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)
250
Indonesiaasanarchipelagiccountryusesshipsas
theirmeansoftransportationtoconnect eachisland.
Moreover, the vision of Indonesia is to become a
globalmaritimeaxis.Incontrast,thequalityofsafety
atsea in Indonesia is still low, which has led to the
occurrence of several accidents
(Bowo, Furusho,
2016).
The objective of this research is to compare the
HRAs in the marine industry, and to find a proper
methodology that can be applied to the marine
industry,especiallywithrespecttoaccidents.
The remainder of this paper is organized as
follows. The second chapter is a
literature review
about HRA generations and the characteristics of
marine accidents. The third chapter presents an
explanation about Human Error Assessment and
ReductionTechnique(HEART)methodologyandthe
4M Overturned Pyramid (MOP) model. Results of
thispaperwillbepresentedinthefourthchapter;and
the discussion and conclusions will be
presented in
chapterfiveandchaptersix,respectively.
2 LITERATUREREVIEW
2.1 HRAGenerations
Since the 20
th
century, researchers have been
developing HRA. Essentially, HRA has three
functions,namelytheidentificationofhumanerrors,
thepredictionoftheirlikelihood,andthereductionof
their likelihood, if required (Kirwan, 1996). Those
HRA functions were developed to assess the
probabilityoferrorinnuclearpowerplants.
Hollnagel summarized HRA
development from
1975–2005,asshowninFigure1below.Inthe1980s,
the development of HRAs had the largest growth,
when compared with other years. Moreover, this
period represents the first generation of HRAs.
Furthermore, in the 1990s, there was also
developmentofHRAs,althoughnotassignificantas
in the
1980s. Moreover, this period represents the
launchoftheso‐calledsecond‐generation(Hollnagel,
2005).
Figure1.CumulatedNumberofHRAMethodspublication
(Hollnagel,2005).
However,as ofrecent, allindustrial sectors,such
astherailway,airplane,medical,andmarinesectors,
use HRA to identify the errors that cause accidents
andincidents.Therefore,thedevelopmentofHRAsis
still ongoing. Owing to the large number of HRA
methodologies, the methodologies are classified by
the differences of
viewpoints of problem‐solving, as
firstgenerationandsecondgeneration.
2.1.1 FirstGeneration
ThefirstgenerationofHRAwasfirstdevelopedin
the 1980s. These HRAs were developed to help risk
assessors predict and calculate the likelihood of
human error. Furthermore, the first‐generation
methods focus on the skill and
rule base level of
human action, and are often criticized for failing to
consider aspects such as the impact of context,
organizationalfactors,anderrorsofcommission(Bell
& Holroyd, 2009). The methodologies which are
includedinthefirstgenerationareasfollows:THERP
(Techniquefor Human Error Rate Prediction), ASEP
(Accident Sequence Evaluation Program), HEART
(HumanErrorAssessmentandReductionTechnique),
andSPAR‐H(SimplifiedPlantAnalysisRiskHuman
ReliabilityAssessment).
2.1.2 SecondGeneration
More modern methods, the second generation of
HRAiscarefullyconsideredandmodelstheinfluence
ofcontextontheerror.Moreover,itutilizesfindings
and insights
from the then developed cognitive
movement (Boring, 2012). The development of this
secondgenerationbeganinthe1990s,andisgoingto
bedevelopedevenfurther.ATHEANA(ATechnique
for Human Event Analysis) and CREAM (Cognitive
Reliability and Error Analysis Method) are included
inthesecondgeneration.
2.2 MarineAccidentsCharacteristics
In all sectors, the development of accident analysis
canbedeterminedusingthreetechniquesofanalysis:
sequential techniques, epidemiological techniques,
and systemic techniques (Underwood & Waterson,
2013).
There are several differences between the three
techniques,asdescribedbelow.
2.2.1 SequentialTechniques
This is a simple, linear cause‐and‐effect model,
where accidents are modeled as a series of falling
dominos,which occurinaspecificand recognizable
order(U.S.DepartmentofEnergy,2012).Thismethod
describes the events leading up to accidents, using
physicalcomponentfailuresortheactionsofhumans
(Leveson,2011).
2.2.2 EpidemiologicalTechniques
An epidemiological technique can also
be
recognized as the latent failure model. With this
technique, accidents are seen as a combination of
unsafe acts (active failures) and unsafe conditions
(latentconditions)(U.S.DepartmentofEnergy,2012).
2.2.3 SystemicTechniques
A systemic technique describes losses as the
unexpected behavior of a system. In other words,