To mitigate global warming, worldwide CO2-emissions have to be cut. Similarly, the shipping industry has to drastically decrease it's emissions. Rules and regulation on emissions are becoming more stringent. Moreover, the public and clients are demanding less pollution from ship operators. While many shipping operators are looking at a suited fuel alternative, no consensus has been reached on the best alternative to entirely dispose of harmful emissions from shipping in the long-term. Fugro is one of the ship operators interested in entirely bringing down the emissions of their vessels that are built from 2030 onwards and therefore this research is conducted. This thesis will research how and if Fugro can achieve the IMO target of 70 percent CO2 reduction in 2050, by deploying net zero-emission fuels in Fugro vessels as of 2030. An evaluation will be done to assess which alternative fuels are feasible and most suitable, based on current technologies and considering future scenarios around technology. The research will approach this problem by firstly conducting an extensive literature research into alternative fuels and Multiple Criteria Decision Analysis methods. Thereafter the Fugro fleet, potential future vessel developments, future scenarios and alternative fuels are researched to set up a technical framework for this case problem specifically. With this technical framework in place, the method itself is conducted. This is done in two ways. First, by conducting a qualitative approach using the Analytic Hierarchy Process, also involving stakeholders from Fugro and the industry. Second, by carrying out a case application of the four best alternatives on a representative Fugro vessel. This approach, including a qualitative approach extended with a quantitative one results in a substantiated advise. Both methods complement each other on points where the other falls short. The AHP includes stakeholders and takes into account criteria that are difficult to quantify. The application approach tests the feasibility of the proposed alternatives and tests the potentially subjective or intuitive outcome of the AHP method. With methanol scoring well in both approaches, the conclusion of this research is that methanol is the advised alternative fuel for future Fugro vessels. Liquid hydrogen is the highest rated fuel alternative from the AHP. The application example shows that this fuel isn't able to comply with the required Fugro operability however. Contrary to that, ammonia is a highly rated alternative in literature and is also scoring well in the application part of this thesis, but is not rated as a suitable alternative by stakeholders in the AHP. The last considered alternative in both parts of the method is synthetic diesel, which is comparable to MDO. This fuel scores well in both methods but is considered to remain too expensive to become a viable alternative in the coming ten years. In this thesis, estimates of different fuel options are presented and some design concepts and considerations on the different alternative fuel applications are given. The next step for Fugro would be to work out a methanol concept, by going through the design spiral more than once and work out the design choices and challenges this fuel brings. With alternative fuels being more expensive than MDO, this thesis pointed out that the economic speed will change when using alternative fuels, which is another point that requires more research. While the AHP proved to be a suitable way to score fuel alternatives while including stakeholders and assessing criteria qualitatively, it also pointed out that it is difficult to solely base an alternative fuel advise on this method. Potentially, the AHP results would improve when the questionnaire is simplified by reducing the amount of considered criteria.

The shipbuilding industry is not a standalone industry. It integrates with the shipping market, and therefore one characteristic of the shipbuilding industry is complexity. Competitiveness is a multi-dimensional concept that can be measured in numerous ways. The author found that these multi-dimensional concepts can better be translated into three objectives. Three methods are evaluated to find which one is the most appropriate when measuring the shipyard's competitiveness. To find which method is the most appropriate, the author conducted the Analytical Hierarchy Process and found that benchmarking analysis is the best method. Data Envelopment Analysis (DEA) is chosen due to its versatility, cautious estimation, and non-parametric characteristics. Data Envelopment Analysis is an operation research method that uses mathematical formulation to find benchmarks among units under study. The first model uses deliveries as output, dock area and number of employees as inputs. The second model uses price/CGT and duration/CGT as inputs, and new contracts as outputs. The models investigated 20 shipyards from Japan, China, South Korea, and Vietnam. The results show that Chinese yards are very efficient when it comes to attracting new orders. However, in terms of allocating its resources, Chinese yards are very inefficient. Japanese yards are very efficient in both models. Most of the Japanese yards are frontiers. Korean yards, on the other hand, are the winner for mega-sized yards, but not in the medium-sized shipyards. Data Envelopment Analysis (DEA)'s results show the efficiency between the output and input of a system with a quantifiable value and provide a point of improvement by increasing output (for output-oriented) or decreasing input (for input-oriented). The insight can be derived from the results by analyzing the efficiency score and lambda values.

The aviation Maintenance, Repair and Overhaul (MRO) industry faces an enormous growth due to the increasing air traffic. To accommodate this growth and increase efficiencies of processes it is required to increase the transparency of demand in the Supply Chain (SC). Research into predicting demand in the MRO industry have mainly been focused on the spare part request (forward logistics). This results in a research gap regarding increasing transparency in the reverse SC. This paper investigates an approach to increase transparency in demand by prediction the arrival times of shipments in the reverse SC based on transport times. The transport times from two customers have been collected and analysed. The analysis showed the presence of different transport behaviour depending on the process which included inefficiencies and waste. Based on the analyses different methods to predict the transport time have been formulated and evaluated. The results show two methods superior to the others which are the median and decision tree. Furthermore, the research indicates the accuracy of predictions increases with more available data and stable processes. The results show to be insufficient to accurately predict the majority of the shipments and therefore require further research.

The maritime emission regulations and incentives require and motivate the selection of different types of energy systems, fuels, and abatement options. In early-stage ship design, the design space of possible combinations can be significant. Therefore, a selection tool has been developed to find the most satisfactory combination of abatement options at minimum costs that at least meet the emission requirements. This decision problem is studied from a general perspective to develop a universal selection tool to enable a widespread application in the maritime industry. The selection tool contains datasets with decision parameters of different energy systems, fuels, and abatement options. The energy systems, including reference fuels, can be selected and assessed on their annual economic and environmental performance. Both upstream and operational emissions are considered and, in addition, the external costs of the emissions are quantified. The abatement options, including fuels, have different effects on fuel consumption and emissions. Moreover, interaction effects occur when combining alternatives. The identified problem is a combinatorial optimisation problem and the objective space is constrained by the emission and compatibility constraints. It is formulated as a multi-objective optimisation problem, whereby the annual internal (investment plus operational) costs and external costs are simultaneously minimised. The external costs can serve as a balancing approach for emission reduction. Furthermore, it can encourage the reduction of the overall environmental impact beyond the regulatory emission constraints. A genetic optimisation algorithm is integrated into the selection tool, which optimises the combinations of abatement options that are subject to the applicable constraints. The functioning of the methodology is evaluated by case studies for the NAVAIS subjects: a battery-electric road ferry and a diesel-electric workboat, both for European waters. The results can provide useful insights into the concept design space of feasible combinations that comply with emission regulations.

There is a significant historical link between maritime trade and economic activity. With the rapid growth of containerised trade over the past 60 years, a significant share of global trade is transported by container nowadays. This suggests there is a connection between container flows and economic activity. There is also a time delay between containers with goods being imported and the announcement of quarterly Gross Domestic Product numbers. With US container imports and exports being publicly available information, this leads to the research question of this thesis: Can loaded and/or empty container flows be used to predict economic activity?” If so, this could prove to be valuable information for economists, policy makers and, as this thesis highlights, professional traders working at investment banks or hedge funds.

This thesis focusses on analysing transpacific containerised trade as a potential so-called forward indicator of US GDP. To measure these transpacific flows, an aggregate of US West coast port data is produced and de-seasonalised. The container flows are separated into Loaded In, Empty Out and Loaded Out flows. It is important to understand the dynamics of container trade and the variables influencing it on this route. Variables are identified that could possibly affect the supply-side of container shipping and therefore the transpacific container flows (the demand-side is seen as an expression of economic activity).
After analysing the Cross Correlation Functions (CCF) of the identified, data based influencing variables, five different GDP growth prediction models are constructed. This is done by performing OLS single and multivariate regressions of the individual container flows (together with the influencing variables) on historical GDP growth data from 2000 to 2017. The resulting models are tested against an existing, commonly used forward indicator: the Purchasing Managers Index (PMI). The results show that three models, all using a form of loaded containers, outperform the PMI when predicting the direction of US GDP growth 3-months ahead over the 17-year time period of the dataset used. The probability of large prediction errors with these models are also smaller than the PMI benchmark model.

The growing pressure on tank storage terminals is pushing the terminals to find ways to accommodate more vessels by increasing the efficiency of the vessel handling process. This research looks at the possibility of implementing forward planning at such a terminal by simulating multiple strategies and testing the impact on the seaside performance of the terminal. The current process is a reactive one due to the first come first serve rule that is enforced. This causes an unpredictable arrival distribution of vessels and a low level of information and collaboration. The simulation results are promising and show that a proactive planning strategy could drastically increase terminal seaside performance by allowing the terminal to control vessel arrival distributions and preventing certain interrupts that often occur within the process. In the best case, the terminal seaside performance increased from the current state performance of 55% to 92%.

Developments in the field of renewable energy technologies have lead to low energy cost in areas with specific characteristics. In the Middle East solar power plants are built for prices around 3 cent per kilowatt-hour and bids below 3 cent have been placed. In Europe wind energy is the second largest source of renewable energy after biomass. Although, wind farms are being built without subsidy the total cost of a wind farm is much higher in the order of 14 cent per kWh and it can decrease to 10 cent by 2030.

In this research an concept study for the transport of electric energy is made to find an answer to the question: “is it possible to ship electric energy which is delivered in a port between areas of generation and consumption over a fixed distance with the use of a ship and deliver it for a levelised cost of electricity (LCoE) which is competitive in the market of unloading.”

The cost of a kilowatt-hour of electric energy in the Dutch market is 8.33 if it is produced by a modern gas fired power plant and if the cost for CO2 emissions are included according to the current prices in the European emission trading scheme. If higher carbon cost scenario's are applied the price could increase to prices over 17 cent per kilowatt-hour. The concepts in this study are tested against four different carbon emission cost scenario's ranging from 8 euro/tCO2 to 190 euro/tCO2

Most electrical energy is transported by electric energy power grids. For long distance high volume energy transmission a high voltage direct current energy cable is the most efficient way to transport energy but at larger distances the transmission system gets more expensive. In this research the electric cable is used as a reference case which the concept has to outperform.

To test the concepts a non-linear model is made which is solved with Matlab-integrated solvers to find the optimal transport concept. The model works by optimising the main dimensions of the ship. With this optimal ship design concepts are analysed. For this research: a concept based on a hydrogen-carrier (ammonia) and a concept based on thermal energy storage are discussed.
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The hydrogen-carrier concept uses ammonia as a carrier. Ammonia has a high energy content and can be produced with the Haber Bosch process or with Solid State Ammonia Synthesis (SSAS). If the energy is produced using the well known Haber-Bosch process in 2018 the levelised cost of energy is 21.2 and 24.2 cent per kilowatt hour depending on the distance. Developments in the field of electrolysers will reduce the capital investment which can reduce the price to 17.8 to 20.8 cent per kilowatt hour by 2025 if the energy is supplied at 2 cent per kilowatt hour to the ammonia production facility.

The SSAS method offers higher energy effincies at lower capital investment cost. The LCOE calculated in this research is 11.4 to 16.4 cent per kilowatt-hour.
The thermal energy storage concept is based on molten salt. Molten salt is being used in concentrated solar power plants as a storage of energy. In the concept used in this project the molten salt is transported to another part where the heat which is stored in the salt is used to generate electricity. The LCoE is already 28.5 cent per kilowatt-hour at a distance of 1,000 nm. The thermal energy storage concept is more expensive than the ammonia concept.

Depending on the distance and the carbon cost scenario's the concept can be competitive to the fixed connection with a cable and fossil fired power plants in the home market. If the low cost carbon scenario's (1 and 2) are applied by law makers the concept of electrical energy by sea is not competitive but if politicians would decide to increase the cost for carbon emissions the transport could become competitive. When the carbon emission cost increase to values over 100 euro/tCO2 the SSAS concept is competitive for distance up to 2,000 nm but it still more expensive than a cable connection. For this distances larger than 4,000 nm and carbon prices according to the 4th scenario (190 euro/tCO2) the SSAS-based concept can be a competitive energy supplier.

Problem statement - Damen Shipyards (Damen) wants to use Big Data analysis to gain new market insights and forecast vessel demand. These insights are valuable because Damen keeps standardised vessels in stock in order to significantly reduce delivery times. This concept gives Damen a competitive advantage, but is not without risk since they must speculatively build vessels beforehand. As a case, Damen wants to improve the current forecasting methods for the thriving offshore wind market - for which they supply vessels - based on data derived from the Automatic Identification System (AIS). AIS data includes among others information about identification, location, speed, date and time of approximately 200.000 vessels worldwide. Made Smart Group (MSG) is an information service provider specialised in nautical information, and maintains the world’s largest AIS database. MSG and Damen joined forces for this research.

Objective - The objective of this research is to determine if and how Big Data analysis can be used to model (future) demand for Crew Transfer Vessels (CTV) being used for Crew Transfer Operations (CTO) in the offshore wind industry.

Methodology - Almost 45 million AIS location reports of 39 CTVs servicing 263 turbines in 3 offshore wind farms throughout 2016 are analysed to derive key figures of the executed CTOs. Key figures are e.g. the weather window, and the number of executed CTOs per hour. The CTV demand is modelled based on these key figures, and three wind farm specific input parameters: number of turbines, distance between wind farm and port, and the sea state distribution.

Results - The CTO demand of the 263 analysed wind turbines was on average 113 per year in 2016. This average CTO demand has a variation of almost 50% between turbine types. With an accuracy of 4%, it is modelled that a yearly average of 12.4 CTVs are needed to service these 263 turbines. Furthermore, the CTV demand decreases on average with 11% in the three analysed wind farms if the CTO limit can be increased from 1.5 m to 2.0 m mean significant wave height. This result in a potential cost saving around € 5.1 million on a yearly basis for these three wind farms alone.

Implications - AIS data can be used to model vessel demand and gain insight into the market size. The accuracy of the developed model can be improved by adding: more wind farm specific variables; and/ or data of more CTVs/ wind farms. The gained knowledge about using Big Data analysis to forecast the CTV market size is useful and important for the introduction and future development of commercial AIS based data analysis. Furthermore, it provides insights into the operational profile of CTVs. This can be used to develop better vessels, better service the market and ultimately help to lower the cost price of offshore wind energy. It is believed that the maritime sector could profit from AIS data analysis.