African Journal of Business Management Vol. 5(4), pp. 1397-1407, 18 February, 2011 Available online at http://www.academicjournals.org/AJBM ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
Benchmarking the efficiency of Asian container ports Susila Munisamy1* and Gurcharan Singh2 1
Department of Applied Statistics, Faculty of Economics and Administration University of Malaya, 50603 Kuala Lumpur, Malaysia. 2 Buckingham Business School, University of Buckingham, MK18 1EG, United Kingdom. Accepted 9 December, 2010
In the last decade, the shipping industry and the global seaborne trade has witnessed a rapid growth due to globalization of the world economy, boom in international trade and borderless investments. As competition among international ports has intensified, the evaluation of port operational efficiency has become increasingly important to enable individual ports to reflect on its current status quo and understand their strengths and weaknesses in the competitive environment. This study aims to investigate the technical and scale efficiency of major container ports in the Asian region. The study employs the non-parametric Data Envelopment Analysis technique to benchmark and evaluate the operating performance of 69 major Asian container ports and generate efficiency ranking. The results indicate that the average technical efficiency of the Asian container ports is 48.4%. The overall technical inefficiency in Asian container ports is due to pure technical inefficiency rather than scale inefficiency. The results of this study can indicate possible improvement(s) in port management and operational planning at local and national levels. Key words: Efficiency, data envelopment analysis, container ports. INTRODUCTION Since the 1980s, the thrust of globalization has propelled the growth of the shipping industry and the global seaborne trade. More than 80% of international trade in goods is being carried by sea transport, accounting for more than 8.17 billions tons in 2008 (UNCTAD, 2009). The boom in international trade has created highly differentiated goods, which helped popularize cargo unitization, or containerisation. Such innovation has prompted integrated logistic services followed by intermodal freight transportation systems. At the same time, container ports have evolved from being labour intensive to capital intensive. Wang et al. (2005) found that due to the deriveddemand nature of port services, its providers are obliged to incorporate cutting-edge technologies to maintain competitiveness. Global networking, freight transportation chains, and investment in technology have all together marked a dramatic growth and improvement in productivity
*Corresponding author. E-mail:
[email protected]. Tel: 60379673669. Fax: 603-79567252.
of the container port industry. Due to its position in the international transport-chain and the importance of sea-traffic, ports play an important role in the region’s economic development. As such ports competitiveness could affect the regions viability, prospects and propensity for growth. In the contemporary container port industry, the rapid sea-traffic growth and intermodal transportation has drastically changed the market structure from monopoly to fierce competition (Cullinane and Wang, 2006). Alongside the emerging Northeast Asia as a major hub of world economy is AFTA (ASEAN Free Trade Area) where both regions have interest in increasing their competitive edge as a production base of the world market. Given the current phase of globalization and competition, port performance is of major importance for port competitiveness and indicators of plausible improvements in port management and operational planning becomes significantly important. In this context, it is important to evaluate the efficiency levels of ports in utilizing the resources which will reflect their current status quo and reveal their strengths and weaknesses in the
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competitive environment. In line with such motivation, this study will benchmark the efficiency of container ports against best-practices in the Asian region. The study will seek out those best practices that will lead to improved efficiency in the context of Asian seaport. This study provides a benchmarking analysis based on the non-parametric Data Envelopment Analysis (DEA) technique to evaluate the operating efficiency and generate efficiency ranking of 69 major Asian container ports. The paper is structured as follows: the next (second) section introduces the related prior studies, the third describes the main framework of the DEA approach and methodology, the fourth outlines the container port operations and describes data variables and the fifth section gives the empirical results and the final section concludes.
LITERATURE REVIEW Benchmarking the efficiency of ports against world bestpractice can help ports to establish appropriate management practices and strategies in order to achieve and maintain a competitive edge in the international markets. Efficiency benchmarking is advantageous to port operators in enabling them to act ex ante by becoming proactive in operational enhancements, rather than ex post, by acting on inference from shippers’ responses and suggestions (Wang et al., 2005). As such, port efficiency literature has gained momentum in the past decade with many applications using the Data Envelopment Analysis (DEA) technique. The theory of efficiency analysis began with the works of Koopmans (1951), Debreu (1951) and Farrell (1957) who made the first attempts at estimating efficiencies for a set of observed production units. Charnes et al. (1978) and Banker et al. (1984) popularized the DEA linear programming with the CCR model and BCC model with impositions of constant (CRS) and variable returns to scale (VRS) assumptions respectively on the production frontier. DEA is a technique for measuring the relative efficiencies of homogeneous decision-making units that use similar inputs to produce similar outputs where the multiple inputs and outputs are incommensurate in nature. The technique attempts to trace out a production frontier based on observed input and output levels for individual ports and a port’s technical efficiency is evaluated relative to the frontier. DEA applications in ports are quite recent with the first attempt being attributed to Roll and Hayuth (1993). Roll and Hayuth (1993) presented a theoretical exposition and used a cross-sectional data for financial reports in order to render the DEA approach operational. Following Roll and Hayuth’s (1993) DEA theoretical work on ports, many empirical studies have used DEA to measure technical efficiency of ports. Martinez-Budria et al. (1999) estimated the efficiency of 26 Spanish ports over the period 1993-1997. Martinez-Budria et al. (1999) classified
the 26 ports into three groups; namely, ‘high complexity’, ‘medium complexity’ and ‘low complexity’ ports. After examining the efficiency of these ports using DEA-BCC models, the authors conclude that the ports of ‘high complexity’ are associated with high efficiency, compared with the medium and low efficiency found in other groups of ports. Tongzon (2001) used both DEA-CCR and DEAadditive models to analyse the efficiency of 4 Australian and 12 other international container ports for the year 1996. Plagued by the small sample size more efficient ports then inefficient ports were identified. Valentine and Gray (2001) applied the DEA-CRS model to 31 container ports out of the world’s top 100 container ports for the year 1998 with the objective of determining the relationship between ownership and organisational structure with efficiency. Turner et al. (2004) employed DEA and Tobit regression analyzing influence of port authority, ocean carrier and rail carrier conduct on port productivity in North America between 1984 till 1997. The presence of economies of scale at the container port and terminal level was observed. Park and De (2004) in their fourstage DEA analysis employed both constant and variable returns to scale analyses due to the existence of economies of scale in ports. Similarly, Barros and Manolis (2004) applied DEA models to estimate the relative efficiency of a sample of Portuguese and Greek seaports using both the DEA-CCR and DEA-BBC models. A detailed review of the application of the DEA technique in ports is provided by Gonzalez and Trujillo (2007). Despite an extensive literature on benchmarking using DEA in the global container ports to investigate operating efficiency, the Asian container port sector is relatively under researched. Furthermore, the issue of discriminating the efficient units and efficiency ranking are discussed less frequently in the port sector literature. This study is by far the most extensive study on the efficiency of Asian container terminals encompassing 69 container terminals from 17 Asian countries and will determine the efficiency ranking of the container ports. METHODOLOGY In a framework based on the works of Koopmans (1951), Debreu (1951) and Farrell (1957), a container port production can be formun
lated as a set of n ports of Sn = {(xi,yi)} i =1 consisting of x inputs used to produce y
∈ R +p
∈ R q+ outputs, which makes a production
possibility set of Ψ: Ψ = {(x,y)
∈ R +p+ q | x can produce y}.
(1)
Based on this production possibility definition, we estimate the Farrell output-orientated technical efficiency measure of for each port operating at (x,y) using λ(x, y) = sup {λ| (x, λy)
∈ Ψ}.
(2)
where λ(x,y) ≥ 1 represents the proportionate expansion in outputs
Munisamy and Singh
without altering inputs. In this framework, λ(x,y) = 1 indicates the container port is on the efficient frontier which serves as the yardstick to benchmark other ports. Thus, they are bounded between unity and infinity, with unity representing a perfect efficiency score of 100%. Non-parametric frontier efficiency estimators In practice, Ψ is unobserved, thus to estimate efficiency scores for each port we use a DEA estimator suggested by Charnes et al.
ˆ CRS, ψ
(1978). We replace Ψ with its DEA estimator
which is the
conical hull of the estimated production frontier, allowing for constant returns to scale (CRS):
ˆ CRS ={(x,y) ∈ R +p+ q | ψ n
n
y ≤ ∑i =1 γ i yi ; x ≥ ∑i=1 γ i xi such that γ i ≥ 0, i = 1,..., n}
(3)
The output oriented Farrell technical efficiency estimator can then be computed by taking the conical hull into consideration for the linear program:
λˆ CRS =sup{ λ > 0 λy ≤ ∑n γ i yi ; x ≥ ∑n γ i xi such that γ i ≥ 0, i = 1,..., n} i =1 i =1 (4)
γi ≥ 0
where
(i=1,..n) are the intensity variables over which the
optimization is made. Alternatively, the variable returns to scale (VRS) estimator, employs a convex hull by including the
∑
n
γ =1
i=1 i
constraint,
while the non-increasing returns to scale (NIRS) estimator relaxes the the
ˆ VRS constraint ψ
by applying
∑
n
i =1
γi ≤ 1
ˆ VRS, ψ ˆ NIRS ψ
instead of
n i =1 i
∑
γ = 1.
To illustrate the different DEA estimators, Figure 1 exhibits 4 units, A, B, C and D, each with one input (X) and one output (Y). The efficient frontier of the CRS DEA estimator is the dotted line that passes through B from the origin. Under the assumption of CRS, B can be extrapolated to points on this dotted line, such that the change in input level causes an equally proportional change to the output level. The frontiers of the VRS DEA estimator consists of the bold lines connecting A, B and C. It is made of convex combination of the extreme points lying on the production surface. The segment AB reflects locally increasing returns to scale (IRS), that is, an increase in the input would result in a greater than proportionate increase in output. The segment BC reflects decreasing returns to scale (DRS), that is, an increase in the input would results in a smaller than proportionate increase in output. The production possibility set (Ψ) is the area consisting of the frontier together with observed or possible activities with a surplus of inputs and/or a shortfall in output compared with the frontiers. A, B and C are on the frontiers and VRS-efficient and receive the efficient score of one. However, only unit B is CRS-efficient. The CRS and the VRS DEA estimators work in the same way to arrive at efficiency. Efficiency is measured as the distance between the decision making unit and the frontier. The assessment of efficiency can be made in terms of input or output orientation. The objective of the input-orientated model is to minimise inputs while producing at least the given output levels. In contrast, the output-
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orientated model attempts to maximise outputs while using no more than the observed amount of any input. There is a “reciprocal relationship” between the input-orientated and output-orientated efficiencies in the CRS efficiency estimator which is not available in the VRS efficiency estimator. In general, under the VRS assumption the orientation of the assessment (input or output) affects the point of projection on the frontier and the resulting efficiencies may not be the same, as can be seen for DMU D.
Super-efficiency The CCR and BCC models dichotomize container ports into inefficient and efficient ones. However, it is not possible to differenttiate between the efficient ports since all efficient ports receive the same efficiency score of one. When there are several fully efficient ports like in this study, it is difficult to tell which port is more efficient and to what extent compared to other ports. To overcome this limitation, several researchers have developed techniques to deal with ranking both efficient and inefficient units within DEA. These can be classified into six, somewhat overlapping categories which are :- evaluation of a cross-efficiency matrix (Sexton, 1986); super efficiency method (Andersen and Petersen, 1993); benchmarking method (Torgensen et al., 1996); ranking with multivariate statistics in the DEA context such as canonical correlation, linear discriminate analysis and discriminate analysis of ratios (Friedman and Sinuany-Stern, 1997, Sinuany-Stern et al., 1994, Sinuany-Stern and Friedman, 1998 respectively); ranking with proportional measure of inefficiency dominance (Bardhan et al., 1996); and ranking using a combination of multiple criteria decision making methodologies with DEA such as preferential information, assurance regions and cone-ratios, fuzzy logic, multiple objectives, analytical hierarchical procedure among others. The review of ranking methods in DEA context can be found in Adler et al. (2002). In this study, we attempt the ranking analysis of container ports using the super-efficiency ranking method proposed by Andersen and Petersen (1993) due to the simplicity of the concept and many published papers have used this approach. The method does not need additional information in providing a complete ranking of the set of units or extensive computations as required in the other methods. The super-efficiency model is identical to the DEA model previously described, but a port under evaluation (k) is excluded from the reference set. The formulation for the super efficient model, follows Equation (4), but is evaluated without unit k i.e. for i= 1,.., n, i ≠ k. For an efficient unit, its exclusion from the reference set will alter the frontier and allow the unit to be located above the efficient frontier that is, to be super-efficient, as can be seen for point B in Figure 2. Therefore, the super-efficiency score for efficient port can in principle take any value less than or equal one in the output orientated DEA models. This enables the ranking of efficient ports that is, the smaller the value the higher the rank. Nevertheless, the scores for inefficient ports remain the same as in the standard models as the exclusion of the port evaluated does not affect any change on the frontier (Figure 1). The super-efficiency measures how much can the inputs be increased (or the outputs decreased) while not become inefficient. Despite its simplicity, it is worth mentioning that the superefficiency method has some drawbacks. The super-efficiency methodology can give ‘specialized’ units an excessively high ranking. To avoid this problem, Sueyoshi (1999) introduced specific bounds on the weights in the model. Sometimes the super efficiency model may be infeasible and the technique cannot provide a complete ranking of all units. Mehrabian et al. (1999) has suggested a modification to the DEA model formulation in order to ensure feasibility.
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CRS Y
VRS
C
D’ DRS D
B IRS
Ψ A
O X Figure 1. The DEA models.
Y
C B B’ B’’
B
VRS
D Ψ
A B
O X Figure 2. The super efficiency VRS DEA model for efficient unit B.
Scale efficiency We can measure the scale efficiency (SE) of the port as:
SE =
λˆCRS λˆVRS
DRS. This is done by running the non-increasing returns to scale (NIRS) DEA model. If the VRS score equals the NIRS score, then the port is said to be operating at DRS. Alternatively, if the VRS score varies from the NIRS score, than the port is said to be operating at IRS (Coelli et al., 1998).
(4)
Where, SE = 1 implies scale efficiency and SE < 1 indicates scale inefficiency. However, scale inefficiency can be due to the existence of either increasing (IRS) or decreasing returns to scale (DRS). To identify the nature of returns to scale, first the CRS efficiency score is compared with VRS efficiency scores. For a given port, if the VRS score equals the CRS score, the port is said to be operating at CRS. On the other hand, if the scores are not equal, a further step is needed to determine whether the company is operating at IRS or
Container port operations and data Generally a container port consists of one or more container terminals. In order to transport containers from ship to shore and within the port itself, the required facilities include berths for vessels to park, area to stock and store container, and handling equipments to upload and unload containers. The container handling equipment can be classified into two main groups: quay crane and yard handling system. Figure 3 provides a graphical representation of the typical container terminal system. On the quayside, containers are transported between ship and shore and container quay cranes mobile cranes and ship shore
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Figure 3. A typical container terminal system. Source: Monaco, Moccia and Sammarra (2009).
Table 1. Major Asian container ports 2007: Descriptive statistics for inputs and outputs.
Statistic Minimum Maximum Mean Std Deviation
Berth length (m) 100 12,610 2,525.9 2,648.6
Terminal Area (m 2) 3200 6,169,837 892,461 1,194,399.9
Inputs Total Refer Points 0 7,422 1,130.3 1,795.8
gantries are the main equipments used for ship loading and unloading. On the yard side, containers are transferred to land transport modes or are arranged to be loaded on to other ships. There are two types of activities which occur in the yard area: stacking of container and horizontal transport. Containers are stacked in the yard area before they are moved away. To stack the containers equipments such as straddle carriers, rubber tired yard gantry cranes, rail mounted yard gantry cranes, reach stackers, top lifters and side lifters are required. Horizontal terminal transport is the movement of containers between the ship to shore, the stacking area, and the landside operation. Equipment for horizontal transport includes yard tractor, prime mover, yard trailer and/or chassis and/or utility trailer and forklifts. To model the port operations, we capture the main resources used by the ports (inputs) for acquiring the main goods and services produced (outputs). Under the traditional microeconomic framework, capital and labour are necessarily the input for production. A common issue in the empirical studies of container port efficiency performance is finding a proxy to reflect labour or the number of workers. According to Notteboom et al. (2000), expert analysis shows that there is a stable relationship between the number of yard gantries with the number of dock workers. Wang et al. (2005) goes to show that the average number of workers per crane is six. Hence, we take the total yard equipments, i.e. sum of straddle carriers, yard gantries, reach-stackers, front-end handlers, and forklifts, as an input factor, reflecting the labour that is required. We enlist another four inputs encompassing berth length, terminal area, total reefer points, and total quayside cranes (and/or mobile cranes) – to reflect the capital inputs in the industry. The single output used is the total throughput of the container port. We obtain secondary data for 71 major Asian container ports from Containerisation International Yearbook 2007 covering 17 countries that is, Bangladesh, Brunei, Cambodia, China, India, Indonesia, Japan, Hong Kong, Malaysia, Pakistan, Philippines, Singapore,
Total Quayside Cranes 1 131 19.2 24.5
Total Yard Equipment 4 674 88.8 119.5
Output Total Throughput (TEU) 20,700 27,935,500 3,239,158.8 5,690,010.8
South Korea, Sri Lanka, Taiwan, Thailand, and Vietnam. The descriptive statistics for variables used to calculate the DEA efficiency estimates are as shown in Table 1. To validate the inputs and output variables, the Pearson’s coefficient of correlation is constructed and the correlation coefficient matrix is tabulated in Table 2. This matrix shows the simple correlation between all possible pairs of variables included in this analysis. The correlation coefficient reveals that the single output variable i.e. total throughput is meaningfully correlated with each of the input variables. The significant correlation between the input and output variables show that the DEA model developed do capture the important factors that influence container throughput, hence, producing reliable results.
RESULTS DEA efficiency The DEA efficiency scores in this study are estimated using the computer program, Efficiency Measurement System, EMS Version 1.3, developed by Professor Holger Scheel, University Dortmund (Scheel, 2000). In this study two ports which were considered to be outliers were removed from the dataset that is, Ningbo and Zhangjiang, and the DEA analysis was conducted using the remaining 69 ports. The DEA models run in this study is under the assumption of output maximization (also known as output orientation) as container throughput is more easily controlled by the port. Table 3 presents the results of the application of the
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Table 2. Pearson correlation matrix of input and output data.
Berth Length Terminal Area Total Refer Points Total Quayside Cranes Total Yard Equipments Total Throughput
Berth length 1.000 0.773 0.798 0.791 0.797 0.658
Terminal area
Total refer points
Total quayside cranes
Total yard equipments
Total throughput
1.000 0.774 0.809 0.698 0.814
1.000 0.767 0.703 0.712
1.000 0.883 0.899
1.000 0.791
1.000
output-oriented DEA analysis giving the CRS and VRS DEA scores, benchmarks, scale efficiency and the nature of returns to scale. The CRS DEA scores provide the overall technical efficiency (average = 2.93). The overall technical efficiency is broken down into pure technical efficiency given by the VRS DEA score (average = 2.07) and scale efficiency (average = 1.42). The results reveal that technical inefficiency is the source of overall technical inefficiency in Asian ports rather than scale inefficiency. This technical inefficiency could be due to inefficient management practices. Thus ports must enhance their handling activities and computerize container terminal operations. The technical efficiency scores presented in Table 3 indicate the potential for ports to increase outputs while maintaining existing inputs. The efficiency scores for the ports indicate the presence and extent of inefficiency of output production. For example, on average, the ports are 48.4% efficient, based on the pure technical efficiency score. The average efficiency suggests that given the inputs, the ports outputs can be expanded by 51.6%. 21 of the 69 container ports (30%) are fully efficient with a score of 1. The remaining 48 ports (70%) are inefficient and have efficiency scores ranging from 1.125 to 4.65. This indicates that the container ports in Asia have room to improve on their technical efficiencies. These ports either need to reduce their inputs or expand their outputs to become efficient. To practically improve the efficiency of container ports, increasing changeable outputs may be more appropriate than decreasing the given inputs. The ports must adopt the hub strategy to increase container throughputs. Hub ports are major nodes within the global transport system and act as an international distribution centers for entire regions or continents. Other strategies that impact on the volume and nature of trade is to seek for World Trade Organization membership, provide dedicated container terminals, seek cooperation strategies between ports and improve on their transport infrastructure to link with the hinterland. Ranking analyses by super efficiency model In column one of Table 3, the ports are ranked using the overall technical efficiency scores. However, there are 12
efficient ports that cannot be distinguished. In order to the rank the efficient container port, we measure superefficiency scores using the Anderson and Petersen (1993) method. The results are shown in Table 4. The table omits the super-efficiency scores of the inefficient container ports since they coincide with the efficiency scores in DEA VRS model presented in Table 3. The results of the comparison among nations show that the most efficient ports in Asia are located in Bangladesh, Philippines, China, Cambodia, India and Singapore. The most efficient container port in the Asian region is Chittagong of Bangladesh with a super-efficiency score of 0.219. It is the principle seaport of Bangladesh and handles about 92% of import-export trade of the country (the other only port in Bangladesh is Port of Mongla which handles the balance trade). Chittagong is located in the Bay of Bengal between two largest economic powers with a huge customer base i.e. India and China. It capitalises on its location and China is Bangladesh’s biggest trade partner with annual turnover worth more than $4 billion. China is Chittagong’s leading origin country for imports as well as the leading foreign seaport for cargo leaving Chittagong. Chittagong also receives China’s financial and construction assistance for its expansion and modernization. The results also reveal that the Chinese container ports (excluding Hong Kong) are amongst the top performers with five out of 12 ports ranked within the top twelve which are Xiamen, Yantian, Lianyungang, Tianjin and Guangzhou. In 2007, the total container throughput of the 12 ports in China is the highest, with a total of 77.9 million TEUs followed by Singapore’s PSA International yielding 27.9 million TEUs. China follows the global trend in concentrating liner services at ports, hence developing their ports into hubs, to cater for the rapid development of its hinterland economy. China has emerged as the world’s manufacturing powerhouse and consumer market (Cullinane et al., 2004). In addition, the ports Yantian and Guangzhou received much expertise and technology transfer from Hong Kong due to entrepreneurial interests. The least technically inefficient port is Muara of Brunei with an efficiency score of 10.36 (Table 3). While the inefficiency on inputs and outputs in efficient container ports are all zero, there are too much inputs or too little output in inefficient container ports. To practically improve the
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Table 3. Efficiency results of major Asian container ports.
Rank Container port
DEA CRS Model Overall technical Benchmarks efficiency 1 12 1 2 1 6 1 18 1 10 1 46 1 26 1 1 1 6 1 5 1 0 1 9 1.102 13, 16, 58 1.175 16 1.420 14, 51 2.908 8, 14 7.164 1, 8, 14 10.591 13, 16 13.293 1, 14 14.525 13, 16 16.477 14, 16 1.315 1, 8, 14 1.296 14, 16
1 1 1 1 1 1 1 1 1 1 1 1 2 3 4 5 6 7 8 9 10 11 12
Chittagong (1) Sihanoukville (3) Guangzhou (6) Lianyungang (8) Tianjin (13) Xiamen (14) Yantian (16) Mumbai (20) Davao (51) General Santos (52) Zamboanga (56) PSA Int. (58) Shanghai (10) Tuticorin (24) Niigata (35) Bintulu (42) Shimonoseki (38) Mitajiri (32) New Mangalore (22) Subic Bay (55) Visakhapatnam (25) Qingdao (9) Yantai (15)
13 14 15 16 17 18 19 20 21 22 23 24 25
Belawan (26) Cebu (50) Kaoshiung (63) Hong Kong Keelung (64) Colombo (62) Tg. Pelepas (47) Incheon (60) Port Klang (46) Busan (59) Jawaharlal Nehru (18) Tg. Priok (27) Qui Nhon (69)
1.740 1.494 1.265 1.595 1.283 1.816 1.441 1.560 2.480 2.390 1.561 2.271 1.876
1, 8, 14 14, 51, 52 14, 16 8, 14, 58 14, 51 1, 8, 20 14, 16 14, 51, 52 1, 8, 14 14, 16 8, 14, 16, 58 14, 51, 52 3, 52
26 27 28
Pasir Gudang Tokyo (39) Shekou (12)
2.359 2.601 2.480
13, 14, 16 8, 14 8, 14, 58
29 30 31 32 33 34
Chennai (17) Dalian (4) Shantou (11) Nagoya (34) Mizushima (33) Karachi (48)
2.526 2.650 4.039 3.134 5.088 2.912
1, 8, 14 13, 16, 58 13, 16, 58 6, 14 6, 14 1, 8, 14
DEA VRS Model Scale Returns Pure technical Benchmarks efficiency to scale efficiency 1 6 1 CRS 1 4 1 CRS 1 5 1 CRS 1 12 1 CRS 1 8 1 CRS 1 35 1 CRS 1 24 1 CRS 1 9 1 CRS 1 21 1 CRS 1 3 1 CRS 1 4 1 CRS 1 18 1 CRS 1 6 1.102 DRS 1 1 1.175 IRS 1 1 1.420 IRS 1 2 2.908 IRS 1 0 7.164 IRS 1 4 10.591 IRS 1 2 13.293 IRS 1 5 14.525 IRS 1 0 16.477 IRS 1.046 1, 14, 58 1.257 DRS 1.060 14, 16, 20, 22 1.223 IRS 8, 14, 20, 22, 1.082 32 1.608 IRS 1.096 14, 32, 51, 52 1.364 IRS 1.125 14, 16, 58 1.124 DRS 1.164 58 1.371 DRS 1.233 3, 14, 51 1.040 DRS 1.359 1, 14, 58 1.337 DRS 1.424 6, 14, 16 1.012 DRS 1.439 3, 14, 51 1.084 DRS 1.459 13, 58 1.700 DRS 1.510 10, 16, 58 1.583 DRS 1.522 8, 14, 16, 58 1.026 DRS 1.546 13, 14 1.468 DRS 1.644 3, 52, 56 1.141 IRS 13, 14, 16, 51, 1.960 55 1.204 IRS 2.146 14, 16, 58 1.212 DRS 2.246 14, 16, 58 1.104 DRS 8, 14, 20, 42, 2.446 51 1.033 IRS 2.621 10, 16, 58 1.011 DRS 2.662 16, 51, 55, 56 1.517 IRS 2.733 6, 14, 16 1.147 DRS 2.862 51 1.777 IRS 2.894 1, 8, 14 1.006 DRS
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Table 3. Cont’’d
35
Fuzhou (5)
2.982
2.939
3.227 4.472 3.059 3.227
14, 16 8, 13, 14, 16, 58 6, 14 6, 14 14, 16, 58
36 37 38 39
Port M. Quasim (49) Laem Chabang (67) Gwangyang (61) Mundra (21)
40 41 42 43 44
Hakata (28) Kitakyushu (30) Jurong (57) Yokohama (41) Manila (54)
3.945 3.839 3.816 5.028 5.349
8, 14, 16 6, 14 1, 14 14, 16 13, 14, 16
3.538 3.632 3.815 3.981 4.120
45 46 47 48 49 50 51 52 53 54
Penang (45) Shimizu (37) Osaka (36) Kawasaki (29) Bangkok (66) Kobe (31) Taichung (65) Iloilo (53) Kochi (19) Pipavav (23)
4.699 4.768 5.009 23.729 5.704 6.598 5.505 7.826 7.068 9.158
13, 14, 16 1, 8, 14 14, 16 14, 16 14, 16 14, 16 13, 16 1, 14 8, 58 6, 14
4.397 4.497 4.593 4.651 5.113 5.369 5.486 6.304 6.741 6.994
55 56 57
Yokkaichi (40) Kuantan (43) Da Nang (68)
11.004 11.029 13.530
14, 16 8, 14 3, 51, 52
8.082 9.335 9.703
58
Muara (2) Geometric Average Standard Deviation
11.571 2.929 4.428
1, 8, 14
10.360 2.065 2.304
the efficiency of container ports, increasing changeable outputs may be more appropriate than decreasing the given inputs. In the case of Muara Port, which shows the lowest score of 10.36, it should increase its container throughput by 90% (an increase of 886,230 TEU) holding the current level of inputs, to become fully efficient. Benchmarks The inefficient container ports have a group of ports as the reference sets for benchmarking. In Table 3, the benchmarks are given next to the technical efficiency scores. Benchmarks or peer referents offer two different interpretations depending upon whether the unit is efficient or inefficient. For the inefficient ports, benchmarks provide information on which port(s) they should emulate in order to be efficient. On the other hand, benchmarks for the efficient ports tell how many inefficient ports are using the particular efficiency unit as their benchmarks. Based on the results of the VRS DEA model, four ports
2.988 3.029 3.045 3.080
14, 16, 51 8, 13, 14, 16, 51 10, 16, 58 6, 14, 51 14, 16, 51, 58 8, 14, 16, 32, 51 6, 14, 51 1, 14, 20 10, 16, 58 10, 13, 16, 58 13, 14, 16, 55, 56 1, 8, 14, 20, 51 14, 16, 58 14, 20, 51, 52 14, 16, 58 10, 16, 58 13, 14, 16, 24 1, 8, 14, 20, 35 8, 51, 58 6, 14, 51 8, 13, 14, 51, 55 8, 16, 20, 32 3, 14, 51 8, 14, 20, 42, 51
1.015
IRS
1.080 1.476 1.005 1.048
IRS DRS IRS IRS
1.115 1.057 1.0002 1.263 1.298
IRS IRS IRS DRS DRS
1.069 1.060 1.091 5.102 1.116 1.229 1.004 1.241 1.048 1.310
IRS IRS DRS IRS DRS DRS IRS IRS IRS IRS
1.361 1.181 1.394
IRS IRS DRS
1.117 1.419 3.091
IRS
that is, Xiamen, Yantian, Davao and PSA International are most frequently used as benchmarks by the other inefficient ports, i.e. by 35, 24, 21 and 18 inefficient ports, respectively. Tuticorin and Niigata is only benchmarked once, while none of the inefficient ports benchmark Shimonoseki and Visakhapatnam. This result can enhance our confidence about the efficiency measure of Xiamen, Yantian, Davao and PSA International that they are genuinely well performing ports because they outperform many other ports in this study. For the inefficient port Muara, it uses five ports i.e. Lianyungang, Xiamen, Mumbai, Bintulu and Davao as its benchmarks. The inefficient ports can emulate their efficient peers to improve themselves and become efficient. Scale efficiency and returns to scale The average scale efficiency of the Asian container ports was 1.42. 12 (17.4%) ports are scale efficient while the rest 82.8% are scale inefficient suggesting that they are not operating at optimal scale. The port of Jurong has a
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Table 4. Super-efficiency results of fully efficient container ports.
Rank 1 2 3 4 5 6 7 8 9 10 11 12
Container port Chittagong Zamboanga General Santos Xiamen Sihanoukville Davao Yantian Lianyungang PSA Int. Tianjin Mumbai Guangzhou
score very close to 1 and is almost scale efficient. Of the 56 scale inefficient ports, nine i.e. Shanghai, Tuticorin, Niigata, Bintulu, Shimonoseki, Mitajiri, New Mangalore, Subic Bay and Visakhapatnam are technically efficient. The remaining 47 ports are both scale and technically inefficient. The study further investigates the status of returns to scale of the container ports. The right most column of Table 3 indicates the nature of returns to scale of each port calculated based on the DEA scores. Here, 33 ports (47.8%) show increasing returns to scale, 24 ports (34.8%) exhibit decreasing returns to scale, while the rest of the 12 ports exhibit constant returns to scale. It is evident that the majority of the container ports with throughput of one million TEUs and higher in a year tend to operate at decreasing returns to scale (DRS). This is due to the fact that port capital investments are often made in large amounts irregularly with the expectation of a long working life. As such, at initiation, ports often design their capacity in advance well in excess to its current business requirement, even if port traffic only builds up gradually over time (Cullinane and Wang (2006)). On the other hand, smaller sized container ports with throughput below one million TEUs a year, exhibit either CRS or IRS. This outcome corresponds to the findings in Wang and Cullinane (2006) in investigating efficiency of 104 container terminals in European ports. For all the ports that experience increasing return to scale in their operations, increases in inputs will results in more than a proportional increase in outputs. Hence, the ports that operate with IRS could achieve significant efficiency gains by increasing its scale of operations. The scale could be altered via expansion or internal growth and building alliances amongst shipping organizations. For the ports that are operating at decreasing returns to scale, further increase in inputs would only results in a smaller proportional increase of outputs. The ports that experience DRS should eliminate their scale inefficiency by decreasing their scale of operations via giving up
Country Bangladesh Philippines Philippines China Cambodia Philippines China China Singapore China India China
Super -efficiency 0.219 0.421 0.328 0.576 0.582 0.698 0.816 0.820 0.821 0.835 0.886 0.905
some of the terminal assets and operational functions to other specialised entities via concessions and leaseholds. This will allow efficient handling and transit of containers as well as help promote intra-port competition between multiple service providers within a port which can lead to higher efficiency gains. Size of port and efficiency In the past studies on ports, it is often suggested that large ports are more efficient than smaller ports due to economies of scale. Figure 4 presents a scatterplot of the association between technical efficiency and total container throughput for the 69 Asian container ports in this study. The results do not provide conclusive evidence that larger ports are more efficient then small port as there are also technically efficient smaller ports. However, Figure 4 suggest that container terminal of a low efficiency level of up to 0.6 are relatively small with container throughput of less than 7 million TEUs. The exception to this is the port of Busan (throughput of 13.261 million TEUs with a technical efficiency level of 0.42). The figure further indicates that all large container ports with a throughput of 25 million TEU and above have a high efficiency level. Ownership and efficiency It is often suggested that private ownership improves efficiency. However, the results do not suggest that publicly owned and operated ports are less efficient then privately owned ones. For example, Chittagong and PSA International are run by government owned companies, whereas the many of the port operations in China are privately owned. Thus, the type of ownership does not necessarily have an impact on port efficiency. Increasing port competition has forced ports to become more market-
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Afr. J. Bus. Manage.
PSA Int Hong Kong
Shanghai
Busan Guangzhou
Yantian Tianjin
Figure 4. Scatterplot of technical efficiency versus total container throughput. The output technical efficiency scores have been inversed, score 1 indicates full efficiency and score below one indicate inefficiency.
orientated. Therefore, publicly owned and operated ports are forced to revise their strategies and set and implement commercial goals to meet customer requirements. Conclusion Given the current phase of globalization and competition, port performance is of major importance for port competitiveness. This study analysed the technical efficiency and scale efficiency of Asian container ports by means of DEA. Our analysis shows that the main source of overall technical inefficiency in Asian container ports is due to pure technical inefficiencies rather than scale inefficiencies. This suggests that port managers must improve their management practices to favour efficient ways and to meet customer requirements. Ports must enhance their handling activities and computerize container terminal operations. The next step would then be to improve their scale efficiencies. About 48% of the ports are exhibiting increasing returns to scale. These ports should increase its scale of operations via expansion or internal growth and building alliances amongst shipping organizations. About 35% of the container ports exhibit decreasing returns to scale. These ports can decrease their scale of operations by giving up some of the terminal assets and operational functions to other specialised private entities via concessions and leaseholds. This will allow efficient handling and transit of containers as well as help promote intra-port competition
between multiple service providers within a port which can lead to higher efficiency gains. Strategies that impact on the volume and nature of trade is to become a hub, seek for World Trade Organization membership, provide dedicated container terminals, seek cooperation strategies between ports and improve on their transport infrastructure to link with the hinterland. The analysis also revealed that Chinese container ports enjoy a clear lead in the Asian region in terms of containers handled and they are very competitive. Size and ownership structure are not determinants of efficiency level in container ports. ACKNOWLEDGEMENT The authors would like to thank the University of Malaya for supporting this study via the UMRG Research Grant. REFERENCES Adler N, Friedman L, Sinuany-Stern Z (2002). Review of ranking methods in data envelopment analysis context. Eur. J Oper. Res., 140: 249-265. Andersen P, Petersen N (1993). A procedure for ranking efficient units in data envelopment analysis. Manage. Sci., 39(10): 1261-1264. Banker RD, Charnes A, Cooper W (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci., 30(9): 1078-1092. Bardhan I, Bowlin WF, Cooper WW, Sueyoshi T (1996). Models for efficiency dominance in data envelopment analysis. Part I: Additive models and MED measures. J. Oper. Res. Society Japan., 39: 322– 332. Barros CP, Manolis A (2004). Efficiency in European Seaports with
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