The purpose of this paper is to benchmark the operational and financial performances of the major Indian seaports to help derive useful insights to improve their performance.

1. A benchmarking study of Indian
seaports
Sajeev Abraham George and Anurag C. Tumma
Department of Operations and Supply Chain,
S P Jain Institute of Management and Research, Mumbai, India
Abstract
Purpose – The purpose of this paper is to benchmark the operational and financial performances of the
major Indian seaports to help derive useful insights to improve their performance.
Design/methodology/approach – A two-stage data envelopment analysis (DEA) methodology has
been used with the help of data collected on the 13 major seaports of India. The first stage of the DEA
captured the operational efficiencies, while the second stage the financial performance.
Findings – A window analysis over a period of three years revealed that no port was able to score an overall
average efficiency of 100 per cent. The study identified the better performing units among their peers in both
the stages. The contrasting results of the study with the traditional operational and financial performance
measures used by the ports helped to derive useful insights.
Research limitations/implications – The data used in the study were majorly limited to the available
sources in the public domain. Also, the study was limited to the major seaports which are under the
Government of India and no comparisons were carried out with other local or international ports.
Practical implications – There is a need to prioritize investments and improvement efforts where they
are most needed, instead of following a generalized approach. Once the benchmark ports are identified, the
port authorities and other relevant stakeholders should work in detail on the factors causing inefficiencies, for
possible improvements in performance.
Originality/value – This paper carried out a two-stage DEA that helped to derive useful insights on
operational efficiency and financial performance of the India seaports. A combination of the financial and
operational parameters, along with a comparison of the DEA results with the traditional measures, provided a
different perspective on the Indian seaport performance. Considering the scarcity of research papers reported
in the literature on DEA-based benchmarking studies of seaports in the Indian context, it has the potential to
attract future research in this field.
Keywords India, Quantitative, DEA, Performance management and benchmarking,
Data envelopment analysis, Operational efficiency, Seaports
Paper type Research paper
1. Introduction
With a GDP of over $2tn and an average annual projected growth rate of over 7 per cent,
India has been among the fastest growing economies in the world. Some of the important
factors driving the growth of the Indian economy include, the high internal demand for
goods and services, the country’s large young working population and its increasing
participation in the global economy. Global trade plays a critical role in the development and
growth of a country. Import and export of goods such as petroleum products, engineering
goods, mineral ores, textiles and agricultural products forms a large majority of India’s
global trade. The physical movement of these goods and commodities requires suitable
infrastructure and systems for management. This is the area where seaports play a critical
role in the country’s economic development. These ports require scarce public and private
resources for their functioning that ultimately drive the performance. It is therefore
JGOSS
13,1
88
Received 11 May 2019
Revised 4 September 2019
Accepted 14 September 2019
Journal of Global Operations and
Strategic Sourcing
Vol. 13 No. 1, 2020
pp. 88-102
© EmeraldPublishingLimited
2398-5364
DOI 10.1108/JGOSS-05-2019-0037
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2398-5364.htm

2. imperative to study the performance of seaports, which are large and complex systems, for
effecting possible improvements.
India has a coastline of 7,517 km which enables maritime trade. Out of the total trade,
maritime trade accounts for 95 per cent by volume and 70 per cent by value in India. There
are 13 major as well as around 200 medium and minor ports in India. The cargo traffic at the
major ports of India in 2016-2017 was 647 MMT (Million Metric Tons) with a growth at 4
per cent CAGR. The total cargo throughput from all ports stood at 1,133 MMT in the same
period. Considering the important role of these ports, the Indian government has recently
taken high profile initiatives for huge investments in the development of the ports and its
associated infrastructure (Ibef.org, 2007). The Government has outlined the “SagarMala”
program for modernization and projects related to seaports. The program includes
enhancing the port connectivity and increasing the port-led industrialization. The Indian
port sector has a healthy outlook due to the investments from government and private
players and also due to the increasing cargo traffic. It is thus necessary to evaluate the
efficiency of ports so that appropriate steps can be taken for improving the performance.
2. Background
On comparing the efficiency of the logistics sector of India with that of the other countries, it
can be observed that the country lags behind with a logistics cost as high as 19 per cent of
the GDP. This obviously leads to high cost of doing business as well as costlier goods and
services. In India, transportation of goods by roads account for over 55 per cent of overall
ton-kilometers which overshadows cheaper and environment friendly modes such as
waterways and coastal shipping. Hence, increasing the port utilization should be a key focus
for the Indian government to optimize the logistics modal mix. One of the steps that could
help to achieve this would be a benchmarking of the existing seaport infrastructure to
understand the current performance and identify shortcomings. This should potentially
enable the various stakeholders to manage the limited resources, in a better way.
The performance measurement system plays a critical role in monitoring performance,
enhancing motivation and communication, and diagnosing problems. Furthermore, it
provides an approach to measure the success and potential of management strategies and
facilitates better understanding of the situation. It can lead to better decision making and
can help to motivate and direct the various stakeholders toward desired end results. An
examination of the literature reveals that performance measurement and benchmarking has
received a great deal of attention from researchers, in the recent past, and number of studies
have been reported that use different methodologies to address this issue. Data envelopment
analysis (DEA) is technique that has been widely used in this regard. However, though
numerous studies in the past have used DEA for performance benchmarking in various
industry sectors, its use in the context of seaports has not been adequately addressed.
Moreover, apparently none of the studies have attempted to link up the operational
performance with the financial performance of these seaports. No major studies have been
reported in the literature from the Indian context using DEA for performance benchmarking
of seaports. This study attempts to address this research gap by addressing the issue of
performance benchmarking of the major Indian ports using a two-stage DEA model that
takes into account both the operational and financial performance measures. While
measures such as cargo volume handled, equipment utilization and average output per ship-
berth-day are used to capture operational efficiencies, the financial benefits of these
efficiencies are measured in terms of the revenues generated and the operating ratio. The
study performs an analysis of the relative efficiencies of major Indian seaports using a
two-stage DEA model with the help of the data from year 2014 to 2016. This study attempts
Benchmarking
study of Indian
seaports
89

3. to build on the previous work carried out by researchers, by combining the operational and
financial parameters to evaluate efficiencies.
The remaining part of this paper is structured as follows: The next section attempts to
provide a supply chain perspective to the operations of seaports. Section 4 contains a review
of the relevant literature, which is followed by a brief discussion on DEA in Section 5. The
application of the DEA methodology for performance benchmarking of the seaports is the
theme of Section 6. Further, the results of the data analysis, together with its managerial
implications are presented in Section 7. Section 8 presents the concluding remarks that
contain the limitations of this work and scope for future research.
3. A supply chain perspective of seaports
The key challenge in a supply chain is the management of the flow of material, informational
and financial resources in an effective and efficient manner to add superior value for the
customer. Logistics is an integral part of a supply chain that facilitates flow of material from
the point of origin to the point of consumption. Seaports form an inseparable part of many
global supply chains which rely on maritime transport. With increased globalization that has
resulted in larger volumes of goods being transported between countries, the need for global
nodes in supply chains keeps increasing. Often, the flow of high volume of goods between the
global nodes is logistically made possible by the port infrastructure. In global supply chains,
since maritime transport is generally cheaper than the other modes, it enables to drive down
supply chain costs. Seaports provide services of transportation, storage and distribution.
They also serve as links between multiple modes of transport. Raw materials such as crude
oil, mineral ores, food-grains can be transported in huge volumes using ships. Seaports
enable the loading, receipt and handling of large quantities of various raw materials and
finished goods at lower costs. As India is dependent on foreign crude oil, major Indian
seaports serve at the key locations for handling petroleum products. Containerization has
helped to create robust maritime transportation, enabling non-bulk cargo to be transported
efficiently. The intermodal transportation is also dependent on containers and the major
seaports in India handle the loading, unloading and storage of these containers.
As mentioned before, seaports perform multiple operations such as storage, loading,
unloading and transshipment of cargo. They also provide access for intermodal transport
and services to the ships, cargo-owners, handlers, government and others. Seaports not only
ensure availability of facilities and equipment for cargo transportation, they also provide
safety for the ships while carrying out the various operations. Some ports even provide
repair and maintenance facilities for the ships and assistance to customs clearance services
for the imports and exports of goods. Therefore, there is a strong need for coordination
between the port personnel, logistics operators, shippers and associated services at the
seaports and they need to work with the changing needs of the customers. Improving the
efficiency of seaports can significantly impact the performance of supply chains that can
result in benefits to customers, companies and the country.
4. Literature review
Studies reporting the analysis and benchmarking of different ports have been presented in the
literature by various authors. One of them by Jim et al. (2008) have used DEA and Revealed
Comparative Advantage (RCA) technique to analyze the competitiveness and efficiency of Indian
container port operations. This study mainly used land and equipment as the input variables,
derived from the transport and freight service industry of India data during 2000-2005. The
physical assets of these ports were mainly used as inputs while the outputs chosen were cargo
throughput and containers handled per hour. Their paper also compared the Indian ports with
JGOSS
13,1
90

4. some other major ports in BRICS countries. In another study, Liu (2008) evaluated operational
efficiency of major ports in the Asia-Pacific region, in which three DEA models were used with
the help of cross-period data of 10 ports. The study used berth characteristics as the main input
while the outputs included cargo volume and number of ships handled. It was observed that
environmental factors, statistical noises and managerial inefficiency were not taken into account
in the models which led to differences in the results of the three models presented. In another
study, used a DEA window analysis model with panel data for the period 2000 to 2005 for 22
ports in the Middle East and East Africa. A comparison of efficiency of DMUs was made for
estimations using normal and super efficiency. The study utilized DEA-BCC model for window
analysis with ship calls and cargo throughput as outputs while berth length, terminal area and
equipment were the inputs. The mean efficiency values for all the DMUs used in the study in the
window analysis were found to be less than 1.
Jiang and Li (2009) in their paper proposed performance measurement of Northeast
Asian ports through radial and non-radial approach of DEA. A model consisting of 12
DMUs with regional GDP, berth lengths and equipment numbers as inputs, while TEU
throughput as the output was presented. Apart from the efficiency scores, frequency and
relative distance analysis were included as comparators. Cullinane and Wang (2010)
asserted the use of window analysis over cross-sectional data in analysis of port production
using DEA, citing suitability for medium and long-term efficiency analysis. The study
comprised of 25 leading container ports worldwide with data collected over 8 years. Number
of equipment was the main input and container throughput along with terminal length and
area were used as outputs. The benefit of using a selected window width was found as
separation of technical efficiency from impact of technological changes.
A recent study by Wiegmans and Dekker (2016) focused on 11 deep sea ports in Europe
in the Hamburg-Le Havre range. A combination of DEA and a single-point benchmarking
method was used to show Dutch deep-sea ports as being the most efficient. Financial
parameters such as depreciation and material cost were used along with number of
employees as inputs. Apart from the usual cargo volume and number of ships handled, sales
and profit were also part of their output set. Input-oriented and output-oriented DEA models
were used to account for possibility of certain ports potentially having better control over
inputs while others over output. Their study found smaller ports to be more efficient than
larger deep-sea container ports. Another study on the relative efficiency of 21 small and
medium sized container terminal Chinese ports was conducted by Ding et al. (2015). DEA
and Malmquist Productivity Index, followed by Tobit regression were employed to evaluate
operational performance and productivity of ports. Inputs for the models were terminal
length, number of equipment and staff while container throughput was the only output.
Almawsheki and Shah (2015) also used DEA for technical efficiency measurement of 19
Middle-Eastern container ports. They employed slack variable analysis, the results of which
could be used by the terminal managers for better resource utilization and steady
improvement of operational efficiency. A CCR input oriented DEA model with berth and
equipment characteristics as inputs combined with cargo throughput as output was used. In
another very recent study by Kutin, et al. (2017), the traditional output-oriented DEA for
comparing the relative efficiencies of 50 ports in ASEAN region was presented. The ports
were categorized into 6 different sets for applying DEA-CCR and DEA-BCC models. Depth,
size of container yard, quay length and no. of equipment served as the inputs and container
throughput as the output. Seaports were found to have better efficiencies than the inland
ports. A brief description of the different performance benchmarking studies of ports,
reported in the literature is presented in Table I.
Benchmarking
study of Indian
seaports
91

5. Author/s
Geography
Methodology
Inputs
Outputs
Cuadrado,
et
al.
(2004)
Europe
Competitive
Benckmarking
(Non-DEA)
Infrastructure,
Logistics
coordination,
Time
,
Cost
and
Safety
NA
Jim
et
al.
(2008)
22-Worldwide
RCA
and
DEA
terminal
area,
total
quay
length,
no.
of
quayside
gantry,
no.
of
yard
gantry,
no.
of
straddle
carrier
Cargo
throughput
,
no.
of
container
per
hour
Liu
(2008)
10-Asia-
Pacific
DEA-CCR,
BCC
and
3-stage
models
Container
lot
size,
the
number
of
bridge
cranes,
container
berth
length
and
the
number
of
deep-
water
berths
Ship
calls,
Volume
of
container
cargo
Salem
Al-Eraqi,
et
al.
(2009)
22-East
Africa
and
Middle
East
DEA-
window
analysis
(6
year
data)
Berth
length,
terminal
area
and
handling
Equipments
Ship
calls,
Throughput
Jiang
and
Li
(2009)
12-Northeast
Asia
DEA
Import/Export
by
customs,
GDP
by
regions,
Berth
length,
and
Crane
numbers
TEU
throughput
Cullinane
and
Wang
(2010)
25-Worldwide
DEA-BCC
model
,
cross-
sectional
study
8
years
Quayside
gantry(No.),
Yard
gantry(No.),
Straddle
carrier(No.)
Container
throughput(TEU),
Terminal
length(m),
Terminal
area
(ha)
Jiang
et
al.
(2011)
24-Asia
DEA
-
Strongly
Efficient
Frontier
and
Strongly
Inefficient
Frontier
Total
area,
container
quay
length,
storage
capacity
Number
of
direct
calls
and
container
throughput
Feng,
et
al.
(2012)
2-UK
and
China
survey
questionnaire
15
factors
–
Wanke
(2013)
27-Brazil
2-stage
DEA
No.
of
berths,
Warehousing
area,
Yard
area
Intermediate
outputs:
Solid
bulk
frequency,
Container
frequency.
Final
outputs:
Container
throughput,
Solid
bulk
throughput
Wiegmans
and
Dekker
(2016)
11-Europe
DEA
Employees(No.),
Depreciation(euro),
Material
cost
(euro)
Cargo(Tons),
Ships(no.),
Sales
(euro),
Profit(euro)
Rajasekar
and
Deo
(2014)
13-India
Input-oriented
DEA,
output-
oriented
DEA
No.
of
berths,
Berth
length,
No.
of
equipment,
No.
of
employees
Container
Throughput,
Total
traffic
Ding
et
al.
(2015)
21-China
DEA
and
MPI
(5-year
data)
Terminal
length,
handling
equipment
quantity,
and
staff
quantity
Container
throughput
(continued)
Table I.
Research papers on
seaport
benchmarking
JGOSS
13,1
92

6. Author/s
Geography
Methodology
Inputs
Outputs
Almawsheki
and
Shah
(2015)
19-Middle
Eastern
region
DEA-CCR
input-oriented
model
Total
area,
quay
length,
quay
crane,
yard
equipment,
maximum
draft
Cargo
throughput
Lu
et
al.
(2015)
20-Worldwide
DEA-CCR,
DEA-BCC,
DEA-
super
efficiency
Yard
area/berth,
No.
of
quay
crane,
No.
of
yard
crane,
yard
tractor/berth,
berth
length
Container
throughput,
productivity
indicator
Kutin,
et
al.
(2017)
50-ASEAN
DEA
-CCR
and
DEA-BCC
Max
depth
at
berth(m),
Size
of
container
yard(m
2
),
Quay
length(m),
No.
of
equipment
Container
throughput
Table I.
Benchmarking
study of Indian
seaports
93

7. 5. Dea
DEA is a non-parametric linear programming technique which utilizes multiple input and
outputs to calculate the relative efficiencies of similar units. These entities under
comparison are called decision-making units (DMUs). Charnes et al. (1978) formulated the
basic DEA technique, also known as the CCR model, to compare the efficiencies of DMUs.
The efficiency score of a unit is obtained by choosing the optimal weights for inputs and
outputs so as to maximize the ratio of weighted sum of outputs to weighted sum of inputs
of that unit. The efficiency score ranges from 0 to 1 and the model allows individual
DMUs to use optimal weights to maximize its efficiency. Another model widely used is
the BCC model by Banker et al. (1984). The CCR model is based on the assumption of
constant returns to scale between the input and output while the BCC model is suited for
variable returns to scale. Further, the models can be input-oriented or output-oriented,
indicating how DMUs can be made efficient by proportionally reducing the input or
output respectively while keeping the other proportions constant. DEA methodology
requires that the DMUs under consideration should be reasonably homogenous. Further,
as a common rule of thumb, for ‘m’ inputs and ‘n’ outputs, the number of DMUs should be
more than the product of m and n. DEA defines the efficiency of each DMU as the ratio of
the weighted sum of outputs to the weighted sum of inputs. The outputs are the products
and or services produced by the units and inputs are the resources used to produce these
outputs. A unit with an efficiency score of 1 (100 per cent) is considered as efficient and a
score of less than one indicates that the unit is inefficient. Each unit is allowed to select
the optimal weights that maximize its efficiency, subject to the condition that the
efficiencies of all the units in the data set when evaluated with these weights are not
allowed to exceed one. In the basic DEA model developed by Charnes et al. (1978) (CCR),
the objective is to maximize the efficiency value of a test DMU ‘p’ from among a reference
set of ‘n’ by selecting the weights associated with the inputs and outputs. Therefore, the
input and output weights are the decision variables in the mathematical programming
problem.
The original CCR mathematical model is formulated as follows:
Max
Xs
k¼1
vkykp
Xm
j¼1
ujxjp
s:t
Xs
k¼1
vkyki
Xm
j¼1
ujxji
#1 8i
vk; uj 0
where:
k = 1, . . . . . . . . . . . ., s (outputs);
j = 1, . . . . . . . . . . . ., m (inputs);
i = 1, . . . . . . . . . . . ., n (DMUs);
yki = amount of output k produced by DMU i;
xji = amount of input j used by DMU i;
vk = weight given to output k; and
uj = weight given to input j.
JGOSS
13,1
94

8. Solving of the above fractional program is computationally difficult and cannot be solved by
an ordinary Linear Programming (LP) software and therefore has been transformed into an
equivalent LP formulation (Charnes et al., 1978). This is done by normalizing either the
numerator or denominator of the fractional programming objective function. The LP
formulation made by maximizing the weighted sum of outputs setting the weighted sum of
inputs equal to one is the output maximization DEA program. Therefore, the equivalent LPP
for a test DMU p, is formulated as follows:
Max
X
s
k¼1
vkykp
s:t
X
m
j¼1
ujxjp ¼ 1
X
s
k¼1
vkyki
X
m
j¼1
ujxji # 08i
vk; uj 08k; j
If,
uj = weight of the output yj;
vi = weight of the input xi;
yjp = output j of the unit p; and
xip = input i of the unit p.
DEA is an effective approach to assess the efficiency of DMUs, where traditional
performance measurement either fail or are difficult to apply. Though DEA generates a
large quantity of information to the decision makers, some its main strengths in overcoming
the pitfalls of traditional performance measurement systems are:
Being able to handle disproportionate multiple inputs and outputs.
Not requiring the decision maker to assign any priory arbitrary weights.
Being able to combine multiple inputs and outputs to a single comprehensive
measure of relative efficiency.
DEA defines efficiency of a DMU as the ratio of sum of the weighted outputs to the sum of
weighted inputs. In a two stage DEA, the overall of efficiency will be the product of the first
and second stage efficiencies and the outputs of the first stage will be the inputs of the second
stage. Unlike techniques such as regression analysis, DEA being a non-parametric method, it
does not need a functional relationship between the inputs and the outputs. The weights for
the inputs and outputs are assigned in such a way that it will maximize the composite
efficiency score for each decision making unit that allows each unit to take advantage of its
unique areas of strength. With DEA, both financial as well as operational measures can be
used together to obtain a composite index. Since it allows multiple inputs and outputs, DEA
can help to eliminate the use of uni-dimensional data that can promote dysfunctional
behavior. DEA can also help measure a unit’s level of continuous improvement as it can be
used longitudinally to monitor the DMUs progress over time and can be used for initial
sorting of units into relatively efficient and inefficient ones. In addition, it can be used to
identify which aspects of performance of relatively efficient units are worthy of further
investigations for identifying good operating practices (George and Rangaraj, 2008).
Benchmarking
study of Indian
seaports
95

9. However, DEA is restricted to comparing like units and managers should use this only as an
initial indicator of inefficiencies. Also, when the number of inputs and output measures
increases, the power of DEA to discriminate decreases and therefore we need more number of
DMUs in the data set to perform the analysis in a meaningful way. Thanassoulis (1993)
provides a detailed account on the comparison of regression analysis and DEA as alternative
methods for performance assessments.
6. Application of data envelopment analysis for benchmarking of seaports
The steps in implementation of DEA would include the selection of DMUs, identifying the
inputs and outputs of the units being assessed, collecting data on the inputs and outputs,
solving the appropriate models and interpreting the results. As the first step, the 13 major
seaports in India that fall under the ministry of shipping, India was selected for the study.
The selected 13 ports have been found similar in their characteristics to a great extent and
were mainly responsible for the major part of the trade volumes. Data on these ports, which
were publicly available in the ‘Basic Port Statistics’ publication by the Transport Research
Wing of Ministry of Shipping (2014, 2015; 2016), Government of India was used in the study.
A window analysis was preferred over a cross-sectional study because of the need to track
the performance of these ports over a period of time. The window width of 3 periods was
used for the study taking into account the port performance data for the financial years
2013-14, 2014-15 and 2015-16. It was observed that a larger window width than this could be
undesirable due to potential impact of technological advances on performance, which
possibly could skew the results. Thus, a port in each year has been considered separately as
a DMU to compare its efficiency scores for different years. This has yielded, in effect, a total
of 39 DMUs for the analysis. This has also helped to address the dimensionality issue of the
DMUs by making sure that the number of DMUs was large enough to maintain the
discriminatory power of the technique, as per the rule of thumb prescribed by Cooper et al.
(2007). Further, the efficiency scores of each port were then found by averaging the
efficiency scores for the three years to get the final overall efficiency score.
The next step was to select the inputs and outputs for the study. It was kept in mind that
both the inputs and outputs must be measurable and related to the performance of seaports.
After examining similar studies reported in the literature and also observing the functioning
of the ports, multiple inputs and outputs were identified. The common inputs generally used
for such studies include the berth characteristics, (e.g. the number of berths, its length and
the depth) the storage capacity, available storage areas, available equipment and the
number of employees. The outputs could be divided into two categories – operational and
financial outputs. The operational performance of ports was typically measured using cargo
traffic handled, capacity utilization, average pre-berthing wait time, average turn-around
time and average output per ship-berth day. The financial performance has been measured
in terms of the sales generated, expenses incurred, profit/loss made and the operating ratio.
In this study, it was decided to carry out a two stage DEA with the first stage capturing
the operational efficiency while the second stage the financial performance. The outputs of
the first stage were used as the inputs to the second stage. While the number of berths,
number of employees and the capacity in Million Metric Tons (MMT) were considered as the
inputs to the first stage, Cargo volume handled in MMT and the Turn-Around Time in days
was used as its outputs. As mentioned before, these first stage outputs have been considered
as the inputs for the second stage. Considering an output maximization objective, in the first
stage, the inverse of the output measure “turn-around time” was used in the analysis. The
second stage outputs were set as the port revenues and operating income, captured in crores
of Indian Rupees (1 Crore = 10m). The input oriented CCR DEA model was selected and
JGOSS
13,1
96

10. MaxDEA 7 Basic software was used for the implementation. The conceptual representation
of the two-stage DEA model is shown below (Figure 1).
7. Results and discussion
Initially, the first stage efficiencies were obtained and it was found that there were 9 ports
with an efficiency score of 1. These operationally efficient ports were Kolkata, Kamarajar,
JNPT and Kandla in year 2014, Kamarajar, Cochin, JNPT and Mumbai in year 2015 and
Kamarajar in year 2016. Vishakapatnam in 2016 was observed as the worst performing port
in this stage with an efficiency score of 0.52. On computing the efficiency scores of the
second stage, it was found that number of efficient ports was reduced to five for that stage.
The financially efficient ports in the second stage were Kolkata in 2014, Mumbai in 2015,
Mumbai-2014, Paradip-2015 and Kolkata-2016. The second stage also showed a wider
dispersion of efficiency scores than the first stage, with the lowest score being 0.297 for New
Mangalore.
On combining the efficiency scores from both the stages, only two ports: Kolkata-2014
and Mumbai-2015, was found to have an overall efficiency score of 1. The New Mangalore-
2014 was identified as the worst performing DMU overall with a combined efficiency score
of just 0.164. To compare the efficiency of each port over the period of three years, the
average efficiency scores were also computed. It was observed that no port could achieve an
average efficiency score of 1 and the best performing ports from the two-stage DEA model
was Mumbai with an overall average score of 0.965 and the worst performing was
New Mangalore with an overall score of 0.181. The results have been presented in Table II
and Table III.
To validate the DEA results, the average overall efficiency scores were compared with
key the performance indicators outlined in the Basic Port Statistics by the Ministry of
Shipping, India. Two key performance measures viz. Average Utilization and Operating
Ratio (ratio of working expenses to the gross earnings) were used in this exercise. As far as
utilization is concerned, it was interesting to see that Mumbai, the best performing port as
per DEA results had the highest average utilization too. However, the other ports with
higher DEA scores such as Kolkata and JNPT showed lower utilization scores than ports
like Kamarajar and Kandla. These ports were able to use most of their capacity for handling
the cargo traffic. Rest of the ports having an average utilization of lower than 70 per cent,
has also been reflected in their efficiency scores. On the financial side, the average operating
ratio of Mumbai was 74.4 per cent which falls in the medium range. Kolkata, with a poorer
utilization ratio and operating ratio has scored high DEA score while, JNPT having better
operational and finical performance has a lower DEA score. Barring minor exceptions like
this, it can be observed that the operating ratios have been more or less in agreement with
the average overall efficiency scores obtained from DEA. The results from the study seem to
Figure 1.
Two-stage DEA
model
Benchmarking
study of Indian
seaports
97

11. align with certain performance indicators, while some results are in disagreement, with
Mumbai being the best performing port.
There are significant inefficiencies involved in ports like New Mangalore which need to
be addressed. Although Mumbai is the most efficient port, a further comparison with other
global counterparts would be desirable. The ports which show higher efficiency scores are
also the ones with high utilization factors. The choice of the variables for inputs and outputs
will affect the results and hence it is important to consider the relevance of these variables
used in the study. Overall, the DEA methodology would be suitable as an alternate approach
for measuring relative efficiency of the Indian seaports. Having incorporated both financial
and operational measures, a different perspective on the seaport performance has been
presented.
Table II.
Efficiency scores
from the two-stage
DEA model
No. DMU Year Stage1 Stage2 Combined
1 Kolkata dock system 2014 1.000 1.000 1.000
2 Haldia dock complex 2014 0.654 0.979 0.640
3 Paradip 2014 0.854 0.697 0.595
4 Vishakapatnam 2014 0.636 0.649 0.413
5 Chennai 2014 0.632 0.448 0.283
6 Kamarajar(Ennore) 2014 1.000 0.542 0.542
7 Chidambaranar (Tuticorin) 2014 0.762 0.407 0.310
8 Cochin 2014 0.962 0.433 0.416
9 New Mangalore 2014 0.554 0.297 0.164
10 Mormugao 2014 0.699 0.411 0.287
11 JNPT 2014 1.000 0.689 0.689
12 Mumbai 2014 0.961 1.000 0.961
13 Kandla 2014 1.000 0.703 0.703
14 Kolkata dock system 2015 0.867 0.910 0.789
15 Haldia dock complex 2015 0.724 0.994 0.719
16 Paradip 2015 0.845 1.000 0.845
17 Vishakapatnam 2015 0.579 0.738 0.428
18 Chennai 2015 0.631 0.508 0.321
19 Kamarajar(Ennore) 2015 1.000 0.583 0.583
20 Chidambaranar (Tuticorin) 2015 0.829 0.476 0.395
21 Cochin 2015 1.000 0.424 0.424
22 New Mangalore 2015 0.665 0.301 0.200
23 Mormugao 2015 0.691 0.446 0.308
24 JNPT 2015 1.000 0.842 0.842
25 Mumbai 2015 1.000 1.000 1.000
26 Kandla 2015 0.773 0.644 0.497
27 Kolkata dock system 2016 0.936 1.000 0.936
28 Haldia dock complex 2016 0.585 0.881 0.515
29 Paradip 2016 0.917 0.738 0.676
30 Vishakapatnam 2016 0.522 0.733 0.382
31 Chennai 2016 0.571 0.555 0.317
32 Kamarajar(Ennore) 2016 1.000 0.791 0.791
33 Chidambaranar (Tuticorin) 2016 0.667 0.492 0.328
34 Cochin 2016 0.836 0.471 0.393
35 New Mangalore 2016 0.602 0.298 0.179
36 Mormugao 2016 0.736 0.383 0.282
37 JNPT 2016 0.932 0.856 0.797
38 Mumbai 2016 0.983 0.950 0.934
39 Kandla 2016 0.786 0.646 0.507
JGOSS
13,1
98

12. Rank
DMU
Average
efficiency
score
Average
Utilization
(%)
Average
Operating
Ratio
(%)
1
Mumbai
0.965
131.8
74.4
2
Kolkata
dock
system
0.908
75.7
87.9
3
JNPT
0.776
82.2
44.3
4
Paradip
0.706
60.7
58.7
5
Kamarajar(Ennore)
0.639
80.5
12.7
6
Haldia
dock
complex
0.625
56.8
73.7
7
Kandla
0.569
88.9
71.4
8
Cochin
0.411
43.3
94.8
9
Vishakapatnam
0.408
59.6
69.0
10
Chidambaranar
(Tuticorin)
0.344
67.7
51.3
11
Chennai
0.307
58.0
84.6
12
Mormugao
0.292
36.1
90.0
13
New
Mangalore
0.181
47.8
65.6
Table III.
Comparison of
average port
efficiency scores with
other key measures
Benchmarking
study of Indian
seaports
99

13. As seaports are an indispensable part of the global trade, there are multiple stakeholders
involved who will be impacted by the performance of the ports. This study can potentially
help the government and policy makers to identify the inefficient ports from the efficient
ones. This would help to prioritize improvement efforts where they are most needed, instead
of following a generalized approach. Once the target ports are identified, the port authorities
and other relevant stakeholders should work in detail on the factors causing inefficiencies
for possible improvements. Companies planning to locate manufacturing sites, warehouses,
distribution centers and other vital business infrastructure, which requires significant
movement of material through sea, can consider the results of the port benchmarking study
for formulating their location and supply chain network strategy. Obviously, locations near
ports with better efficiency scores will be more desirable. Businesses looking to significantly
scale up operations in the country can consider if the relevant ports performance can be
suited to their rising requirements for movement of goods in the future. Also, planners and
policy makers can study how the ports with high efficiency scores can support speed and
delivery of operations for exports and imports via sea route. For sourcing strategy, even
foreign businesses can utilize the port benchmarking data to see the effect on sourcing from
suppliers providing materials through efficient and inefficient ports in India. Since, the
study also incorporates financial factors, the financial planners could potentially utilize the
scores while considering investments and associated risks in different ports. Ports with poor
efficiency scores will be carrying more financial risks, which should be factored in financial
decisions. Finally, it would be useful for the providers of port infrastructure to look at the
results of this benchmarking study while considering potential demand from ports that
require improvement in efficiency scores. This would help them in their demand planning
and therefore in enabling timely infrastructure upgradation.
In the recent past, the government of India has been taking high profile initiatives for
making India a preferred destination as a global manufacturing hub. Large investments in
infrastructure have been proposed and steps are being taken including the “SagarMala”
project of the Ministry of Shipping for attracting and optimizing the container traffic. This
potentially presents many opportunities for the seaports to change their current style of
operation and to be ready for the foreseen surge in demand and for meeting the expectation
of faster evacuation of containers.
8. Conclusion
The methodology followed in this study and the results of the analysis were shared with
some of the senior officials connected to the Indian seaports. The input and output variables
used in the study were largely considered favorable by them. A few of them have observed
that private participation in handling the infrastructure of Indian seaports has been
beneficial, while some others have found it to be even having a detrimental impact. Further,
all of them seemed to believe that dedicated facilities for different commodities were more
beneficial than shared facilities. They opined that apart from productivity of employees,
high level of bureaucracy in the administrative processes of the ports as well as in other
related departments such as the customs has significant impact on their efficiencies. It has
been observed that there has been an increase in the overall port capacity, but a proportional
increase in cargo traffic has not been achieved. This could be attributed to the lack of long
term planning of infrastructure to support the increase in demand. The government and port
officials should address these issues and should take necessary steps to boost investments
in activities for increasing traffic at the ports. Further, appropriate key performance
indicators (KPI) should be created for the employees to elicit desired behavior and superior
performance. Automation is also an important factor which can increase the efficiency.
JGOSS
13,1
100

14. Merits of flexible facilities vs dedicated facilities could be decided based on the future
demand for commodities to be handled. Managers should also look at the financial measures
to consider the expenditure and capital investments. This can help to reduce the government
intervention in the finances of the port bodies.
The study was limited to the major ports of India which are governed by the public
institutions under the Government of India. The study is thus constrained to the geography
of India. There are certain private ports in India which play an important role in the trade
dynamics of the country. One such port is the Mundra port run by Adani Ports and Special
Economic Zone (APSEZ) which could not be incorporated due to insufficient data as well as
due to the homogeneity issues. The variables considered for the study have been supported
in the literature. However, the performance of seaports could be affected by many other
variables too. Factors such as the number of equipment, contract and non-contract facilities
and labor, berth characteristics, productivity of employees have been dropped due to either
non-availability of sufficient data or lack of changes in the three-year period. The port
policies also play an important role in deciding the baseline for ship-performance at a
particular port and due to complexity in its measurements, they were not considered in the
analysis. A sensitivity analysis can be further conducted to see the effect of changes in the
inputs and outputs for the selected seaports. Changes in the port inputs usually can occur
over long periods which can also be explored. A broader comparison with ports in Asia or
other major international ports elsewhere could also be carried out. This will help in
benchmarking the developing port infrastructure in India with the other developed and
developing ports of the world.
This study has made an attempt to benchmark the performance of Indian seaports in
terms of its efficiency and performance, considering them as units that transform inputs to
outputs. The authors of this paper believe that considering the scarcity of research papers
reported in the literature on DEA based benchmarking studies of seaports in the Indian
context, it has the potential to attract researchers to extend this study further to derive
useful academic and practical insights.
References
Almawsheki, E. and Shah, M. (2015), “Technical efficiency analysis of container terminals in the middle
eastern region”, The Asian Journal of Shipping and Logistics, Vol. 31 No. 4, pp. 477-486.
Banker, R.D., Charnes, A. and Cooper, W.W. (1984), “Some models for estimating technical and scale
inefficiencies in data envelopment analysis”, Management Science, Vol. 30 No. 9, pp. 1078-1092.
Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision making units”,
European Journal of Operational Research, Vol. 2 No. 6, pp. 429-444.
Cooper, W.W., Seiford, L.M. and Tone, K. (2007), Data Envelopment Analysis: A Comprehensive Text with
Models, Applications, References and DEA-Solver Software, 2nd ed., Springer, New York, NY.
Cuadrado, M., Frasquet, M. and Cervera, A. (2004), “Benchmarking the port services: a customer
oriented proposal”, Benchmarking: An International Journal, Vol. 11 No. 3, pp. 320-330.
Cullinane, K. and Wang, T. (2010), “The efficiency analysis of container port production using DEA
panel data approaches”, Or Spectrum, Vol. 32 No. 3, pp. 717-738.
Ding, Z.Y., Jo, G.S., Wang, Y. and Yeo, G.T. (2015), “The relative efficiency of container terminals in
small and medium-sized ports in China”, The Asian Journal of Shipping and Logistics, Vol. 31
No. 2, pp. 231-251.
Feng, M., Mangan, J. and Lalwani, C. (2012), “Comparing port performance: western European versus
eastern Asian ports”, International Journal of Physical Distribution and Logistics Management,
Vol. 42 No. 5, pp. 490-512.
Benchmarking
study of Indian
seaports
101

15. George, S.A. and Rangaraj, N. (2008), “A performance benchmarking study of Indian railway zones”,
Benchmarking: An International Journal, Vol. 15 No. 5, pp. 599-617.
Ibef.org (2017), “Ports in India: Market size, investments, economic development, Govt initiatives | IBEF”,
available at: www.ibef.org/industry/ports-india-shipping.aspx (accessed 3 December 2017).
Jiang, J., Chew, E., Lee, L. and Sun, Z. (2011), “DEA based on strongly efficient and inefficient frontiers
and its application on port efficiency measurement”, Or Spectrum, Vol. 34 No. 4, pp. 943-969.
Jiang, B. and Li, J. (2009), “DEA-based performance measurement of seaports in northeast Asia: radial
and non-radial approach”, The Asian Journal of Shipping and Logistics, Vol. 25 No. 2, pp. 219-236.
Jim, Y.-C., Wu, C.-W. and Lin, (2008), “National port competitiveness: implications for India”,
Management Decision, Vol. 46 No. 10, pp. 1482-1507.
Kutin, N., Nguyen, T. and Vallée, T. (2017), “Relative efficiencies of ASEAN container ports based on
data envelopment analysis”, The Asian Journal of Shipping and Logistics, Vol. 33 No. 2, pp. 67-77.
Liu, C. (2008), “Evaluating the operational efficiency of major ports in the Asia-Pacific region using data
envelopment analysis”, Applied Economics, Vol. 40 No. 13, pp. 1737-1743.
Lu, B., Park, N.K. and Huo, Y. (2015), “The evaluation of operational efficiency of the world’s leading
container seaports”, Journal of Coastal Research, Vol. 73 No. sp1, pp. 248-255.
Rajasekar, T. and Deo, M. (2014), “Is there any efficiency difference between input and output oriented
DEA models? An approach to major ports in India”, Journal of Business and Economic Policy,
Vol. 1 No. 2, pp. 18-28.
Thanassoulis, E. (1993), “A comparison of regression analysis and data envelopment analysis as
alternative methods for performance assessments”, Journal of the Operational Research Society,
Vol. 44 No. 11, pp. 1129-1144.
Transport Research Wing, Ministry of Shipping (2014), Basic Port Statistics of India 2013-15,
Government of India, New Delhi.
Transport Research Wing, Ministry of Shipping (2015), Basic port statistics of India 2014-15,
Government of India, New Delhi.
Transport Research Wing, Ministry of Shipping (2016), Basic port statistics of India 2015-16,
Government of India, New Delhi.
Wanke, P.F. (2013), “Physical infrastructure and shipment consolidation efficiency drivers in Brazilian
ports: a two-stage network-DEA approach”, Transport Policy, Vol. 29, pp. 145-153.
Wiegmans, B. and Dekker, S. (2016), “Benchmarking deep-sea port performance in the Hamburg-Le
havre range”, Benchmarking: An International Journal, Vol. 23 No. 1, pp. 96-112.
Further reading
Al-Eraqi, S.A., Mustafa, A. and Tajudin Khader, A. (2010), “An extended DEA windows analysis:
Middle east and east African seaports”, Journal of Economic Studies, Vol. 37 No. 2, pp. 208-218.
Corresponding author
Sajeev Abraham George can be contacted at: sajeev.george@spjimr.org
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
JGOSS
13,1
102