Considering 91 countries with seaports, this study conducted an empirical inquiry into the broader economic contribution of seaborne trade, from a port infrastructure quality and logistics performance perspective. Investment in quality improvement of port infrastructure and its contribution to economy are often questioned by politicians, investors and general public. A structural equation model (SEM) is used to provide empirical evidence of significant economic impacts of port infrastructure quality and logistics performance. Furthermore, analysis of a multi-group SEM is performed by dividing countries into developed and developing economy groups. The results reveal that it is vital for developing countries to continuously improve the quality of port infrastructure as it contributes to better logistics performance, leading to higher seaborne trade, yielding higher economic growth. However, this association weakens as the developing countries become richer.
This study introduces a state-of-the-art volatility forecasting method for container shipping freight rates. Over the last decade, the container shipping industry has become very unpredictable. The demolition of the shipping conferences system in 2008 for all trades calling a port in the European Union (EU) and the global financial crisis in 2009 have affected the container shipping freight market adversely towards a depressive and non-stable market environment with heavily fluctuating freight rate movements. At the same time, the approaches of forecasting container freight rates using econometric and time series modelling have been rather limited. Therefore, in this paper, we discuss contemporary container freight rate dynamics in an attempt to forecast for the Far East to Northern Europe trade lane. Methodology-wise, we employ autoregressive integrated moving average (ARIMA) as well as the combination of ARIMA and autoregressive conditional heteroscedasticity (ARCH) model, which we call ARIMARCH. We observe that ARIMARCH model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a weekly as well as monthly level. We also observe remarkable influence of recurrent general rate increases on the container freight rate volatility.
This paper evaluates the influence of forecast horizon and observation fit on the robustness and performance of a specific freight rate forecast model used in the liner shipping industry. In the first stage of the research, a forecast model used to predict container freight rate development is presented by exploring the relationship between individual company’s rates and aggregated market rates, and thus assists in dealing with uncertainty and market volatility for a given business situation. In the second stage, a design of experiment approach is applied to highlight the influence of the forecast horizon and observation fit and their interactions on the forecast model’s performance. The results underline the complicated nature of creating a suitable forecast model by balancing business needs, a desire to fit a good model and achieve high accuracy. There is strong empirical evidence from this study; that a robust model is preferable, that overfitting is a true danger, and that a balance must be achieved between forecast horizon and the number of observations used to fit the model. In addition, methodological guidance has also been provided on how to test, design, and choose the superior model for business needs.
A mixed integer linear programming model is presented for the operational level cargo allocation and vessel scheduling problem of a liner shipping company in Turkey, where flow-dependent port-stay lengths, transit times and transshipment synchronizations are considered. The proposed model aims to assign shipments to routes to decrease total tardiness and construct partial vessel schedules for establishing coordination with port authorities to comply with the berthing time windows. In addition to the mathematical model, novel valid inequalities and benders decomposition algorithm are implemented. Performance of the developed algorithm is evaluated on real-life problem instances. The results show that benders decomposition with valid inequalities yields the best performance.
This study exploits service modularity in front-end logistics services in e-fulfillment, from a customer-centric approach, particularly in order management, delivery, and return. Through an online survey of UK customers, the service priorities of 494 respondents via AHP (Analytic Hierarchical Process) were analyzed. Extracting customers' service priorities, ordering behavior, and demographic information as input data, the clustering algorithm KAMILA (KAy-means for MIxed LArge data sets) was further applied. The three identified customer clusters (multichannel shoppers, infrequent shoppers, and online fans) provide preliminary evidence on how commonality and variability aspects of service modularity in front-end logistics services can optimize the number of service options and their performance levels. Therefore, our study, building on value co-creation and modularity, proposes a systematic way of exploiting service modularity for the customer segmentation process that addresses heterogeneous customer preferences cost-efficiently and uncomplicatedly. Furthermore, we provide a framework for the governance of front-end logistics services, guiding outsourcing decisions. Accordingly, it reveals the implications of customer priorities and service decomposition logic choices on value creation. Finally, the propositions formulated aim to develop theoretical foundations for explaining how the heterogeneity in customer priorities for logistics services can be managed with modularity, creating value both for customers and retailers.
Aiming at reducing CO2 emissions from shipping at the EU level, a system for monitoring, reporting, and verification (MRV) of CO2 emissions of ships was introduced in 2015 with the so-called ‘MRV Regulation’. Its stated objective was to produce accurate information on the CO2 emissions of large ships using EU ports and to incentivize energy efficiency improvements by making this information publicly available. On 1 July 2019, the European Commission published the relevant data for 10,880 ships that called at EU ports within 2018. This milestone marked the completion of the first annual cycle of the regulation’s implementation, enabling an early assessment of its effectiveness. To investigate the value of the published data, information was collected on all voyages performed within 2018 by a fleet of 1041 dry bulk carriers operated by a leading Danish shipping company. The MRV indicators were then recalculated on a global basis. The results indicate that the geographic coverage restrictions of the MRV Regulation introduce a significant bias, thus prohibiting their intended use. Nevertheless, the MRV Regulation has played a role in prompting the IMO to adopt its Data Collection System that monitors ship carbon emissions albeit on a global basis.
In supporting multimodal freight transport services, Activity 3.2-3 focuses on identifying the priorities of the shippers (cargo owners) in relation to intermodal logistics solutions that comprise the core of multimodality.
A literature search was performed on this subject. It identified a long list of characteristics that shippers consider necessary for efficient and effective intermodal transport. They include price, delivery time, time reliability of delivery, frequency of shipments, cargo safety and security, reliability of pick up time, ability to respond to customer needs, proactive notification of problems, etc. A number of pre-conditions
were also identified. They include the commodity type, value, density and time-sensitivity, freight distance, direction of haul (head-haul/back-haul), meaningful load factors and transhipment costs. Based on the results of the literature search, a questionnaire was designed for obtaining shippers’ assessment of their experience with intermodality, the factors driving it and the measures proposed for its promotion. It is noted that the latter subject has not been treated by the previous studies examined.
After being revised on the basis of feedback received from logistics experts, the questionnaire was promoted through shipper associations in the five study countries (Germany, Denmark, Sweden, Norway and Finland). Responses were received through an electronic survey lasted from July 2017 to June 2018. The questionnaire was also distributed in paper form to the participants of the event “Future transport and logistics in the Fehmarnbelt Region – How to be prepared for changing cargo flows” on 29 May 2018 during the Fehmarnbelt days 2018 in Malmö, Sweden. Furthermore, responses were enriched by a number of interviews from selected companies and associations.
The majority of the 33 usable responses obtained comes from Germany and Denmark. The companies that have arranged intermodal shipments during 2016 find their experience more than satisfactory.
Germans appear to be 25% happier with intermodality than their Danish counterparts, who are still satisfied. The differential is greater with regard to business types. Freight forwarders, who are more exposed to intermodal realities than shippers, display a much higher satisfaction than the latter, who fall a bit short of the satisfactory level albeit still on the positive side.
Among the reasons for going intermodal, the specific customer/supplier instructions appear to be the most important one. This finding suggests the need to identify the right decision-makers prior to designing activities promoting intermodal transportation. Competitive pricing follows suit surpassing all other quality characteristics (in Germany, it is even more important than customer preferences). This
result contradicts the findings of other studies that assign more importance to attributes such as frequency of service, reliability, etc. The appropriateness of shipment size and the convenience of transit time follow price concerns in the scale of importance. It is interesting to note that the advantages offered by intermodality in terms of low emissions and improved company image appear very low in the
importance spectrum despite the emphasis placed on them by the policy makers.
As expected, the type of business has a bearing on these priorities. Competitive pricing is the main concern of shippers, while from the freight forwarders’ perspective, customer preferences remain the decisive factor. An interesting observation is that the only occasion that environmental concerns climb higher than shipment size and transit time is when it comes to other businesses, probably pointing to the more distant positioning of this type of respondents to the realities of the market place
The paper proposes a methodology for freight corridor performance monitoring that is suitable for sustainability assessments. The methodology, initiated by the EU-funded project SuperGreen, involves the periodic monitoring of a standard set of transport chains along the corridor in relation to a number of Key Performance Indicators (KPIs). It consists of decomposing the corridor into transport chains, selecting a sample of typical chains, assessing these chains through a set of KPIs, and then aggregating the chain-level KPIs to corridor-level ones using proper weights. A critical feature of this methodology concerns the selection of the sample chains and the calculation of the corresponding weights. After several rounds of development, the proposed methodology suggests a combined approach involving the use of a transport model for sample construction and weight calculation followed by stakeholder refinement and verification. The sample construction part of the methodology was tested on GreCOR, a green corridor project in the North Sea Region, using the Danish National Traffic Model as the principal source of information for both sample construction and KPI estimation. The results show that, to the extent covered by the GreCOR application, the proposed methodology can effectively assess the performance of a freight transport corridor. Combining the model-based approach for the sample construction with the study-based approach for the estimation of chain-level indicators exploits the strengths of each method and avoids their weaknesses. Possible improvements are also suggested by the paper.
This paper presents a literature survey on the fleet size and mix problem in maritime transportation. Fluctuations in the shipping market and frequent mismatches between fleet capacities and demands highlight the relevance of the problem and call for more accurate decision support. After analyzing the available scientific literature on the problem and its variants and extensions, we summarize the state of the art and highlight the main contributions of past research. Furthermore, by identifying important real life aspects of the problem which past research has failed to capture, we uncover the main areas where more research will be needed.
This paper addresses the fleet renewal problem and particularly the treatment of uncertainty in the maritime case. A stochastic programming model for the maritime fleet renewal problem is presented. The main contribution is that of assessing whether or not better decisions can be achieved by using stochastic programming rather than employing a deterministic model and using average data. Elements increasing the relevance of uncertainty are also investigated. Tests performed on the case of Wallenius Wilhelmsen Logistics, a major liner shipping company, show that solutions to the model we present perform noticeably better than solutions obtained using average values.