An Analysis Of Turnaround Time In Ref. To Chennai Port Trust

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CHAPTER 1
INTRODUCTION
AN ANALYSIS OF TURNAROUND TIME OF CONTAINER VESELS AT
CHENNAI PORT
1.1 INTRODUCTION
The shipping industry is one of our oldest industries and still plays an important role in
our modern society. Today, over 55 000 cargo ships are active in international trade. The
fleet is represented in over 150 countries, crewed with over 1.5 million sailors working
around the world. The different types of cargo being transported are goods for consumers,
food, raw material, cars and fuel, just to name a few. The following are some advantages of
sea transportation:
➢ Shipping industry has the most competitive freight costs, as is one of the most cost-
effective ways of goods transportation through long distances.
➢ Vessels are built to carry huge amounts of goods and raw materials in comparison
with the capacity of airplanes or trucks. In addition, shipping allows the movement
of liquids, gas and dangerous cargo. For this matter, there are certain regulations to
keep the safety of the vessel, the crew, and the cargo.
➢ In comparison with the road transport, the maritime industry is less dangerous for
the environment. The shipping industry is responsible for only 12% of the total of
pollution generated by human economic activities.
For port terminals, measuring key performance indicators to improve operational
efficiencies and productivity is crucial. With vessel sizes on the rise, shipping companies
are more demanding than ever. The best way to evaluate the relevance of a key performance
indicator is to use the SMART criteria. SMART is an acronym that typically stands for
Specific, Measurable, Attainable, Relevant, and Time-bound. You can’t manage what you
don’t measure, so when the time comes, use these five conditions to determine the best key
performance indicators.
Since the turnaround time being the important performance indicator of the port, its
role is critical for the port’s operation.

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1.1.1 GENERAL – PORT
Ports are places wherever there are facilities for berthing or anchoring ships and
wherever there's container handling instruments to carry containers from ships to shore,
shore to ships, or ships to ships. There are different roles of ports, including
1. ports as places.
2. ports as operating systems.
3. ports as economic units.
4. ports as administrative units.
Maritime transport is that the transportation of products (cargo) different people and
folks by ocean and other waterways. Ports represent a crucial economic activity in coastal
areas. The higher the output of products and passengers year-on-year, the more
infrastructure, provisions and associated services are required. These can bring variable
degrees of advantages to the economy and to the country. Ports also are vital for the support
of economic activities within the backcountry since they act as an important affiliation
between ocean and land transport. As a provider of jobs, ports do not only serve an economic
but also a social function. In terms of the load carried, seaway transportation is the cheapest
and most effective transportation system compared to other systems. Industries need a
secure and low-cost suggests that of mercantilism finished merchandise and commerce raw
materials. Hence the majority of industries in the world are located in the coastal belts, in
the vicinity of major ports.
Port operations are a necessary tool to change maritime trade between mercantilism
partners. To ensure swish port operations and to avoid congestion within the harbor it's
inevitable to for good upgrade the port’s physical infrastructure, invest in human capital,
fostering connectivity of the port and upgrade the port operations to prevailing standards.
Hence, port operations may be outlined as all policies, reforms, and rules that influence the
infrastructure and operations of port facilities as well as shipping services.
More than eightieth of world trade is carried by ocean, constituting far and away the
foremost vital suggests that of transport of products. Maritime transport has been growing
annually by around three. 1% for the past three decades. Although there are several shipping

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corporations within the maritime trade, most of them are small with insignificant market
shares.
The increasing economic process of trade and high quality of port operations needs
the applying of a complicated ICT system. In recent years the scale of ships has doubled
and has extra to the issue in managing port operations and demanded an excellent larger
supply effort. The trend towards just-in-time manufacture needs a permanent improvement
of the data flow and integration of the transport business within the production method.
International maritime transport prices tend to air average between 2 to a few times as high
as custom duties of commerce countries. Still, it's the most affordable approach of
transporting giant amounts of products compared to alternative transport ways.
1.1.2 CONTAINER TERMINAL
A container port or container terminal is a facility where container cargos are
transshipped between different transport vehicles, for onward transportation. The
transshipment may be between container ships and land vehicles i.e. trains and trucks, in
which case the terminal is described as a maritime container port. Alternatively, the
transshipment may be between land vehicles, typically between trains and trucks, in which
case the terminal is described as an inland container port.
Maritime container ports tend to be part of a larger port, and the biggest maritime
container ports can be found situated around major harbors. Inland container ports to be
located in major cities, with good rail connections to maritime container ports.
The main facilities in container terminals include the quay, the container yard, the
container freight station, the interchange area, the gate facility, and the railhead. The process
at container terminals can be divided into subprocesses: arrival of the ship, cargo unloading
and loading, transport of containers from the ship to stack, stacking of containers, and
interterminal transport and other modes of transport. As containers move along the container
transport chain, they can have a different status, including empty container, full container
load, and less than container load. Generally, the network of nodes and links involved in the
container transport chain can be classified into four principal functions, i.e., consignment
assembly, consignment consolidation, carriage, and port handling.

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Fig.1.1. Container Terminal
1.1.3 PORT PERFORMANCE
Port performance is the basis on which the port is targeted by many trading
companies to do trading of its goods or do business with the port. The port performance
provides information on how effectively and efficiently the port operations are carried out.
The operational performance of a port is generally measured in terms of certain efficiency
parameters and indicators such as:
➢ Turn Around Time.
➢ Pre-Berthing Waiting Time.
➢ Average Ship Berth-day Output. Etc.
1.1.4 TURNAROUND TIME
Ship’s Turn Around Time (TAT) in the port is the primary indicator to judge the
quality of service being given by the port to the ships. TAT is the total time taken by a vessel
from immediately when the vessel is ready for berthing till it leaves the berth. However,

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the time taken for waiting due to non-port account shall not be taken into account. The total
time spent by a vessel at the port from its arrival at reporting station till its departure from
the reporting station. It thus includes pre-berthing waiting time, navigation time (inward
movement and outward movement time), stay at working and non-working berths and
shifting time. However, the detention/idle time due to litigation, fire, repair/dry docking,
delay in the decision regarding dismantling, etc. is not to be included.

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1.2 INDUSTRY PROFILE
India has a coastline spanning 7516.6 kilometres, forming one of the
biggest peninsulas in the world. According to the Ministry of Shipping, around 95 per cent
of India's trading by volume and 70 per cent by value is done through maritime transport. It
is serviced by 13 major ports (12 Government-owned and one private) and 187 notified
minor and intermediate ports. The total 200 non-major ports are present in the following
State i.e. Maharashtra, Gujarat, Tamil Nadu, Karnataka and others.
Fig.1.2. Ports of India

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Major ports handled over 74% of all cargo traffic in 2007. All except Kamarajar Port
Limited are government administered, but private sector participation in ports has increased.
There are also 7 shipyards under the control of the central government of India, 2 shipyards
controlled by state governments, and 19 privately owned shipyards.
While increasing the capacity of major ports, Ministry of Shipping has been striving
to improve their operational efficiencies through policy interventions, procedural changes
and mechanization. As a result, key efficiency parameters i.e. Average Turnaround Time
are shown below:
Table 1.1 Turnaround time of Major ports in India
Ports in India Turnaround time in days
Kolkata D.S 4.18
Haldia D.C 3.37
Paradip 7.01
Vishakhapatnam 5.67
Ennore 4.32
Chennai 2.54
Tuticorin 3.55
Cochin 1.77
New Mangalore 2.46
Mormugao 4.15
J.L.Nehru 2.24
Mumbai 5.28
Kolkata D.S 5.38
Chart 1.1 Turnaround Time of Major ports in India

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1.3 COMPANY PROFILE
1.3.1 CHENNAI PORT TRUST
Chennai Port, the third oldest port among the 12 major ports, is an emerging hub
port in the East Coast of India. This gateway port for all cargo has completed 137 years of
glorious service to the nation’s maritime trade.
Maritime trade started way back in 1639 on the sea shore Chennai. It was an open road -
stead and exposed sandy coast till 1815. The initial piers were built in 1861, but the storms
of 1868 and 1872 made them inoperative. So, an artificial harbor was built and the
operations were started in 1881. The cargo operations were carried out on the northern pier,
located on the northeastern side of Fort St. George in Chennai. In the first couple of years
the port registered traffic of 3 lakh tonnes of cargo handling 600 ships.
Being an artificial harbor, the port was vulnerable to the cyclones, accretion of sand
inside the basin due to underwater currents, which reduced the draft. Sir Francis Spring a
visionary skillfully drew a long-term plan to charter the course of the port in a scientific
manner, overcoming both man-made and natural challenges. The shifting of the entrance of
the port from eastern side to the North Eastern side protected the port to a large extent from
the natural vulnerabilities. By the end of 1920 the port was equipped with a dock consisting
of four berths in the West Quays, one each in the East & South Quay along with the transit
sheds, warehouses and a marshalling yard to facilitate the transfer of cargo from land to sea
and vice versa. Additional berths were added with a berth at South Quay and another
between WQ2 & WQ3 in the forties.
Fig.1.3. Chennai Port layout.

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India’s Independence saw the port gathering development, momentum. The
topography of the Port changed in 1964 when the Jawahar dock with capacity to berth 6
vessels to handle Dry Bulk cargoes such as Coal, Iron ore, Fertilizer and non-hazardous
liquid cargoes was carved out on the southern side.
In tune with the international maritime developments, the port developed the Outer
Harbour, named Bharathi Dock for handling Petroleum in 1972 and for mechanized
handling of Iron Ore in 1974. The Iron ore terminal is equipped with Mechanized ore
handling plant, one of the three such facilities in the country, with a capacity of handling 8
million tonnes. The Chennai port’s share of Iron ore export from India is 12%. However, at
present due to Hon'ble High Court's order handling of Ore is stopped. The dedicated facility
for oil supports the expansion of the CPCL's oil refinery in the hinterland. This oil terminal
is capable of handling Suezmax vessels.
In 1983, the port heralded the country’s first dedicated container terminal facility
commissioned by the then Prime Minister Smt. Indira Gandhi on 18th December 1983. The
Port privatized this terminal and is operated by M/s. D.P. World (Chennai Container
Terminal Private Limited). The port is ranked in the top 100 container ports in the world.
Witnessing a phenomenal growth in container handling year after in 2009 commenced the
Second Container Terminal operated by M/s. PSA (Chennai International Terminals Private
Limited) with a capacity to handle 1.5 M TEU's to meet the increasing demand.
The Port now with three docks, 24 berths and draft ranging 8.5 m to 16.5 m has
become a hub port for Containers, Cars and Project Cargo in the East Coast
Chennai Port is one among major ports having Terminal Shunting Yard and running
their own Railway operations inside the harbor. The port is having railway lines running up
to 41 Kms, 8 sidings to handle wide range of cargo like Granite, Food grains, Dry Bulk, etc.
For handling containers separate sidings are available.
The Port has handled 51.88 Million Tonnes of cargo volume for 2017-18 vis-a-vis
50.21 Million Tonnes of Cargo in 2016-17. Container Volume increased to 1549457 TEU's
against 1494831 TEU's in 2016-17. Physical performance parameters like Pre-Berthing
Detention, Turn Around Time and Ship Berth day Output continued to improve.

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The existing Cruise Terminal was being upgraded to International Standards as a
part of Cruise Shipping Policy. The induction of Mobile Harbor Cranes 100T - 2 Nos is
expected to improve the productivity of Cargo Handling of the Port.
Aggressive marketing initiatives are being undertaken to improve the Port Trade,
Logistics and also to attract new Cargoes.
Vision
To be recognized as a futuristic Port with foresight.
Mission
➢ Achieve excellence in Port operations with State-of-the-Art technologies.
➢ Enhance competence and enthuse workforce to maximize customer satisfaction.
➢ Anticipate and adapt to the changing global scenario.
➢ Act as a catalyst for sustained development of the Region.
Quality Policy
➢ Provide efficient, prompt, safe and timely services at optimum cost.
➢ Ensure quick turn round of vessels by providing facilities for efficient handling of
cargo.
➢ Maintain total transparency in all our transaction.
➢ Continually improve our services to meet the expectations of the port users, employees
and the society.
1.3.2 CHENNAI CONTAINER TERMINAL Pvt. Ltd. (CCTPL)
Chennai Container Terminal (CCT) is the first container terminal in Chennai port
built in 1983. The container terminal was privatised in 2001 and is operated by DP World
since 30 November 2001 with a capacity of 1.2 million TEUs. CCT is managed under a 30-
year build-operate-transfer agreement set up with the Chennai Port Trust of the Government
of India. The terminal is capable of handling fifth generation vessels up to 6,400 TEU and
has direct services to China, West Africa, Europe and the United States. The terminal
crossed the "one million TEU" mark in 2007. In 2011, it handled 1.12 million TEUs. It

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enjoys a quay length of 885 m (2,904 ft) and has 4 berths with an alongside depth of 13.4 m
(44 ft), height (ISLW to Top of Cope) of 34 m (112 ft), channel length of 6,700 m
(22,000 ft) and channel depth of 19.2 m (63 ft). The total terminal area covers 21.1 hectares,
and yard stacking area covers 17 hectares (42 acres).
Fig.1.4. Chennai container Terminal Private Ltd.
The terminal has an on-site rail track. It has a berth productivity of 22 moves per
hour and an average turnaround of 26 hours. The operator has invested around US$128
million to get new equipment at the terminal. At present, 7 quay cranes with Super
Post Panamax handling capacity and 24 rubber-tyred gantry cranes (RTGs) form part of the
inventory. The operator has also taken over from Chennai Port 4 quay cranes, 10 RTGs, 3
reach stackers, 240 reefer plugs, and 2 top lifters and one empty container handler.
Fig.1.5. CCTPL Panoramic view

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CCT is ranked at the 79th position among the top 100 container terminals in the
world. It is one of the fastest growing terminals in India with a CAGR of 20 per cent. It
presently has four mainline services with direct connectivity to Mediterranean, Europe,
Thailand, Vietnam, China and Korea. The mainline services are complemented by seven
weekly feeder services and one coastal service to Colombo, Vizag, Penang, Port Klang,
Singapore, Yangon and Port Blair, respectively. Presently, CCT is connected to 50+ ports
worldwide. A container freight station, with a covered area of 6,500 m2
(70,000 sq. ft),
operates within the port offering such services as inspection, LCL de-stuffing and delivery
of import cargo. CCT has plans to invest ₹ 1 billion to install two quay cranes.

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1.4 NEED FOR STUDY
Maritime transport is the backbone of global trade and the global economy. Shipping
helps ensure that the benefits of trade and commerce are more evenly spread. No country
is entirely self-sufficient, and every country relies on maritime trade to sell what it has and
buy what it needs. Much of what we use and consume in our everyday lives either has been
or will be transported by sea, in the form of raw materials, components or finished articles.
So, this study revolves in resolving the factors that affect the ports performance so
that it can operate efficiently and effectively, so that the port can act as an operating system
with smooth transactions of goods which helps to reduce the backlogs in operations of transit
of goods. This helps the goods to reach the user at the right time and at the right place
creating an effective logistics system.
1.5 OBJECTIVE OF THE STUDY
1.5.1 PRIMARY OBJECTIVE
To analyze the Turnaround time of the port and determine solutions in improving
the productivity and efficiency of the port.
1.5.2 SECONDARY OBJECTIVE
➢ To construct the process layout model for the current model followed in the port.
➢ To construct the cause and effect diagram for the factors that affect the turnaround time.
➢ To determine how the dependent factor influences the turnaround time using multiple
regression analysis.
➢ To forecast the data for using linear trend regression.

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1.6 SCOPE OF THE STUDY
➢ This study helps to understand how the port function and how each operation is carried
out.
➢ It mostly revolves around Turnaround time which is the main performance indicator
which helps trading companies how the port performs and choose the efficient port to
do its business.
➢ This study suggests some ways in how the turnaround time can be minimized and how
the performance of the port can be improved.
1.7 LIMITATION OF THE STUDY
➢ Since the data collected is secondary data, not all the facts can be derived from the
available data.
➢ During the study period some of information where disclosed as confidential by the
officials at the port.

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CHAPTER 2
LITERATURE REVIEW
2.1 GENERAL - PREVIOUS LITERATURE
The following are the previous studies that were referred:
Kokila and Abijath (2017) studied in detail about the vessel turnaround time at Cochin
Port Trust (CPT), how it can be calculated and its relative importance on the overall port
performance. The project aims to study the causal factors for low turnaround time of ships
at CPT. The project was conducted for two months duration. Only the traffic department
was chosen for their study. From their study, it has been understood that the vessel
turnaround time of port increased from the preceding year. The vessel turnaround time was
1.69 during 2014-15 and it increased to 2.18 days in 2015-16. So, their study focused on
exploring the ways to decrease the vessel turnaround time at the port and its feasibility. The
analysis to reduce the operational delays of the vessel was carried out using FMEA, the
factors causing delays are ranked based on the severity and recommendations are suggested
to avoid such issues in future.
Kasypi Mokhtar and Dr. Muhammad Zaly Shah (2006) whose study were motivated by
the rapid development in port container terminal, in providing efficient and effective
services and high port productivity, with the aim to achieve optimum port performance.
Research arises from the issue between port throughputs TEU (i.e. Teus- Twenty-Footer
Equivalent Unit) and port facilities (e.g. quay crane, prime movers etc.), as currently it is
not possible to determine significant factors that influence port performance, in terms of
turnaround time. For this purpose, their research proposes a regression model that relates
these variables, i.e. turnaround time and port facilities. Two ports in Port Klang – Westport
and Northport – were used as the subjects from which actual vessel call data collected
between August 1 and August 31, 2005 were used. The results showed that vessel
turnaround time is highly correlated with crane allocation as well as the number of
containers loaded and discharged. The benefits of such model include giving port operators
opportunity to determine optimum crane allocation to achieve the desired turnaround time
given the quantity of containers to be processed.

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Keith Ronald Studer (1966) whose study concentrates on the loading of grain ships in the
port of Vancouver, the operations of the port were examined and the constituent factors of
turnaround time was delineated. Some of the possible causes of delay were investigated.
The costs associated with unproductive ship time were then estimated and it was seen that
many of the development in the shipping industry was placing increased emphasis on a fast
turnaround, the latter was often difficult to achieve because of disorganization and
conflicting interest in the port. The loading records of a sample of 1305 grain ships are then
examined with a view to determining the degree of size dependency inherent in the loading
time and loading rate attained. The study was concluded by stating that there is an
appreciable positive correlation between ship size and that the portion of the variation
explained by linear regression analysis is not inconsiderable.
Hartmann (2004) introduced an approach for generating scenarios of sea port container
terminals. These scenarios were used as input data for simulation models. Furthermore, they
were employed as test data for algorithms to solve optimization problems in container
terminal logistics such as berth planning and crane scheduling. The scenario consists of
arrivals of deep-sea vessels, feeder ships, trains, and trucks together with lists of containers
to be loaded and unloaded. Moreover, container attributes such as size, empty, reefer,
weight, and destination are included. The generator is based on a large number of parameters
that allow the user to produce realistic scenarios of any size. The generator discussed here
has been developed within the simulation project at the HHLA Container-Terminal
Altenwerder in Hamburg, Germany. Optimization problems include the allocation of berths
to arriving vessels as well as scheduling the loading and unloading operations of quay
cranes. Simulation models are developed to evaluate the dynamic processes on container
terminals. This allowed to generate and analyze statistics such as average productivity,
average waiting time and average number of shuffle moves in the stack. By this way,
potential bottlenecks can be identified.
Ximena Clark et. al, (2004) investigated the determinants of shipping costs to the U.S. with
a large database of more than 300,000 observations per year on shipments of products
aggregated at six-digit HS level from different ports around the world. Distance, volumes
and product characteristics matter. In addition, they found that ports efficiency is an
important determinant of shipping costs. Improving port efficiency from the 25th
to the 75th
percentile reduces shipping costs by 12 percent. Inefficient ports also increase handling

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costs, which are one of the components of shipping costs. Reductions in country
inefficiencies associated to transport costs from the 25th
to 75th
percentiles imply an increase
in bilateral trade of around 25 percent. Finally, they tried to explain variations in port
efficiency and found that the variations are linked to excessive regulation, the prevalence of
organized crime, and the general condition of the country's infrastructure.
Anindita Mandal, Soma Roychowdhury et.al., (2016) examined the performance of 13
major ports of India in respect of key operational performance indicators. Their study
presents a systematic analysis of different performance indicators for a ten-year time period
(2003 to 2013) using a variety of statistical methods and evaluates status of each port in
different categories of performance. This enables the ports to gauge their own effectiveness
and appraise reasons for their shortcomings. In this context, the work further develops an
integrated composite performance index by relegating comparative weightages to different
indicators, to assess the relative overall performance of different ports. Their study
underlines the need of such estimates to adjudge the consistency of performance, internal
and across ports to enable planning and development of measures for enhanced
performance.
Atul Deshmukh (2002) tried to study and compare the efficiency of Major Ports in India.
It also tries to make the comparison of Indian Ports to that of the Ports like Singapore and
other developed countries ports. Various parameters are taken into consideration while
comparing the ports. To derive the conclusion from the comparison of major ports in India,
it can be said that JNPT is the only port that has shown positive efficiency for the past five
to six years. The cost of handling cargo per tonne has reduced at JNPT port while other ports
has shown a continuous rise. Though the total cargo handled by JNPT is not the highest as
Vishakhapatnam, Mumbai or Kandla, the traffic handled has shown a continuous rise. About
50% of the Container cargo in the country is handled by JNPT. This paper also compares
the major ports in Maharashtra like JNPT and MPT with that of the port of Singapore and
calls for major reformation at JNPT and MPT to be at par with the international standards.

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Dr. Padmasani, K Tamilselvi (2016) made an attempt to assess the operational
performance of ports in terms of vessel, container and total traffic handled during the last
decade. As per the results among thirteen major ports, seven ports performance seems to be
increasing but still inefficient with their existing infrastructure and other ports are efficient
ports. Therefore, the optimum utilization of the infrastructure is needed for sustainable
growth of the nation.

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CHAPTER 3
RESEARCH METHODOLOGY
3.1 RESEARCH DESIGN
The research design is the conceptual structure within which research is conducted;
it constitutes the blueprint for the collection, measurement and analysis of data. Research
design is needed because it facilitates the smooth sailing of the various research operations,
thereby making research as efficient as possible yielding maximal information with minimal
expenditure of effort, time and money.
The research design used in this study is Exploratory research design, since the data
collected for this study is secondary data which was collected from the Ports Operations and
Management Systems department (POMS) from Chennai port Trust.
3.2 COLLECTION OF DATA
The first step in an exploratory study is a search of the secondary. Studies made by
others for their own purpose represent secondary data. It is inefficient to discover anew
through the collection of primary data or original research what has already been done and
reported at a level sufficient for management to make a decision.
In this study the data collected is secondary data which is collected for the past years
by the Port Operations and Management Systems department of Chennai Port Trust.
3.3 TOOLS USED FOR ANALYSIS
3.3.1 SPSS
The tool used to analyze the collected data is SPSS. SPSS uses a base spreadsheet
format and has easy to use Windows based drop-down menus for commands. The advanced
user can do programming with the syntax command function if they wish. SPSS can read
data files from common formats such as Excel, Access, SAS and dBase. The base package
allows you to perform basic descriptive (e.g. frequencies, sums, mean, etc.) and univariate
(e.g. Pearson correlation, one-way ANOVA, t-test, etc.) statistics. Add-on packages are
available for advanced statistics such as multivariate regression modeling.

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3.3.2 EXCEL
Microsoft Excel is a spreadsheet program. It features calculation, graphing tools,
pivot tables, and a macro programming language called Visual Basic for Applications.
Although Excel is typically thought of as only a spreadsheet program, it can also be used to
conduct most of the basic statistics. On the Tools menu, simply click Data Analysis to get a
view of the possible statistics. The ability to analyze data is a powerful skill that helps to
make better decisions. Microsoft Excel is one of the top tools for data analysis and the built-
in pivot tables are arguably the most popular analytic tool.
3.3.3 POM QM
POM-QM is software for production/operations management, quantitative methods,
management science and operations research. QM for Windows provides mathematical
analysis for Operations Management, Quantitative methods, or Management Science. It
features calculation methods for PERT/CPM, Linear Programming, Decision Analysis,
Transportation problem, Statistical functions, Game Theory, Goal Programming, etc.
3.4 TECHNIQUES USED FOR ANALYSIS
3.4.1 CAUSE AND EFFECT ANALYSIS
Cause and Effect Analysis is a technique that helps you identify all the likely causes
of a problem. This means that you can find and fix the main cause, first time around, without
the problem running on and on. It is also known as Fishbone diagram. The technique was
developed by Professor Ishikawa in the 1960s. It is called that because of the resemblance
of the finished diagram to a fish bone. The diagram was initially used for quality
improvement, but soon proved a highly effective problem analysis tool as well, used to
analyse the causes of impediments within corporate processes, as well as potential way to
improve these processes.
The Cause and Effect Analysis entails two important steps which enable the problem
solver to both look back and ahead in time. In looking back, the analysis is geared towards
identifying the areas where mistakes were made, or money was lost. This is how the diagram
helps understand what happened before. By establishing the causes of the problem, the

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problem solver is able to work towards swiftly solving, or entirely avoiding, future instances
of it.
When looking ahead, the analysis attempts to find workable solutions that can be
easily implemented in the future as a means to build upon the success of the organization.
3.4.2 MULTIPLE REGRESSION
Multiple regression is a multivariate statistical technique used to examine the
relationship between a single dependent variable and a set of independent variables. The
objective of multiple regression analysis is to use the independent variables whose values
are known to predict the single dependent variable. Each independent variable is weighted
by the regression analysis procedure to estimate the maximal prediction from the set of
independent variables. The weights denote the relative contribution of the independent
variables to the overall prediction and facilitate interpretation as to the influence of each
variable in making the prediction.
Y = a + b1X1 + b2X2
Regression analysis is the most widely used technique for business decision-
making. It is the foundation for building business forecasting models. It can also be used to
study the factors influencing consumer decisions. It enables to evaluate the expected return,
a stock option etc.
3.4.3 LINEAR REGRESSION
Linear regression is a statistical tool used to help predict future values from past
values. Linear regression models are used to show or predict the relationship between
two variables or factors. The factor that is being predicted (the factor that the equation solves
for) is called the dependent variable. The factors that are used to predict the value of the
dependent variable are called the independent variables.
Y = a + bX
It is commonly used as a quantitative way to determine the underlying trend and when
prices are overextended. A linear regression trendline uses the least squares method to plot

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a straight line through prices so as to minimize the distances between the prices and the
resulting trendline. This linear regression indicator plots the trendline value for each data
point.
3.4.4 LINEAR PROGRAMMING
Linear programming is a method to achieve the best outcome in a mathematical
model whose requirements are represented by linear relationships. It is a mathematical
technique for maximizing or minimizing a linear function of several variables, such as
output or cost. Linear programming is used for obtaining the most optimal solution for a
problem with given constraints. In linear programming, we formulate our real-life problem
into a mathematical model. It involves an objective function, linear inequalities with subject
to constraints.

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CHAPTER 4
ANALYSIS AND INTERPRETATION
4.1 PROCESS LAYOUT
Initially the analysis involved was understanding the existing process carried out in
the field to determine the turnaround time. The process has then been identified and has
been formulated into process layout flow chart. The process layout flow chart is given
below:
In order to determine the turnaround time, it is important to study the operation
process carried out. Turnaround time is the time interval between when the vessel (ship)
reaches and leaves the port premises. The turnaround time is calculated by dividing them

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into processes and the time taken to carry out each process is determined. The sum of all the
time carry out each individual process gives the turnaround time.
The turnaround time shall comprise the following components:
➢ Pre-Berthing Waiting Time
➢ Inward Navigation
➢ Stay at Berth
➢ Outward Navigation
4.1.1 Pre-Berthing Waiting Time:
The process begins with the vessel arriving at the outer anchorage zone which is at
a distance of 4-5 nautical miles from the port. Then the crew either contact the reporting
station to allocate a berth for the vessel which involves a berth meeting between the agent
and the port authorities. The time taken to complete this process is known as Pre-Berthing
Waiting time.
Fig.4.1. Outer anchorage area
4.1.2 Inward Navigation:
Once the vessel has been allocated to a berth, Tugboats are deployed from the port
to maneuver the vessel to the allocated berth. The time taken from when the pilot boards the

25
tug boat till the vessel is moored or secured to the berth or dock is known as the Time of
Inward Navigation.
Fig.4.2. Inward Navigation
4.1.3 Berthing time:
Berthing time is time the vessel spends at the berth where operations like loading
and unloading of cargos takes place. The berth time is classified into productive and
unproductive time i.e. working and non-working time of the workers. The berthing time is
measured as the time between the vessel is secured to the dock till the mooring lines are
disconnected from the dock.
The time for the berthing period is initially scheduled for a particular period for
which the vessel is docked at the port. The vessel has to be at the wait until the scheduled
time even after the operation is complete so that there is no alteration in the port operations
and scheduling process.

26
Fig.4.3. Berthing time
During the Berthing period of the vessel there are factor that affect the operations
carried out i.e. loading and unloading of container cargos. These factors are given in the
cause and effect diagram that leads to Non-working time which is discussed later in this
study.
4.1.4 Outward Navigation:
Once all the operations such as loading and unloading of cargo has been done in the
port the ship is made to set sail to its new destination, but before that the ship is maneuver
to the outer anchorage area. The time taken between the time the vessel is detached from
the docks to the time it reaches the outer anchorage area is known as the Time of Outward
Navigation.
Fig.4.4. Outward Navigation

27
4.2 CAUSE AND EFFECT ANALYSIS
The Cause and effect analysis were conducted during this study where the factors
that influence or affect the turnaround time were determined using the annual reports
collected from the Chennai port website and the cause and effect diagram is framed.
4.2.1 CE DIAGRAM OF PRE-BERTHING WAITING TIME
There are some factors that affects the affects the Pre-Berthing Waiting time and
causes delay, this delay is known as Pre-Berthing delay or detention. These factors are listed
in the cause and effect diagram i.e. all the factors that lead to Pre-berthing detention which
is given below:
4.2.2 CE DIAGRAM OF WAITING TIME DURING OPERATIONS
From the Annual report the factors that affect the waiting time during operations.
Once the vessels has been docked the main operations i.e. loading and unloading of cargo
takes place. During this operation many factors affect the turnaround time and delay the
process thus increasing the turnaround time and reducing the productivity of the operation.
In practice, cargo containers are generally stacked or piled up in multiple separate columns,
heaps or stacks at ports. So, the cranes need to often rearrange or shuffle such container
stacks, in order to pick up any required container. This affects the efficiency of the port
making traders shift their business to other ports.

28
The CE diagram is given below:
4.3 ANALYSIS OF DATA
In this study the data has been analyzed using SPSS software, where multiple
regression analysis is carried as the collected data consist of one dependent variable i.e.
turnaround time and various independent variables that affect turnaround time. The equation
found from the SPSS analysis is used to carry out the LPP in determining the minimum
turnaround time that can be achieved. And further the data was forecasted for the next 10
years using linear trend regression models using MS Excel.

29
4.3.1 Analysis of Factors that affect the Turnaround time with 2 variables
Initially, the analysis was carried out by considering two variables i.e. Pre-Berthing
Waiting time and Waiting time after operations. The data is given for the following data in
the unit of “days”. The data has been analyzed and the following output has been
determined.
Table 4.1 Model Summary of 2 variables
Table 4.2 Anova of 2 variables
Table 4.3 Coefficient of 2 variables
Y = a + b1X1 + b2X2
Y = Turn Around time of Vessels days
X1 = Pre-berthing Waiting time days

30
X2 = Waiting time after operations days
Y = 1.153 + 0.898 X1 – 0.224 X2
From the above analysis the Pre-berthing waiting time positively influences the
Turnaround time i.e. if Pre-berthing waiting time increases, turnaround time increases and
Waiting time after operations negatively influences the turnaround time.
4.3.2 Analysis of Factors that affect the Turnaround time with 3 variables
In order to get a more accurate solution another variable is added to the independent
variable i.e. Crane productivity. The relationship between these variables were found out
using the linear regression equation was found out using SPSS and the output is given
below:
Table 4.4 Model summary of 3 variables
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .863a
.744 .649 .11036
a. Predictors: (Constant), Crane productivity, Waiting time after
operations, Pre berthing waiting time
Table 4.5 Anova of 3 variables
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression .284 3 .095 7.768 .009a
Residual .097 8 .012
Total .381 11
a. Predictors: (Constant), Crane productivity, Waiting time after operations, Pre berthing waiting
time
b. Dependent Variable: Turnaround time

31
Table 4.6 Coefficients of 3 variables
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 2.358 .486 4.848 .001
Pre berthing waiting time .814 .221 .670 3.681 .006
Waiting time after operations -.654 2.083 -.057 -.314 .762
Crane productivity -.049 .020 -.455 -2.501 .037
a. Dependent Variable: Turnaround time
Y = a + b1X1 + b2X2 + b3X3
Y = Turn Around time of Vessels days
X1 = Pre-berthing Waiting time days
X2 = Waiting time after operations days
X3 = Crane productivity MPH
Y = 2.358 + 0.814 X1 – 0.654 X2 – 0.049 X3
From the above equation it is found that the other two variables have similar
relation and it is proven from the above that when productivity of crane increases the
turnaround time decreases due to the negative relationship with turnaround time.
4.3.3 Analysis for determining the Minimum Turnaround Time
The multi regression equation collected from SPSS is subjected to LPP in
determining the minimum turnaround time that can be achieved. However, since the
decision variables on the RHS of the objective function has to be of same unit of measure
so only the Multi regression equation of only the 2 variables is considered i.e. Pre-berthing
waiting time and Waiting time after operations is considered since both the variables are the
time factors that affect turnaround time.

32
The LPP was carried out using POM QM and the following results were obtained,
Table 4.7 LPP of Turnaround time
X1 X2 RHS Dual
Minimize .898 -.224
Constraint 1 1 0 <= .0041 0
Constraint 2 0 1 <= .0041 .224
Solution 0 .0041 -.0009
From the above we obtain realistic results i.e. Pre-berthing waiting time or delay can
be completely be avoided by making berth reservations a month or a couple of weeks before
the vessel arrives at the port, so that the berth can be made available as soon as the ship
arrives near the coast. The waiting time after operations is restricted to 0.0041 days.
Therefore, from the obtained results the minimum turnaround time that can be
obtained is
Y = 1.153 + 0.898 X1 – 0.224 X2
X1 = 0
X2 = 0.0041
Y = 1.153 + 0.898 (0) – 0.224 (0.0041)
Y=1.15 days
The minimum turnaround time that can be achieved is 1.15 days by considering only
2 variables.
4.3.4 Forecasting of turnaround time
The collected data on turnaround time has been forecasted for next 10 years using
MS- Excel. The following equation has been determined using linear trend regression
analysis

33
Table 4.8 Forecast of turnaround time for the upcoming 10 yrs.
YEAR TAT forecast
2007 1.43424
2008 1.1925
2009 1.153333
2010 1.28
2011 1.658333
2012 1.215833
2013 0.956667
2014 1.243814
2015 1.26
2016 1.0075
2017 1.086667
2018 1.162 1.162
2019 1.071573
2020 1.048599
2021 1.025624
2022 1.00265
2023 0.979675
2024 0.956701
2025 0.933726
2026 0.910752
2027 0.887777
2028 0.864803

34
Chart 4.1 Turnaround time of vessels
Y = a + bX
Y = Turnaround time of vessel
X = Year
Y = 47.45 – 0.022 X
From the forecasted data it is found that the turnaround time seems to have a
steady decline i.e. the turnaround time improves for the next 10 years.
0
0.5
1
1.5
2
2005 2010 2015 2020 2025 2030
No.
of
days
Year
Turnaround time of vessels
TRT forecast

35
CHAPTER 5
CONCLUSION AND RECOMMENDATIONS
5.1 MAJOR FINDINGS
The following results were obtained during the course of the study, these results not
only prove real time facts but also have proven to be effective to derive solutions.
➢ The various factors that increases the turnaround time were determined.
➢ From Multi regression analysis of 2 variables it was found, the Pre-berthing waiting
time positively influences the Turnaround time i.e. if Pre-berthing waiting time
increases, turnaround time increases and Waiting time after operations negatively
influences the turnaround time.
➢ And in 3 variable multi regression analysis it is found that the other two variables
have similar relation as that of the 2 variable equation and it is proven from the
equation that when productivity of crane increases the turnaround time decreases
due to the negative relationship with turnaround time.
➢ In linear programming analysis, the minimum turnaround time that can be achieved
is 1.15 days.
➢ From the forecasted data it is found that the turnaround time seems to have a steady
decline i.e. the turnaround time improves for the next 10 years.
5.2 RECOMMENDATIONS
The following are the recommendations offered by the Experts after applying the
statistical tools.
➢ Turnaround Time speaks volumes of the Terminal’s operational efficiency. From the
data, it is known that the Turnaround Time continues to reduce and Pre-Berthing
Detention (waiting time before and after operations) of the vessel shall be made zero.
This will further reduce the Turnaround Time and would entail the terminal to ensure
vessel’s berthing on arrival. The terminal is recommended to adhere to this fact as any
dilution in the efforts would impose the threat of levy of congestion surcharge.

36
➢ The gantry crane productivity can be increased not only by making mother vessels to
call the terminal, but by making more number of such vessels to call. By doing so, the
terminal’s cargo throughput volume will accelerate. The terminal authorities are
recommended to call on the consortium partners i.e., steamer agents and enthuse them
that their vessels call their terminal.
➢ Reduction in the Truck Turnaround Time and the Dwell time of containers can be
possible only by providing better road and rail connectivity to and from the terminal.
Connectivity does augment in keeping the vessels’ Turnaround Time under control or
at minimum. The yard operations management can be optimized only by seamless
distribution of containers to and from the Container Parking Yard. Hence the conclusion
drawn from the null hypothesis that there is no impact on the lesser Truck Turnaround
Time, Average Dwell Time of containers cannot be tenable.
The Terminal is recommended to hold discussions with the State government’s
department to provide better road connectivity. It is also recommended that the terminal
authorities join hands with the Port and the government making all out efforts to ensure
completion of the EMRIP (Ennore Manali Road Improvement Project & Chennai Port to
Maduravoyal Elevated Corridor Project at the earliest) and gain the competitive edge over
the mushrooming container terminals in and around Chennai.
➢ Gantry Crane productivity is calculated per gantry deployed for vessel work whereas
the ship output per berth per day is not considering the number of gantries deployed for
vessel. It simply takes the boxes handled per day irrespective of the gantries deployed.
As such, the terminal’s productivity is on par with the global standards.
➢ In order to sustain and take a giant leap towards accelerating its productivity, the
terminal authorities are recommended to subject their gantry cranes into periodical and
preventive maintenance schedules. They are also recommended to maintain necessary
inventories of spares considering the Pareto’s principle of 80: 20 rule.
➢ The terminal’s traffic volume has been decreasing steadily. The forecast made by the
Regression Techniques is no doubt – alarming. The container industry is growing and
so the emergence of newer terminals and great interest shown by the private terminal
operators being witnessed. Hence, it is high time that necessary steps be taken to sort

37
out with the consortium partners’, appreciate the productivity strengths of the terminal
and enthuse them to make the terminal their only preferred choice.
Hence, provision of
➢ Connectivity to the hinterland by railway network and good quality road capable of
handling high traffic flow,
➢ Investment in dredging of channel to increase the draft at ports to pave way for the
handling of Main Line Vessels to attract and develop the EXIM trade between Chennai
and far-off countries,
➢ Road and rail infrastructure involving several government agencies for approvals,
acquisition of land and development, completion of the projects that are already in the
pipeline.
5.3 CONCLUSION
Since the main objective of the study is to identify the factors that affect the turnaround
time and determining how each factor affect the turnaround time and operations of the port.
The data collected from the port have proven that they correspond to real world scenarios
through the analysis conducted. The study is limited due to limitation in details regarding
some information that were disclosed from the port and the lack of tools i.e. inhouse tools
and software necessary to carry out a more detailed analysis. But from the results obtained
in this study help how each factor influence the turnaround time and how the turnaround
time will be for the upcoming ten years.

38
.
ANNEXURE
CCTPL'S PERFORMANCE INDICATORS FOR THE GIVEN PERIOD FROM APR TO MAR
(UPDATED TILL JAN 2019)
S.No
PARTICULARS 2007-08 2008-09 2009-10 2010-11 UNITS
1 IMPORT TEUS 578080 583628 581860 588383 TEUS
2 % W.R.T. TOTAL TEUS 51.84 51.58 51.48 51.26 %
3 EXPORT TEUS 537106 547897 548370 559395 TEUS
4 % W.R.T. TOTAL TEUS 48.16 48.42 48.52 48.74 %
5 NO. OF TEUS HANDLED (excluding Restows) 1115186 1131525 1130230 1147778 TEUS
6 RESTOWS 6314 9564 8098 7738 TEUS
7 TOTAL TEUS 1121500 1141089 1138328 1155516 TEUS
8 IMPORT BOXES 432305 432823 437265 426779 BOXES
9 EXPORT BOXES 406217 406983 413052 408259 BOXES
10 TOTAL BOXES 838522 839806 850317 835038 BOXES
11 NO. OF VESSELS CALLED 759 756 619 578 VSLS
12 BERTH OCCUPANCY % 58.28 54.48 45.46 52.72 %
13 BERTH PRODUCTIVITY (INCLUDING HATCH) 38.57 47.06 54.96 50.50 MPH
14 CRANE PRODUCTIVITY (INCLUDING HATCH) 20.10 22.83 26.29 25.37 MPH
15
PRE BERTHING WAITING TIME (AWAITING VESSEL
READINESS)
0.00 0.10 0.05 0.08 DAYS
16 WAITING TIME AFTER OPERATIONS 0.02 0.02 0.04 0.01 DAYS
17 TURN ROUND TIME 1.43 1.19 1.15 1.28 DAYS
18 OUTPUT PER SHIP BERTH DAY 1062 1280 1613 1598 TEUS
19 AVERAGE PARCEL SIZE 1485 1425 1863 2011 TEUS
20 TRUCK TURN ROUND TIME 80 57 49 54 MINS
21 AVG. DWELL TIME 2.61 1.97 2.06 2.96 DAYS

39
ANNEXURE
S.No
PARTICULARS 2011-12 2012-13 2013-14 2014-15 UNITS
1 IMPORT TEUS 552296 459069 391937 423767 TEUS
2 % W.R.T. TOTAL TEUS 52.94 52.24 53.60 56.77 %
3 EXPORT TEUS 491051 418284 339257 396986 TEUS
4 % W.R.T. TOTAL TEUS 47.06 47.76 46.40 53.18 %
6 RESTOWS 5232 4624 3846 5152 TEUS
7 TOTAL TEUS 1048579 881977 735040 825905 TEUS
12 BERTH OCCUPANCY % 60.03 38.63 27.94 35.81 %
15
READINESS)
0.56 0.05 0.01 0.00 DAYS

40
ANNEXURE
S.No
PARTICULARS 2015-16 2016-17 2017-08 2018-19* UNITS
1 IMPORT TEUS 452367 367301 274889 321003 TEUS
2 % W.R.T. TOTAL TEUS 54.78 57.24 56.86 56.64 %
3 EXPORT TEUS 410413 274342 208594 245776 TEUS
4 % W.R.T. TOTAL TEUS 45.22 42.45 42.80 43.05 %
6 RESTOWS 4768 4676 3842 4078 TEUS
7 TOTAL TEUS 867549 646319 487325 570857 TEUS
12 BERTH OCCUPANCY % 39.96 25.50 26.06 28.99 %
15
READINESS)
0.04 0.02 0.03 0.05 DAYS

41
BIBLIOGRAPHY
BOOKS
1. Vehicle Scheduling in Port Automation, Dr. Hassan Rashidi and Professor Edward
Tsang.
2. Operations Research, Kanti Swarup, P.K. Gupta, Mam Mohan.
3. Administration Report 2016 – 2017, Chennai Port Trust.
4. Administration Report 2017 – 2018, Chennai Port Trust.
JOURNAL
1. Anindita Mandal, Soma Roychowdhury and Jhumoor Biswas (2016), “Performance
analysis of major ports in India: a quantitative approach”, Int. J. Business Performance
Management, Vol. 17, No. 3, 2016.
2. Atul Deshmukh (2002), “INDIAN PORTS – THE CURRENT SCENARIO” ,Dr.
Vibhooti Shukla Unit in Urban Economics & Regional Development, Working Paper
No. 14.
3. Dr. Padmasani, K Tamilselvi (2016), “An assessment of Indian major sea ports
performance and efficiency”, International Journal of Multidisciplinary Research and
Development, Online ISSN: 2349-4182, Print ISSN: 2349-5979, Impact Factor: RJIF
5.72, Volume 3; Issue 6; June 2016; Page No. 323-327.
4. Hartmann (2004), “Generating scenarios for simulation and optimization of container
terminal logistics”, OR Consulting, Bornstraße 6, 20146 Hamburg, Germany, and
Institute of Business Administration, Department of Production and Logistics,
Christian-Albrechts-University Kiel, 24098 Kiel, Germany.
5. Kasypi Mokhtar & Dr.Muhammad Zaly Shah (2006), “A REGRESSION MODEL
FOR VESSEL TURNAROUND TIME”, Tokyo Academic, Industry & Cultural
Integration Tour 2006, 10-19 December, Shibaura Institute of Technology, Japan.
6. Keith Ronald Studer (1966) “A Study of Ship size and Turnaround time in the port of
Vancouver”, University of British Columbia, May 1969.

42
7. Kokila A V & Abijath V (2017) "Reduction of Turnaround Time for Vessels at Cochin
Port Trust" International Journal of Pure and Applied Mathematics Volume 117 No. 20
2017, 917-922, Cochin Port.
8. Ximena Clark, David Dollar & Alejandro Micco (2004) “Port Efficiency, Maritime
Transport Costs and Bilateral Trade”, NBER Working Paper No. 10353, March 2004,
JEL No. F1, L41, L92.
WEBSITE
1. www.chennaiport.gov.in
2. www.ipa.nic.in
3. www.ijpam.eu
4. www.sciencedirect.com
5. www.shipping.nic.in

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