Search results

This paper inspects in detail the seasonality (deterministic) in container freight rates, and compares seasonality patterns in different freight rate indices. A deterministic seasonality unit root test is performed to achieve set objectives. This study concludes that all the indices (tested in this paper) exhibit significant deterministic seasonality. For January and August, there is no seasonal effect observed in all five series. At the same time, all the indices except Exports from Europe Rate Index (EEI) exhibit significant seasonal patterns in February, September, and December. All five indices exhibit significant seasonality during May, and the coefficient sign shows a drop in the freight rates. During March, October, and November; it is observed that only EEI exhibit significant seasonal patterns. The results could be beneficial for carriers and agents who are involved in the containerised freight transport business. Also, shippers could get a clear idea about the freight rates' nature across various trade routes.

While previous studies focused mainly on East Asia to Europe or United States trade routes, in recent years, trade among South-East Asian countries has increased notably. The price of transporting a container is not fixed and can fluctuate heavily over the course of a week. Besides, extant literature only identified seasonality patterns in the container freight market, but did not explore route-varying seasonality patterns. Hence, this study analyses container freight seasonality patterns of the six South-East Asian routes of the South-East Asian Freight Index (SEAFI) and the index itself and forecasts them.

Data of the composite SEAFI and six routes are collected from the Shanghai Shipping Exchange (SSE) including 167 weekly observations from 2016 to 2019. The SEAFI and individual route data reflect spot rates from the Shanghai Port to South-East Asia base ports. The authors analyse seasonality patterns using polar plots. For forecasting, the study utilize two univariate models, autoregressive integrated moving average (ARIMA) and seasonal autoregressive neural network (SNNAR). For both models, the authors compare forecasting results of original level and log-transformed data.

This study finds that the seasonality patterns of the six South-East Asian container trade routes are identical in an overall but exhibits unique characteristics. ARIMA models perform better than SNNAR models for one-week ahead test-sample forecasting. The SNNAR models offer better performance for 4-week ahead forecasting for two selected routes only.

Major industry players such as shipping lines, shippers, ship-owners and others should take into account the route-level seasonality patterns in their decision-making. Forecast analysts can consider using the original level data without log transformation in their analysis. The authors suggest using ARIMA models in one-step and four-step ahead forecasting for majority of the routes. The SNNAR models are recommended for multi-step forecasting for Shanghai to Vietnam and Shanghai to Thailand routes only.

This study analyses a new shipping index, that is, the SEAFI and its underlying six routes. The authors analyze the seasonality pattern of container freight rate data using polar plot and perform forecasting using ARIMA and SNNAR models. Moreover, the authors experiment forecasting performance of log-transformed and non-transformed series.

The purpose of this paper is to explore the extent to which supply side flexibility tactics are deployed by operators in the container liner shipping sector in 2009/200 to restrict supply in a market which is characterised by over‐supply (as well as under demand).

Taking a case study approach using the Far East‐Europe trade lane, secondary data are reviewed for each type of internal flexibility capability. This is supplemented by a qualitative Delphi‐based research method so that findings are iteratively verified with leading practitioner personnel.

In 2009, directly after the severe imbalance between demand and supply emerged, liner shipping providing companies were only partially able to exploit the flexibility tactics that were available to them. This improved in 2010 and contributed to an upturn in performance.

Ocean freight logistics provides a vital foundation for contemporary international commerce. However, the viable provision of this service has become significantly more challenging and this research examines why this is the case and what supply side responses are being deployed. Taking a case study approach focussing on 2009/2010 restricts the generalisability of the research that could now be examined on a longer time scale across the whole sector.

This research is novel as there has been no previous research which has looked at the deployment of supply side flexibility tactics in the container liner shipping sector. The findings have considerable bearing on how the industry is run and understood by its providers, customers and regulators.

This paper presents an overview of the recent development of Eurasian rail freight in the Belt and Road era and further evaluates its service quality in terms of transit times and transport costs compared to other transport modes in containerised supply chains between Europe and China.

A trade-off model of transit time and transport costs based on quantitative data from primary and secondary sources is developed to demonstrate the market niche for Eurasian rail freight vis-a-vis the more established modes of transport of sea, air and sea/air. In a scenario analysis, further cargo attributes influencing modal choice are employed to show for which cargo type Eurasian rail freight service is favourable from a shipper's point of view.

At present, Eurasian rail freight is about 80% less expensive than air freight with only half of the transit time of conventional sea freight. Our scenario analysis further suggests that for shipping time-sensitive goods with lower cargo value ranging from $US1.23/kg to $US10.89/kg as well as goods with lower time sensitivity and higher value in a range of $US2.46/kg to $US21.78/kg, total logistics costs of Eurasian rail freight service rail is cheaper than all other modes of transport.

As an emerging competitive solution, Eurasian rail freight demonstrates to be an option beneficial in terms of transport cost, transit time, reliability and service availability, which offers a cost-efficient option enabling shippers to build up agile and more sustainable supply chains between China and Europe.

Our study firstly provides a comprehensive assessment of present Eurasian rail freight including a thorough comparison with alternative modes of transport from a shipper's point of view.

The purpose of this paper is to examine the long-term cointegrating relationship between ocean, rail, truck and air cargo freight rates, as well as the short-term dynamics between these four series. The authors also test the predictive ability of these freight rates on major economic indicators.

The authors employ a vector error-correction model using 16 years of monthly time series data on freight rate data in the ocean, truck, rail and air cargo sectors to examine the interrelationship between these series as well as their interrelationship with major economic indicators.

The authors find that truck freight rates and as well as dry bulk freight rates have the strongest predictive power over other transportation freight rates as well as for the four major economic indicators used in this study. The authors find that dry bulk freight rates lead other freight rates in the short-run but lag other freight rates in the long run.

While ocean freight rate time series have been examined in a large number of studies, little research has been done on the interrelationship between ocean freight rates and the freight rates of other modes of transportation. Through the use of data on five different freight rate series, the authors are able to assess which rates lead and which rates lag each other and thus assist future researchers and practitioners forecast freight rates. The authors are also one of the few studies to assess the predictive power of non-ocean freight rates on major economic indicators.

In recent years, the business of container lines has faced severe challenges such as overcapacity and low profitability. To survive in such a competitive market, container lines need to maintain long-term customer relationships by enhancing the satisfaction and loyalty of customers. The purpose of this paper is to adopt a social exchange theory (SET) approach and investigate the impact of relational bonding strategies on the satisfaction and loyalty of customers in container shipping.

Drawing on SET, a theoretical model that specifies the relationships between relational bonding strategies, customer satisfaction and loyalty was proposed. Survey data were collected from 175 freight forwarders. The obtained data were analyzed using structural equation modelling.

The results indicate that financial bonding strategies have the most significant direct effects on customer satisfaction, while social bonding strategies have the strongest direct impact on customer loyalty. Financial bonding strategies, on the other hand, have the strongest total effects on customer loyalty. Intermodal and basic operations are found to have the equal total effects on customer loyalty.

By identifying the most effective relational bonding strategies for enhancing customer satisfaction and loyalty, this study’s findings allow container lines to better allocate their resources and implement effective relational marketing policies to satisfy and retain their customers.

This research analyses and validates the determinants of customer satisfaction and loyalty from a relational lens and empirically contributes to the field of relational marketing in the container shipping industry.

Since the 2010s, market conditions for container shipping companies have been deteriorating owing to decreasing container cargo trade and increasing supply capacity. This study aims to contribute to the empirical literature on the container shipping industry market structure. Specifically, this study aims to investigate the extent of market competition.

This study analyzes the market structure and evaluates the market power of shipping companies through a non-structural test.

The H-statistic for the entire period of 2004–2018 was 0.37, which is significantly different from zero. This indicates the absence of monopoly pricing throughout the entire period. For the time-phased estimates, the H-statistic between 2004 and 2008 is 0.15, which is not significantly different from zero. On the other hand, the H-statistic from 2009 to 2018 was 0.40, which differs significantly from zero.

As the Far East Freight Conference had released tariffs and charge rates by item for container shipping routes, monopolistic pricing is said to have appeared until the European Union abolished the European Economic Community (No. 4056/86) in 2008, before the economic crisis. However, this study indicates that pricing in the container shipping industry has been distinctly non-monopolistic; further, competition seems to have intensified since 2008. Industry competitiveness is of interest not only to academics but also to practitioners, including policymakers, especially when considering competition policies.

This paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.

The decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.

In the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.

The decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.