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Proceedings of 25th International Scientific Conference. Transport Means 2021.
Smart Ports’ Influence on Coastal Sustainability
V. Paulauskas1, R. Philipp2, L. Henesey3, D. Paulauskas4, A. Sutnikas5, C. Meyer6, L. Gerlitz7,
N. Heine8, K. Kozyczkowski9, A. Zigus10, A. Silonosov11
1Klaipeda University, H. Manto 84, 92294 Klaipơda, Lithuania, E-mail: vytautaskltc@gmail.com
2Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Philipp-Müller-Str. 14, 23966
Wismar, Germany, E-mail: robert.philipp@hs-wismar.de
3Blekinge Institute of Technology, Biblioteksgatan 4, 37440 Karlshamn, Sweden, E-mail: lhe@bth.se
4Klaipeda University, H. Manto g. 84, 92294 Klaipơda, Lithuania, E-mail: paulauskasd75@gmail.com
5Klaipeda Science and Technology Park, V. Berbomo 10, 92221 Klaipeda, Lithuania, E-mail: projects@kmtp.lt
6Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Philipp-Müller-Str. 14, 23966
Wismar, Germany, E-mail: christopher.meyer@hs-wismar.de
7Hochschule Wismar, University of Applied Sciences: Technology, Business and Design, Philipp-Müller-Str. 14, 23966
Wismar, Germany, E-mail: laima.gerlitz@hs-wismar.de
8Institute for Sustainable Economics and Logistics, Dierkower Damm 29, 18146 Germany, E-mail: heine@inwl.de
9Motus Fundation, Kazimierza Pulaskiego 8, 81368 Poland, E-mail: kk@motusfoundation.com
10Klaipeda State Seaport Authority, J. Janonio 24, 92251 Klaipeda, Lithuania, E-mail: a.zygus@port.lt
11Blekinge Institute of Technology, Biblioteksgatan 4, 37440 Karlshamn, Sweden, E-mail: alexandr.silonosov@bth.se
Abstract
Nowadays, ports are actively seeking ways to improve their safety and operational activity. An essential driver in this
context is digitalisation. Since seaports are also key actors for the sustainable development of coastal regions, it is
important that they transform into smart port ecosystems. Hence, the automation and digitisation of ports’ operations are
important not only for the ports themselves, but also for the regions and countries hosting regional port ecosystems.
Studies on the digitalisation level of ports bear the potential to detect optimal ways for increasing safety, security and
visibility in terms of the digital transformation, as well as attracting passengers and freight flows, which in turn positively
affects not only the ports, but particularly also the sustainable development of coastal regions.
Therefore, the paper presents the results of a conducted assessment of small and medium-sized ports’ digitisation level as
well as introduces ways and recommendations how to improve the level of digitisation on the path towards becoming a
smarter port ecosystem. The research builds upon key insights from the Connect2SmallPorts project, part-financed by
INTERREG South Baltic Programme 2014–2020. Thereby, the research utilises collected primary data concerning ports
located in the Baltic, North and Mediterranean Sea Regions. Thus, the study bases on well-grounded theoretical and
practical findings in the maritime science field in the nexus of digital transformation.
KEY WORDS: smart port; port digitalisation; digitalisation level; port ecosystem
1. Introduction
Small and medium-sized ports are important for the sustainable development of regions and even countries,
because they represent essential parts of the local and regional economy [1-5]. Hence, a smart regional development is
directly linked to (port) logistics performance improvements [6-8]. Concerning small and medium-sized ports, recent
studies noticed that they face big challenges in comparison to their larger counterparts due to limited financial resources
and the lack of suitable human capital [9, 10]. Especially the latter two pitfalls represent grave problematic aspects
regarding the digital transformation – i.e. smart port development. The main objective behind the smart port concept is to
reach the highest digitalisation status [11], which – in turn – is expected to have a powerful influence on regions’
sustainability, due to arising radical spill-over effects emanating from ports’ strong interrelationships to other key
industries.
Next to this, regional sustainability areas are immediately affected by ports’ potential to – for instance – attract
passengers and cargo flows, favour regional labour market via the creation of additional working places, as well as
encourage tourism plus education and research in transport and logistics [12-14]. The digitisation in terms of smart
logistics operations is not only important for ports, but also for the corresponding regions and countries that strongly
depend on the regional port ecosystems [15-18]. In this context, studies on the digitalisation level of ports bear the
potential to detect optimal ways for increasing safety, security and visibility in terms of the digital transformation, as well
as for attracting passengers and freight flows, which in turn positively affects not only ports, but particularly also the
sustainable development of coastal regions [19-22].
In line with this, the current paper aims to present the findings of a performed assessment of small and medium-
sized ports’ digitisation level as well as to introduce ways and recommendations how to improve the level of digitisation
on the path towards becoming a smarter port ecosystem. The study builds upon key insights from the still ongoing
397
Connect2SmallPorts project, which is part-financed by the INTERREG South Baltic Programme 2014–2020. Thereby,
the research bases on collected primary data that refers to ports located in the Baltic, North and Mediterranean Sea
Regions. Therefore, the study sets upon well-grounded theoretical and practical findings concerning the maritime
transport science field in connection with the digital transformation. In accordance with recent studies [9, 23, 24], the
results of the present examination show that the digital efforts in small and medium-sized ports cover differ and thus, are
dispersed, whereby the digital transformation is important for both, ports and their regions.
The paper is structured as follow: In the second section, the theoretical background is outlined, whereas in the third
part of the article, the applied methodology is set out. Afterwards the main findings are highlighted. The paper ends with
some conclusions.
2. Theoretical Background
Seaports – regardless their size – are essential parts of cities as well as surrounding local industries, and as such,
attract tourism in form of passengers as well as are responsible for in- and out-going cargo for settled enterprises [15, 19].
Accordingly, also small and medium-sized ports have a great influence on the sustainability of their regions [2, 16, 25].
For describing small and medium-sized ports, only a limited number of factors is necessary. By taking Europe as
an example, small and medium-sized ports [26-28]:
x are no core ports in the sense of the Trans-European Transport Network (TEN-T);
x handle less than 10 million tonnes of cargo per year;
x are specialized or non-specialized ports (also known as universal ports);
x are mainly municipality ports;
x face limits regarding capacities and expansion possibilities.
Smart small and medium-sized ports – in certain ways – are similar to smart organisations, since the main
operations are managed through a single centre, too, which also [9, 17, 20]:
x regulates information concerning shipping operations and navigation;
x determines the optimal flow of cargo to, within and from the port;
x optimally distributes port equipment for ship handling activities;
x effectively employs marketing;
x efficiently regulates the access of passengers and cargo to or from terminals.
Nowadays, port processes and operations shall be as much as possible automated and digitalised, but on the other
hand must be economically useful and sustainable. Therefore, the crux of the matter for small and medium-sized ports is
to identify and initiate sustainable measures and investments in order to reach the processual smart port status.
The main areas of port digitalisation are usually [22, 27]:
x digitalisation of the management functions;
x digitalisation of port’ or terminals’ operations and port service;
x safe navigation;
x control of real (actual) depths in the port;
x emergency situation management in port;
x port control institutions;
x legal documents validation in the port (port rules, navigational regulations, etc.);
x port dues and tariffs;
x ships in the port;
x ETA and ATA of the ships;
x cargo in port;
x passenger entrance to the port;
x service companies in port and its activities;
x port statistics;
x port annual reports;
x port development programmes (sustainability and digitalisation strategies);
x port promotion materials (video, audio etc.), etc.
In contrast, the main objectives or tasks of the digitalisation in ports are commonly to:
x improve environmental and safety;
x make the best choice on best practice applications;
x increase transport efficiency;
x reach the final digitalisation level, which is associated by the smart port stage.
Generally, this implies the detection of operational areas in which digitisation is needed. As a suitable tool,
digitisation audits can assist such endeavours and at the same time shall point out the effectiveness of the digitalisation
case-by-case actions. On the other hand, if such digital audits are conducted additionally on a bigger scale, this allows for
comparative assessments of ports’ level of digitisation in the course of a benchmarking, which in turn allows for the
derivation of best practices. [9, 16, 18, 20, 22-24]. For evaluating the effectiveness of ports’ digitalisation progress some
indicators are needed. An innovative tool that hosts a set of suitable indicators is the digital readiness index for ports
398
(DRIP), which can be used as well for an evaluation of ports’ digital transformation and benchmarking purposes [23, 24].
Within this so-called DRIP, the five main pillars – which accumulates numerous digital performance indicators and port
performance indicators (PPIs) – refer to [ibid.]:
(1) Management – associated indicators deal with port’s digitalisation strategy and openness to implement new
digital solutions;
(2) Human Capital – associated indicators determine employees’ knowledge, skills and capabilities;
(3) Functionality (IT) – associated indicators evaluate the functionality and effectiveness of IT systems and
efficiency of processes;
(4) Technology – associated indicators refer to used enabling technologies and digital solutions;
(5) Information – associated indicators measure the degree of knowledge procurement sources.
3. Methodology
According to the DRIP model, the final DRIP score is calculated based on the results of examined pillars for which
different weighting factors apply [23, 24]: (1) Management = 20%, (2) Human Capital = 20%, (3) Functionality (IT) =
25%, (4) Technology = 30%, (5) Information = 5 %. For the benchmarking, the ports in the sample had been grouped
according to different characteristics, such as the achieved DRIP score, port classification in the sense of the TEN-T (i.e.
core ports, comprehensive ports, Non-TEN-T ports), but also according to other useful determinants such as cargo
turnover and port location (country) [8, 15]. However, the benchmarking results presented in the present study showcases
basically ports’ digital auditing results. Moreover, ports are specified depending on their achieved DRIP score, importance
in logistics chains and cargo turnovers. This is necessary because different ports have varying possibilities and resources
for implementing digitalisation programmes. In respect of the mentioned features, it will be possible to figure out which
digitalisation level is typical for certain port characteristics. In order to respect the confidentiality of data provided by the
audited ports, the port’s names were anonymised.
In the frame of the benchmarking, it is good practice to test the quality of accurateness concerning port auditing
results. For such a purpose, it is appropriate to use the maximum distribution method [23]. Mathematical conditions for
the auditing and benchmarking base on random factors. Therefore, interviews were conducted with responsible top-level
managers for IT implementations, who have a great overview on digitalisation information in the port and actively
participate in the digitisation progresses in the port or terminals. However, in case of data with big random factors it is
possible use the Normal (Gaussian) principal [13].
Applied method in the present study targets on the DRIP scoring band analysis. To calculate the size of the random
error or the DRIP scoring band, dispersion and/or “maximal distribution”, mathematical methods can be used. It was set
that the size of the random error (
e
or
P
t'
) in the dispersion method is comparable with dispersion ( ) [13, 26, 29].
The dispersion method was implemented to evaluate the DRIP scoring band and can be expressed as follow [29, 30]:
2
21
1
yiy
tt
n
V
¦
, (1)
where
n
– the number of the measurements (audited ports); ti – particular measurement results (ports’ DRIP score); ty –
mathematical expectation of the average DRIP scores, which can be calculated as follows:
1
n
i
i
y
t
tn
¦
. (2)
Finally, the DRIP scoring band (
P
t
'
) with determined probability (e.g. 63-68%) can be presented as follow:
2
Py
et
'V
r
. (3)
The DRIP scoring band
P
t
is calculated as follow:
Py P
tt t
'
r
. (4)
Similarly, the DRIP scoring band can be calculated using the maximum distribution method. In the frame of the
present research, it can be expressed as follow [3, 30]:
'
Py t
ttPtk
'
r
, (5)
y
V
399
where
'P
–probability coefficient (according to the literature, it is recommended that in case of a probability of 63-68%,
the probability coefficient should be equal 1; in case of a probability of 95%, the probability coefficient should be equal
2; and in case of a probability of 99.7%, the probability coefficient should be equal 3);
t
'
–difference between maximum
and minimum DRIP score results; kt–coefficient, which depends on the number of measurements (the number of
processed data) (if the number of data is 3, this coefficient will be 0.55; if the number of data is 4, this coefficient will be
0.47; and similarly, if the number of data is 5, this coefficient will be 0.43; 6 ؙ0.395; 7 ؙ0.37; 8 ؙ0.351; 9 ؙ0.337;
10 ؙ0.329; 11 ؙ0.325; 12 ؙ0.322; etc.; the minimum value of the coefficient is 0.315, if the number of collected data
is greater than 15).
4. Results
Within a first step, the digital auditing results of a large range of the ports from the Baltic, North and Mediterranean
Sea were contrasted. Building upon the detailed digital auditing results, the corresponding DRIP scores as well as DRIP
filtration results were calculated for 30 ports. However, first, the cargo turnover and corresponding DRIP scores for large,
medium-sized and small ports are showcased in Fig. 1.
Fig. 1 Cargo turnover and DRIP scores of large (L), medium-sized (M) and small (S) ports
According to Fig. 1, the strong relationship between cargo turnover and DRIP score becomes apparent, whereby
a detailed analysis of corresponding indicators and pillars revealed that the main reasons for this detected circumstance
can be traced back to the lack of financial and human resources in medium-sized and small ports.
Nevertheless, smart ports indicator results can be linked to regions’smart systems elements –especially
concerning environmental and safety factors. Next to this, it should be noted that the achieved DRIP score by ports
depends on the individual, who participated in implementation and evaluation process. This has a big influence on the
accuracy of the received DRIP scoring results. Consequently, for the accuracy evaluation of the DRIP scoring results,
dispersion and maximum distribution methods were used. The corresponding results are highlighted in Table and Fig. 2,
whereby a selection was made, which culminated in a focus on small ports from BSR.
Table
Calculated and filtrated DRIP scoring results for small ports located in BSR
Ports
1
2
3
4
5
6
7
8
9
10
DRIP
2.54
2.55
3.08
3.37
3.43
3.47
3.90
3.91
3.99
4.03
Filtrated DRIP
2.65
2.66
3.05
3.35
3.42
3.46
3.88
3.89
3.92
3.98
The analysis of the DRIP scoring accuracy shows a great fluctuation. Thereby, the calculated mathematical
expectation DRIP score is 3.456, the DRIP score band is 0.506 and the accuracy of the DRIP score results is 14.7%. From
the received results, it can be derived that according to the DRIP assessment, some smaller ports are close to score up to
4.5 (cf. possible DRIP score spread: 1.0 to 6.0), which implies that it is possible to link or use ports’ high digitalisation
level with respective regions’ and cities’ operation, safety and security systems, as well as to promote the creation of joint
regional or urban smart systems.
400
Fig. 2 BSR small ports DRIP scoring accuracy band (X-axis –port numbers, presented in Table 1; Y-axis –DRIP scoring
results)
5. Conclusions
The implementation of digital solutions to improve small and medium-sized ports’operations and management is
essential for their sustainable development, but also for achieving the visions of both smart ports and smart regions
likewise.
However, the study turned out that the digitalisation level of small and medium-sized ports is much lower,
compared to the level observed in the case of investigated large ports. On average small ports’ DRIP scores ranged from
2.54 up to 4.02, medium-sized ports’ DRIP scores ranged from 2.92 up to 4.32, and large ports’ DRIP scores ranged from
3.15 up to 4.90.
Moreover, the accuracy of evaluation results differs regarding ports importance: core ports up to 18.0%,
comprehensive ports up to 10.8%, and Non-TEN-T ports up to 15.5%. Furthermore, the accuracy of evaluation results
differs regarding cargo turnover: large ports up to 15.3%, medium-sized ports up to 9.8%, and small ports up to 16.4%.
Nevertheless, overall, it could be derived that the digitalisation level of small and medium-size ports is about 30%
less in comparison to large ports. Nevertheless, it can be concluded that an increasing digitalisation level in small and
medium-sized ports can stimulate their activities and increase port service options and smart port development progress,
which in turn will promote the creation of regional or urban smart systems.
The DRIP model used as a methodological concept to evaluate the port digitalisation level and smart port
development progress appear to be a suitable tool, since it builds upon 5 pillars and 38 indicators that are well-defined.
Nevertheless, also other than these 38 indicators could have a decisive influence on the development of smart ports, which
in turn allows if necessary to reconsider the model and to include further or other indicators in the frame of future studies.
On the other hand, for the present study, this represents a methodological limitation, due to the lack of other substitutable
tools to the DRIP model, since the DRIP model is still unique and the first of its kind. Hence, currently there do not exist
other digital readiness indexes or maturity models for ports in the scientific literature and in practice.
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