Measurement of efficiency of major ports in India a data envelopment analysis approach Rajasekar T, Sania Ashraf P Malabika Deo P 1

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1 INTRNATIONAL JOURNAL OF NVIRONMNTAL SCINCS Volume 4, No 5, 2014 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Measurement of efficiency of major ports in India a data envelopment analysis approach Rajasekar T, Sania Ashraf P Malabika Deo P 1 Ph. D Scholars, Department of Commerce, Pondicherry University Pondicherry Professor & Head, Department of Commerce, Pondicherry University rajakudal@gmail.com doi: /ijes ABSTRACT The present research work examines the operational efficiency of select major ports in India over the period of time 1993 to 2011 through data envelopment analysis. Hypothesis tested in this study is, size does not determine port efficiency. Based on the results it is found that both bigger ports i.e. Mormugao, Jawaharlal Nehru Port Trust and smaller ports nnore, Tuticorin were proved to have efficient port operations all through. Through the result of super efficiency analysis, the study found that JNPT port rated as number one super-efficient port among the selected major ports in India. Keywords: Major Ports in India, Data nvelopment Analysis, Operational efficiency, Super efficiency model 1. Preamble India being surrounded by ocean in three sides, it is obvious that India s economic growth enormously depends upon its international trade as 90% of the India s export and import were carried through seaports. In this direction major ports play an important role for transactions the export and import of the country. The major ports of India effect more than 75% of total India s trade. The goods are transacted through logistics chain movement, and logistics becomes very important for any country for its trade competitiveness. Based on the report of Asian Development Bank (2007), if the current trend flows out to the future, around trillion tons of cargo are expected to be handled by the Indian ports during the year This shows that measuring efficiency of the ports should be given priority and necessity steps should be taken to make inefficient port perform better. To improve the efficiency and cargo volume traded the hub-strategy should be implemented by every port i.e. one place can handle all types of container traffic, this will improve the trade capacity of the port. The study attempts to investigates on the validity of the statement size is a contributing factor for port efficiency. Some of the earlier studies evidence that bigger ports are inefficient Coto-Millan et al. (2000), Cullinane et al. (2004). While some of the studies argued that bigger ports are efficient Cullinane et al. (2002), Turner et al. (2004), Tongzon and Heng (2005) and Wang and Cullinane (2006). Apart from this, the studies like Cullinane et al (2004) and Susila Munisamy et al (2011) found that indistinct relationship between size and efficiency. On the above background the research question whether size determines the efficiency of major ports in India? is being investigated in this piece of work. To answer this question, the present study carried out the evaluation of the operational efficiency of selected major ports in India during the study period Received on March 2014 Published on April

2 2. Literature Review There are several studies on evaluation of operational performance of the ports in different region. The initial study was done by Roll and Hayuth (1993) measured the port efficiency through data envelopment analysis (DA). Spanish port performance was measured by Martinez Budria et al. (1999). Tongzon (2001) who evaluated the port performance of 16 container terminals in various countries in the world. Greek and major four Portuguese ports were evaluated by Barros and Athanssiou (2004). The study done by Cullinane et al (2005) was on privatization and port performance of world s top 30 container terminals. Some of the studies related size with and its efficiency. Al raqi A.S. et al (2008) found the efficiency of 22 major seaports in the region of Middle astern and ast African and concluded that bigger ports are efficient. Coto-Millan, P (2000) measured 27 Spanish ports performance and it found that smaller ports are more efficient in comparison to bigger ports. Sohn, J and Jung, G (2009) studied operational efficiency of 16 Asian ports and concluded that larger ports shows better efficiency. Finally Turner, H et al (2004) analysed top 26 ports in the region of United States and Canada and categorically asserted that bigger ports are efficient. From the existing literature it was also found that most of the studies adopted data envelopment analysis (DA) for measuring the efficiency, Itoh (2002) suggested that DA is the most suitable model for measuring port efficiency. In this direction present study is interested to carried out evaluation of operational efficiency of selected major ports in India during with sophisticated model i.e. Data nvelopment Analysis models. 3. Methodology 3.1 Objective and Data The present research work carried out operational effectiveness of selected major ports in India during In turn it tries to investigate whether size is influencing the operational efficiency of ports in India. The study used input variables on the basis of land, labour and equipments, whereas outputs was represented by container traffic and total cargo traffic of the selected major ports. The present study is based on secondary data which was collected from the port authorities, CMI data bases and India Stat websites. 3.2 Data nvelopment Analysis Overview Data nvelopment Analysis (DA) an analytical tool for efficiency measurement has been invented by Charnes et al (1978). This technique is based on linear programming and it considers the input and output variables by converting into linearity technique for measuring the efficiency of the decision making units (DMU). According to Thanassoulis (2003) data envelopment analysis will give relative efficiency of decision making units instead of absolute efficiency. Data envelopment analysis is based on mathematical calculation and also it is called as non-parametric test because it does not follow any distribution method. According to Athanassopoulos and Curram (1996) who studied the bank and restaurant efficiency, suggested that DA is not only suitable for public sector companies but also suitable to measure the efficiency of private sector firms. Soares de Mellor et al (2003) highlighted the application of DA in identifying the inefficiencies and making benchmark for the efficient and inefficient units. Borenstein et al (2004) stated that DA can differentiate both efficient and inefficient units and it suggests the adjustment needed. Siems (1992) 927

3 carried out measurement of efficiency based on weighted sum of inputs and outputs. Saha and Ravisankar (2000) pointed out that DA cannot produce negative weight and the efficient ratio is restricted to 1 or 100%. According to Marinho (2003) DA set score for each decision making units for its relative performance. In general, DA scores are restricted with in the range of 0 to 1% or 0 to 100% the efficient unit obtain the value equal to 1 or 100%. Basically data envelopment application can be classified into input and output-oriented. Input-oriented model minimize the inputs for a desire level of output, output oriented model maximize the outputs while input is kept at constant level. Both the input and output oriented models seek to maximize the outputs, minimize the inputs and maximize the efficiency. In general, input oriented model closely focus on operational and managerial issues whereas output oriented model are more oriented towards planning and strategy (Cullinane et al, 2005). In a competitive world most of the ports need to continuously review their productivity and efficiency in order to make sure that they can give better services to the society and port users. Considering the above necessity the present study used output oriented DA model for measuring the efficiency of the major ports in India. The list of variables considered for the study are as follows number of berth, berth length, number of equipments, number of employees, container throughput and total traffic. Table 1: Compilation of Input and Output Variables Variables Contents Relevant Literature Input Variable No of Berth Rios and Macada (2006), Liu (2008) Berth Length Al-raqi A. Salem (2008), Cullinane K et. al (2006), Cullinane and Wang (2006), No of quipments Al-raqi A. Salem (2008), Rios and Macada (2006), Wu and Lin (2008), Cullinane and Wang (2006), Liu (2008) No of mployees Roll and Hayuth (1993), Rios and Macada (2006), Output Variable Container Throughput (TU) Total Traffic Valentine and Gray, Wu and Lin (2008), Cullinane K et. al (2006), Cullinane and Wang (2006), Coto-Millan et. al. (2000), Valentine and Gray, Al-raqi A. Salem (2008), Liu (2008) Before selecting the input and output variable, a thorough research was done on the earlier studies in the port sector. The above table presents the input and output variables based on land, labour and equipments. After checking the multicollinearity through correlation test, the input variables like number of berth, berth length, number of equipments and number of employees were selected. The reason behind selecting the number of berth, berth length as input variable is that they play a vital role for cargo handling whereas number of equipments and number of employees are important for its loading and unloading activities. Keeping these facts the above variables in mind were given priority in selecting as input variables. The container throughput and total traffic were considered as the output variables in the present study for evaluating the port efficiency. 928

4 4. Results & Discussion Table 1: Standard DA CCR Mormugao Chennai Paradip Tuticorin Cochin nnore Mumbai JNPT Mean Rank Note: nnore port stated its operations in 2002only. Table 1, shows standard CCR results and its ranking for selected major ports in India. The results are interpreted in the manner that those units obtained exactly 1.00 will be treated as efficient and less than 1 will be treated as inefficient. From the table it is evident that the ports of Mormugao, Tuticorin, nnore and JNPT were proved to be efficient ports during , whereas Chennai port shows inefficiency throughout the study period except in the year The other ports like Mumbai and Paradip showed fluctuating performance during the study period. The port of Mumbai was efficient in the initial periods and later it became inefficient. Paradip port was seen to be efficient in the year 2001, 2009, 2010 and 2011 rest of the years it was inefficient. The port of Cochin operated inefficiently throughout the study period. Based on the DA CCR results the ports like Mormugao, Tuticorin, nnore and JNPT jointly occupied top position. Followed by Paradip port ranked fifth position and Chennai port ranked sixth position. The other ports like Mumbai and Cochin obtained last two position among the selected major ports in India. Table 2: Standard DA BCC Mormugao Chennai Paradip Tuticorin Cochin nnore Mumbai JNPT

5 Mean Rank Note: nnore port stated its operations in 2002only. The efficiency scores based on standard DA BCC model is presented in the table 2. The port of Mormugao, Tuticorin, nnore and JNPT in this model also were rated as efficient port operations in India, while Chennai also showed efficiency all though except in the year Paradip port also was rated as efficient ports all the year except in the year 2002, 2004 and 2005 when it was rated as inefficient. Mumbai port had efficient operations in the initial periods i.e. first eight years later it was rated as inefficient. Cochin port was again rated as inefficient throughout the study period. The results indicate that DA BCC obtained better results comparatively DA CCR results. The reason behind BCC operates variables returns to scale whereas CCR operates constant return to scale. The port of Mormugao, Tutiocorin, nnore and JNPT again occupied the top position with efficient throughout the years. The other ports like Chennai and Paradip occupied the fifth and sixth respectively. The least efficient ports like Mumbai and Cochin remained in the last two positions among the selected major ports in India. Table 3: Relative fficiency of Major Ports in India JNP Mormuga Tuticori nnor Chennai Cochin Mumbai Paradip T o n e 1993 SI IRS IRS FF FF FF - FF - FF NOP 1994 SI IRS IRS FF FF FF - FF - FF NOP 1995 SI IRS IRS FF FF SI IRS FF - FF NOP 1996 SI IRS IRS FF FF SI IRS FF - FF NOP 1997 SI IRS IRS FF FF SI IRS FF - FF NOP 1998 SI IRS IRS FF FF SI IRS FF - FF NOP 1999 SI IRS IRS FF FF SI IRS FF - FF NOP 2000 SI IRS IRS FF FF SI IRS FF - FF NOP 2001 SI IRS IRS FF FF SI IRS FF - FF NOP 930

6 2002 SI IRS IRS FF FF SI IRS DRS FF FF 2003 SI IRS IRS FF FF SI IRS IRS FF FF 2004 SI IRS SI DRS FF FF SI IRS IRS FF FF 2005 SI IRS SI DRS FF FF SI IRS SI IRS FF FF 2006 SI IRS IRS FF FF SI IRS SI IRS FF FF 2007 SI IRS IRS FF FF SI IRS IRS FF FF 2008 SI IRS IRS FF FF SI IRS IRS FF FF 2009 SI IRS IRS FF FF SI IRS DRS FF FF 2010 SI IRS IRS FF FF SI IRS DRS FF FF 2011 IRS SI IRS FF FF SI IRS DRS FF FF SI Scale inefficient, Pure technical inefficient, FF fficient, NOP Not in operation IRS Increasing Returns to Scale, DRS Decreasing Returns to Scale Note: nnore Port started its operations in 2002 only ***** - represents efficient units Relative efficiency analysis of selected major ports in India during was done and the results are presented in the table 3. The model finds out the efficient as well as inefficient units and the reasons thereof. The score report indicates that the ports like Mormugao, Tuticorin, nnore and JNPT operated as efficient ports among selected major ports in India. The ports of Chennai and Paradip showed inefficiency in utilization of its resources. These inefficient ports need to improve their utilization capacity to become efficient one. The port of Cochin showed pure technical inefficiency throughout the study period, which indicates technologically this port is behind compared to other ports under consideration. So technological up gradation should be taken care to make it an efficient unit. Port of Mumbai has operated efficient in the initial years and later it was regarded as inefficient port because of lack of utilization capacity. Properties of scaling techniques four ports are constant return to scale prevails and on an average three ports are increasing returns to scale and one port prevailed as decreasing returns to scale. Table 4: DA Additive Constant Return to Scale Mormugao Chennai Paradip Tuticorin Cochin nnore Mumbai JNPT

7 Mean Rank DA Additive models measures how the firms are efficiently using their resources. Standard DA- BCC and DA CCR models are input and output oriented models respectively whereas DA-Additive model a combination of input and output oriented model. The result of additive model presented in table 4 revealed that the ports like Mormugao, Tuticorin, nnore and JNPT are using their resources efficiently throughout the study period. The ports of Chennai shows inefficient utilization during all the years except in the year Paradip port also found to be inefficient in utilization of resources in all the years except 2001, Mumbai port showed efficient utilization during initial years and later it was identified to be operations inefficiently. The port of Cochin shows inefficient utilization throughout the study period. As per the table the port of Mormugao, Tuticorin, nnore and JNPT were ranked the top most positions followed by Paradip at fifth position among selected major ports. The port of Chennai remained at six position in utilization capacity and ports like Mumbai and Cochin were adjudged least performing ports as they occupied the last two positions during the study period. Table 5: DA Additive Variable Return to Scale Mormugao Chennai Paradip Tuticorin Cochin nnore Mumbai JNPT

8 Mean Rank Note: nnore port stated its operations in 2002only. Table 5 presents the results of the measures of utilization of capacity under variable returns to scale for the major ports in India during The port operation of Mormugao, Chennai, Tuticorin, nnore and JNPT were efficiently utilizing it resources throughout the study period. Paradip port was rated efficient in all the years except the year 2002, 2004, 2005 and Mumbai port showed efficient utilization of resources in the initial years but later it went down to become inefficient. The port of Cochin was identified again for inefficient utilization through variable returns to scale during all the years. Rank wise classifications put the ports like Mormugao, Chennai, Tuticorin, nnore and JNPT at top positions during the study period. The ports like Paradip and Mumbai ranked sixth and seventh positions during The lease efficient port Cochin rated last among selected major ports in India. Table 6: DA A&P Super fficiency Model Mormugao Chennai Paradip Tuticorin Cochin nnore Mumbai JNPT Mean Rank DA Anderson & Peterson model measures super efficiency among selected major ports in India. Super efficiency ranking method was developed by Anderson and Peterson in the year 1993, is regarded as the most popular method for measuring the super efficiency on decision making units. Super efficiency score are derived for all the DMUs. All the super-efficient units get the score as per decreasing order of efficiency unlike the other models where 933

9 efficiency is identified in score 1. Whereas the inefficient units gets the same score as determined by other models. From the table it is found that the port of JNPT had highest efficiency score among selected major ports in India with followed by nnore port with and was placed in second position. Mormugao port also rated as super-efficient with The port of Tuticorin showed super efficient during the study period with the score of Other ports like Chennai, Paradip, Mumbai and Cochin were rated as inefficient ports, because DA A & P do not measure inefficient observations. 5. Concluding remarks Measuring the efficiency of ports through data envelopment analysis is appropriate. The present study used DA models for measuring the relative efficiency of selected major ports in India during The input variables have been selected for this study are number of berths, berth length, number of equipments and number of employees whereas output variables considered are container throughput and total traffic. Based on the results the ports like Mormugao, Tuticorin, nnore and JNPT rated as efficient ports under the standard DA- CCR, BCC and DA Additive CRS and VRS methods. The study contributes to classify the doubts as regard common belief that bigger ports perform well. In this direction the study reveals that size is not a determinant factor for port efficiency as bigger ports like Mormugao, JNPT and smaller ports nnore, Tuticorin were proved to be efficient ports all though. It leads to a conclusion that size always does not ensure efficiency. Based on the results it is necessary to take efforts to focus on the reasons for such inefficiency i.e. whether scale or technical to make them efficient. Few suggestions can be made through this study to reinforce the performance of major ports in India. From the study it can be understood that some of the ports are operating inefficiently so this is the time to think about long term plan for boost up by upgrading infrastructure facilities in the major ports. The inefficiencies mostly is found in terms of scale inefficiency and the port of Cochin showed pure technical inefficiency. For scale inefficient ports it is time to modernize the ports to revise turn the inefficiency. For improving technical efficiency the technological up gradation should be undertaken for the particular ports. However, the caution should be taken while interpreting the efficient ports among the existing ports. Mormugao, Tuticorin, nnore and JNPT are found better in comparison to the other ports of India but it does not mean they are the most efficient and there is no room or need for further betterment. 6. References 1. Al-raqi. A S., Mustaffa. A., Khader. A T., and Barros. C P, (2008), fficiency of middle astern and ast African seaports: application of DA using Window analysis, uropean Journal of Scientific Research, 23(4), pp Andersen. P., and Petersen. N C, (1993), A procedure for ranking efficient units in data envelopment analysis, Management Science, 39, pp Athanassopoulos. A D., and Curram. S, (1996), A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units, Journal of Operational Research Society, 47(8), pp Banker. R D., Charnes. A., and Cooper. W (1984), Model for estimating technical and scale efficiencies in data envelopment analysis, Management Science, 30(9), pp

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