Influencing factors of crude oil maritime shipping freight fluctuations: a case of Suezmax tankers in Europe-Africa routes

PurposeThe paper aims to identify the contributors to freight rate fluctuations in the Suezmax tanker market; this study selected the refinery output, crude oil price, one-year charter rate and fleet development as the main influencing factors for the market analysis.Design/methodology/approachThe paper used the VEC (vector error correction) model to evaluate the degree of impact of each influencing factor on Suezmax tanker freight rates, as well as the interplay between these factors.FindingsThe conclusion and results were tested using the 20-year data from 1999 to 2019, and the methodology and theory of this paper were proved to be effective. Results of this study provide effective reference for scholars to find the law of fluctuations in Suezmax tanker freight rates.Originality/valueThis paper provides a decision-making support tool for tanker operators to cope with fluctuation risks in the tanker shipping market.

Modeling the freight rate for container transportation by rail in the Russian Federation

Objective: To develop proposals for improving freight rates for container transportation by rail in the Russian Federation in the current context. Methods: Comparative method, analysis, synthesis, statistical method, and system approach have been applied. Results: Multi-factor complex digital models of rates per wagon with containers and per container train have been developed. One of the options for the algorithm for evaluating individual components of these models is proposed, additional comments to the presented models are provided. Practical importance: The findings can be used to improve freight rates for container transportation by rail in the Russian Federation in the current context, as well as for further research in the field of pricing.

PECULIARITIES OF CURRENT WORLD SCIENTIFIC RESEARCHES ON MARITIME ECONOMICS AND SPECIFICALLY SEABORNE TRADE

The article analyzes and systemizes current studies of leading world scientists on maritime economics and seaborne trade with the aim to reveal current trends and venues for future researches in this field. Special attention is paid to researches evaluating how the outbreak of coronavirus pandemic impacted shipping industry as a main global supplier of goods. All studies under review are conceptually grouped into two main branches. The first branch comprises papers focused on the world seaborne trade data dynamics, including official maritime reports. As opposed to Ukrainian and Russian maritime economics papers which predominantly describe and portray the statistical data available in official maritime reports issued by international organizations and shipping services providers, leading world scholars use this statistics as a baseline for individualized researches, mainly focused on investigation of correlation between various shipping indicators and prediction of same. The second branch comprises papers investigating trade of certain types of cargo, such as containers, crude oil, dry bulk. Several general peculiarities of both branches of researches are defined. Almost all of them attempt to provide an insight into the nature of a freight rate and to forecast the development of either general freight market or specific cargo related one. The utilized methodology is also identical. Depending on the aim of research and data availability, scholars employ various models of regression analysis, a standard tool of statistical modeling, which estimates the average relationship between two or more variables. No matter which freight market is under investigation, studies usually try to examine the connection of this market with others by evaluating the spillover effects between vessel types and vessel sizes. Distinguishing features of researches lie in the target stakeholders who could benefit from, either the industry in general or particular groups of market participants. In addition, nowcasting trade data is a real problem raised by the industry to modern science, which tries to tackle it by proposing innovative digitalized solutions.

Comprehensive Evaluation Model and Empirical Research on Railway Freight Rates in a Competitive Environment

This study aims to establish criteria to evaluate performance in railway freight pricing policy and to strengthen the marketing ability of the railway freight enterprise. The evaluation method used in our study is known as the improved grey relation analysis evaluation. Fifteen factors that influence freight pricing strategy are selected in this paper to establish a freight rate evaluation index system and comprehensive evaluation index values for monthly freight rates are calculated. The improved grey relation analysis not only considers the relation between each index and freight rate but also examines the relationship between each index. We find that the comprehensive evaluation state using the improved grey relation analysis evaluation method can better fit the 2015–2017 changes in monthly railroad freight revenue than the traditional grey relation analysis evaluation. Furthermore, an optimal pricing model for freight shipping by train is built and calculated using the bi-level programming model. Finally, the S Railway Bureau’s actual coal transportation in 2017 is taken as an experiment to support the validity of the improved grey relation analysis method. We find that when the monthly results of improved grey evaluation grades are compared with the grade evaluation of the gap between the historical shipping coal rates and the optimal freight shipping rates, the comparison of results matches well. The research work is helpful to policymakers who need to judge the performance of the market price.

The interrelationship between ocean, rail, truck and air freight rates

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueWhile 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.

Research on Logistics Cost Control of E-commerce Enterprise from the Perspective of Value Chain– A Case Study of Pinduoduo

Recently, with the rapid development of social platforms, social e-commerce enterprises are also rising. However, in the process of development, logistics cost has become a big constraint. Taking Pinduoduo as an example, this paper adopts case analysis method and literature review method to study how to help e-commerce enterprises control logistics costs from the perspective of value chain. By analyzing, this paper finds that for internal value chain of Pinduoduo, it faces problems of inadequate supervision of delivery cost, unreasonable freight rate, and high reverse logistic cost. For external value chain, Pinduoduo faces problems of low loyalty from users, high competitiveness from competitors and imperfect sinking market value chain. Aiming at these problems, this paper puts forward the following suggestions. For internal value chain, Pinduoduo should adopt JIT and ABC method, strengthen the supervision of delivery cost, and improve the efficiency of after-sale service. For external value chain, Pinduoduo needs to establish strong relationship with suppliers and customers. Most importantly, forming an infallible information system is essential.

A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction

China Coastal Bulk Coal Freight Index (CBCFI) reflects how the coastal coal transporting market’s freight rates in China are fluctuated, significantly impacting the enterprise’s strategic decisions and risk-avoiding. Though trend analysis on freight rate has been extensively conducted, the property of the shipping market, i.e., it varies over time and is not stable, causes CBCFI to be hard to be accurately predicted. A novel hybrid approach is developed in the paper, integrating Long Short-Term Memory (LSTM) and ensemble learning techniques to forecast CBCFI. The hybrid LSTM-based ensemble learning (LSTM-EL) approach predicts the CBCFI by extracting the time-dependent information in the original data and incorporating CBCFI-related data, e.g., domestic and overseas thermal coal spot prices, coal inventory, the prices of fuel oil, and crude oil. To demonstrate the applicability and generality of the proposed approach, different time-scale datasets (e.g., daily, weekly, and monthly) in a rolling forecasting experiment are conducted. Empirical results show that domestic and overseas thermal coal spot prices and crude oil prices have great influences on daily, weekly, and monthly CBCFI values. And in daily, weekly, and monthly forecasting cases, the LSMT-EL approaches have higher prediction accuracy and a greater trend complying ratio than the relevant single ensemble learning algorithm. The hybrid method outperforms others when it works with information involving a dramatic market recession, elucidating CBCFI’s predictable ability. The present work is of high significance to general commerce, commerce-related, and hedging strategic procedures within the coastal shipping market.