721
1 INTRODUCTION
From the crisis of 2008‐2009, the sector of maritime
shipping, and in particular its container sector has
been in the state of permanent recession. It can be
observed in the form of unbalanced market. The
potential supply of global container operators,
namely the maximum carrying capacity of
the
containerfleetisstillbyfarhigherthantheeffective
demand for their services (UNCTAD 2018). It is a
resultofdynamicgrowthofworldcarryingcapacity
in TEUs based on annual increments, stimulated to
great extend by increasing use of mega‐container
vessels(VLCC),whichismuchhigher
thanlessthan
expected upswing in demand for container carring
services.(seefig.1.)
Figure 1. Figure 1. Growth of demand and supply in
containershipping,2007–2017(Percentage)
Source:(DHL2018)
Freight Markets in the Global Container Shipping
–
Their Dynamics and Its Impact on the Freight Rates
Quoting Mechanism
A.S.Grzelakowski
GdyniaMaritimeUniversity,Gdynia,Poland
ABSTRACT:Themainsubjectofresearchinvolvestheglobalfreightmarketinthesectorofcontainershipping,
andinparticularthemechanismforsettingupfreightrates.Theaimofresearchistoidentifyandanalysethe
featuresandcharacteristicstypicalofthismarket,resultingfrom
thecharacterofmarketmechanism,aswellas
toindicatefactorswhichdeterminemarketfluctuationsandevaluateitsimpactforthecarriersandshippers.
Moreover,thecausesandeffectsoffluctuationsineffectivedemandandpotentialsupplyinthesectoroffreight
market were identified in terms of their impact
on freight rates. The analysis includes three main types of
containermarkets,existingtodayonaglobalscale,andcharacterizesthestrategiesandmodesofbehaviourof
global container operators undertaking activity on these markets. By applying the theory of mass random
serviceandqueueingtheorytoexaminethecontainerfreight
marketmechanismsthemethodoffreightrates
short‐termforecastingwaspresentedwhichcanbeusedasaninstrumentfortakingdecisionsinthesectorof
globalmaritimelogistics.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 12
Number 4
December 2018
DOI:10.12716/1001.12.04.11
722
Thistrendcanbemaintaindforthenextfewyears
in the world container shipping. Ocean effective
demand fails to rise as predicted between 2011 and
2015 and today it is assumed that the potential
demandwillequalthepotentialsupplyonlywithin3‐
4subsequentyears,i.e.in2020
‐2021(seefig.2).
Figure2. Supply demand growth (annualized), in %.
Source:(DHL2018)
Potential supply growth rate is limited by
scrappingthetonnageanddelayingthedeadlinesfor
thedeliveryofnewvesselsorderedintheshipyards
worldwide. This peculiar disconnection between the
supply and demand side of the container freight
marketindicatesnotonlythelevelofimpactofglobal
economicsituationon
theshippingsector,butalsoits
structural weakness, and consequently its market
mechanism. In these conditions, the supply side
under significant pressure of global competition
visibleinthenetworkofalltransportroutesandsub‐
markets is very susceptible to any forms of subject‐
ownership consolidation (concentration processes,
including mergers,
takeovers, and operational
integration‐alliances). The other, in turn – the
demand side is characterised by very high rate of
change over time – but first of all, short‐ and mid‐
termfluctuationswhichsignificantlyaffectthemarket
situation (. In such circumstances, i.e. related to
widening the gap between the
supply side, co‐
determinedbytheinvestmentbaseddecisionsofthe
carriers and medium‐term investment cycle of
shipbuildingsector,andthedemandside,determined
bythedecisionsofexportersandimportersandshort‐
term trading cycle, the market mechanism as
effectivelyoperatingsubsystemcontrollingthesector
fails, in general.
The market mechanism, as the
instrument not only potentially able to ba lance this
unusuallyunstablesystem,butalsoastheinstrument
designed to co‐develop in an effective manner the
allocation‐related decisions of carriers and
distribution processes – redistribution of benefits
(valueadded)betweenthemandshippersaswellas
otherentitieswithinthegloballogisticsupplychains,
fails to perform its function in an effective manner
(Grzelakowski2014).
In such circumstances, the basic functions of
freight market become significantly distorted, which
result from, and at the same time, result in
inefficiency of price mechanism (Carlier et al. 2011).
Its
distortionisexpressedinthedetachmentoffreight
and charter rates from the demand‐supply relation,
i.e. effective demand from potential supply. The
analysisoffreightmarketexpostandfreightaswell
ascharterindexes,developedforparticularsegments
ofthecontainermarketandforecastingfluctuationsof
effectivedemand,with
relativelylowpriceelasticity
of the demand, even at short time intervals, fail to
provide real grounds for evaluating changes in the
level of freight rates based on the evaluation
(prediction) of changes in the traditional market
parameters – based mainly on macro‐economic
indicators (increase/decrease of GDP, PMI, trade
growthrate,currentaccountbalance, levelofnational
debt,etc.).Insuchcircumstances,i.e.withsuchhigh
leveloffailureoffreightmarketmechanism,weshall
look for other methods and forecasting systems
(evaluation)ofdirectionandrateofchangesinfreight
ratesinparticularsegmentsofthemarket(Cole2009,
Carlieretal.2011).
Theaimandalsotheresultofresearchpresented
inthisarticleistoindicatesuchmethodwhichwould
help,inarelativelysimpleandunambiguousmanner,
with the use of available data base, to define
tendenciesinshapingfreightratesonanannualbasis.
Since
itis importanttoensurethereliability of such
evaluationsrelatedtopredictedlevelof freightrates
which should constitute grounds for taking rational
decisions by shippers (forwarders/logistic operators)
regarding the formula for defining the price in the
contract of carriage, i.e. spot or negotiated rates
usually for a year (contractual
rates). The formula
mayalsobeusefulinthesegmentofchartersandin
the model based on slots (NVOCC). However, it is
onlyindicativeinitscharacterandshouldbeapplied
asauxiliarymethod.Itcanbeusedasaninstrument
supporting the related decision making processes,
basedon
thetraditionallyappliedheuristicmethods,
supported by knowledge and experience of the
forwarder/logisticoperator.
Inordertodevelopthismethoditwasnecessaryto
analyseandevaluate:1/modelofmarketmechanism
inthesegmentofglobalcontainershipping, 2/main
typesoffreightmarketswithinmaritimeshipping,3/
standard solutions
and activities of shipping
operators in the condition of crisis. Each element
constituting the methodological assumptions
developing the structure of presented price formula
wassyntheticallypresentedinthisstudy.
2 GLOBALCONTAINERSHIPPINGMARKET
MECHANISM
The freight market, as one of the types of transport
market,hasallthefeatures
andpropertiestypicalof
the service market (Cowie 2010. By performing
detailedsegmentationofthetransportmarket,global
freight container markets shall be included into the
categoryofintermodalmarketswhichfallwithinthe
group of global logistic markets (OECD 2008).
Therefore, by analysing their structure and
mechanisms,weindicatethat:
1 in their character the markets are secondary
markets.Theyarethemarketsresultingfromprior
tothemtrademarkets,andconsequentlytheprice
elasticityofsupplyanddemandfortheservicesof
marine container operators results from the price
elasticityofsupplyanddemandforgoodscarried
incontainers
bysea,
723
2 price elasticity of supply and demand in the
segment of marine container transport is lower
thanthepriceelasticityofsupplyanddemandfor
goods carried in containers by sea. It arises from
relatively high rigidity of the supply side of the
container transport market – its low reaction
to
changes in effective demand. It is caused by
relatively long investment cycle in the
shipbuilding sector (renewal of tonnage)
compared to the commercial cycle shaping the
demandside(RICSResearch2009),
3 the demand side is characterised by particularly
highrateofchangeovertime– highamplitudeof
changesmainlyinshort‐ butalsomid‐and long‐
termperiodsoftime(seasonal,business,structural
fluctuations). As a result, the adapting processes
on the supply side usually occur with significant
delay and consequently the container operators
are forced to maintain steady tonnage surplus of
carryingcapacitycomparedtotheaverage
levelof
demandfortheirservices.Itgeneratesadditional,
usuallyhighfixedcostsinthistransportsector.
4 freight rates, defined in general as annual tariffs,
arenotabletoreflectsignificantrateofchangein
demandandsupplyparametersofthismarket.In
such situation, they are more indicative
and
informative in their character, and in the
conditions of unbalanced market they are
frequentlydetachedfromtherealsystemofother
marketelements(Cole2009).
5 pricerigidity–freightpricesdefiningthelevelof
revenue for container operators, which make the
operators apply compensatory mechanisms
(numerous freight surcharges,
GRI formula, etc.)
which only aim to reach at least the break‐even‐
pointortheforecastedlevelofEBITDA,inorderto
make the relation between the tonnage operating
costs and revenue real, in pa rticular during
economicdownturn.
Such features of marine container freight market
unambiguously indicate that to perform
its current
analysis, and first of all, to forecast the change in
particular ma rket elements, we cannot apply the
balance model based on pricing, known from the
general theory of the market, the so‐called Pareto
distribution.Since,duetotheir rigidity,lowelasticity
andprinciples todefine them,the
freightratesarenot
the component which is able to create the state of
balance in this respect. They are practically external
components of the market, functioning to a larger
extentasinstrumentscreatingthefinancialbalanceof
the carriers, rather than as the creator of market
balance–andeven
thebalanceperceivedinthemid‐
andlong‐termtimeinterval(Mallard,Glaister2008).
In such situation, to analyse the maritime
containertransportmarketweneedtoapplyanother
method,basedonthetheoryofmassrandomservice
orqueuing theory. With the use of such methodwe
can define
in terms of probability (i.e. in real terms,
namely in line with the features of this market) the
system of applications and system for meeting the
demandfortransportservicesofcontaineroperators
rendering their services on a particular shipping
market.
For the market operates as the system of mass
random
service,withtypicalflowofapplicationsand
distribution in time (week, month, quarter, or year)
and the demand‐meeting mechanism. It means that
each segment of the container market has, at a
particular time, its own mechanism of applications
and effective demand‐meeting mechanism, which is
generally random in character
and possible to be
described only with the use of random variables,
namely the theory of probability. Without the
knowledge of this mechanism and random
parameterswecannot correctly definetheprinciples
fordefiningthefreightandcharterrates,andevaluate
the activities and decisions of carriers and
shippers/forwarders based on
the typically
microeconomiccriteria(Kieletal.2013.
The mechanism of demand applications for
operator’sservices(alliance,groupofoperators)ina
particular port or ports handled in a particular
relation(loop),namelytheprocessofapplicationsand
itsdistributionintimecanbedescribedwiththeuse
of
one random variableλ
’
. Depending on the needs
andpurposeofthestudyaswellasaccesstodata,the
variable can be presented in two variants
(Grzelakowski2014):
1 λ
’
1–presentsthestochasticprocessofapplications
asperformula:„average”numberofapplications
for particular time per time unit (e.g. 100, 500 or
1000TEUpermonth):characterisesdensityofthe
flowofapplications,
2 λ
’
2‐presentsthestochasticprocessofapplications
asperformula:„average”time intervals between
the demand subsequent applications (e.g. 2000
TEU on average per week); characterises the
intensityoftheflow(Daughtey2008).
The knowledge on random variableλ
’
makes it
possible to determine the distribution of probability
of demand applications in formula 1 or 2 and,
therefore, defines well the character and type of
processes occurring on a particular ma rket from its
demand side, i.e. the applications generated by
transport markets handled by a particular operator.
The information
and data should be clearly
recognizedbythecontaineroperator,butalsoknown
(examined) by forwarders handling a pa rticular
market, since the knowledge on container transport
(container shipping) market mechanisms facilitates
the decision making in the supply chain, and in
particularmakesthepossibilitiesofpricenegotiations
real(spotratesversus
contractualrates).
However,forthecompletecharacteristicsofglobal
maritime container shipping market it is
indispensable to know the stochastic processes
regardingthe demand, namely meeting the demand
ofshippers/forwarders.Theprocessesaredefinedby
thecarrier,indicatingthetimeandconditionsofships
voyage/journey.However,duetoa number
offactors
theprocessisrandomandthereforeitsmechanismis
alsodescribedwiththeuseofrandomvariableλ
”
.It
can reflect, depending on the needs and purpose of
analysis(Carlieretal.2011,Kieletal.2013):
1 λ
”
1‐„average”withinthetheoryofprobabilityfor
a particular market (line, loop) number of
complete production cycles, e.g. performed
voyages(thedemandmeetingstage)bythecarrier
within the defined time unit (month, quarter,
year), namely the number of calls at a particular
portorports,
2 λ
”
2‐ „average” (as above) time intervals between
subsequent production cycles of ship/ships,
724
namely subsequent calls at a particular port; it
defines the frequency of calls – intensity of
shipping operations performed by the carrier
taking on the tonnage within particular service
(Cole2009).
Parameterλ
”
makes it possible to determine the
distribution of demand‐meeting flow per particular
containeroperator(alliance)intime,indicatinginthe
majorityofcasesthatthedistributionisnot–contrary
to what can be expected (fixed voyage schedule) –
normalbutusuallyexponential.Itdefinesatthesame
time
the average, by stochastic category, time for
meeting the demand, and determines the time and
costofsuchprocess.These,inturn,constitutecrucial
parametersdefiningtheefficiencyofcontainerfreight
marketinthelogisticterms(RICSResearch2009).
Both random variables defining the process of
applications and demand‐meeting process
on the
analysedcontainershippingmarketmakeitpossible
not only to define correctly the mutual relations
between the effective demand and potential supply
but also to learn better and understand the
mechanismofselectedcontainermarketsegmentwith
therelatedpricingmodel.Itdeterminestheirpractical
value. It can
also be used effectively to define the
price formula and the tonnage operational
productivity which reflects the level of surplus
tonnageonthemarketanddeterminesthelevelofits
operational costs. The costs, in turn, constitute an
importantfactorco‐definingtheleveloffreightrates
inashortand
longperiodoftime.
3 TYPOLOGYOFMARITIMEGLOBAL
CONTAINERMARKETSANDSHIPPING
OPERATORS’PRICINGSTRATEGIES
The sole knowledge on the mechanism of particular
segmentofmaritimecontainertransportmarketisthe
prerequisite but it is insufficient to learn the
principles and criteria, based on market premises,
related to taking
decisions on defining freight rates
andintroducing changes by containeroperators. For
theyaredecisionsofmicroeconomiccharacterandare
conditional,toalargeextent,uponthetypeofmarket
whereparticularcarriersoperate.Therefore,thereisa
needonthepartofoperatorstoanalysetheirmarkets
in detail in
terms of its typology (and previously
segmentation).Suchanalysis isnecessaryto develop
efficient and effective marketing strategy taking
account of price parameters. Therefore, the correctly
performedmarkettypologyincreasestheknowledge
on its mechanisms and the related decisions and
behaviourofcarriers(Carliereta.2011).
In general, we
can differentiate 27 types of
markets,aseconomiccategory.However,inpractice,
basedontheanalysisofstrengthandmarketreactions
regarding the demand and supply side, we can
differentiateonlyninetypesofmarkets,fromamong
which in the analysed segment of global freight
marketonlythreetypesareof
significantimportance.
Theyinclude(Grzelakowski2014):
1 limited monopoly (supply), the so‐called
competitivemonopoly,
2 oligopoly(occasionallyreferredtoasoligopson),
3 bilateraloligopolyi.e.duopoly.
The specified types of markets unambiguously
indicate that in the segment of maritime container
shipping the oligopolistic market and its various
forms occurs
as the dominant type of market. It is
significantly affected by the production and capital
concentration processes (both vertically and
horizontally–mergers,takeovers)andtheincreasing
integration in the operational and functional area
occurringmainlyonthesupplyside(alliances).
Theconsolidationofsuchtypesofmarketsproves
significant strength
and position of container
operators able in such conditions to exert effective
impact on the behaviours of particular entities –
participantsinthelogisticchain/supplychain(Carlier
etal.2011).Moreover,intheconditionsofincreasing
concentration of maritime transport sector, their
market position is increasing as well as their
bargaining power towards the demand side of the
market,i.e.shippers (exporters and importers). As a
result, they begin to function as the supply chain
operatorswithinthewholesegmentoftransport(not
onlymaritimetransport).Bytakingtheleadingrolein
theareaofsupplychainmanagement,theygradually
introduce
theirownpricingsolutions,basedontheir
strategies, which we could have been observed
recentlyinextendingtherangeoffreightsurcharges
andtheintroductionandoveruseofGRI(GeneralRate
Increase)mechanism(Carlieretal.2011).
In the current market situation, the pricing
strategiesofglobal container operators
arebased on
twobasiccriteria(Daughtey2008):
1 achieving the economic optimum within the
operational and commercial area, which the
oligopolistscanreachwhen their final short‐term
cost equals the final revenue; it is actually their
economic (but not financial) point of production
profitability,namelytheequivalentofbreak
‐even‐
point of operators undertaking activity on the
oligopolisticmarket,
2 achievingthetechnicaloptimum(technological)of
production, perceived in the category of higher
use of tonnage capacity, which the carriers can
reach when their final short‐term cost equals the
averagetotal cost of transport services (constants
and variables).
Within such optimum – level of
production, the final cost of transport of each
subsequent unit is already much higher than the
final revenue obtained by the carrier, and
consequently the production profitability is
steadilydecreasing(Grzelakowski2014).
Depending on the type of market where the
containeroperatorsprovidetheirservices,
thepointof
economic and technological optimum and its
production will be shaped differently in terms of
usingthecarryingpotential.And,intheconditionsof
duopoly, the economic optimum is usually reached
already with the use of min. 55% of carrying
potential, whereas on the oligopolistic market the
limit
amounts to ca. 62‐65%, and the competitive
monopolyonlyabove70%(highereconomiesofscale
resultingfromthereductioninfixedcosts).Inthecase
of technological optimum, the limit is shifted along
the axis of production, i.e. the level of using max.
carrying capacity (potential supply). At duopoly, it
amountsto ca. 65%, oligopolyca. 75 %, and limited
monopoly83–88%(Grzelakowski2014).
725
Inpractice,theindicatorsdefinethepotentialareas
andformsofthecontaineroperatorpricingreaction‐
oligopolists operating on such market characterized
by the established distribution of applications and
effective demand meeting system. The lack of
possibilitytoreachtheeconomicoptimum,e.g.dueto
limitationsfromthedemandside
–lackofdenseflow
ofapplicationswithsignificantsupplysurplusatthe
same time, will make operators turn towards the
technological optimum. It is more oriented to
minimizethetonnageoperationalcosts,andstriveto
maintain the existing level of freight rates and
eliminatepressure to reduce them. In
such case, the
operator’s area of activity is included between the
economic and technical optimum of production –
actuallyitisshiftedfromthefirsttowardsthesecond
one. Such activities are usually accompanied by the
pricing strategy, characteristic of such system,
expressedingradualdeparturefromthetendencyto
maximize
the service production profitability, i.e.
surplus of final revenue over the final costs of
production towards the tendency to minimize the
costsandstrivetomaintainthepreviousorpossibly
stable level of revenue (minimal profitability of
production).
The change in distributing the flow of effective
demand and meeting the
demand, expressed in the
form of increasing intensity of applications and
parallelreductionofsurplussupply,willgenerate,in
turn,anothertypeofpricingstrategywithinthistype
of market. The process related to gradual departure
fromtheapproachorientedtothetechnicaloptimum
ofproductiontowardstheeconomicoptimumwill
be
accompanied by constant pressure to increase the
freightratesandimprovetheproductionprofitability
ratioperunit.Itindicatestheattemptsofoperatorsto
maximize the marginal revenue and minimize the
marginalshort‐termcosts.
4 FREIGHTRATESSHORT‐TERMFORECASTING
INTHESEGMENTOFCONTAINERSHIPPING–
RATE
ESTIMATIONMETHOD
Theknowledgeofcontainermarketmechanismsand
behaviour of operators undertaking activity on
particularmarketsmayfacilitate,toalargeextent,the
correct assessment of change in the level of freight
rates within a few or several months. Therefore,
takingaccountofindicatedparameters,crucialforthe
correctassessment
ofthemarketregardingbothareas
ofanalysis,i.e.mechanismsandtypol ogy ofmaritime
container markets, we can define factors (variables)
whichcanconstitutegroundsforestimatingthescale
ofchangeintheleveloffreightrateswithinafewor
severalsubsequentmonthsatthemost.Theyinclude
the
followingvariables(indicators):
1 V‐surplus/shortage of tonnage of
operator/operators on a particular line, which
determines the level of tonnage operational
productivity (TEU/dwt) and indirectly indicates
howthetonnageoperational costsareshapedand
howtheselectedcomponentsofcostscanchange
inrelationtothisparameterinthenearestfuture.
The varia ble directly reflects the standing of the
market where the container operators undertake
theiractivity,
2 Y‐forecasted increase/decrease in effective
demand for carriers’ services operating within a
particular relation, estimated under official,
published statistical data related to commodity
exchange, and commodity freight and exchange
indexes,
3 Z –
predicted changes in the level of ship’s
operational costs (their increase or decrease),
triggered, however, not by the factors of freight
market where the shipping operator undertakes
their activity, but by other types of markets (e.g.
energy markets, labour markets, capital markets,
otherservicemarkets).
Taking into account the said variables
we can
determinethefunctionofchangeinpricelevelinthe
formof:
X
t+n=Xt xV
e1
t+n xY
e2
t+nxZ
e3
t+n,
where:
X
t+n‐estimatedlevelofrateintimet+n,
X
t ‐freightrateintimet,
V
t ‐ surplus/shortage of tonnage (correlation
betweenpotentialsupply/effectivedemand)intimet
+n[elasticitycoefficiente1definingthetendenciesof
changesintimet+n],
Y
e2
t ‐ increase/decrease in effective demand –
predicted changes in time t + n expressed in the
estimatedelasticitycoefficiente2,
Z
e3
t‐changesintheleveloftonnageoperationalcosts
intimet+ndefinedbyelasticitycoefficiente3.
These factors, to different degree and different
strengthmayaffectthedecisionsofthecarrierrelated
toquotingfreightrates–theirchangesintime.Each
ofthemshouldbemonitoredand
analysedcarefully
andindividually„balanced”bytheforwarder/logistic
operatorintermsofforecasting(estimating)changes
infreightrates.Sincetheimportanceofeachofthese
parametersmaydevelopdifferentlyindifferenttime
intervals (increase in some of them, decrease or
stabilization of the other). Therefore, the significant
variability of these parameters
in time and different
direction of changes require due caution when
balancing each of them, i.e. assigning a particular
elasticity coefficient to each parameter. The decision
shouldnotonlybeprecededbythoroughanalysisof
the market, but also supported by evaluating the
activities taken by carriers and shippers on
other
container transport markets but of similar profile of
the flow of applications (Carlier et al. 2011, RIOS
Research2009).
Detailedanalysisoftheprocessofshapingfreight
rates at the selected types of markets (ex post)
indicatesthattheelasticitycoefficients which can be
„assigned” to any of these factors
– variables fall
withinthefollowingranges:
1 V–[0,65 – 1,30]; lower limitofindicatordenotes
the ultimately high surplus of carrying potentia l
located on a particular market, upper limit –
ultimate shortage of tonnage relative to the
effectiveandpotentialdemand(t+n),
2 Y‐[0,85
–1,25];lowerlimitofindicatordenotes
ultimately pessimistic forecast related to the
economic growth and increase in commodity
exchange, and the upper limit – ultimately
optimisticforecast,
726
3 Z‐[0,90–1,35];lowerlimitof indicatorincludes
optimistically estimated tendencies regarding the
decrease in tonnage operational costs (e.g.
significant decrease in bunker prices), and the
upper limit – definitely pessimistic estimates –
significant growth rate of operational costs and
among them mainly the cost of the bunker
(UNCTAD2018,IHSMarket2018).Thedynamics
ofchangesinbunkercostoverthe lasttwoyears
areshowninFigures3and4.
Figure3.Bunkerpriceindex(QtoQ2017‐2018)
Source:(DHL2018)
Figure4. Average cost of ISO bunker fu 2018el in the
leadingAsian,USandEuropeanports(Jan.2017‐July2018)
Source:(IHSMarkt2018,UNCTAD2018)
Inordertoestimatecorrectlytheleveloffreightin
timet+nweneedtoincludealldependentvariables,
assessing them through the economic situation in
particularregionsintheworldmanagedbyparticula r
containerservice.
5 CONCLUSIONS
The presented model – formula for estimating the
level of change
in freight rates in the segment of
containershippingisonlyindicative initscharacter.
It shows from ex post perspective the character and
type of correlations within the process of predicting
freightratesonaparticulartypeofcontainer market
(oligopolistic) with proper application and demand‐
meeting mechanism. The
grounds for this study
included data obtained from selected, real segments
of maritime container shipping market (UNCTAD
2018).Themodelwasalsopartially(ca.57%relative
to the output data base). The obtained correlation
indicatorsfellwithintherangebetween0.63and0.76
(Grzelakowski 2014). However, such high
concurrence does
not mean that the function is a
universal instrument for predicting the levels of
freight rates since the intensity of change occurring
withineachoftheparametersdefining thedemanded
variableexantemaybesosignificantsothatitwould
require changing the elasticity range within each of
the
parameters or adding another variable defining
parameter X (e.g. EBITDA). However, according to
the author the model provides sufficient and solid
grounds for estimating the direction and rate of
changeinfreightrateswithin12‐18months.
REFERENCES
Carlier, K., Tavasszy, L., Perrin, J‐F. Minderhoud M.,&
Notteboom, T. 2011. Worldwide Container Network
Model.Leuven/Delft:TML/TNO.
Cole, S. 2009. Applied transport economics. Policy,
management & Decision Making. London and
Philadelphia: The Chartered Institute of Logistics and
Transport(UK).
Cowie, J. 2010. The Economics of Transport. A theoretical
and applied
perspective. London and New York.
Routledge.
Daughtey, F., 2008. Analitycal Studies in Transport
Economics.Cambridge:CambridgeUniversityPress.
DHL. 2018. Ocean Freight Market Update. DHL Global
Forwarding,Freight.September(2018).DHLPublic.
Grzelakowski, A. S. 2014. Container shipping operators as
integrators of global logistics supply chains.Logistics
andTransport,Vol.
21,No.1(2014):47‐49.
HISMarket.2018.
Kiel,J.,Smith,R.andUbbels,B. 2013.ReviewofTransport
and Economic Models. D3.1 of the I‐C‐EU project.
Brussels:EuropeanCommission.
Mallard G. & Glaister S. 2008., Transport Economics.
Theory, Application and Policy. New York: Palgrave
Macmillan.
RICS Research. Construction
Supply Chain Management:
ConceptsandcaseStudies.2009.S.Pryke(ed).Oxford:
WileyBlackwell.
TransportInfrastructureInvestment.OptionsforEfficiency.
2008. Transport Research Centre. International
TransportForum:ParisOECD.
UNCTAD, 2018. Review of maritime transport. Geneva:
UNCTADsecretariat.
