Page 204 - ΝΑΥΤΙΚΑ ΧΡΟΝΙΚΑ - ΜΑΙΟΣ 2024

FLEET OPTIMISATION

feasible in the shipping industry. These
robust techniques can be applied within
a vessel-specific framework and not on
a black-box approach in conjunction with
the high-frequency data derived from the
onboard data acquisition system.
Statistical learning methodologies are
applicable in terms of pattern recogni-
tion of machine performance, enabling
conditional-based monitoring and predic-
tive maintenance. Additionally, machine
learning methodologies can be applied
to voyage optimisation, utilising route
The crew plays a significant role optimisation to mitigate the effects of
in implementing technological the uncertainties derived from financial
developments to expedite the return perspectives and port operations.
on investment and ensure
the ship’s safe operation. How efficient and safe is the interoperability of
Hence, crew training is required the new digital platforms for monitoring ship per-
to ensure the proper operation of formance, ensuring compliance, and facilitating
the vessel and build up the crew’s reporting?
confidence in the operation of The efficient interoperability of the new
energy-saving devices. digital platforms requires robust mod-
els to ensure accurate ship performance
monitoring while simultaneously facil-
itating reporting through continuous
and granular data flows, either in noon
reports or high-frequency data. Assuming
the reliability and abundance of high-fre-
quency sensors and data sources, the
granularity of the reporting sequence
from the crew on these digital platforms
can be efficient and robust while ensuring
optimal vessel performance monitoring
and facilitating reporting.
of their resources to trial installations in The human element certainly increases
order to investigate the feasibility and potential risks in the reporting proce-
efficiency of a fleet optimisation strate- dure. However, proper and accurate
gy’s technical and operational measures. crew training in the new digital platforms
If these trial installations prove success- can support the crew during the transi-
ful, the company can then proceed to tional period from conventional to more
install and implement the examined advanced systems.
measures across its entire fleet.
What, in your view, is the future of connectivity
How can shipping capitalise on AI? Are there any and telecommunications in the shipping industry?
specific applications particularly relevant to fleet In recent years, digitalisation has been
optimisation? accelerating within the shipping indus-
Artificial Intelligence (AI) applications try, with connectivity and telecommuni-
as a broadband superset of machine cation being at the forefront. In coming
learning are applicable to the shipping years, data flow information derived from
industry. Specifically, we utilise genetic vessels is expected to be more secure in
algorithms with high-fidelity meteoro- terms of granularity and continuity. Inter-
logical and oceanographic data, along net coverage is also central in this pro-
with navigational databases, to achieve cess, enabling the optimisation of vessel
multi-objective voyage optimisation. performance and facilitating the smooth
Μachine learning methodologies and transition to new digital platforms.
statistical learning techniques are also

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