Container shipping is generally considered a global business. This truth may not hold from a single-company perspective. The companies’ physical operation networks show that container carriers operate differently and follow different paths in their internationalisation development. Additionally, the degree of internationalisation, measured on the basis of sea-oriented operations, differs from that measured according to land-oriented front-end marketing and sales activities. The purpose of this study is to further examine the internationalisation patterns of shipping lines. An examination of the front-end activities and the structures of leading container-shipping companies is conducted. The sales office networks of the sector’s 20 largest companies worldwide (by twenty-foot equivalent unit capacity) are analysed as key indicators. The numbers of sales offices are measured by analysing the websites of the sample (20 companies), as well as annual reports and other publicly available data sources. The findings show that not all shipping companies are international, by virtue of the industry. While it is difficult to observe differences in the overall patterns of the sales networks at a macro level, some companies differ in their activities. The data set also shows that market share and total capacity are not necessarily good indicators of a carrier’s worldwide presence. This research is based on secondary data. Other important transactional and market-oriented considerations should be examined before drawing conclusions about the internationalisation of container-shipping companies and of the industry. This paper contributes to the relevant existing research, particularly by adding its view on the demand-oriented criteria as suggested by Dunning and Lundan (2008).
Decisions regarding investments in capacity expansion/renewal require taking into account both the operating fitness and the financial performance of the investment. While several operating requirements have been considered in the operations research literature, the corresponding financial aspects have not received as much attention. We introduce a model for the renewal of shipping capacity which maximizes the Average Internal Rate of Return (AIRR). Maximizing the AIRR sets stricter return requirements on money expenditures than classic profit maximization models and may describe more closely shipping investors׳ preferences. The resulting nonlinear model is linearized to ease computation. Based on data from a shipping company we compare a profit maximization model with an AIRR maximization model. Results show that while maximizing profits results in aggressive expansions of the fleet, maximizing the return provides more balanced renewal strategies which may be preferable to most shipping investors.
We consider a strategic infrastructure and tanker fleet sizing problem in the liquefied natural gas business. The goal is to minimize long-term on-shore infrastructure and tanker investment cost combined with interrelated expected cost for operating the tanker fleet. A non-linear arc-based model and an exact solution method based on a set-partitioning formulation are developed. The latter approach allows very fast solution times. Computational results for a case study with a liner shipping company are presented, including an extensive sensitivity analysis to account for limited predictability of key parameter values, to analyze the solutions’ robustness and to derive basic decision rules.
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.
Purpose: A service production system has a structure composed of task execution, agents performing tasks and a resulting service output. The purpose of this paper is to understand how such a service production system changes as a consequence of offshoring.Design/methodology/approach: Drawing on practice theory, the paper investigates how offshoring leads to reconfiguration of the service production system. Through a multiple case methodology, the authors demonstrate how agents and structures interact during reconfiguration.Findings: The paper analyses the reconfiguration of components of a service production system in response to change ignited by offshoring. The authors find recurring effects between structures that enable and constrain agents and agents who shape the structure of the production system. Research limitations/implications: The paper offers a novel contribution to the service operations management literature by applying practice theory. Moreover, the authors propose a detailed, activity-driven view of service production systems and service offshoring. The authors contribute to practice theory by extending its domain to operations management.Practical implicationsService production systems have the ability to self-correct any changes inflicted through offshoring of the systems, which helps firms that offshore.Originality/valueThe paper is aimed at service professionals and offshoring managers and proposes a novel presentation of the service production system with a description of how it responds to offshoring. The authors contribute to theory by applying practice theory to the fields of service operations management and offshoring.
Green shipping as an emerging concept which aims to mitigate the negative environmental impacts caused by shipping activities has received more and more attentions recently. However, there is a gap in knowledge how to take the efficacious measures, which makes it difficult for the stakeholders of shipping activities to promote green shipping. In order to fill this gap, this chapter proposed a generic methodology for establishing a criterion evaluation system for greenness assessment of shipping, including the identification of the success factors, the development of some strategic measures, and the analysis of the measures for enhancing the greenness of shipping. A criterion evaluation system which consists of multiple criteria in five aspects including: technological aspect, economic aspect, environmental aspect, social aspect, and managerial aspect has been firstly established. Subsequently, Analytic Network Process (ANP) has been employed to determine the relative importance of these factors in green shipping with the consideration of the interdependences and interactions among these criteria for evaluating the greenness of shipping, and they have been ranked from the most important to the least. Accordingly, the key success factors for green shipping have been obtained. Then, some strategic measures for helping the stakeholders enhance the greenness of shipping have been proposed. Finally, Interpretative Structuring Modeling (ISM) has been employed to analyze the cause-effect relationships among these measures and the features of these measures.
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.
Maersk Line er verdens førende containerrederi og blandt de mest betydningsfulde virksomheder i Danmark. Den globale førerposition blev opnået på relativt kort tid og var et resultat af rederiets beslutning i 1973 om at gå helhjertet ind i containerskibsfarten. Beslutningen blev startskuddet til Maersk Lines dybe internationalisering, hvor rederiet ændrede sig fra at være en overvejende danske virksomhed, der betjente internationale markeder, til at være en genuin transnational virksomhed. Med fokus på opbygningen af Maersk Lines globale organisation og særligt etableringen af egne kontorer i udlandet indkredses rederiets tilpasning og udvikling i perioden fra 1973-1999.
In this paper speed optimization of an existing liner shipping network is solved by adjusting the port berth times. The objective is to minimize fuel consumption while retaining the customer transit times including the transhipment times. To avoid too many changes to the time table, changes of port berth times are only accepted if they lead to savings above a threshold value. Since the fuel consumption of a vessel is a non-linear convex function of the speed, it is approximated by a piecewise linear function. The developed model is solved using exact methods in less than two minutes for large instances. Computational experiments on real-size liner shipping networks are presented showing that fuels savings in the magnitude 2–10% can be obtained. The work has been carried out in collaboration with Maersk Line and the tests instances are confirmed to be representative of real-life networks.
Major liner shipping companies offer pre- and end-haulage as part of a door-to-door service, but unfortunately pre- and end-haulage is frequently one of the major bottlenecks in efficient liner shipping due to the lack of coordination between customers. In this paper, we apply techniques from vehicle routing problems to schedule pre- and end-haulage of containers, and perform tests on data from a major liner shipping company. The paper considers several versions of the scheduling problem such as having multiple empty container depots, and having to balance the empty container depot levels. The influence of the side constraints on the overall cost is analysed. By exploring the fact that the number of possible routes in the considered case is quite limited, we show that the model can be solved within a minute by use of column enumeration. Alternative constraints and problem formulations, such as balancing empty container storage level at depots, are considered. Computational results are reported on real-life data from a major liner shipping company.