additional capacity in the period meaning that the port of Rotterdam will most likely not be able to increase its total market share and the total volume of cargo transported through the port to a level as in the situation described above, despite of the introduction of Maasvlakte II. Scenario 3: Sensitivity to port dues The overall picture of the effect of a pricing strategy of the port of Rotterdam on the development of the ports is equal to that of the base case scenario, implying that the port of Rotterdam seems to have fairly high pricing power considering that the port dues are raised with 50%. General conclusions from the Case Study Three important general conclusions can be drawn from the scenarios: The model is sensitive to capacity constraints. The utilization rates of the ports never exceed the 90% congestion limit for none of the scenarios and if this limit is reached within a port the total volume of cargo transported through the port stays at a constant level. The model is very sensitive to hinterland congestion. This can be concluded from the course of the total market shares over the years. If the congestion level of the A15 increases the total market share of the port of Rotterdam drops drastically from about 30% to merely 15%. The model gives plausible results on the short term ( ). On the long term (as from 2020) the competitive position of Rotterdam depends on additional capacity expansion plans of the other June

104 9 Conclusions and Recommendations ports. As a consequence there are many uncertainties about the future market share and throughput volumes of Rotterdam as from 2020, which is crucial for further development of the port. These conclusions display the importance of the development of the Port Competition Model and the contribution of this research. 9.3 Recommendations The development and application of a tool for the planning of port capacity has been focused on port expansion as strategy for a single port to deal with competition as well as with autonomous demand growth. Limitations of this study and the Port Competition Model relate to the scope of the research, the analysis of port congestion, analysis on the port of Antwerp, and (the lack of) modeling the full dynamics of port competition. In this paragraph some recommendations and suggestions for further studies and improvement of the model are formulated. Widen the scope of the research Competition on transshipment flows is not included in the research, only transit flows. As a large part of the generalized chain costs of container transport arises with (un)loading the competition on transshipment containers is severe as well and more research is needed in order to determine the market shares of the ports for the whole container market. The focus of the research is on capacity expansions. Attention paid to non-structural measures is concise. The effects of less capital-intensive strategies, such as tariff strategies and cooperation between ports, on port competitiveness need to be further investigated. The French and Italian ports are not considered. However, these ports compete to a large extent for the same hinterland countries as the ports within the Hamburg-Le Havre range. Including the Italian and French ports in the research gives a better picture of the market positions of Rotterdam, Antwerp, Hamburg and Bremen. The level of competition is restricted to inter-port competition at the level of authorities. In addition to the previous item the scope of the research can be widened to inter-range competition, e.g. competition between the Hamburg-Le Havre range and the Mediterranean range. Further analysis of port congestion In this research, a port is considered as a point entity with an overall capacity instead of as a set of inter-dependent links, which need to be optimally tuned to each other. Any inefficiencies in these links and their joint functioning lead to higher service times than ideally can be performed by the port. These higher service times are interpreted in this study as port congestion. Congestion behavior within a port is more complicated than congestion of the highway network. In this research an assumption is made for the shape of the congestion function. As is concluded in chapter 7 the model is not sensitive to a decrease of the influence of the level of congestion on the service time and the service cost. However, the model is very sensitive to an increase of the influence of the level of congestion on the service time and the service cost. Further research on the determination of the congestion behavior within a port and the associated congestion function is needed. The behavior of congestion within a port could be simulated with Harboursim. Further analysis of the port of Antwerp The port of Antwerp has a big disadvantage for shipping companies relative to the other ports which is not incorporated in the model. A thorough analysis on the port of Antwerp needs to be carried out to explain the disadvantage relative to the other ports. Modeling the full dynamics of port competition Substantial investments in the port of Rotterdam, e.g. Maasvlakte II, and potential reactions of other competing ports have focused attention to the dynamics of port competition. The dynamics of port competition are incorporated in the Port Competition Model to the extent that varying market shares are assumed and calculated for every year of the modeling period. In order to incorporate the full dynamics 90 June 2007

105 9 Conclusions and Recommendations of port competition in the model the competitive strategies of other ports and the sensitivity of decisions on port investment for such strategies need to be simulated, e.g. with Gaming theory. In paragraph attention is paid to the capacity expansion pattern to meet linearly growing demand. An extension to this approach for non-linearly growing demand is illustrated in Appendix IV. This approach is used to determine the optimal phasing and sizing of an expansion strategy. When the approach for non-linearly growing demand is applied in the Port Competition Model, the optimal relief interval between two succeeding capacity expansions and the associated optimal expansion size can be calculated, with dynamic planning of capacity expansions as a result. A first start has been made to incorporate the full dynamics of port competition in the model. June

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107 References References Cariou, Corrail (2001). Strategic Alliances in Liner Shipping: An Analysis of Operational Synergies. University of Nantes, France. Coppens, F., Lagneaux, F., Meersman, H., Sellekaerts, N., Van de Voorde, E., Van Gastel, G., Vanelslander, Th., Verhetsel, A. (2007). Economic impact of port activity: a disaggregate analysis The case of Antwerp. Working paper No. 110, February, Microeconomic Information Department, NBB, Brussel / Department of Transport and Regional Economics, University of Antwerp, Belgium. CPB (2004). Verruiming van de vaarweg van de Schelde Een maatschappelijke kosten-batenanalyse. Centraal Planbureau, the Hague, the Netherlands. CRA (2004). Study on the Port of Rotterdam Market Definition and Market Power. Charles River Associates, Brussels, Belgium. Dekker, S. (2005). Port Investment Towards an Integrated Planning of Port Capacity. PhD-Thesis, TRAIL / Faculty of Civil Engineering, Delft University of Technology, the Netherlands. De Langen, P.W., Chouly A. (2004). Hinterland access regimes in seaports. European Journal of Transport and Infrastructure Research, Vol. 4, No. 4, pp De Langen, P.W., Nijdam, M.H., Van der Lugt, L.M. (2005). Port Economics. Erasmus University Rotterdam, the Netherlands. Fourgeaud, P. (2000). Measuring Port Performance. The World Bank. Accessed February, Freidenfelds, J. (1981). Capacity Expansion Analysis of Simple Models with Applications. North Holland, New York, U.S./Oxford, U.K.. Frima, G.A.J. (2004). Capacity study for the Rio de la Plata waterway, Argentina. Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Garcia, B. (2006). Unclogging ports of entry through inland ports or Port Agility. American Planning Association Conference, April. Groenveld, R. (2002). Service Systems in Ports and Inland Waterways. Lecture notes CT4330/CT5306, Department of Hydraulic Engineering, Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Günther, H.O., Kim, K.H. (2006). Container terminals and terminal operations. OR Spectrum, Vol. 28, pp Haezendonck, E., Notteboom, T. (2002). The competitive advantage of seaports. In: Huybrechts, M. et al. (2002). Port Competitiveness An Economic and Legal Analysis of the Factors Determining the Competitiveness of Seaports. De Boeck Ltd, Antwerp, Belgium, pp Heaver, T., Meersman, H., Van de Voorde, E. (2001). Co-operation and competition in international container transport: strategies for ports. Maritime Policy & Management, Vol. 28, No. 3, pp Huybrechts, M., Meersman, H., Van de Voorde, E., Van Hooydonck, E., Verbeke, A., Winkelmans, W. (2002). Port Competitiveness An Economic and Legal Analysis of the Factors Determining the Competitiveness of Seaports. De Boeck Ltd, Antwerp, Belgium. Ligteringen, H. (2000). Ports and Terminals. Lecture notes CT4330/CT5306, Department of Hydraulic Engineering, Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Manne, A.S. (1967). Investments for Capacity Expansion Size, Location, and Time-Phasing. George Allen & Unwin Ltd, London, U.K.. June

108 References Meersman, H., Pauwels, T., Van de Voorde, E., Vanelslander, T. (2006). Het effect van Havenconcurrentie op het project Ijzeren Rijn. Department of Transport and Regional Economics, University of Antwerp, Belgium. Meersman, H., van de Voorde, E. (2006). Dynamic Ports Within a Globalised World. Department of Transport and Regional Economics, University of Antwerp, Belgium. Paper presented at the 17 th International Symposium on Theory and Practice in Transport Economics and Policy, Berlin, Germany, October Moonen, H., Lang, N., van Nunen, J.A.E.E., van der Velde, S.L. (2006). Creating Virtual Capacity Utilizing real-time information to boost the performance and reliability of container supply chains. TRAIL Research School, Delft, the Netherlands. Municipality of Rotterdam and Port Authority Rotterdam (2004). Havenplan 2020, Ruimte voor kwaliteit. Rotterdam, the Netherlands. NEA (2005). Maritieme Goederenstromen in de Hamburg Le Havre Range; Nadere Analyse Achterlandvervoer NEA Transport Research and Training, Rijswijk, the Netherlands. Notteboom, T.E. (1997). Concentration and load centre development in the European port system. Journal of Transport Geography, Vol. 5, No. 2, pp Op het Veld, J.H.G.P.L. (2003). Capaciteitsplanning onder havencompetitie Containeroverslag in de Hamburg - le Havre range. Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Porter, M.E. (1990).The Competitive Advantage of Nations. New York: Free Press, U.S.. Port of Rotterdam (2007). Port Tariffs Rotterdam, the Netherlands. Rudzikaite, L., Kiel, J. (2005). Revenue use from transport pricing. Contribution to the Colloquium Vervoersplanologisch Speurwerk, Antwerp, Belgium, November Sanders, F.M., Verhaeghe, R.J., Dekker, S. (2005). Investment dynamics for a congested transport network with competition: application to port planning. Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Slack, B. (1999). Satellite terminals: a local solution to hub congestion? Journal of Transport Geography, Vol. 7, No. 4, pp Smits, R.M. (2006). Capacity study for the Port of Buenos Aires, Argentina, Volume I and II. Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Suykens, F., Van de Voorde, E. (1998). A quarter of a century of port management in Europe: objectives and tools. Maritime Policy & Management, Vol. 25, No. 3, pp Tavasszy, L. (2003). SMILE: Strategic Model for Integrated Logistics and evaluation. Accessed February, UNCTAD (2006). Review of Maritime Transport. UNCTAD Secretariat, Presented at United Nations Conference on Trade and Development, New York, U.S./Geneva, Switzerland. Van der Hoest, A. (2003). Rotterdam en de Italiaanse concurrentie. Faculty of Civil Engineering, Delft University of Technology, the Netherlands. Van der Jagt, N. (2005). Breathe easy. Containerisation International, April, pp. 39. Van de Voorde, E., Winkelmans, W. (2002). A general introduction to port competition and management. In: Huybrechts, M. et al. (2002). Port Competitiveness An Economic and Legal Analysis of the Factors Determining the Competitiveness of Seaports. De Boeck Ltd, Antwerp, Belgium, pp Verhaeghe, R.J. (2006). Plan and Project Evaluation. Lecture notes CT4740, Department of Transport and Planning, Faculty of Civil Engineering, Delft University of Technology, the Netherlands. 94 June 2007

109 References Visser, J.G.S.N., Konings J.W., Pielage, B.A., Wiegmans, B. (2007). A new hinterland transport concept for the port of Rotterdam: organisational and/or technological challenges? Delft University of Technology, the Netherlands. Wong, K.Y. (2005). Strategy formulation using Game Theory: The competition between seaports: Rotterdam versus Antwerp. Faculty of Civil Engineering, Delft University of Technology, the Netherlands June

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111 List of Appendices List of Appendices Appendix I Appendix II Appendix III Appendix IV Appendix V Appendix VI Appendix VII Appendix VIII Appendix IX Appendix X Appendix XI Appendix XII Appendix XIII Container Transfer Process Port Capacity Measures Scale Factor Freidenfelds-Approach The Ports of Antwerp, Hamburg and Bremen Comparison Transport Cost Call Pattern Value of Time General Information Ports HLH Range Vessel Sizes Distances by Mode Data OD-Matrix Balance Import-Export Appendix XIV Cargo Flow Prognosis 2010 Appendix XV Appendix XVI Appendix XVII Calibration Base Case Scenario Sensitivity for Congestion June

112 Appendix Appendix I Container Transfer Process origin maritime section ocean vessel arrival passage trough entrance channel transshipment flow unloading at berth berth-yard transportation transshipment storage yard storage port land section yard-gate transportation loading at gate hinterland transportation hinterland destination truck train inland shipping optional: transfer at inland terminal(s) hinterland destination Source: Dekker, June 2007

113 Appendix Appendix II Port Capacity Measures Port capacity measures Structural measures Non-structural measures - dredging works - removal of obstacles - additional berths - application of locks - more cranes - additional road and rail connections - land reclamation Supply-management measures: - exchange of information - loading/unloading without berth interference - improved berth capacity - better terminal design - improved yard capacity - improved gate capacity - improved port-land interface - spreading of activities to other regions - reallocation of activities - privatization - private funding of investments Demand-management measures: - congestion pricing - peak pricing - demurrage charges - redirection of cargo flows - slot auctioning Improved handling capability Affected port users behavior Facility expansion Improved utilization Source: Dekker, 2005 June

114 Appendix Appendix III Scale Factor Manne/Freidenfelds have developed an optimal expansion concept in which a trade-off is made between capital financing costs and scale effect(see also Appendix IV). For a cost function of the type b C ( x ) = ax (scale factor b) and interest rate r, the optimum is defined by: r = b 1 * τ * r e τ With τ * is the optimal time interval. This can be solved iteratively. The optimum for a particular scale factor b and interest rate r can also be determined graphically as presented below. Source: Sanders, Verhaeghe, Dekker, June 2007

115 Appendix Appendix IV Freidenfelds-Approach In the simplest engineering approach for dealing with expansion problems, it is assumed that demand for additional units of capacity will grow linearly at rate g over an unbounded horizon; the aspects of competition and congestion are ignored. Starting from time t = 0, gτ additional units will be required at time τ (i.e. the relief interval) in the future. The figure below illustrates the demand and expansion pattern. It is further assumed that the investment cost of additional units comprises an exponential function, specified by C(x) = ax b with 0<b<1. This function represents the present value of providing x units of capacity forever. This model is meant to capture some important phenomena in capacity expansion problems in the simplest possible setting. For example, the interaction between demand and expansion strategy is not accounted for. A thorough discussion for the exponential investment cost function can be found in Freidenfelds (1981, pp ). The remainder of this appendix gives a brief summary of the most important results. The present value W of an infinite stream of investments in expansion can be represented as: W ax 1 e b = r ( x / g ) (F.1) in which represents the social discount rate. In terms of the relief interval τ = x/g the present value can be represented as: W b b = ag τ 1 rτ e (F.2) The number of parameters can be reduced by looking at W/a, rτ, and g/r: W ( g / r) ( rτ ) 1 e b b / a = (F.3) rτ June

116 Appendix A necessary and sufficient condition for the optimal relief interval can be found by setting the derivative of Eq. F.3 to 0, which yields: e r τ 1 1 = rτ b (F.4) Using a Taylor series approximation for e rτ, the optimal relief interval τ* is approximated by: r b τ * (F.5) It can be observed that the optimal relief interval is independent of the growth rate g. Furthermore, an increase of the scale factor b leads to a decrease of the optimal relief interval. Suppose that demand grows at rate g = 64,000 TEU/year, and the investment cost of capacity expansion is given by C(x) = 3.125x 0.829, where x is the number of TEU s and C is expressed in millions of euros. The social discount rate r is From Eq. F.5 follows that the optimal relief interval is τ*=10.3 years, thus the optimal size x*=gτ*=659,200 TEU. Because a port productivity of 24,000 TEU/ha/year is assumed, this corresponds to a port area expansion of about 27.5 hectares. The associated investment cost is 48.8 million. Source: Dekker, June 2007

117 Appendix Appendix V The Ports of Antwerp, Hamburg and Bremen Antwerp Hamburg June

118 Appendix Bremen Source: June 2007

119 Appendix Appendix VI Comparison Transport Cost Road Rail June

120 Appendix Inland waterway Source: NEA, June 2007

121 Appendix Appendix VII Call Pattern June

122 Appendix Source: CRA, June 2007

123 Appendix Appendix VIII Value-Of-Time In this research the VOT is approximated by the daily loss on capital for the receiver of the container in transit. This is established by determining the steepness of the linear approximation of the curve i D (opportunity of time) that can be constructed with the help of OC ( D) = V [(1 + ρ) 1] as function of the duration (in days). This is conceptually shown in the figure below. i The linear approximation of the opportunity cost of time is here based on a Taylor series approximation for (1 + ρ) D, which can be set equal to 1 + ρ D because ρ 1. The approximated opportunity cost of i time, OC ( D ), is then expressed by: app OC i ( D ) = V ρ D app i The value of the slope of this function, V i ρ, represents the approximated VOT for commodity group i. For the sake of simplicity, the average value per TEU, V av, is used instead of V i. Source: Dekker, 2005 June

124 Appendix Appendix IX General Information Ports HLH Total container throughput (mln TEU) Rotterdam Antwerp Hamburg Bremen Total Market shares (%) Rotterdam Antwerp Hamburg Bremen Total Total container throughput (mln tons) Rotterdam Antwerp Hamburg Bremen Total Capacity (mln TEU) and Utilization rate (%) Capacity 2003 (mln TEU) Utilization (%) Capacity shares 2003 (%) Capacity 2006 (mln TEU) Utilization (%) Capacity shares 2006 (%) Rotterdam Antwerp Hamburg Bremen Total June 2007

125 Appendix Modal split (%) 2005 Mode Rotterdam Antwerp Hamburg Bremen Transshipment Hinterland transport Total Truck Train Barge Total Source: CRA, 2004 and websites ports June

126 Appendix Appendix X Vessel Sizes Vessel sizes Class TEU capacity DWT (average) L (m) D (m) B (m) 1 st generation nd generation rd generation th generation Post Panamax Jumbo > Source: Ligteringen, 2000 Technological development Source: CRA, June 2007

127 Appendix Appendix XI Distances by Mode Truck distances (in km) Duisburg Basel Milan Vienna Prague Warsaw Rotterdam Antwerp Hamburg Bremen Train distances (in km) Duisburg Basel Milan Vienna Prague Warsaw Rotterdam Antwerp Hamburg Bremen Barge distances (in km) Duisburg Basel Milan Vienna Prague Warsaw Rotterdam Antwerp Hamburg Bremen Source: NEA, 2007 June

128 Appendix Appendix XII Data OD-Matrix Destination Country Port of Origin Total Total Destination Country Port of Origin Total 201 6% 16% 17% 13% 11% 12% 7% % 44% 61% 24% 11% 12% 30% % 10% 14% 40% 60% 46% 38% % 30% 8% 23% 18% 30% 25% Total 100% 100% 100% 100% 100% 100% 100% Destination Country Port of Origin Total % 9% 8% 4% 4% 3% 100% % 6% 7% 2% 1% 1% 100% % 1% 1% 2% 4% 2% 100% % 5% 1% 2% 2% 2% 100% Total 86% 4% 3% 2% 3% 2% 100% 114 June 2007

129 Appendix Mode Origin Destination Total % 6% 17% 100% % 23% 0% 100% % 22% 29% 100% % 20% 1% 100% % 10% 0% 100% % 18% 9% 100% Total % 10% 15% 100% % 4% 30% 100% % 70% 0% 100% % 72% 17% 100% % 53% 18% 100% % 35% 1% 100% % 52% 4% 100% Total % 13% 27% 100% % 20% 2% 100% % 26% 0% 100% % 82% 1% 100% % 52% 0% 100% % 31% 1% 100% % 37% 10% 100% Total % 23% 2% 100% % 12% 2% 100% % 81% 0% 100% % 74% 11% 100% % 52% 0% 100% % 19% 0% 100% % 16% 0% 100% Total % 17% 2% 100% Total 72% 18% 11% 100% June

130 Appendix Commodity Origin Destination Total % 1% 0% 0% 33% 2% 1% 10% 41% 0% 100% 500 8% 1% 0% 0% 34% 1% 0% 14% 42% 0% 100% % 1% 0% 0% 28% 2% 0% 6% 46% 0% 100% % 1% 0% 0% 43% 2% 0% 6% 42% 0% 100% % 3% 0% 0% 31% 1% 0% 8% 20% 0% 100% % 0% 0% 0% 4% 2% 0% 3% 45% 0% 100% Total % 1% 0% 0% 32% 2% 1% 9% 41% 0% 100% % 1% 0% 1% 10% 2% 0% 3% 77% 0% 100% 500 6% 1% 0% 0% 7% 0% 0% 2% 84% 0% 100% % 1% 0% 0% 8% 0% 0% 2% 85% 0% 100% % 1% 0% 1% 28% 2% 0% 3% 58% 0% 100% % 10% 0% 0% 7% 0% 0% 5% 55% 0% 100% % 3% 0% 0% 15% 2% 0% 2% 67% 0% 100% Total 309 5% 1% 0% 1% 10% 1% 0% 3% 77% 0% 100% % 3% 0% 0% 9% 6% 0% 2% 70% 0% 100% 500 0% 0% 0% 0% 2% 0% 0% 1% 97% 0% 100% % 1% 0% 0% 8% 0% 0% 1% 86% 0% 100% % 1% 0% 0% 8% 0% 0% 2% 83% 0% 100% % 4% 0% 0% 3% 0% 0% 1% 63% 0% 100% % 4% 0% 0% 12% 0% 0% 2% 67% 0% 100% Total % 3% 0% 0% 9% 6% 0% 2% 71% 0% 100% % 2% 0% 0% 12% 6% 0% 2% 66% 0% 100% 500 1% 1% 0% 0% 0% 1% 0% 1% 96% 0% 100% % 2% 0% 0% 15% 0% 0% 4% 73% 0% 100% % 2% 0% 0% 16% 0% 0% 1% 74% 0% 100% % 2% 0% 0% 6% 1% 0% 1% 85% 0% 100% % 1% 0% 0% 3% 1% 0% 0% 67% 0% 100% Total % 2% 0% 0% 11% 5% 0% 2% 68% 0% 100% Total 9% 2% 0% 1% 11% 4% 0% 3% 70% 0% 100% 116 June 2007

131 Appendix Appendix XIII Balance Import-Export Rotterdam Antwerp June

132 Appendix Hamburg Bremen Source: NEA, June 2007