The Efficiency of European Container Terminals - Case Study Example

The Efficiency of European Container Terminals and Implications for Supply Chain Management Table of Contents Methods 5 Results 7 Discussion 8 References 10 Introduction Container port could be defined as an element in the global supply chain industry that deals with exports and imports of goods. This supply chain industry, there are several other players apart, is basically the intermediary between the producer and the consumers. In this case container terminal is considered in stead of container port. This is because in case of container ports required data would not be available. Major objective of this study is to measure the level of efficiency of the container terminals in Europe. This objective is also reflected in the title of the paper, “The Efficiency of European Container Terminals and Implications for Supply Chain Management”. This title precisely represents the core subject of the paper. The abstract of the paper briefly discuss the main objective of the study, methodology used in the process and the final outcome of the study. It can be concluded from the statement of purpose of the abstract that the core content of the introduction of the paper is significantly matching with the basic statement of the abstract. The major purpose of the study is to determine the level of efficiency of the European container terminals. In the introduction of the study various information are given in a proper sequence. All these information are directly or indirectly related to the main purpose of the study. The main objective of this study involves the determination of the efficiency of container terminals and its implication in the global supply chain management. But before going straight into the determination process the term container terminal and its role in the global supply chain management needs to be defined. Container port functions as a link between the consumers and the producers of goods and it is mainly used in the export import industry. Container port industry, simply a part of the global supply chain industry where there is many other players who actually influence the main role of the container port industry. In the last two decades there have been two main changes in the container shipping industry. These changes have taken place in the form of globalization in case of service coverage and provision in case of extension of logistics services. Apart from this, the introduction part also includes the nature of the competitiveness of the port industry and various external and internal challenges that this industry is likely to face. All these information are indirectly related with the main purpose of the study. It is quite clear form the introduction part of the paper that there could be several ways to reduce the cost and thereby attracting more customers by charging less price. This is more crucial in case of European container industry is highly competitive in nature as compare to the rest of the world. One of the main reasons of this competitiveness could be the geography of the Europe. This competitive nature might have direct influence on the main purpose of the study. In the last part of the introduction is directly related to the purpose of the study as it discusses the main objective of the paper that is to determine the efficiency of the European container terminal industry. Methods There are two methods which are mainly applied in order to measure the efficiency of port, one is Data Envelopment Analysis (DEA) and another one is Stochastic Frontier Analysis (SFA). Stochastic Frontier Analysis is an economic model which could be used in order to measure the technical efficiency of container terminals (Kumbhakar, Lovell, 2003). Data Envelopment Analysis (DEA) method is a non parametric method to determine the level of efficiency of any decision making unit where there is multiple inputs and/or outputs. In this approach a single virtual output need to be constructed to a single virtual output. In this case the production function need not be pre-defined (Ray, n.d.). In this case Data Envelopment Analysis approach is applied to measure the efficiency of the port. Among various models of DEA two models namely DEA-CCR and BCC, are mainly used in the determination of the efficiency of a port. The main assumption in the DEA-CCR model is the return to scale is constant. Main reason of this assumption is to scale up or down the observed production combinations proportionally. On the other hand DEA-BCC model assumes that the return to scale is variable. In this case DEA method is used as it deals with input and output variables. In case of determining the efficiency of a port input and output variables could be chosen according to the requirement. For instance when the objective of the port is profit maximization then the input variable would be the employment or anything regarding labor but when objective is to increase employment, then it is the output variables. Since in this case DEA method is applied for the purpose of measuring the efficiency of the container ports, there would be requirement for different input and output variables. Here all the required information regarding the possible input and output variable of a port, are provided. In case of a container terminal, container throughput would be the output variable. As far as input variable is concerned in case of container terminals, land, labor and equipment are crucial. In the process of measurement of efficiency of a port main input variables would be aggregate annualized expenditure regarding the terminal equipments, quay length and the area of container terminal. Apart from these other important input variables include proximity of major trade lanes, berth accessibility, berth occupancy, and crane operating hours, equipment maintenance etc. Although there are several variables it is quite difficult to obtain data regarding all these variables. One of the major problems that are likely to be faced in the process is the difficulty in collection of labor data as there are very few sources of these data. A significant effort was put in to gather various data on labor input. A sample was surveyed for this purpose. Data from previous research could have been used but the responses were not enough consistent and logical. To overcome this problem a predetermined relationship between various terminal facilities is used, although this relationship could not be applied to any type of ports with different production characteristics. The main reason behind this is that different terminal equipment and labor arrangements are used in case of different production scale. As far as sample selection is concerned, this study targeted to select all the European container terminals which are having throughput of 10,000 TEUs. But it was difficult to obtain all the data regarding all the container terminals. Finally 104 container terminals were selected for the study. These terminals were distributed in 29 European countries. All these 104 container terminals were having the throughput of 10,000 TEUs. Moreover, Containerisation International Yearbook and Lloyd’s Ports of the World are used for the all the data which are required for cross-sectional analysis for the year 2003. These two trade publications directly gather information regarding the container terminals. In order to overcome the problem of data unavailability a proper questionnaire was prepared and sent to different ports. All these data was finally used in the final sample. It is quite clear from the sequence of statement regarding methodology, that it is a clear and understandable method. In this method the first job is to identify and collect data on input and output variables. A specific sample was chosen for the analysis. Various trade publications and a questionnaire was used to solve the problem of data unavailability. Results There are two tables and two figures consisting various information regarding the study. Table 1 provides different statistical data of the sample of 104 container terminals. Various statistical data include mean, median, standard error, standard deviation, skewness etc. All these statistical variables are calculated on the output variable and the input variables of the sample. Output variable is container throughput and input variables include terminal length, terminal area and equipment costs. Figure 1 represents the relationship between the efficiency and the container throughput. This efficiency is calculated in both CCR and BCC method. When the efficiency of the port is equal to one, it is called a totally efficient port. It is quite clear from this figure that efficiency of most of the ports is quite poor in both the methods. There are only few ports which are actually efficient. The relation between the efficiency of a port and its throughput is further presented by a Tobit Regression model. Table 2 shows the results of this Tobit model. Both CCR and BCC approach has been applied to find out the relationship. According to this figure there is a positive relation between the ports efficiency and its throughput, because in both the method the calculated value of chi square is above the critical value i.e. 6.63 at 1% significance level where the degree of freedom is also 1. Finally, figure 2 shows the different efficiency level of ports from different regions of Europe. Here also, both CCR and BCC methods are applied. It can be concluded from this figure that ports in British Isles and Western Europe have high level of efficiency as compared to ports in other regions. Information and results which are given in these tables and figures are also explained in the content of the study. There are no discrepancies between the text and the tables. All the data in these tables and figures are obtained by calculation of various statistical methods. These methods are briefly discussed in the text. Values which are presented here in these tables and figures are explained properly in the text of the study. The major objective of this study was to measure the efficiency level of different container terminals in Europe. This objective has been specifically attained by figure 1 and figure 2. These figures reflect the efficiency of container terminals of different regions in Europe. Figure 1 shows the efficiency level of different ports having different throughput, whereas figure 2 reflects the different efficiency levels in different regions in Europe. These results are based on the research on a sample of 104 European container terminals. So, it can be concluded that the objective of the study is successfully attained by the study. Discussion Table 1, which provide different statistical values of different input and output variables of container terminals. This table is not properly interpreted in the paper. Figure 1 is properly interpreted in the paper. Proper explanation is given regarding the co-efficient of correlation of mean efficiency. It is quite clear from the interpretation of this figure that the efficiency of a container terminal is significantly dependent on the production scale of the terminals i.e. the throughput of the terminals. Table 2 which also shows the inter dependency between the efficiency and the terminal throughput. The interpretation of this table state that there are positive relationship between throughput and efficiency. This relationship is further validated by the fact that the large terminals generally use state-of-the-art technology and better management as compare to smaller terminals in order to improve their efficiency. All these interpretations are logical and in accordance with the facts and figures given in these tables and figures. Figure 2 shows the average efficiency of ports placed in different locations in Europe. The interpretation of this figure shows that average efficiency of ports is higher in case those which are located in British Isles and Western Europe, whereas efficiency is very low in case those terminals which are located in Scandinavia and Eastern Europe. So, finally it can be concluded that all the tables and figures are properly interpreted. This study is compared with a similar type of research which involves the efficiency measurement of container terminals of China. In this research also efficiency is measured by applying the DEA method. Here both DEA-CCR and DEA-BCC methods are applied. In the research paper that involves the efficiency measurement of terminals in China, include The Malmquist Index but in the paper of study there is no use of this index. This index actually reflects the factor productivity and technical efficiency. Apart from this, like the research paper of Chinese terminals all the key factors has been taken care of in the paper of study (Liu, Liu, Cheng, n.d.). According to the author of this paper any further research on efficiency or inefficiency of terminals would be interesting but at the same time it would also be more complicated. Any further data regarding the reason behind the efficiency of the terminals would be more wide ranging and also would be difficult to quantify. References Kumbhakar, S.C. Lovell, C.A.K. 2003, Stochastic frontier analysis, Cambridge University Press Liu, B.L. Liu, W.L. Cheng, P. C. No Date, Efficiency Analysis of Container Terminals in China: An Application of DEA Approach, appc 2006 Seoul, Retrieved from appc.snu.ac.kr/files/1128_paper.pdf Ray, S.C. No Date, Theory and Techniques for Economics and Operations Research, Data Envelopment Analysis, Retrieved from assets.cambridge.org/97805218/02567/.../9780521802567ws.pdf Read More