IJLSM 2018 Container inventory management Introducing the 3F model.pdf

Int. J. Logistics Systems and Management, Vol. 31, No. 3, 2018 363
Copyright © 2018 Inderscience Enterprises Ltd.
Container inventory management: introducing
the 3F model
Lalith Edirisinghe*
College of Transportation Management,
Dalian Maritime University,
1 Linghai Rd, Ganjingzi,
Dalian, Liaoning, China
and
Faculty of Management and Social Sciences,
CINEC Maritime Campus,
Malabe, Sri Lanka
Email: lalith.edirisinghe@cinec.edu
*Corresponding author
Zhihong Jin
College of Transportation Management,
Dalian Maritime University,
1 Linghai Rd, Ganjingzi,
Dalian, Liaoning, China
Email: jinzhihong@dlmu.edu.cn
A.W. Wijeratne
Department of Agribusiness Management,
Faculty of Agricultural Sciences,
Sabaragamuwa University of Sri Lanka,
P.O. Box 02, Belihuloya 70140, Sri Lanka
Email: aw.wijerratne@gmail.com
Abstract: Container inventory imbalances (CII) can primarily be attributed to
global trade imbalances that cause a substantial indirect cost in shipping.
Therefore, it is imperative that carriers adopt highly efficient and effective
container inventory management (CIM) systems to overcome the CII issues.
This research intends to develop realistic guidelines for CIM that minimise CII
costs. The components of the 3F model are chosen based on the rating scores
given by the experts for 22 common CIM strategies followed by a standard
filtration process. It ultimately comprises six strategies, namely: reduce import
freight; reduce export freight; service agreements; synchronised budget; agile
inventory; and export priority. These variables are rooted in three dimensions
namely: freight, forecasting and flexibility. To identify the relative contribution
by each component, this paper proposes a double weight allocation procedure
for each strategy. The 3F model helps managers to think beyond traditional
container reposition and increased profits.

364 L. Edirisinghe et al.
Keywords: container inventory management; CIM; imbalance; freight;
forecasting; flexibility; strategy; CIM mix.
Reference to this paper should be made as follows: Edirisinghe, L., Jin, Z. and
Wijeratne, A.W. (2018) ‘Container inventory management: introducing the 3F
model’, Int. J. Logistics Systems and Management, Vol. 31, No. 3, pp.363–386.
Biographical notes: Lalith Edirisinghe is a Doctor in Transport Planning and
Logistics Management. He has 35 years of experience in international supply
chain including shipping, customs and border management, IMDG, marketing
and teaching. He has published more than 20 articles in international journals
and conferences. He is a member of scientific committee of Sri Lanka Society
for Transport and Logistic, editorial board member of R4TLI, Journal of
Shipping and Ocean Engineering, New York; and reviewer of International
Journal of Shipping and Transport Logistics, International Journal of Supply
Chain and Operations Resilience; Journal of Business and Economics; Indian
Journal of Science and Technology; Case Studies in Business and
Management – Macrothink Institute. He is a chartered member of Institute of
Logistics and Transport; Chartered Marketer. He represents Sri Lanka in Indian
Ocean Rim Association–(IORA) Project for Development of Transnational
Skills Standards of Department of Education and Training, Australia.
Zhihong Jin is a member of Teaching Guiding Committee of Ministry of
Education, China, editorial board member of International Journal of Shipping
and Transport Logistics, Associate Editor-in-Chief of Journal of Dalian
Maritime University. His research interests are logistics system planning and
management, transportation planning and management technology, and supply
chain design and management. He has published four books and more than
160 papers, and obtained five projects from National Natural Science
Foundation of China (NSFC), seven projects granted by Ministry of Education
of China, Ministry of Transport of China, and Liaoning Province, and
two international cooperation projects.
A.W. Wijeratne obtained his Doctoral in Mathematics from Harbin Institute of
Technology, China in 2008. He has been working as a Senior Lecturer in
Statistics and Mathematics at the Department of Agribusiness Management,
Sabaragamuwa University of Sri Lanka. He has published over two dozen of
research papers in refereed journals covering a wide range of subject areas. He
has been supervising a number of doctoral candidates affiliated to reputed
national and international universities for last six years. Moreover, he has given
his active contribution as a statistician for projects at the national level. His
research interest includes mathematical modelling in business, experimental
designs and applied statistics.
1 Introduction
Containerised cargo is widely recognised as the most dynamically developing sector of
global seaborne trade (Miler, 2015). The sheer volume of international maritime
container traffic is approximately 420 million containers shipped yearly (United Nations
Office on Drugs and Crime, 2009). A major problem revolves around repositioning
empty reusable containers within a global network of ports after the product arrives at its
destination (Ross et al., 2010). Containers have made greater impact on emerging

Container inventory management 365
countries as container ships replace the less efficient traditional vessels and enabled
global production and distribution, which reduces transaction costs remarkably (Lun and
Browne, 2009).
According to alphaliner.com (2016), there are 5,170 fully cellular ships that could
carry 20.3 million TEU1
and the World Shipping Council (2017) reports 34.5 Million
TEU of containers according to their data available as of 2013. The indispensable
allocation of empty containers plays the squanderer in container logistics, which has
become an urgent problem yet to be solved in practice and an interesting topic being
studied in academic circles (Qing-kai et al., 2014). There is no actual storage issue in
container transportation, but a problem of ‘time storage’ does exist, which is also called
‘safety time’ or ‘buffer time’ (Bocheng et al., 2008). The lead time in container supply
chain and the cost for a carrier heavily depends on the routes and speed choosing (Zhang
and Li, 2016). It is quite a paradox to learn that there is no standard system to regulate the
management of container inventories except some basic inventory control practices
adopted by individual carriers.
According to Edirisinghe et al. (2016a), 96% of carrier representatives consider CII as
a grave issue. However, only 58% of carriers have a standard CIM policy. Moreover,
only 42% of carrier representatives are satisfied with the existing CIM policy. The
carriers’ emphasis is mainly focused on empty container repositioning which is popularly
known as smart repo. Many articles are written on this subject facilitating carriers to
select best option that suits their individual market condition. Although these practices
may help the situation as far as the individual carriers are concerned there is no effective
knowledge sharing among carriers. As there are no common standards set for container
inventory management (CIM) the present scenario hampers the learning curve advantage
required by the industry. The individual carriers ‘learn by mistakes’ in isolation and those
learnings are never scientifically evaluated against any CIM fundamentals.
The remarkable differences between the container shipping industry and other
industries are that the capacity has two dimensions, that are volume and weight, and there
are many container types in the cargo transportation (Bingzhou, 2008). This reality makes
the container controllers’ job highly complicated. The fundamental duty of container
controllers is to supply the required quantity of containers, in right quality, right sizes,
and right types as demanded by exporters at the correct time, and at correct place. It is not
easy to know the exact amounts of empty containers required in the future port area with
multiple depots; customer demands and returning containers in depots per unit time are
assumed to be serially correlated and dependent on random variables (Dang et al., 2010).
Commonly, there are three main sources of container supply namely, laden imports;
empty container imports (or newly manufactured in the same port); and leased containers.
The use of shippers’ own containers is another source but the management of these
containers does not come to the carriers preview. However, carriers prefer to use their
own containers as much as possible rather than using leased units as the containers are
considered a part of carriers’ branding strategy (Edirisinghe et al., 2015). The main
drivers of the container system are speed and low cost (Scholz-Reiter et al., 2012) thus it
makes it very essential to manage its inventories effectively and efficiently. However, as
the fleet of containers grows in relation to the increasing vessel capacities the container
inventory imbalance (CII) becomes inevitable. The dramatically increased container fleet
size and the complexity of the container shipping network brings more challenges to the
operation of the container shipping system (Dong et al., 2013). One major source of

complexity arises due to imbalances of demand and supply for empty containers in each
port (Ross et al., 2010). Therefore, managing the container inventory is a complicated
matter. During the initial growth stage of containerisation carriers faced an identical issue
with respect to ship space. Subsequently, they opt to exchange slots. However,
behavioural patterns of CSL with respect to sharing ship space and pooling containers are
not the same (Edirisinghe et al., 2015).
Total cost concept, economies of scope, demand complementarity, market power
theories and obstacles to integration are applicable in explaining the evolutions in
container shipping in the supply chain context (Lam, 2013). Improvements in logistics
and supply chain practices act as important tools to achieve competitive advantage
(Gorane and Kant, 2014). The empty container statistics of a specific country or port is a
good indicator to ascertain the significance of the issue.
For example, during the year 2015 the cost of empty container re-positioning in Sri
Lanka is estimated2
[Brito and Konings, (n.d.)] at USD 152.4 million based on 381,090
TEUs of containers moved empty as per (CASA Per. Review, 2015). The empty
container (MTY) reflects 31.3% of total domestic containers handled in the country and
this additional cost is ultimately borne by consumers in Sri Lanka. Unlike other shipping
operations container ships cannot offer service to customers without empty containers
equivalent to the cell capacity of the ships available at a given port. The ideal CIM
system should deliver on the specific needs of each individual carrier, which can be
achieved by assessing the effectiveness of the existing CIM practices and formulating a
standard CIM system. This helps firms formulate strategic decisions that are necessary
for carriers’ competitive advantage. The objective of inventory management in general is
to provide uninterrupted production, sales, and customer-service levels at the minimum
cost. While the other industries such as manufacturing rely on lean inventory techniques
the container carriers are at a dilemma due to derived demand factor in shipping. They
are compelled to strike a strategic balance between ‘agile’ and ‘lean’ activities that they
employ in maintaining the optimum number each container category. For example,
highly effective techniques such as just in time (JIT) are unlikely to be practised in
container shipping. Therefore, the proposed CIM mechanism is expected to fill an
existing gap in the industry.
The objective of this paper is to introduce 3F CIM model based on the perception of
industry experts towards identifying set of criteria, and to explore best practices and
evaluate their relevance and merits; and facilitate carriers earn the learning curve
advantage through continuous improvement that helps carrier organisations and the
industry. Therefore, this study primarily identifies the issue from the industry point of
view. Improvements in logistics and supply chain practices are essential to the
competitiveness of businesses (Diaz et al., 2011). The CIM model (3F) introduced in this
paper would help make decisions to minimise empty CIM costs while optimising
inventory usage. This broader approach may enhance organisational core competencies
rather than depending on individual (tacit) knowledge of container controllers. As far as
the significance of the paper is concerned, the 3F model not only benefits the shipping
industry. If transport costs are reduced, the prices of goods and services are expected to
decrease, thus decreasing inflation. Similarly, a country’s exports will be more
competitive in the global market if transport costs are lower. These factors would have a
direct impact on the country’s economy.

2 Literature review
Supply chain networks look to transformational leadership and innovation as sources of
competitive advantage (Goffnett and Goswami, 2016). Containers today play a key role
in global supply chain and the reverse logistics is one of critical areas that need careful
consideration of firms. Various supply chain performance evaluation models have been
presented in literature and managers need identify the root causes of weakness points and
improve supply chain’s performance through analysing and solving the recurring
problems (Moharamkhani et al., 2017). Logistics have a major impact on economic
activity (Edirisinghe and Ratnayake, 2015). The management of container inventories is
not only a concern of carriers but also of any international trader, manufacturer, supplier,
service organisations such as hospitals, schools and even non-profit organisations as
performances of any such organisation depend on the container supply chain in one or
more stage of their work process. Researchers map a positive relationship between
logistic supply chain performance and organisational performance which ultimately leads
to enhance the performance improvement within firms and across the supply chain
(Kamble et al., 2016). In other words, prompt and effective responsiveness of the supply
chain has become the differentiator between the competing supply chains (Raghuram and
Saleeshya, 2016).
The emergence of empty container allocation results from imbalance demands of the
two ports, and the imbalance is ultimately manifested by freight forwarder canvassing
goods in the market (Bu et al., 2012). Determining the quantities of containers in
shipping routes and to keep them stable at all calling ports during the shipping route
operation has received more attention in theory and practice (Chidiac, 2013). The
problems of ship size, container deployment, and slot allocation are inter-related, and the
interrelations should be considered when optimising resource allocation of container
shipping lines (Zeng et al., 2010). Container ships are built to carry containerised cargo
and nothing else (International Maritime Organization, 2009). A major challenge
revolves around repositioning empty reusable containers (Ross et al., 2010). Container
movement is ever increasing with declining freight. As per estimated number of shipping
containers moving through only the ports of Melbourne and Hastings alone is expected to
triple by 2035 (Victorian Auditor-General’s Report, 2014). The terminal related variable
fees are connected to different segments and services (e.g., fee per handled container,
trailer, swap-body, storage of load units, etc.) and the types of commodities, etc.
(Bergqvist and Monios, 2014). Providing containers help increase the utilisation rate of
containerships (Rodrigue, 2013). Economies of scale are pushing towards the largest
container possible as it implies for inland carriers’ little additional costs (Notteboom and
Rodrigue, 2009).
This paper earlier proposed that there is a gap in the industry with respect to CIM.
Göpfert and Wellbrock (2016) which suggests that innovation management is not yet
sufficiently implemented around logistics and a structured innovation process is also
absent in many companies surveyed. Some innovative proposals with respect to CIM are
evident in the literature. The collapsible or foldable containers might represent a solution
to minimise both regional and international movements. The potential cost savings of
operating collapsible containers extends beyond decreasing marine and surface transport

costs: because several empty containers can be folded and handled in one package,
incremental break-down and assembly costs can be offset with the efficient use of space
(at terminals and aboard ships) and reduced trucking, handling, and storage costs (Hanh,
2003). However, foldable container does not make any impact on reducing the number of
units that needs repositioning, except the fact that number of slots that occupy the same
number of units have been reduced (Moon et al., 2013)
Another application being practiced is a flexible destination port policy. This type of
policy only specifies the direction of the MTY flows, whereas ports of destination are not
determined in advance, and MTYs are unloaded from vessels as needed (Song and Dong,
2011). The effectiveness of this method is limited to the relevant line’s service routes,
container inventory and fleet size. Container leasing is part of a carrier’s inventory
management strategy. Carriers prefer to lease containers in shortage areas and off-hire
them in surplus areas to avoid repositioning costs (Hanh, 2003). Rattanawong et al.
(2011) cite 27 papers that attempts to solve the empty container issue. Their paper’s
analysis is based on the players in the container supply chain, namely, the principal, the
port, the customer, and the container depot. The key issues discussed in those papers
include imbalances of empty containers, container allocation problems, trade imbalances,
uncertain demand on ports, the movement and flow of empty containers, container
scheduling problems, distribution planning problems, and fleet management. Chou et al.
(2010) consider the empty container allocation problem by determining the optimal
volume of a port’s empty containers and repositioning empty containers between ports to
meet exporters’ demand over time. Olivo et al. (2005) propose an operational model
considering the empty container management as a min cost flow problem whose arcs
represent services routes, inventory links and decisions concerning the time and place to
lease containers from external sources. The container off hire allocation problem involves
transportation demand forecasting, container inventory planning, vessel and voyage
scheduling as well as redelivery priorities of various container types (Ii et al., 2012). The
container deployment depends on the ship size, number of calling ports, number of
loading and unloading containers at each calling port, the turnaround time of containers at
each calling port, and the slot utilisation of each ship. The number of loading and
unloading containers at each port depends on the slot allocation scheme (Zeng et al.,
2010). Not only the demand of empty containers have stochastic factors, but the supply of
empty containers also have stochastic factors (Li and Han, 2009) and in order to satisfy
the Asian demand in empty containers, ocean carriers take the empty containers from
importers country like the USA or Europe (Belmecher et al., 2009).
A key objective for liner shipping operations is to utilise their fleets optimally (Lun
and Browne, 2009). According to Yur and Esmer (2011), the CIM issue has recently
received increased research attention. For example, there were only 14 publications on
the issue from 1972–2005 (33 years) compared with 50 publications in five years from
2006–2011. Savi (n.d.) suggests three specific areas that benefit by proper container
monitoring namely:
1 inventory reduction at manufacturing location
2 improving replenishment efficiency at customer
3 controlling container management costs.
Increases in the efficiency of container utilisation can only be achieved if carriers more
efficiently manage the origins and destinations of their cargo bookings and the operation

of their equipment depots, are more careful about where and when they will position
empty containers, and develop stricter regulations about how long customers can wait to
pick up their cargo or store containers without paying a fee (WSC, 2011). Karmelic et al.
(2012) suggest six factors that should be considered by carriers to optimise their empty
container logistics. These factors include:
1 trade imbalances between particular markets in determining liner service
2 the type of container equipment available in determining container capacities based
on the ratio between the numbers of a carrier’s own containers and those to be leased
3 the optimal leasing arrangement category if containers are leased
4 the availability of new containers for purchase
5 optimal repositioning routes; and special empty-container repositioning tariffs and
storage tariffs imposed by container terminals and depots.
The main drivers of the container system are speed and low cost (Scholz-Reiter et al.,
2011). Organisations constantly strive to enhance its performance through collaborative
supply chain management techniques (Borade and Bansod, 2012). Many service
agreements between carriers already have provisions to exchange containers in addition
to slot exchange between consortium partners (Edirisinghe and Zhihong, 2016).
Therefore, carriers should explore possibilities of interchanging their containers between
alliance partners. Edirisinghe et al. (2016b) introduced five variables that may influence
carriers’ decision with respect to container exchange namely, operational, legal, branding,
benefits, and feasibility. This could be a very economical strategy because most of the
alliance agreements even provide necessary provisions for this. The container exchange
has both benefits and costs thus assessing the cost of exchange is a crucial factor for the
carriers. However, it is important to ensure that the costs incurred in the coalition will be
fairly allocated to participating companies in the coalition (Cheng et al., 2013). Some
benefits from joining the chain are difficult to quantify in monetary terms (Chiadamrong
and Wajcharapornjinda, 2012). According to Min et al. (2005), collaboration is defined as
two or more companies sharing the responsibility of exchanging common planning,
management, execution, and performance measurement information (Min et al., 2005).
Collaboration, by definition, is a process where two or more parties (individuals or
organisations) join resources and knowledge to achieve common goals (Feyzioglu et al.,
2011). Two carrier companies can exchange their empty containers between each other at
various ports to eliminate the transportation cost of empty containers. To minimise costs,
a container leasing company should find the maximum number of pairs of carrier
companies that can exchange containers (Shao et al., 2015). Inter firm cooperation is a
source of competitive advantage (Solesvik and Encheva, 2010). Collaboration will reduce
the duplication of processes, cut inventories (Islam et al., 2010). Shipping lines rely on
horizontal integration through operating agreements and mergers and acquisitions
(Notteboom and Cesar, 2012). The shipping industry has a long history of cooperation
since the 1990s with the formation of consortia and alliances (Caschili and Medda, 2012).
The CIM is a highly complicated matter unlike the common inventory management
of a manufacturing firm or a super market because of the derived demand nature of
shipping business. Owing to the chronic trend of increasing trade imbalance across the
oceans (Karmelic et al., 2012), the empty container management problem has become a

major issue for the container shipping industry during the last decade. According to the
analysis of Boile et al. (2009), tariff imbalance and the related costs of repositioning
empty containers from surplus to deficit areas; cost of inland transportation; marginal and
volatile profitability of the leasing industry; cost of manufacturing and purchasing new
containers in relation to the cost of leasing containers; leasing contract terms; the cost of
inspection and maintenance of aged containers; and the cost of disposal are some of
additional causes to the problem. The list continues citing the uncertain customer
demand, the widespread allocation of container ports and customers, the dynamic nature
and increased complexity of container shipping (Dang et al., 2013).
More than 50% of the container management costs are in the empty container
distribution and container leasing thus distribution and leasing of empty containers
scientifically at the same time is an urgent issue (Sun and Yang, 2006). A great increase
in China’s export throughput further resulted in the rapid increase of container throughput
and the supply-demand imbalance of empty containers (Kang et al., 2012). The
transportation of containers is an essential economic activity that involves a substantial
number of complex operations for the shipment of multiple products using vehicles of
several types and various modes (Liu et al., 2010). As cited in Qu et al. (2013) studies
exist for two dynamic deterministic formulations to deal with the single and
multi-commodity cases; for empty container relocation, they also put forward an ordinary
model.
The CIM problem is further aggravated by the multiple types (i.e., general purpose,
high cube, reefer, flat-rack, open top, etc.) and sizes (i.e., 20’, 40’, 45’, etc.) of the
containers that are required to cater to the exporters demand. Other core issue in CII is
the types of commodities that need to be moved because the said customers’ demands are
exclusively dependent on that. CSL presently mitigate the impact of CI imbalance
primarily through internal controls. For example, some CSL (principals) penalise their
regional offices or agents for any idle containers that remain in their respective territories
(Edirisinghe et al., 2015). The CII issue demands serious attention from the shipping
industry. However, the majority of the literature on container management refers to
reducing the ‘cost’ of empty container repositioning, not minimising the ‘need’ for
repositioning (and thus addressing the cause of CII). Therefore, in addition to the existing
efforts optimisation methods of container reposition and the identification of most
effective and efficient methods that minimise the idle time of containers is required. The
CIM model will benchmark the most successful CIM practices and mechanisms that
optimise the container inventory utilisation. Sometimes the industry or respective
authorities may not see the real gravity of the problem due to involvement of queueing
theory in container management. The seriousness of the problem is usually evaluated
periodically say once in six months or annually. Therefore, the hidden impact of real
imbalance will not be much visible that can only be explained using the queueing theory.
The mathematics underlying queueing theory is quite like those underlying seemingly
unrelated subjects as inventories, dams, and insurance (Cooper, 1981).
The efficient management of empty containers becomes a source of competitive
advantage for shipping companies to improve their customer-service levels and
productivity (Dang et al., 2010). However, if empty container flows are not managed
carefully, the entire shipping network will operate inefficiently (Cole et al., 2013). It is
identified that managing containers has various parameters such as high detention cost,
rising inventories, vessel misses, rejection of cargo by the buyers (Kathleen, 2016) and
the load times have a great impact in the economic profit as the freight containers are

exchanged in intermodal stations or terminals (Barro-Tores et al., 2010). The special
types such as tank container operators use decision support tools based on mathematical
programming (Savelsbergh et al., 2005). The problems in CIM include idle containers,
slow velocity of turnover of containers, disordered containers management, and
undeveloped information communication (Ke et al., 2008). The low container
management level is the main cause of empty container allocation (Wang et al., 2008).
Generally, a successful reverse logistics could help to increase the service level of
companies and reduce the costs (Tseng et al., 2005). The CIM problem consists of:
1 choosing the best way to reposition empty containers between several sites for next
rounds of use
2 purchasing the right quantity of new containers at the right period to meet system
requirements for product transportation (Chandoul et al., 2009).
For optimal management of containers and reduction of the time required for
transportation, the use of infrastructure based on modern technologies is something
inevitable (Rezapour et al., 2014).
The key concern that pushes the carriers for effective and efficient CIM is the
consequential environmental pollution. Failure to deliver a global and uniform CO2
reduction regime for international shipping will greatly reduce the ability of the shipping
sector to reduce its emissions (International Chamber of Shipping, 2014). In 2009,
shipping was estimated to have emitted 3.3% of global CO2 emissions and according to
the International Maritime Organization’s (IMO) 2nd Greenhouse Gas (GHG) Study 2, if
unabated, shipping’s contribution to GHG emissions could reach 18% by 2050
(Rightship, 2013). Over the last one and a half years, slow steaming has reduced Maersk
Line’s CO2 emissions by about 7% per container moved. Their goal is to reduce CO2
emissions by 25% by 2020 (Shortsea Promotion Centre, 2012).
3 Methodology
3.1 Population, sample and approach
The study was conducted in Sri Lanka with the intention of generalising its outcome in
the global context. The researchers are confident that their results can be generalised for
the benefit of the global shipping community, given Sri Lanka’s maritime background.
Sixteen of the top 20 CSLs in the world operate regular services in the busiest
commercial port in the country, Colombo, primarily because of the strategic geographic
location of port of Colombo in Sri Lanka. Approximately 75% of the global container
capacity is operated (alphaliner.com, 2016) by those top carriers. Therefore, the sample is
expected to be relatively reflective of the general views of the global shipping industry.
The Ceylon Association of Shipping Agents (CASA) is composed of 135 licensed ships’
agents, representing all reputable international shipping lines. The interviews were
conducted with 30 senior CSL representatives from the administration, marketing,
container control and vessel operations departments.
The questionnaire contains two sections. The general section investigates the
company’s container stock position and its CIM policy, namely, annual empty container
movement, empty repositioning costs as a percentage of freight earnings, the frequency

of inventory monitoring, the availability of a CIM policy, whether the respondent was
satisfied with the current policy, and whether the respondent considered imbalances to be
a serious issue. Section 2 comprises 22 questions pertaining to strategies that could be
adopted by the carriers to better manage their container inventory. These strategies were
identified by the researchers during the interviews. The respondents were required to
mark their preferences for all questions on an 11-point scale ranging from +5 to –5,
representing highly agree to highly disagree, respectively, and neutral (0). The
questionnaire was very brief and was deliberately drafted in an objective form given the
nature of the respondents and based on previous experiences. According to unpublished
industry sources, CSL operations in Sri Lanka can be categorised into five strata based on
the imbalance between exports and imports in the container volumes handled in the
country. Carriers that experience imbalances of less than 50 TEUs in 2013 were not
considered because of their lesser practical involvement with the issue. Accordingly, an
opinion survey was conducted using 105 selected shipping agents, as elaborated in
Figure 1.
Figure 1 Sample selection and responses received (see online version for colours)
Seventy-two out of the sample of 105 responded in the survey. However, according to the
key survey informants, this response rate was acceptable because usually the employees
in the shipping industry are reluctant to participate in surveys. It was also noted that some
carriers do not allow their agents to reveal any information because of data
confidentiality issues. More specifically, following the European Union’s strict
implementation of antitrust laws, agents are very careful about sharing information that
could be detrimental to their respective principal carrier.
3.2 Development of the model
The steps that have been followed in developing the 3F model are explained below.
The sum of the average scores of each strategy in each dimension was weighted
firstly by the overall sum. The weighted mean scores were secondly weighted by the sum
of scores of the each dimension.
Notice that the preference (qij) of ith
respondents for the jth
strategy is such that
: [ 5, 5].
ij ij
q q

] Altogether there are 72 respondents and the average for the jth

strategy is given by
72
1
72,
j ij
i
q q
¦ j = 1 … 22. Then : [ 5, 5]
j j
q q

R and the
researchers retain the strategies of the following subset where : (0, 5].
j j
q q

R Each
item retained in the subset j
q
is tested for internal consistency by Cronbach’s alpha
value. The items of the subset j
q
reporting higher Cronbach’s alpha values will be taken
to formulate the CIM mix for the CIM model.
Let the average scores of the items of the subset reporting higher Cronbach’s alpha
values are redefined under three dimensions as follows:
x Freight
1 ,
jk
q
where k = number of strategies falling under the dimension freight.
x Forecasting
2 ,
jk
q
where k = number of strategies falling under the dimension forecasting.
x Flexibility
3 ,
jk
q
where k = number of strategies falling under the dimension flexibility.
Then the sum of the average scores for each component of CIM is calculated as follows:
1 2
1 2
3 3
for freight, for forecasting and
for flexibility.
jk jk
jk
q q q q
q q

¦ ¦
¦
Thereafter, the overall sum was obtained in order to determine the share of the dimension
and the weight for each strategy as follows:
1 2 3
q q q q

Then the share of the each dimension is calculated as follows:
1 1 2 2
3 3
for frieght, for forecasting and
for flexibility.
sq q q sq q q
sq q q

The next step is the calculation of the proportion to the weight of each dimension for its
each strategy as follows:
x Freight
1 1
1
jk jk
p q sq

x Forecasting
2 2
2
jk jk
p q sq

x Flexibility
3 3
3 .
jk jk
p q sq

Finally, the weight for the each strategy of each dimension is calculated as follows:
x Freight
1 1 1
jk jk
w p sq
u
x Forecasting
2 2 2
jk jk
w p sq
u
x Flexibility
3 3 3 ,
jk jk
w p sq
u
where _ 1.
jk
w
¦
The final weights reflect the relative importance given to each strategy within each
dimension by the respondents. However, the weight for a single strategy can vary from 0
to 1.0. If a particular strategy scores 1.0, it would become “absolutely perfect” and
become a single dominant strategy. The rest of the strategies would eventually become
obsolete. In practical settings, it is hard to believe that any strategy would become
“absolutely perfect”. The relative importance of each strategy across each dimension is
taken by ranking the weights. For each qij, there will be different dominant strategy
profiles.
4 Results and discussions
The core issue in the industry is the absence of an effective mechanism to mitigate the
negative impacts of container imbalances for the better utilisation of resources. It has also
been noted that there is no common mechanism or approach that focuses on minimising
the idle time of empty containers in storage. Because carriers attempt to resolve this issue
in isolation, the industry lacks any learning-curve advantage. Based on the somewhat
complex scenario set forth above, the researchers attempt to identify existing CIM
practices and formulate a common CIM system, develop application techniques for the
CIM model and introduce an efficient CIM system that can be generalised in the global
context. The respondents were asked to indicate the range of their annual empty container
movement in Sri Lanka. Accordingly, 19 respondents (26%) stated that they have
101–1,000 TEUs of empty containers per year, whereas 16 (22%) stated that they have
1,001–5,000 TEUs. In addition, 11 respondents (15%) have more than 10,000 empty
containers that move from/to Sri Lanka, which is a substantial quantity overall. We
inquired about the annual cost of empty repositioning as a percentage of freight income.
The results reveal that the percentage share for 16 of the respondents (22%) is less than
5%. The share is 6–10% for 21 participants (29%) and 11–20% for five participants.
There were three respondents who claim that their empty repositioning cost as a
percentage of freight income is greater than 20%. However, 27 respondents (38%) did
not comment on that issue.
It is also important to understand how often the respondents experience this issue; the
study reveals that 31 out of the 72 respondents suffer from this problem very
often, whereas 19 respondents always have empty container problems. However,

12 respondents state that they sometimes experience this issue sometimes or only rarely
suffer from this problem. Seven respondents did not comment on this issue.
It was surprising to note that only 42 respondents (58%) have a standard CIM policy,
whereas ten respondents did not comment, and 20 confirmed that they do not have a CM
policy. The researchers also asked whether the respondents are satisfied with their
existing CIM policy; only 30 (42%) responded affirmatively. Therefore, it is quite clear
that there is a substantial need for an effective, efficient CIM policy to minimise the
negative impacts of the CIM problem. This need is clear based on the responses received
when the respondents were asked whether they consider container imbalances to be a
serious problem. CIM problems must take high priority: according to the study,
69 respondents (96%) consider container imbalances to be a serious issue. Only three
responded negatively.
4.1 CIM strategies
The CIM strategies of CSLs usually depend on the physical quantity of inventory, short-
and long-term cargo booking forecasts, market trends, competitor activities, and other
factors. It is very common for CSLs to consider a combination of multiple approaches to
address complicated market situations. Some CSLs simply reposition empty containers
(MTY) out from any location; they do not store MTY pending future export bookings.
Another strategy is to simply import MTYs from an excess port to a location that has
deficit inventory. Wherever possible, CSLs substitute one 40’ with two 20’ MTYs at the
same freight rate (FR). In addition, CSLs may offer exporters a 40’ MTY at the same FR
as a 20’ MTY when there are excessive 40’ MTYs in a particular location. However, the
various logistics costs (explained in the previous chapter) of a 40’ MTY are usually
higher than those of a 20’ MTY. CSLs usually rely on an ‘inward and outward
container-flow forecast’ to control their inventory. The seaborne trade cargo volume
positively affects the freight rate. More demand for shipping services lead to higher
freight rates (Lun and Quaddus, 2009). Most CSLs strategically reduce freight rates for
imports to a specific port when they foresee a future deficit by analysing the containers in
the pipeline, (i.e., containers on board vessels loaded from other ports and already in
route). This analysis is usually performed by closely examining past records and carefully
monitoring market forecasts. This freight centric strategy is reversed when carriers
foresee a potential excess at a particular location in the future.
In most cases, an imbalance occurs because of inaccurate forecasts. The agents of
CSLs are responsible for submitting the most realistic export and import forecasts for
their ports. To improve the accuracy of their individual forecasts, some CSLs impose
penalties on agents for inaccurate forecasts because any deviation between the forecast
and the actual results leads to excess/deficit units. Another CSL strategy is to implement
a tight forecasting system that is constantly monitored at various stages of the process. In
addition to line-owned containers, CSLs can hire containers from container leasing
companies (CLC) at a given port and off-hire them somewhere else, depending on the
contract terms. Some leading CSLs (particularly in ‘supplier-driven’ markets) impose an
empty repositioning surcharge (ERS) on customers to recover the cost of repositioning.
MTYs would be instantly positioned to such locations and then promptly reused.
However, some CSLs maintain an agile inventory to satisfy customers regardless of the
costs associated with idle MTYs. This situation is common with CSLs that primarily

depend on service contract volumes. At the other extreme, certain CSLs maintain a lean
inventory to reduce their costs, even though the unavailability of MTYs has a negative
impact on customer service.
Some service agreements contain provisions to exchange containers among alliance
members. However, paradoxically, no container sharing is currently taking place. A more
recent strategy is to negotiate with any shipping line to interchange slots and move empty
containers in and out from the lines’ respective ports (with no freight paid by either
party). This strategy is an alternative to the sharing of containers, which some carriers are
reluctant to do because of potential legal implications (Edirisinghe et al., 2015).
Accordingly, carriers may be able to reduce the cost of repositioning empty containers,
particularly when certain port pairs are not regularly served by a particular carrier.
4.2 Formulating the 3F CIM mix
This section explains two steps that use the mean value and Cronbach’s alpha value
respectively to derive the 3F model.
4.2.1 Step 1: Mean value
The data were analysed based on the mean value of each strategy. The mean values of the
22 strategies are presented in Table 1.
Table 1 Twenty-two CIM strategies
j Label Description of strategy Mean ( )
j
q
1 Inventory lean Maintain lean inventory to reduce cost to line
notwithstanding negative impact on customer service
3.17
2 Reduce export
freight
Reduce freight rates for exports when realising excess
inventory
2.74
3 SVC agreements Improve the accuracy of demand forecasting by
entering into service agreements with customers
2.67
4 Reduce import
freight
Reduce freight rates for imports when foreseeing a
future deficit at a particular location
2.51
5 BGT synchronise Synchronise the annual budget with the container
management system and penalise agents for variations
2.49
6 ERS Introduce empty repositioning surcharge for
customers and recover cost of repositioning
2.29
7 Agile inventory Maintain agile inventory to satisfy customers
regardless of cost
2.19
8 Exporters’
priority
Give exporters priority and simply reposition when in
deficit
1.36
9 FRT increase E Increase freight rate for exports when realising a
deficit inventory
1.14
10 FRT increase I Increase freight rate for imports when foreseeing a
future excess
0.85

Table 1 Twenty-two CIM strategies (continued)
j Label Description of strategy Mean ( )
j
q
11 Priority import Give importers priority and simply reposition out
when in excess
0.72
12 Substitute Substitute one 40’ with two 20’ containers wherever
possible at the same freight rate or slightly less to
compensate for double handling
–0.17
13 Swapping Offer 40’ containers to exporters at the same rate as
20’ containers when 40’s are in excess
–0.18
14 FCST reliance Strictly follow the long-term forecast for import-laden
units and reuse them for exports upon return by
customers
–0.65
15 PNL agents Improve the accuracy of demand forecasting by
penalising agents for excess/deficit units
–0.76
16 FCST accuracy Implement tight forecasting system that updates
demand on daily/weekly/ monthly/quarterly/annual
basis to improve forecast accuracy
–0.79
17 CTR hiring ‘On hire’ and ‘off hire’ containers for all requirements
when the need arises
–0.87
18 RND voyage Arrange regular empty repositioning in round voyages
(WB laden/EB empty; EB laden/WB empty)
–0.88
19 Seasonal Reposition empties from nearby ports to cater to
seasonal demands
–1.32
20 Alliance Interchange containers with alliance partners in
response to export demands
–2.03
21 Interchange Interchange containers with any shipping line to cater
to the export demand
–2.08
22 Free slots Negotiate with any shipping line to interchange slots
(ship space) and move empty containers ‘in’ and ‘out’
from respective ports (no freight paid by either party)
–2.28
It was assumed that only the components with a mean value greater than 0 were to be
considered for further study. Accordingly, 11 strategies – inventory lean, freight drop
export, SVC agreements, freight drop import, BGT synchronise, ERS, inventory agile,
priority export, FRT increase E, FRT increase I, and priority import – have been selected
for further analysis.
4.2.2 Step 2: Cronbach’s alpha value
Next, the researchers carried out a reliability test on the responses to the questionnaire.
The Cronbach’s alpha value for the 11 items was below the acceptance level. To correct
this initial error, frequency reliability tests were conducted until a satisfactory Cronbach’s
alpha value was obtained, with the most satisfactory combination of components filtered
from the questionnaire. The results revealed that six strategies out of the 11 considered in
the questionnaire conformed to an acceptable Cronbach’s alpha value when measuring
the internal consistency of the variables, that is, how closely related a set of items are as a

group. The Cronbach’s alpha value is 0.971 for the six items that have been filtered from
the 11 components, which suggests that the items have a relatively high internal
consistency. Based on the above description, the researchers also realised that the
Cronbach’s alpha value could not be increased to a value greater than 0.971 by deleting
additional items.
Table 2 shows the extract of the final output of the test that indicates that the filtration
process has reached the highest performance level. Therefore, the researchers propose
that the six items mentioned in Table 3 provide the best combination for an effective and
efficient CIM.
Table 2 Item-wise statistics of the reliability test output
Scale mean if
item deleted
Scale variance
if item deleted
Corrected item –
total correlation
Estimated
Cronbach’s alpha
(if item deleted)
1 Reduce import freight 11.44 42.786 0.969 0.962
2 Reduce export freight 11.22 41.358 0.889 0.967
3 SVC agreements 11.29 42.688 0.870 0.969
4 BGT synchronise 11.47 40.647 0.903 0.965
5 Exporters’ priority 12.60 37.258 0.922 0.965
6 Agile inventory 11.76 37.338 0.942 0.962
The proposed CIM model (3F model) is a combination of various CIM decision variables
that are being used by carriers to ensure that their exporters are effectively and efficiently
supplied with empty containers at all times.
Table 3 Descriptive statistics of CIM strategies
Strategy Minimum Maximum
Mean j
( q
reporting higher
Cronbach’s alpha values)
Std. deviation
Reduce import freight 0 4 2.51 1.088
Reduce export freight 0 5 2.74 1.289
SVC agreements 0 5 2.67 1.199
BGT synchronise –2 5 2.49 1.332
Agile inventory –3 3 2.19 1.562
Exporters’ priority –2 3 1.36 1.595
4.3 The rationale and justification of the dimensions of ‘3F model’
The researchers have identified the existing gap in the literature with respect to CIM per
se. It is evident that previous studies mainly focused on empty repositioning solutions
(a reactive approach) instead of investigating methods to reduce the ‘need’ for empty
container repositioning through a proactive approach. The 3F model may offer the
optimum combination of all of the ingredients for successful CIM so that companies can
realise their goals, e.g., profit, sales volume, market share, and return on investment. The
elements of the 3F model (or CIM mix) that are extracted based on above analysis are
explained in Table 4 along with the core area that the respective strategy is rooted.

Table 4 Elements of the 3F model
CIM strategy Key areas (dimensions) in which the
1 Reduce import freight
` Freight rate is offered by carriers
2 Reduce export freight
3 Service agreements
` Container volume forecasting is made by carriers
4 Synchronised budget
5 Agile inventory
` Carriers’ flexibility in customer service
6 Exporters’ priority
As illustrated in Table 4, the first two components represent ‘freight’. Two ‘forecast’
related strategies are service agreements and budget synchronisation. The last two CIM
strategies in the model reflect the carriers’ flexibility towards the customer demand.
Accordingly, the CIM mix is grouped under three dimensions, i.e., freight, forecasting,
and flexibility.
The CIM mix, as illustrated in Figure 1, provides an independent opinion about the
key dimensions that should be the focus of a carrier’s attention when managing its
container inventories. Under each of these three dimensions, six strategies are elaborated
that facilitate effective and efficient CIM. This approach provides an objective,
‘proactive’ solution rather than the more common, ad hoc ‘reaction’ to market conditions
related to empty container repositioning. This mechanism enables carriers to act more
effectively and efficiently as they regularly evaluate their decision through an indicator
that consists of criteria validated by the industry experts.
4.4 The application techniques
Once the indicators and their corresponding cut-offs have been selected, the next step is
to define the weights that each indicator will have in the measure. The CIM model
represents freight, forecasting and flexibility, whereas each dimension consists of two
indicators, thus totalling six ingredients (CIM strategies). Table 5 illustrates the
computation of weights of the model.
Table 5 Summary of computation of weights of the CIM model
Strategy Indicator (jk)
Dimension
(D)
Average
_
( )
jk
q
Share of
dimension
_
( )
sq
Proportion to
the weight of
_
( )
jk
D p
w_jk
Reduce import freight 2.514 0.479 0.182
Reduce export freight 2.736 0.521 0.198
Freight 5.25 0.376
Service agreements 2.67 0.517 0.191
Synchronised budget 2.49 0.483 0.179
Forecasting 5.16 0.37
Agile inventory 2.19 0.617 0.154
Exporters’ priority 1.36 0.383 0.096
Flexibility 3.55 0.254

This paper proposes a common weight allocation for each strategy by which carriers can
mix the CIM ingredients for improved results. The three dimensions of the CIM mix,
freight, forecasting, and flexibility, receive weights of 0.376, 0.370, and 0.254,
respectively. First, reducing import freight to a container deficit location and/or reducing
export freight from a container excess location are viewed as effective, efficient
strategies. The freight component has the highest level of control, with 37.6% of the mix.
The dimension, forecasting, has a 37.0% representation in the model and comprises
long-term service agreements with customers and synchronising the annual budget with
monthly export/import forecasts. Flexibility is 25.4% of the mix that includes prioritising
exports from a specific location and maintaining agile container inventory.
Figure 2 Dimensions of the CIM mix – 3F Model© Authors (see online version for colours)
The 3F model (or CIM mix) in Figure 2 represents freight, forecasting and flexibility. It
has six variables representing key CIM strategies, namely,
1 reduce import freight
2 reduce export freight
3 service agreements
4 synchronised budget
5 agile inventory
6 export priority.
Container controllers should be trained to mix these six ingredients depending on varying
market conditions. The strategies within each dimension are also weighted in the same
manner, i.e., the mean value of each individual variable is calculated in its relative degree
to the total mean value of responses. Thus, the ‘freight drop export’ and ‘freight drop
import’ strategies within the freight dimension receive weights of 0.182 and 0.198,
respectively. Similarly, the weights for the next dimension, forecasting, are 0.191 and
0.171 for the ‘SVC agreement’ and ‘BDGT synchronise’ strategies, respectively.

Flexibility, the third dimension, receives weights of 0.154 for the ‘inventory agile’ and
0.096 for the ‘priority exports’ strategies. Based on the final weights for each strategy,
the CIM decisions should be taken by carriers.
5 Conclusions
In conclusion, CIM mix will play a very important role in carriers’ attainment of
competitive advantages. An accurate blend of the six elements in the model may help
carriers achieve organisational goals and profit maximisation through the optimum
utilisation of container inventory, which can help the carriers reduce various costs
associated with containers. Consequently, carriers can lower their existing freight rates,
leading to possible price reductions for consumer goods.
5.1 The unique contribution
CII is a significant issue, but many carriers do not have a standard CIM policy to
effectively combat its negative consequences. Moreover, even the carriers who have a
CIM policy are not satisfied with their existing CIM policy. The analysis clearly indicates
that container shipping lines need to adopt a proper CIM system. This serious gap in the
industry gets wider as the CIM is generally considered as an individual problem of
carriers rather than a widespread problem of the industry. Carriers usually attempt to
improve their CIM system in isolation thus the industry is hardly benefited through their
individual research and development. Contrary to which this paper introduces a
systematic CIM mechanism that helps carriers minimise CII thus lowering their CIM
cost. The proposed 3F model will also help reduce the empty container repositioning thus
encouraging the concept of green logistics.
5.2 Theoretical and managerial implications
The ‘3F model’ provides theoretical insight to CIM and helps solve serious managerial
implications. It provides general guidelines to container controllers based on scientific
research facilitating them to take effective CIM decisions. This model does not compete
with the smart container repositioning methods that are currently popular in trade, but
rather it complements those conventional mechanisms. Overall it helps reduce the amount
of empty container reposition. CIM is a recurring management issue as CII is
unavoidable and uncontrollable phenomena due to global trade imbalances; therefore,
adequate control should be exercised in terms of CIM. The container controller is
considered to be a ‘mixer of CIM ingredients’, i.e., one who is constantly engaged in
creatively fashioning a mix of CIM strategies in his efforts to minimise the impact of a
CII. According to the overall analysis, reduce export freight reflects the most popular
CIM strategy, whereas service agreements, reduce import freight, synchronised budget,
agile inventory, and exporters’ priority represent the rest of the strategies in descending
order of popularity. Therefore, carriers can focus on deriving their ‘best-suite’ mix
accordingly. Through this model carriers may derive the most effective and efficient
combination of respective strategies in the 3F model.

This paper introduces a theoretical model that is like the marketing mix. Borden
(1984) reiterates that a marketing executive is a ‘mixer of ingredients’ who is constantly
engaged in fashioning a creative mix of marketing procedures and policies to obtain
profitability. The CIM mix is a set of controllable variables that a carrier can use to
generate the most viable outcome for its container inventory. The container controller
who manages the container inventory may take over the duty of mixing the three
ingredients namely, freight, forecasting, and flexibility in the shipping environment. The
CIM mix involves decisions related to the empty containers that are made available at a
port.
5.3 Limitations and future research directions
As far as limitations are concerned, the research has been conducted in Sri Lanka. Thus,
it may not necessarily reflect the global situation although it presently attracts 16 out of
top 20 container carriers. It is recommended to extend the same research to other key
maritime countries such as Shanghai, Singapore, Dubai, Hong Kong and Rotterdam.
Empty container repositioning also increases carriers’ carbon footprint. Thus, carriers
have a social responsibility to reduce empty container repositioning. Accordingly, further
research in this context would be beneficial.
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Notes
1 TEU = 20-foot equivalent units.
2 Brito and Konings (n.d.) suggest that the empty repositioning cost is USD 400 per container.

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