With the current revolution in Shipping 4.0, a tremendous amount of data is accumulated during vessel operations.
Data quality (DQ) is becoming more and more important for the further digitalization and effective decision-making
in shipping industry. In this study, a practical DQ assessment method for raw data in vessel operations is proposed.
In this method, specific data categories and data dimensions are developed based on engineering practice and existing
literature. Concrete validation rules are then formed, which can be used to properly divide raw datasets. Afterwards,
a scoring method is used for the assessment of the data quality. Three levels, namely good, warning and alarm,
are adopted to reflect the final data quality. The root causes of bad data quality could be revealed once the internal
dependency among rules has been built, which will facilitate the further improvement of DQ in practice. A case study
based on the datasets from a Danish shipping company is conducted, where the DQ variation is monitored, assessed
and compared. The results indicate that the proposed method is effective to help shipping industry improve the quality
of raw data in practice. This innovation research can facilitate shipping industry to set a solid foundation at the early
stage of their digitalization journeys.
With the current revolution in Shipping 4.0, a tremendous amount of data is accumulated during vessel operations.
Data quality (DQ) is becoming more and more important for the further digitalization and effective decision-making
in shipping industry. In this study, a practical DQ assessment method for raw data in vessel operations is proposed.
In this method, specific data categories and data dimensions are developed based on engineering practice and existing
literature. Concrete validation rules are then formed, which can be used to properly divide raw datasets. Afterwards,
a scoring method is used for the assessment of the data quality. Three levels, namely good, warning and alarm,
are adopted to reflect the final data quality. The root causes of bad data quality could be revealed once the internal
dependency among rules has been built, which will facilitate the further improvement of DQ in practice. A case study
based on the datasets from a Danish shipping company is conducted, where the DQ variation is monitored, assessed
and compared. The results indicate that the proposed method is effective to help shipping industry improve the quality
of raw data in practice. This innovation research can facilitate shipping industry to set a solid foundation at the early
stage of their digitalization journeys.
This paper describes the challenges of the maritime supply chain compared to land transport and discusses the new digital initiatives to simplify the processes and enable a better plan for the entire supply chain. First, the background is outlined with an example of the extensive admin processes in maritime transport compared to road transport, followed by a case example presenting the processes of a booking. The case study concludes that the lack of integration is costly in terms of both admin resources, as well as lost capacity on some ships and missing capacity on others. Finally, the evolution of new digital initiatives are discussed, both in general and in terms of competing “alliances” as seen in the airline industry. The paper concludes that the information exchange in the maritime industry has moved drastically in the last 3 years and that one initiative, TradeLens, seems to have gained a position as maritime standard despite a problematic start with many competing initiatives.
This paper addresses the connection between added wave resistance and required propulsion power of ships, having focus on the early stage of new ship designs, notably tankers and bulk carriers. The paper investigates how mean added wave resistance affects the required torque of a fixed pitch propeller and thus also the operational conditions of a directly coupled main engine. The interest of the study has its background in the assessment of minimum propulsion power, and the study considers the prescriptive guidelines of the IMO as basis. Specifically, the study focuses on an assessment of the minimum forward speed attainable under consideration of the propeller light running margin and static load limits of engines in the early phase of new ship designs, where details of hull geometry are not available. The study considers three semi-empirical methods for predicting mean added wave resistance. All methods are known to be applied in the industry, emphasising that only methods relying solely on main particulars, together with information about sea state and advance speed, are of interest. The paper contains a case study used to illustrate the importance of the added wave resistance prediction with respect to the loading of the main engine. It is shown that, despite small absolute differences, the consequence in relation to the loading of the propeller and hereby the directly coupled main engine can be relatively large. Furthermore, the study illustrates that the propeller light running margin of a fixed pitch propeller directly coupled to the main engine has crucial influence on the attainable speed during adverse weather conditions.
Port selection is of vital importance for both port operators and shipping lines. In this contribution, an Automatic Identification System (AIS) big data approach is developed. This approach allows identifying container ships using only AIS data without the need for supplementary information from commercial databases. This approach is applied to investigate the port selection statistics of container ships between Shanghai and Ningbo Zhoushan Port, two of the largest ports in the world in terms of calling frequency, to generate practical insights. Results show that: i) the ratios among large ships, medium ships and small ships of these two ports are both approximately 1: 4: 5; ii) these two ports both have an exclusive (i.e., more feeder ports covered in geographical coverage) and intensive (i.e., more feeder ships deployed in shipping service frequency) collection and distribution network mainly consisting of small ships, but that of Shanghai is more intensive; iii) in terms of ultra-large ships over 380 m, Shanghai has accommodated an extra 18.5% compared to that of Ningbo Zhoushan, this indicates Shanghai's attraction for such vessels in global fleet deployment; iv) the feeder network between Shanghai and Ningbo Zhoushan is weak, and their relationship is actually in competition; v) Ningbo Zhoushan could offer more choices for ultra-large container ships (over 380 m), which implies its greater potential in future port competition; vi) when the depth of channels and berths is sufficient, the distance to hinterland and the convenience of a collection and distribution network begin to get more important in port selection. The empirical findings unveil the decision-making of container lines, competition between ports and implications for shipping policy.
This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that 26% of the ships of the dataset’s fleet warrant further investigation.
Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches.
Different procedures for estimation of the extreme global wave hydroelastic responses in ships are discussed. Firstly, stochastic procedures for application in detailed numerical studies (CFD) are outlined. The use of the First Order Reliability Method (FORM) to generate critical wave episodes of short duration, less than 1 minute, with prescribed probability content is discussed for use in extreme response predictions including hydroelastic behaviour and slamming load events. The possibility of combining FORM results with Monte Carlo simulations is discussed for faster but still very accurate estimation of extreme responses. Secondly, stochastic procedures using measured time series of responses as input are considered. The Peak-over-Threshold procedure and the Weibull fitting are applied and discussed for the extreme value predictions including possible corrections for clustering effects.
This paper describes the methodological aspects of calculation of incidence rates from incomplete data in occupational epidemiology. Proportionate measures in epidemiological studies are useful e.g. to describe the proportion of slips, trips and falls compared to other types of injury mechanisms within single age-strata. However, a comparison of proportions of slips, trips and falls among the different age-strata gives no meaning and can hamper the conclusions. Examples of a constructed example and some selected studies show how estimates of incidence rates can be calculated from the proportionate data by applying estimates of denominators available from other information. The calculated examples show how the risks based on the incidence rates in some cases differ from the risks based on the proportionate rates with the consequence of hampering the conclusions and the recommendations for prevention. In some cases the proportionate rates give good estimates of the incidence rates, but in other studies this might cause errors. It is recommended that estimates of the incidence rates should be used, where this is possible, by estimation of the size of the population. The paper is intended to be useful for students and teachers in epidemiology by using the attached Excel training file.
Sensing data from vessel operations are of great importance in reflecting operational performance and facilitating proper decision-making. In this paper, statistical analyses of vessel operational data are first conducted to compare manual noon reports and autolog data from sensors. Then, new indicators to identify data aberrations are proposed, which are the errors between the reported values from operational data and the expected values of different parameters based on baseline models and relevant sailing conditions. A method to detect aberrations based on the new indicators in terms of the reported power is then investigated, as there are two independent measured power values. In this method, a sliding window that moves forward along time is implemented, and the coefficient of variation (CV) is calculated for comparison. Case studies are carried out to detect aberrations in autolog and noon data from a commercial vessel using the new indicator. An analysis to explore the source of the deviation is also conducted, aiming to find the most reliable value in operations. The method is shown to be effective for practical use in detecting aberrations, having been initially tested on both autolog and noon report from four different commercial vessels in 14 vessel years. Approximately one triggered period per vessel per year with a conclusive deviation source is diagnosed by the proposed method. The investigation of this research will facilitate a better evaluation of operational performance, which is beneficial to both the vessel operators and crew.