Use of HFACS–FCM in fire prevention modelling on board ships Omer Soner a,⇑ , Umut Asan b , Metin Celik c a Department of Maritime Transportation and Management Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey b Department of Industrial Engineering, Istanbul Technical University, Macka 34367, Istanbul, Turkey c Department of Marine Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey a r t i c l e i n f o Article history: Received 5 January 2015 Received in revised form 24 February 2015 Accepted 11 March 2015 Keywords: Ship safety management Root cause analysis Fire prevention FCM HFACS a b s t r a c t This research proposes a proactive modelling approach that combines Fuzzy Cognitive Mapping (FCM) and Human Factors Analysis and Classification System (HFACS). Principally, the suggested model helps predicting and eliminating the root causes behind the frequently repeating deficiencies on board ships. Sup ported with qualita tive simu lations, the HFACS–FCM model is dem onstrate d on a fire rela ted de fic ien cy sample database. Th e fin din gs in dicateth at th e roo t cau ses of a fir e related de fici ency on boa rd ship migh t be reve aled in various levels such as unsa fe acts , pre-cond ition s for unsafe acts, uns afe supervision, and organization influences. Considering the determined root causes and their priorities, the Safe Ship System Mechanism (SSSM), Safe Ship Ope ratio n Mechanism (SSOM), and Safe Ship Exec ution Mechanism (SSEM) are con stitu ted. Con sequ ent ly, the pap er has add ed valu e to both predicting the root causes and enhancing fire-fighting potential which provides reasonable contributions to safety improvements at sea. 2015 Elsevier Ltd. All rights reserved. 1. Introduction Fire accident is one of the most challenging and fatal events on board ships. The control of fire in such operational environments req uires imm edia te resp onse and grea t effort. Thu s, rele vant systems should be timely functioning under operator (ship crew) control without any interruption ( Kuo and Chang, 2003). In addi- tion to firefighting systems on board ships, proactive approaches against fire related non -con for mit ies, accidents and haz ardous situations ar e req ui red to avoid the op era tio na l pr oblem s. Schröder-Hinrichs et al. (2011) argued that organizational factors were usually not noticed in maritime accident investigations offires and explosions in machinery spaces, instead, lower echelons such as unsafe acts are just considered. In the current situation, Port State Control (PSC) organizations have attemp ted to e nable standar d level of achievements insafety, security , and environmental aspects. The strict control requir e- me nts at int ernatio nal level enf orce the ship -ow ner s and ope rato rs and thereby ship management companies to ensure compliance with the relevan t rules and regulat ions.Li and Zheng (2008)made sug gest ions on the imp rove men t of the enf orcement of PSC. Otherwise, the lack of effective and systematic implementations of the requirements might cause deficiencies, nonconformities or major -nonc onformities on board ships, which eventuate in deten- tion. Besides its cost and unexpected catastrop hic consequence s, it aff ects th e reputation of sh ip managem en t co mpanies in the glo ba l market. Above all, reoccurrence of the mentioned issues (i.e. fire accident s) will threat the sustainable maritim e transpo rtation. A brief review of PSC survey results on shipboard deficiencies highlights the significance of the problem. For example, according to the Tokyo MOU PSC annual report, fire safety measures consti- tute 18% of the total deficiencies (Tokyo MoU, 2013), while Paris MOU statistics about fire safety related deficiencies illustrate 15% (Par is Mo U, 2012). Th ese st atistical re po rt s ser ve also as key documents for following the distribution of several deficiencies at ope rational leve l. F or i nst ance, Det Norske Ver itas (DN V), a pre s- tigi ous me mbe r of International Asso ciat ion of Clas sific atio n Societies (IACS), reported that fire safety measures, with 19%, is the most common category of defi cie nc ies de alt with (DNV, 2012). Another report pub lished by American Bureau Ship ping (ABS) illustrates that fire safety measures, as the most common defi cien cy, has the ratio of 16% compared to oth er cate gor ies (ABS, 2012). Finally, the report published by Nippon Kaiji Kyokai (Clas sNK or NK ) emphasises th e impo rt an ce of fir e safet y measu re s rela ted defi cien cies wh ose per cen tage is arou nd 24% (ClassN K, 2012). In order to prevent fire risk on board ships, the ship managers and responsible decision makers should ensure effective imple- mentation of a safety man agem ent syst em in accordance wit h http://dx.doi.org/10.1016/j.ssci.2015.03.007 0925-7535/ 2015 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +90 216 3951064; fax: +90 216 3954500. E-mail address: [email protected](O. Soner). Safety Science 77 (2015) 25–41 Contents lists available at ScienceDirect Safety Science journal homepage: www.elsevier.com/locate/ssci
Transcript
7/26/2019 Use of HFACS–FCM in Fire Prevention Modelling on Board Ships
Use of HFACS–FCM in fire prevention modelling on board ships
Omer Soner a,⇑, Umut Asan b, Metin Celik c
a Department of Maritime Transportation and Management Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkeyb Department of Industrial Engineering, Istanbul Technical University, Macka 34367, Istanbul, Turkeyc Department of Marine Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey
a r t i c l e i n f o
Article history:
Received 5 January 2015Received in revised form 24 February 2015Accepted 11 March 2015
Keywords:
Ship safety managementRoot cause analysisFire preventionFCMHFACS
a b s t r a c t
This research proposes a proactive modelling approach that combines Fuzzy Cognitive Mapping (FCM)and Human Factors Analysis and Classification System (HFACS). Principally, the suggested model helpspredicting and eliminating the root causes behind the frequently repeating deficiencies on board ships.Supported with qualitative simulations, the HFACS–FCM model is demonstrated on a fire relateddeficiency sample database. The findings indicate that the root causes of a fire related deficiency on boardship might be revealed in various levels such as unsafe acts, pre-conditions for unsafe acts, unsafesupervision, and organization influences. Considering the determined root causes and their priorities,the Safe Ship System Mechanism (SSSM), Safe Ship Operation Mechanism (SSOM), and Safe ShipExecution Mechanism (SSEM) are constituted. Consequently, the paper has added value to bothpredicting the root causes and enhancing fire-fighting potential which provides reasonable contributionsto safety improvements at sea.
2015 Elsevier Ltd. All rights reserved.
1. Introduction
Fire accident is one of the most challenging and fatal events onboard ships. The control of fire in such operational environmentsrequires immediate response and great effort. Thus, relevantsystems should be timely functioning under operator (ship crew)control without any interruption (Kuo and Chang, 2003). In addi-tion to firefighting systems on board ships, proactive approachesagainst fire related non-conformities, accidents and hazardoussituations are required to avoid the operational problems.Schröder-Hinrichs et al. (2011) argued that organizational factorswere usually not noticed in maritime accident investigations of fires and explosions in machinery spaces, instead, lower echelonssuch as unsafe acts are just considered.
In the current situation, Port State Control (PSC) organizationshave attempted to enable standard level of achievements in safety,security, and environmental aspects. The strict control require-ments at international level enforce the ship-owners and operatorsand thereby ship management companies to ensure compliancewith the relevant rules and regulations. Li and Zheng (2008) madesuggestions on the improvement of the enforcement of PSC.Otherwise, the lack of effective and systematic implementationsof the requirements might cause deficiencies, nonconformities or
major-nonconformities on board ships, which eventuate in deten-tion. Besides its cost and unexpected catastrophic consequences, itaffects the reputation of ship management companies in the globalmarket. Above all, reoccurrence of the mentioned issues (i.e. fireaccidents) will threat the sustainable maritime transportation.
A brief review of PSC survey results on shipboard deficiencieshighlights the significance of the problem. For example, accordingto the Tokyo MOU PSC annual report, fire safety measures consti-tute 18% of the total deficiencies (Tokyo MoU, 2013), while ParisMOU statistics about fire safety related deficiencies illustrate 15%(Paris MoU, 2012). These statistical reports serve also as keydocuments for following the distribution of several deficienciesat operational level. For instance, Det Norske Veritas (DNV), a pres-tigious member of International Association of ClassificationSocieties (IACS), reported that fire safety measures, with 19%, isthe most common category of deficiencies dealt with (DNV,2012). Another report published by American Bureau Shipping(ABS) illustrates that fire safety measures, as the most commondeficiency, has the ratio of 16% compared to other categories(ABS, 2012). Finally, the report published by Nippon Kaiji Kyokai(ClassNKor NK) emphasises the importance of fire safety measuresrelated deficiencies whose percentage is around 24% (ClassNK,2012).
In order to prevent fire risk on board ships, the ship managersand responsible decision makers should ensure effective imple-mentation of a safety management system in accordance with
http://dx.doi.org/10.1016/j.ssci.2015.03.007
0925-7535/ 2015 Elsevier Ltd. All rights reserved.
the International Safety Management (ISM) Code. The main objec-tive of the system is to improve safety level on board ships whilepreventing human injury, loss of life, and damage to marineenvironment. According to recent amendments to the ISM Code,identifying measures intended to prevent recurrence of deficien-cies and near misses has become one of the core issues. It intro-duces a relatively new concept called preventive action planning,
which strictly requires detailed analysis in order to makeconsistent decisions on actions to be taken (ISM Code, 2010).There is no doubt that systematic analysis on causation is the mostessential aspect of preventive action practices along with the shipoperations and management.
This study proposes a novel preventive action planningapproach to enhance fire safety measures on board ships. The restof this paper is organized as follows: The current section discussedthe significance of controlling and monitoring the fire relateddeficiencies on board ships. Then, a wide range of maritime safetyliterature is reviewed in Section 2. Section 3 introduces the concep-tual framework of the model which is based on combination of Fuzzy Cognitive Mapping (FCM) and The Human Factors Analysisand Classification System (HFACS). To demonstrate the suggestedmodel, a case study concerning a fire related deficiency sampledatabase is analysed in Section 4. In the final section, the researchoutcomes and potential contributions through ship fire safety pre-paredness are extensively discussed.
2. Literature review
2.1. Maritime literature review
Maritime safety is a significantly important element of sustain-ability in world trade since maritime transportation has been car-rying 80% of the global cargo (Asariotis et al., 2013). Furthermore,maritime transportation system has long been monitored byInternational Maritime Organization (IMO), whose primarypurpose is to maintain comprehensive regulatory framework at
international level (Wieslaw, 2012) Shipping might be consideredas one of the most dangerous and global industries of the world.The shipping industry seeks for a modern and user friendly safetysystem since the maritime accidents might cause catastrophic con-sequences (Hetherington et al., 2006). Hence, the contribution tosafety at sea are highly expected and appreciated by maritimesociety. This section draws together a wide range of existingliterature on a range of issues on maritime safety. These issuesinvolve the following four important aspects of maritime safety:(i) regulatory framework, (ii) human factor, (iii) technologicalimprovements and (iv) methodological approaches.
From the regulatory framework perspective, various conven-tions have been developed and adopted by IMO in order to pro-mote the safety, security, and environmental sensitiveness in
shipping industry. However, the effects of the mentioned conven-tions on shipping industry have been argued and discussed bymaritime researchers, rule-makers, and responsible executives.For instance, Vanem and Skjong (2006) criticized the regulationrequirements along with the evacuation procedures in which effec-tive assessment is not possible to conduct. On the other hand,Tzannatos and Kokotos (2009) investigated ship accident duringthe pre- and post- ISM period so as to assess the effectiveness of the ISM Code. In addition, Knapp and Franses (2009) studied onthe major international conventions regarding safety, pollution,search and rescue measures. To strength the safety relatedregulations, Celik (2009) proposed a systematic approach to evalu-ate the compliance level of the ISMcode with the ISO 9001:2000 toadopt an integrated quality and safety management system. The
study illustrated that safety management system implementationson board ships can be enhanced via quality management
principles. Besides international conventions’ enforcement,Knudsen and Hassler (2011) believed that there are additionalefforts required to challenge with the main causes of the ship acci-dents which have not been reduced to a desired ratio. Karahalioset al. (2011) also conducted research to perform a cost-benefitsanalysis along with the maritime regulations. Furthermore,Schinas and Stefanakos (2012) investigated feasibility of the
environmental measurements defined within InternationalConvention for the Prevention of Pollution from Ships (MARPOL).Human factor is another core topic in maritime safety studies.
To find out the role of human element in safety at sea, Hee et al.(1999) conducted one of the pioneering researches on maritimesafety assessment. Furthermore, Hetherington et al. (2006) con-cluded a research that reviews a number of studies to eliminatethe human errors in ship accidents. As another study, Celik andEr (2007) examined the potential role of design errors which trig-ger the human error in shipboard operations. To enhance humanfactor analysis, HFACS was utilized in order to make quantitativeassessment of shipping accident (Celik and Cebi, 2009). To clarifythe exact reasons, Wang et al. (2013) proposed a new method inorder to enable accident causations. Recently, Akhtar and Utne(2014) investigated human fatigue effects to bridge team manage-ment demonstrated with ship grounding case.
Besides human element, technological improvements and mar-itime innovations are one of the significant aspects of maritimesafety. At system safety level, Tzannatos (2005) investigatedprobable equipment failures and their effects in terms of reliabilitymonitoring of the Greek coastal passenger fleet. Moreover, Eideet al. (2007) developed an intelligent model to prevent oil spill asanother catastrophic event at sea. Beyond, Lun et al. (2008)investigated the technological adoption to manage securityenhancement especially in container transport. In a further study,Lambrou et al. (2008) introduced the Intelligent MaritimeEnvironment (i-MARE) framework and technological platform forcargo shipping. Vanem and Ellis (2010) investigated the feasibilityof adapting a novel on board passenger monitoring and communi-
cation system based on RFID technology which provides a decisionsupport in emergency situations. Similarly, LiPing et al. (2011) tookthe advantage of the video surveillance technology for safe nav-igation. It can be clearly seen that new technologies have potentialto enhance safety at sea; however, it is still a great deal to managethe gaps among regulation implementations, human element andrecent technologies in order to increase the overall utility of suchattempts in safety improvements.
Methodological approaches, as the fourth important aspect of maritime safety, have been playing a key role in transformingoperational data, facts, and figures into useful information alongwith safety enhancement. With this purpose in mind, severalresearchers, such as Rothblum (2000), O’Neil (2003), Darbra andCasal (2004), and Toffoli et al. (2005), have conducted statistical
analyses, especially, on accidents and their prevention. On theother hand Lee et al. (2001), Wang and Foinikis (2001), Wang(2002), Lois et al. (2004) used formal safety assessment (FSA)particularly supported with well-known techniques. Specifically,Bayesian network modelling has been utilized in maritime safetyrelated studies (Antao et al., 2008; Trucco et al., 2008; Kelangathet al., 2011; Zhang et al., 2013; Hänninen et al., 2014) in order todeal with the inherent uncertainty and complexity in maritimesafety problems. Moreover, various methods derived from fuzzyset theory have been cited (Sii et al., 2001; Balmat et al., 2009,2011; Abou, 2012; John et al., 2014) in maritime safety literature.There are also some hybrid quantified models (Celik and Cebi,2009; Celik et al., 2010; Pam et al., 2013; Akyuz and Celik,2014a,b; Karahalios, 2014; Wang et al., 2014) that provide satisfac-
tory approaches to the specified operational problems in maritimesafety context.
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Despite the various contributions at international level, thenumbers of ship accidents or detention rates have still not reachedtheir desired levels. Moreover, the number of maritime safety stud-ies in the literature has been increasing at a relatively slow rate.Researchers will need to focus more on operational fieldworksand specific cases, as it appears to be the next phase of maritimesafety studies. This paper, hence, attempts to investigate the root
causes of fire safety related deficiencies in order to provide anapplicable proactive model for ship operation and management.Considering both the theoretical and practical insights provided,this study makes valuable contributions to the maritime safetyliterature.
As the proposed model integrates Fuzzy Cognitive Mapping(FCM) and The Human Factors Analysis and Classification System(HFACS), the following sections introduce the theory of bothmethods.
2.2. Human Factor Analysis and Classification System (HFACS)
HFACS is initiated from Swiss Cheese Model by Reason (1990).HFACS is a comprehensive tool to analyse the human contributionto catastrophic events, accidents, hazardous occurrences, anddeficiencies. Basically, HFACS investigates active failures and latentconditions at four levels. Active failures are sets of inappropriateactions by operators while latent conditions deal with the differentlevels of organization (Chauvin et al., 2013). The described fourlevels in HFACS are (i) unsafe acts, (ii) pre-conditions for unsafeacts, (iii) unsafe supervision, and (iv) organization influences. If the vulnerabilities in different levels cannot be controlled, theoccurrence probability of accidents might be arisen. HFACS wasfirst developed for the aviation accident investigation (Shappelland Wiegmann, 2000, 2001). In the last decade, the HFACS modelwas not only successfully applied in the aviation industry byShappell et al. (2007), but also in the railway (Reinach and Viale,2006) and mining industry (Patterson and Shappell, 2010). Forinstance, the study of Rothblum et al. (2002) was the pioneer scien-
tific research that aimed to investigate human factor in maritimeaccidents. Celik and Cebi (2009) combined HFACS with fuzzy settheory in order to provide a quantitative approach to analyse a sin-gle accident case with error distribution in accordance with theoperational evidences given in accident reports. Recently, Akyuzand Celik (2014a) used HFACS supported with cognitive mappingapproach to confirm the dependencies between causation factors.
2.3. Fuzzy Cognitive Map (FCM)
Fuzzy Cognitive Mapping, advanced by Kosko (1986) from theclassical cognitive mapping method, is an illustrative causativerepresentation of complex systems and can be used to model andmanipulate the dynamic behaviour of systems (Papakostas et al.,
2008). Combining elements of fuzzy logic and neural networks,fuzzy cognitive mapping has been proven to be a promisingmethod for making inferences in cases with substantial uncer-tainty, imprecision and vagueness (Vasantha Kandasamy andSmarandache, 2003; Tsadiras, 2008). Compared to expert systems,fuzzy cognitive maps (FCMs) are relatively quicker and easier toacquire knowledge (Papageorgiou and Stylios, 2008). FCMs havebeen successfully applied in a variety of scientific areas, suchsupervisory control systems (Stylios and Groumpos, 2000), dis-tributed systems (Stylios et al., 1997), decision support system(Tsadiras et al., 2003), organizational behaviour (Craiger et al.,1996), medical informatics (Papageorgiou, 2011), marketing(Nasserzadeh et al., 2008) and risk analysis (Lazzerini andMkrtchyan, 2011), among others.
Most of the FCM models are constructed basically by expertknowledge and experience in the operation of the system.
Questionnaire survey, documentary coding and interviews arethe most common ways for this purpose. FCMs can be developedfor a single expert or a group of experts, where the latter has thebenefit of improving the reliability of the final model (Yamanand Polat, 2009). The aggregation of knowledge from multipleexperts is a relatively simple process in fuzzy cognitive mapping(Stach et al., 2005). Each expert describes every interconnection
with linguistic variables (weights) which are later composed (e.g.by fuzzy arithmetic or defuzzification methods) to produce thecombined map. Several procedures have been proposed forcombining multiple FCM models into a single one (see e.g. Kosko,1992; Stylios and Groumpos, 2000).
A FCM can be represented either as a graph, consisting of concepts (e.g. entities, states, or characteristics of the system)and weighted interconnections between these concepts, or as anadjacency matrix, which has entries wij’s indicating the directrelationship between concept i and concept j. Fig. 1 (Asan et al.,2011) illustrates a simple FCM consisting of five concepts C i(i = 1, . . ., 5) where wij represents the influence degree from causeC i to effect C j. FCM does not allow any direct connections betweena concept and itself, thus all wii elements equal to zero. All other wij
elements take values in [1, 1] and Papageorgiou (2011) explainsthe meaning of these values as;
w ij>0 indicates a causal increase (i.e., C j increases as C i increases,and C j decreases as C i decreases).
w ij<0 indicates causal decrease (i.e., C j decreases as C i increases,and C j increases as C i decreases).
w ij=0 indicates no causality.
Once the FCM is constructed it is used to perform qualitativesimulations in order to predict possible changes and to observewhether the system converges toward a steady state. During thesimulations a model can reach three possible states that are listedbelow (Kosko, 1997):
A steady state where the output values are stabilizing at fixednumerical values.
A limit cycle behaviour where the concept values are falling in aloop.
A chaotic behaviour where concept values wanders foreverwithout apparent structure or order.
A more formal definition of the iterative procedure can bedescribed as follows. The FCM should be first initialized. In otherwords, the activation level of each concept takes a value basedon expert opinion about its current state or measurements from
the real system. Let each concept take its initial value as Aðt Þ
i , where
Ai is the value of concept i at step t , and simulated iteratively. Thenthe value of each concept in an iteration is calculated as
(Papageorgiou et al., 2009)
Aðt þ1Þ
i ¼ f Aðt Þ
i þXn
j ¼ 1;
j–i
Aðt Þ
j w ji
0BBBBB@
1CCCCCA ð1Þ
In Eq. (1), Aðt þ1Þi is the value of concept at step (t + 1), Aðt Þ
i is the value
of concept at step (t ), w ji is the weight of interconnection between C jand C i. f is the threshold function that reduces the result of the mul-tiplication into a normalized range (within [0, 1] or [1, 1]). The
most common activation functions are (Tsadiras, 2008): bivalent,trivalent, sigmoid, hyperbolic tangent.
O. Soner et al./ Safety Science 77 (2015) 25–41 27
Utilizing the HFACS and FCM model, a new framework on firesafety related deficiency analysis is introduced. Conceptual frame-work of the proposed model is presented in Fig. 2. Principally, it
performs a great extent of proactive safety modelling throughdeficiency causation, root cause identification, prioritization, and
preventive action generation. The database source in the model,gathered from ship operational level, might incorporate PSCinspections reports, company audits reports, near-miss reports,hazardous occurrences reports, accident reports, and vetting sur-veys reports. Then, deficiency database are distributed to HFACSto ensure satisfactory deficiency causation where FCM techniquehighlights the relationships among the designated contributing
causes of fire related deficiencies on board ships. Considering theinitial results, it is decided whether a contributing cause is a root
DEFICIENCIES
(Fire safety)
PSC inspections
Company audits
Near-miss reports
Accident reports
Vetting survey results
Other
DATA
S h i p m a n a g e m e n t c o m p a n y
Ship fleet
Deficiency causation
(HFACS)
Root cause
identification
(FCM)
ANALYSIS
Preventive action
planning
INTEGRATION
Root cause prioritization
and verification
(Simulation)
R o o t c a u s e a n a l y s i s
Fig. 1. A hypothetical FCM model and the corresponding adjacency matrix (Asan et al., 2011).
C1
C2
C5
C3
C4
w12
w23
w51
w42
w34
w54
w15
w25
Fig. 2. Conceptual framework of the model.
28 O. Soner et al./ Safety Science 77 (2015) 25–41
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cause or not. Finally, the integration phase enables preventiveaction adaptation at ship operational level. It is considered as aphase to promote ship safety against fire related occurrences atsea.
3.2. Modelling causes of fire related deficiencies
Before preventive actions are suggested against deficiencies, itis crucial to identify the initiating causes of the current causalchain that leads to fire related deficiencies on board ships.Dealing with only a small number of these root causes will reason-ably prevent many of the undesirable deficiencies. In order to iden-
tify root causes and their priorities, a fuzzy cognitive map isconstructed and analysed as summarized below (see Fig. 3).
3.2.1. Step 1: Identification of causal relationships
As previously explained, the causes (i.e. concepts in the fuzzycognitive map) are identified by reviewing diverse reports onfire related deficiencies and employing the human errorframework HFACS. In this step, the causal relationships betweenconcepts are identified by providing domain experts orderedpairs of concepts in a questionnaire format (see Fig. 4). Thisallows systematic examination of all relationships. Here, acausal relationship is characterized with vagueness, sinceit represents the influence of one qualitative concept on anotherone and will be determined using linguistic terms (Papageorgiouand Stylios, 2008). In this way, an expert transforms his
knowledge and experience on the behaviour of the system intoa fuzzy weighted graph.
Identification of Causes
of Fire Related Deficiencies
Identification of Causal
Relationships
Analysis of Direct
Relationshipsa
Analysis of Indirect
Relationships b
Inference through
qualitative Simulationsc
Aggregation of Weights from
Multiple Experts
Decision on the Final
List of Root Causes
c FCM Simulation
Algorithm, What-
If Scenarios
Reports on Fire Related
Deficiencies and HFACS
Questionnaire Survey,
Linguistic Variables
b Reachability Matrix, Normalization,
Outdgree, Indegree,
Impulse Index
D a t a C ol l e c t i on
An al y s i s
List ofPotential
Root Causes
a Adjacency Matrix,
Outdegree, IndegreeImpulse Index
Defuzzification
Max/Sum Method
Center of Gravity
Tools and Techniques:
List ofPotential
Root Causes
Priorities
Fig. 3. The flow of the proposed FCM methodology.
Fig. 4. Ordered pairs of concepts in a questionnaire format.
O. Soner et al./ Safety Science 77 (2015) 25–41 29
The linguistic variable used in this study, expressing the degree
of the causal relationship between two concepts, takes values inthe universe U ¼ ½0; 1 and consists of the term set {does not affectat all (N), affects weakly in a positive way (W), affects weakly-moderately in a positive way (WM), affects moderately in a posi-tive way (M), affects moderately-strongly in a positive way (MS),affects strongly in a positive way (S)}. Using these six linguisticterms, an expert can describe in detail the influence of one concepton another and can discern between different degrees of influence.Fig. 5 depicts the membership functions of the fuzzy sets that areused to characterize these terms.
3.2.2. Step 2: Aggregation of individual weights and defuzzification
In order to improve the reliability of the final model, a groupmap is developed. The weights obtained from multiple experts
are combined to produce the overall linguistic weights and thegroup adjacency matrix. The well-known SUM or MAX methodsin fuzzy logic can be employed for this purpose. Fig. 6 providesan example of the MAX operation where the two linguistic terms‘‘moderately-strongly’’ and ‘‘strongly’’ are aggregated. If theexperts have different priorities of importance, the differentinfluences are reflected in the results through multiplying the mem-bership functionl suggested by thekth expertby the correspondingcredibility weight (Stylios and Groumpos, 2004; Saaty, 2004).
Once the overall linguistic weights are obtained, they are trans-formed to numerical (crisp) values using the Center of Gravity(CoG) defuzzification method. CoG is computed from the followingequation (Ross, 2004):
z
¼R l~wij
ð z Þ z dz R l~wij
ð z Þdz ð2Þ
where R
denotes an algebraic integration. The transformed numeri-
cal values will be within the range [0,1]. The same procedure isapplied to all the causal relationships among the n concepts of the map.
3.2.3. Step 3: Identification of potential root causes (analysis of direct
relationships)
As we aim to identify root causes (i.e. initial causes in a causalmap), the role of each concept in the map needs to be carefullyexamined. An adjacency matrix W allows analysing thecontribution of a concept in the map and articulates how thisconcept is connected directly to other concepts (Kosko, 1986).Each variable is defined by its outdegree (od) and indegree (id).Outdegree shows the cumulative strengths of connections exitingthe concept and is expressed as the row sum of absolute weightsof a concept in the adjacency matrix (Nozicka et al., 1976;Özesmi and Özesmi, 2004).
odðiÞ ¼Xn
j¼1
jw jij ð3Þ
On the other hand, indegree shows the cumulative strength of
connections entering the concept and is expressed as the columnsum of absolute weights of a concept in the adjacency matrix(Nozicka et al., 1976; Özesmi and Özesmi, 2004)
idðiÞ ¼Xn
j¼1
jwijj ð4Þ
Thus, a concept (i.e. a cause) that is less affected by the rest of the causal system than it has impact on it can be characterizedas a potential root cause. To identify the role of variables in a com-plex system different criteria or rules have been proposed in theliterature (Godet, 1994; Gausemeier et al., 1996; Asan et al.,2004). Here, the rules defined by Asan et al. (2004) are adaptedto the root cause identification problem. Thus, a root cause i shouldfulfil the following rules
odðiÞP xod ð5aÞ
IPI i P 2 ð5bÞ
where xod denotes the average outdegree taken over the entire con-cept set and IPI i denotes the so called Impulse Index which is calcu-lated for each concept i as follows
IPI i ¼ odðiÞ
idðiÞ ð6Þ
The maximum range of IPI i extends from 0 (no influence on thesystem) to 1 where the concept is not influenced by other con-cepts, but has an impact on others.
3.2.4. Step 4: Identification of potential root causes (analysis of indirect relationships)
Examining the adjacency matrix reveals only potential rootcauses based on the direct relationships between concepts whichare represented by causal chains of length one. However, this isnot enough to reveal the hidden root causes which sometimesgreatly influence the problem under study. Therefore, the diffusionof causal impacts through reactionpaths and loops needs also to beconsidered (Godet, 1994; Serdarasan and Asan, 2007). Theseindirect relationships, which are represented by causal chains of length greater than one, can be revealed by raising the adjacencymatrix to successive powers. The raw and column sums of eachresulting matrix (raised to a certain power) are normalized toenable a comparison among the results of successive powers. The
normalization originally developed in this paper can be expressedas follows:
Fig. 5. The membership functions describing the linguistic terms.
Fig. 6. MAX method and CoG.
30 O. Soner et al./ Safety Science 77 (2015) 25–41
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indegree values of concept i for the adjacency matrix raised to theqth power, respectively; and odðiÞ
q and idðiÞq denote the outdegree
and indegree values of concept i for the adjacency matrix raised tothe qth power, respectively. Thus, the total outdegree and indegreevalues of concept i can be calculated by the following expressions
RodðiÞ ¼ odðiÞ þ NodðiÞ2
þ .. .þNodðiÞn1
=Q ¼
XQ
q¼1
NodðiÞq
!,Q
ð9Þ
RidðiÞ ¼ idðiÞ þ NidðiÞ2
þ .. .þ NidðiÞn1
=Q ¼
XQ
q¼1
NidðiÞq
!,Q
ð10Þ
where RodðiÞ and RidðiÞ denotes the total outdegree and totalindegree of concept i , respectively and indicate the reachability of concept i. In fact, the multiplication process continues until theadjacency matrix is raised to a certain power ðQ Þ in which the con-cepts’ order proves to be stable (for more detail see Godet, 1994).The resulting indicators are used to reveal potential root causeswhich might be assumed to be unimportant in the previous analysisbut play a leading role because of indirect relationships. For thispurpose the concepts are examined according to the same rulesdescribed in Step 3.
3.2.5. Step 5: Inference through qualitative simulations
Once the direct and indirect relationships are examined, the
fuzzy cognitive map is used to perform qualitative simulations tocapture the transmission of influence along all paths and toobserve whether the system converges toward a steady state.From the steady state calculation we can get an idea of the rankingand thereby of the overall priorities of the variables in relation toeach other (Özesmi and Özesmi, 2004).
Thesimulationprocessis initialized through assigning a value in[0, 1] to the activation level of each concept, basedon experts’ opin-ion about a certain state. The value of zero indicates that a givenconcept is not present in the system at a particular iteration, whilethe value of one suggests that a given concept is present to itsmaxi-mum degree (Papageorgiou and Kontogianni, 2012). In a particulariteration, the value of each concept is determined by its previousvalue and the preceding values of all concepts that exert influenceon it through non-zero relationships (Papageorgiou, 2011). Thisiterative process does not produce exact numerical values; insteadit allows analysing the dynamic behaviour of the system.
The FCM simulation algorithm originally developed by (Kosko,1988) utilizing Eq. (1) consists of the following five stages(Papageorgiou and Kontogianni, 2012):
Stage 1. Define the initial vector A. Stage 2. Multiply the initial vector A and the matrix W . Stage 3. Update the resultant vector A at time step t + 1.
Stage 4. Consider the new vector Aðt þ1Þ as the initial vector in thenext iteration.
Stage 5. Steps 2 to 4 are repeated until Aðt þ1Þ Aðt Þ6 e ¼ 0:001
(where e is a residual describing the minimum error difference
among the subsequent concepts) or A
ðt þ1Þ
¼ A
ðt Þ
. A
ðt Þ
will be thefinal vector.
Note that the iterative method applied here is not necessarilyconcerned about the structure, but the outcome, or inference of the map (Özesmi and Özesmi, 2004).
In order to prioritize the potential root causes, identified in Step3 and 4, simulations are performed for different initial statevectors. In each ‘‘what-if’’ scenario, only one particular concept(i.e. cause) is activated by assigning a value of one to its activation
level. In this way, it is possible to observe the changes in theactivation levels of other concepts throughout the simulation.The higher the number of concepts influenced (i.e. activated) inthe early iterations by a particular concept, the more likely theconcept is a root cause.
Consequently, the decision on the final list of root causes ismade by synthesizing the results of Step 3, 4 and 5. A casestudy on a set of fire related deficiency data is conducted inSection 4.
4. Case study
4.1. Fire related deficiency sample database
Supporting the fire related deficiency database, the researchtends to a great variety of maritime sources such as DNV’ annualdeficiency report (DNV, 2012), ABS’ Reducing the Port StateDetention Factor report (ABS, 2012), Paris Mou’ Taking PSC to theNext Level Annual report (Paris Mou, 2012), Tokyo Mou’ Annualreport on PSC (Tokyo MoU, 2013), and ClassNK’ annual report onPSC (ClassNK, 2013). The field investigation addressed the fre-quently encountered fire related deficiencies on board ships. Thespecific deficiency items are categorized into twenty main groupsgiven as follows:
1. Fire-dampers.2. Emergency fire pump.3. Fire prevention.
4. Firefighting equipment and appliances.5. Fire detection.6. Fire doors within main vertical zone.7. Fixed fire extinguishing installation.8. Ready availability of firefighting equipment.9. Ventilation.
10. Inert gas system.11. Division – main zones.12. Main vertical zone.13. Personal equipment.14. Means of control (opening, closure of skylights, pumps, etc.
machinery spaces.15. Jacketed piping system for high pressure fuel lines.16. Fire control plan - all ships.
17. International shore connection.18. Main fire pumps.19. Emergency Escape Breathing device (EEBD).20. Other firefighting equipment.
The inoperable fire dampers might lead to minor or majordeficiencies. The causes of the inoperable fire dampers are inade-quate familiarization, poor maintenance, adjustment mechanicalparts, functional malfunctions, corrosion, sealing materials, flappositions, installations, etc. Another system related deficiencyon board ships is poor condition of emergency fire pump. Indetail, starting failures, self-priming issues, loss of pressure, leak-ages, remote control interruptions, electrical shortages, malfunc-tioned gauges, driven engine failures, fuel related matters are
the reasons for such deficiency item. Fire prevention measures,might cause ship detention, are highly critical aspect of the fire
O. Soner et al./ Safety Science 77 (2015) 25–41 31
safety on board ship. Furthermore, oil leaks and improper storageof combustible materials have great potential to spread of fire.The poor condition of fire-fighting equipment and appliancesare also the key reason of firefighting vulnerabilities at sea interms of leaking line and fire hose, certification incompliances,incomplete firemen’s outfits, etc. PSC audits state number of causes evidenced with disconnected or covered alarm systems,
unavailable previous testing records. Marvellously, non-functionalfire doors, keeping the fire doors in an open position, unautho-rized cuts in fire zone boundaries, blocking the emergency escaperoutes might be observed as a result of substandard ship opera-tions. The fixed fire extinguishing installation, addressing in com-pany audits and vetting reports addressed especially empty orblocked fire boxes, expired fire extinguisher and breathingapparatus. Beyond all these, quite a number of ventilationdeficiencies are reported with critical failures such as corrosionrelated malfunctioning. The surveys also pointed out performanceand condition matters in different equipment/system such asinert gas system, personal protective equipment, control systems,international shore connection, main fire pumps and emergencyescape breathing device.
4.2. Deficiency causation (HFACS)
Considering the four level of human factor such as unsafe acts,pre-conditions for unsafe acts, unsafe supervision, and organiza-tion influences, the next step is to determine causes leading to firerelated deficiencies. Reviewing the fire related deficiency sampledatabase, the possible causes in ‘‘unsafe acts level’’ are determinedas follows:
C1. Late responding to the sudden operational failure in criticalcomponents of fire system.
C2. Misperception of fire related emergency situationscomplexity.
C3. Responding to emergency fire related situations in panic.
C4. Omitted step in fire safety related procedure. C5. Neglected items in fire frightening equipment routine
inspection checklist. C6. Failed to prioritize actions to be taken during firefighting
drills. C7. Misunderstanding of fire safety procedures. C8. Unorganized responding to fire related emergency situation. C9. Violated firefighting training and practice. C10. Failed to fulfilment of designated responsibilities in fire
prevention. C11. Failed to properly use of firefighting equipment and
appliances. C12. Failed to test and maintain standby arrangements of fire
frightening alarm and equipment.
C13. Use of fire firefighting’s tool/equipment with a knowndefect.
C14. Incorrect placement of portable tools, equipment or mate-rial in firefighting system.
C15. Lack of safety culture about the use of personnel protectiveequipment.
C16. Disable or remove safe guards, warning system or safetydevices.
C17. Inappropriate team integration and discipline infirefighting.
C18. Lack of information due to poor emergencycommunication.
C19. Distributed storage of materials and spares on board ship. C20. Missing and wrong labelling on firefighting equipment and
appliances. C21. Violation of drugs and alcohol policies on board ship.
Reviewing the fire related deficiency sample database, the possiblecauses in ‘‘preconditions for unsafe acts level’’ are determined as
follows: C22. Loss of situational awareness in fire safety on board. C23. Misplaced motivation of crews on board. C24. Impaired physiological states of crews on board. C25. Lack of familiarization about fire safety. C26. Excessive self-confidence of crew members. C27. Physical fatigues of crews on board. C28. Time constraints on crew members’ reaction in operational
level. C29. Incompatible intelligence/aptitude of crews. C30. Insufficient physical capability in emergency response and
actions. C31. Failed to communicate among ship and shore based
organization in emergency situations. C32. Failed to coordinate the actions during the fire related
emergency situations. C33. Failed to conduct adequate operational planning and
briefing. C34. Failed to use all available firefighting resources. C35. Poor coordination of fire frightening equipment and
system. C36. Lack of warnings and signals about fire safety. C37. Increased concentration demands in fire related emer-
gency situations. C38. Poor safety attitudes of crew due to basic health problems
and illness. C39. Impairment of crew due to drug, alcohol or medication. C40. Other emotional overload of crews.
Reviewing the fire related deficiency sample database, thepossible causes in ‘‘unsafe supervision level’’ are determined asfollows:
C41. Failed to provide guidance’s about fire prevention system. C42. Failed to provide dynamic operational plans against fire
situation on board. C43. Failed to provide considerable level of supervision on
board. C44. Failed to provide specific firefighting training along with
different scenario. C45. Failed to ensure about qualification of crews embarkation
on board ship. C46. Failed to continuously monitoring crew performance on
board.
C47. Failed to provide adequate audit time. C48. Ill-defined rules and responsibilities in fire safety plans. C49. Ignored crew resting hours. C50. Failed to update/revise documentation in fire safety plans. C51. Failed to identify fire related hazards on board ship. C52. Failed to initiate fire safety related corrective actions. C53. Failed to report unsafe fire prevention tendencies. C54. Failed to comply with fire safety rules and regulations. C55. Failed to collect data and evidences about fire safety
measurement. C56. Lack of conditions assessment program for firefighting
equipment.
32 O. Soner et al./ Safety Science 77 (2015) 25–41
7/26/2019 Use of HFACS–FCM in Fire Prevention Modelling on Board Ships
C57. Failure to correct repeating unsafe occurrences on boardship.
C58. Lack of methodological tools and background to performtechnical safety analysis.
C59. Poor communication between supervisor and crewmembers on board ship.
Reviewing the fire related deficiency sample database, the possiblecauses in ‘‘organizational influences level’’ are determined asfollows:
C60. The integration problem of company safety policy intooperational level.
C61. Poor design of ship fire system components. C62. Ergonomic design errors in fire safety installations. C63. Lack of systematic personnel selection and recruitment
procedures. C64. Lack of managerial skill in shore-based personnel. C65. Financial resourcing/budget constraints to timely meet
running/operational expenses. C66. Insufficient scope of crew training program. C67. Ineffective promotion system for crews. C68. Purchasing of firefighting equipment and appliances in low
quality. C69. High level of documentation bureaucracy. C70. Lack of policy to monitor the required revisions in safety
procedures. C71. Inefficient fire safety communication planning. C72. Fire control plans’ inconsistencies. C73. Incorrect behaviour enforced by shipping companies. C74. Excessive time pressure due to improper operational
scheduling. C75. Lack of management tools to implement suitable preven-
tive action planning on board. C76. Lack of effective system to determine adequate risk control
options on board. C77. Management review input data incompleteness.
C78. Lack of consistent improvement decisions and follow-upactions in management review output.
4.3. Root cause analysis
In order to identify the initiating causes of the causal systemdescribed above, a fuzzy cognitive map is constructed and anal-ysed. First, the causal relationships between concepts are specifiedusing a self-administered questionnaire where domain experts areasked to indicate for each ordered pair of distinct concepts (C i, C j)whether, ceteris paribus, a change in C i has a significant impacton C j. To express the degree of the causal relationship (weights)
between two concepts the experts use the linguistic scale givenin Fig. 5. Since the number of causes considered in our FCM modelis very high (i.e. 78 distinct causes), it becomes a difficult andtedious task for experts to answer all pairwise questions (i.e.78 ⁄ (78–1) = 6006) and the likelihood of the experts to introduceerroneous data increases (Asan and Soyer, 2009). To overcome thisdrawback and make the administration of the questionnaire more
manageable, the adjacency matrix is divided into 16 distinctregions with respect to the four levels in HFACS, as shown inFig. 7. Seven different groups of experts from the academia andindustry are, then, assigned to one or more of these regions consis-tent with their area of expertise (see Fig. 7). These groups consistsof (i) Maritime researchers (Group #1), (ii) Maritime stakeholders(Group #2), (iii) Port state control officers (Group #3), (iv) Shipmanagement executives (Group #4), (v) Safety researchers(Group #5), (vi) Industrial engineers (Group #6), and (vii)Experienced seagoing officers/engineers (Group #7). This approachnot only reduces the number of questions for each group of experts, it also improves the accuracy of judgments and the overallefficiency.
Next, weights obtained from a group of experts are combinedusing the Max operator to produce the overall linguistic weightsand, thus, the group adjacency matrix. The overall linguisticweights are then transformed to crisp values using the CoGdefuzzification method. The calculations involved in the aggrega-tion and defuzzification process regarding the impact of ‘‘Insufficient scope of crew training program (C66)’’ on ‘‘Failed toprovide specific firefighting training along with different scenarios(C44)’’ in Region 15 are illustrated below. The linguistic weightsobtained from two experts regarding this causal relationship are‘‘moderately-strongly’’ and ‘‘strongly’’ (see Fig. 6). Thus, the CoGfor the overall linguistic weight is calculated as follows
z ¼
R 0:80:6
ð z 0:6Þ
0:2 zdz þ
R 0:90:8
ð z 1Þ0:2
zdz þR 10:9
z 0:80:2
zdz
R 0:8
0:6 z 0:60:2
dz þ
R 0:9
0:8ð z 1Þ0:2
dz þ
R 1
0:9 z 0:80:2
dz ¼ 0:834
Note that, in this study, it is assumed that the experts have equalweights of credibility. The adjacency matrices (for only Region 15)of both experts’ and the resulting matrix with crisp values areshown in Figs. 8–10, respectively.
In the following step, the direct relationships represented in theaggregated adjacency matrix are examined. Using Eqs. (3) and (4),the outdegree and indegree, in other words the cumulativestrengths of connections entering and exiting the concepts are cal-culated. These values, which serve to identify the role of each con-cept in the system, are depicted in Fig. 11. For example, it can besuggested that the concepts C66, C73 and C74 are highly influentialcauses, while C8, C10 and C11 are highly dependent on the rest of
Fig. 7. The partitioned adjacency matrix and the assigned expert groups.
O. Soner et al./ Safety Science 77 (2015) 25–41 33
the system. Since we are basically interested in identifying thepotential root causes, concepts deserving this characteristic shouldfulfil the rules provided in Eqs. (5a) and (5b). In this particular case,the average outdegree taken over the entire concept set is found to
be approximately 13.6. Accordingly, for concept i , if odðiÞP 13:6and IPI i P 2 then the concept will be considered as a potential rootcause. These rules are depicted in Fig. 11, where the dashed linerepresents IPI ¼ 2 and the solid line represents the average outde-gree. For example, C66 is a potential root cause, sinceodð66Þ ¼ 28:1, idð66Þ ¼ 4:8 and IPI 66 ¼ 28:1=4:8 ¼ 5:85. Table 1summarizes the results of the direct relationships analysis.Finally, according to the direct relationship analysis the potentialroot causes identified are C21, C26, C48, C49, C58, C62, C64, C66,C67, C71, C72, C73, C74, and C75.
A similar classification is performed in the analysis of indirectrelationships. Here, the diffusion of causal impacts through reac-tion paths and loops are considered to explore hidden root causes.To do this, the adjacency matrix is raised to successive powers. In
this study, the adjacency matrix is raised to the sixth power ðq ¼ 6Þ
where the concepts’ order proves to be stable. The outdegree andindegree values in each resulting matrix ðq ¼ 1; . . . ;6Þ are thennormalized to enable a comparison among the results of successivepowers. For example, the normalized values of C66 for q ¼ 2 are
calculated using Eqs. (7) and (8) as follows
Nodð66Þ2
¼ maxi¼1...78fodðiÞg odð66Þ2
maxi¼1...78fodðiÞ2
g¼ 33:3 342:3
513:2 ¼ 22:2
Nidð66Þ2
¼ maxi¼1...78fidðiÞg idð66Þ2
maxi¼1...78fidðiÞ2
g¼ 37:3 56:1
496:4 ¼ 4:2
Consequently, the total outdegree and indegree values of C66,which indicate the reachability of this concept, is calculated asfollows
The total outdegree and indegree values of the concepts aredepicted in Fig. 12. To identify the potential root causes, the samerules suggested in the direct relationships analysis are employedhere. In other words, those concepts fulfilling the rulesRodðiÞP 11:9 and IPI i P 2 are considered as potential root causes(see Fig. 12). Table 2 summarizes the results of the indirect relation-ships analysis, where C48, C58, C64, C65, C66, C67, C71, C72, C73,and C74 are labeled as potential root causes. Notice that C65 is
one hidden root cause which is supposed to be unimportant withrespect to the direct relationships. Therefore, comparing the resultsof the two analyses can help to confirm the importance of certainconcepts as potential root causes and can reveal hidden root causeswhich are previously thought to be unimportant but play a criticalrole because of indirect impacts.
In the final step of root cause analysis, qualitative simulationsare performed to analyze the transmission of influence along allpaths and observe changes initiated by the root causes. These sim-ulations give an idea of the overall priorities of potential rootcauses determined in the previous two steps. In this study, 78alternative what-if scenarios are considered for the simulation of the causal system. In each scenario, a FCM is first initialized, i.e.the activation level of each concept in the map takes on a valuein the set {0,1} based on the choice of the concept to be analysed
for its initiating role in the causal system. For example, Að0Þ1 ¼ [0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 00 0 0 0 0 0 0 0 0] represents the initial vector state where onlythe concept ‘‘Insufficient scope of crew training program’’ isactivated/fired. Then the concepts are set free to interact accordingto Eq. (1); here, the hyperbolic tangent is used as the thresholdfunction. The iterations are repeated until the t = 4, where
Aðt þ1Þ
i Aðt Þ
i 6 e ¼ 0:000001 for all i. Fig. 13 depicts the dynamic
behaviour of the concepts for scenario 66, where only C66 is acti-vated in the initial state.
The results of the 78 different scenarios suggest that the systemconverges toward a steady state in maximum four iterations, and
in all scenarios only 25 causes reach an activation level of exact1 (the rest ends up between 0.98–0.99). A critical indicator in these
simulations is the number of concepts influenced in the early itera-tions by a particular concept. Those concepts influencing a highernumber of concepts in the early iterations are supposed to be morelikely a root cause. Consequently, the decision on the final list of root causes is made by synthesizing the results of Step 3, 4 and5. The results are provided in Table 3. The priorities are determinedbased on the averages of rank orders of the scenarios with respectto iterations one and two. For example, the most influential rootcause is ‘‘Incorrect behaviour enforced by shipping companies’’which activates 41 concepts in the first iteration and 77 conceptsin the second. Finally, the root causes listed according to theirpriorities are C73, C74, C66, C48, C58, C64, C72, C21, C65, C62,C67, C71, C26, C75 and C49.
4.4. Integration (preventive action planning)
The model clearly reveals the common root causes of firerelated deficiencies based on their priorities stated as follows:
Cause odðiÞ idðiÞ IPI i odðiÞP 13:6 IPI i P 2 Potential root cause
C1 5.1 31.9 0.16 No No NoC2 8.7 14.7 0.59 No No NoC3 9.4 22.2 0.42 No No NoC4 11.0 15.1 0.73 No No NoC5 13.5 10.8 1.25 No No No
C6 7.1 19.1 0.37 No No NoC7 7.5 9.9 0.76 No No NoC8 6.5 37.3 0.17 No No NoC9 20.1 15.5 1.30 Yes No NoC10 5.5 35.5 0.15 No No NoC11 9.8 33.8 0.29 No No NoC12 10.9 17.9 0.61 No No NoC13 9.6 11.1 0.87 No No NoC14 10.9 12.1 0.90 No No NoC15 10.3 6.5 1.60 No No NoC16 14.6 10.1 1.45 Yes No NoC17 11.8 26.6 0.44 No No NoC18 9.5 9.6 0.99 No No NoC19 5.4 10.3 0.52 No No NoC20 9.5 10.1 0.94 No No NoC21 24.7 10.1 2.46 Yes Yes YesC22 9.9 19.3 0.52 No No No
C23 15.7 22.3 0.70 Yes No NoC24 14.7 15.8 0.93 Yes No NoC25 16.5 14.9 1.11 Yes No NoC26 14.6 6.9 2.13 Yes Yes YesC27 6.2 18.4 0.34 No No NoC28 13.2 6.2 2.13 No Yes NoC29 9.5 4.8 1.99 No No NoC30 4.1 7.4 0.55 No No NoC31 1.7 26.2 0.07 No No NoC32 7.9 30.4 0.26 No No NoC33 11.0 18.4 0.60 No No NoC34 9.7 28.6 0.34 No No NoC35 8.5 15.3 0.55 No No NoC36 7.6 10.1 0.75 No No NoC37 2.8 5.0 0.56 No No NoC38 6.9 8.5 0.81 No No NoC39 11.0 7.9 1.40 No No NoC40 11.9 8.6 1.38 No No No
C41 23.4 20.1 1.16 Yes No NoC42 12.0 19.5 0.62 No No NoC43 30.5 20.6 1.48 Yes No NoC44 13.9 16.3 0.85 Yes No NoC45 11.3 20.8 0.54 No No NoC46 9.0 13.8 0.65 No No NoC47 21.0 23.8 0.88 Yes No NoC48 24.7 7.6 3.27 Yes Yes YesC49 18.3 5.5 3.31 Yes Yes YesC50 8.0 10.3 0.78 No No NoC51 15.8 14.9 1.06 Yes No NoC52 24.8 16.1 1.54 Yes No NoC53 17.0 21.1 0.81 Yes No NoC54 14.3 24.6 0.58 Yes No NoC55 23.7 23.8 1.00 Yes No NoC56 12.4 7.9 1.57 No No NoC57 28.8 23.8 1.21 Yes No No
1. Incorrect behaviour enforced by shipping companies (C73).2. Excessive time pressure due to improper operational
scheduling (C74).3. Insufficient scope of crew training program (C66).4. Ill-defined rules and responsibilities in fire safety plans
(C48).5. Lack of methodological tools and background to perform
technical safety analysis (C58).6. Lack of managerial skill in shore-based personnel (C64).7. Fire control plans’ inconsistencies (C72).8. Violation of drugs and alcohol policies on board ship (C21).9. Financial resourcing/budget constraints to timely meet run-
ning/operational expenses (C65).10. Ergonomic design errors in fire safety installations (C62).11. Ineffective promotion system for crews (C67).12. Inefficient fire safety communication planning (C71).13. Excessive self-confidence of crew members (C26).14. Lack of management tools to implement suitable preventive
action planning on board (C75).15. Ignored crew resting hours (C49).
Now, it is an onerous task to successfully eliminate the featuredfactors via suggesting effective preventive actions systematically.In this stage, so-called integration, preventive actions are exploredrather than relatively simple corrective actions which are mainlypreferred in majority of ship fleets. Hence, it requires a geniusapproach to produce comprehensive solutions especially alongwith the latent error sources. For instance, in unsafe act level, vio-lation of drugs and alcohol policies on board ship (C21) is found asthe most contributing factor since it directly influences thecognitive, physical and mental performance during operations.
The nature of fire events on board ships extremely require team
integrity which might be influenced by excessive self-confidenceof crew members (C26). On the other hand, the combination of inefficient emergency communication (C71), ill-defined rules andresponsibilities in fire safety plans (C48) and relevant inconsisten-cies (C72) will reduce the response level to the complex situations.In addition to the key operational challenges that manyorganizations are recently facing, such as crew resting hours(C49), excessive time constraints (C74), and running costs (C65),the design phase should also consider additional ergonomicaspects (C62) to support safety level at sea. Furthermore, a ship-owner tendency in terms of affecting organizational behaviour of company (C73) is one of the dominating factors whose effects
are widely seen in each level. As another core aspect, operatingcrew qualifications, highly depend on consistent promotion (C66)and effective training (C67), should be enough to overcomecomplex hazardous occurrences. Finally, managerial capabilities(C64) supporting with advance analysis and execution tools (C58,C75) have been playing a crucial role to ensure continuousimprovement of fire-fighting capability on board ships.
It canbe easily seen that the determined root causes directly fallwithin the scope of prevention, mitigation, preparedness, responseand recovery strategies against fire related emergency situations.Establishing a continuous proactive system is the final step of thisresearch. Considering the determined root causes, ship operatingenvironment and recent marine technologies, it is a great necessityto produce effective prevention actions comprehensively. That
means, a preventive action proposal should be in generic form,originated fromcertain root causes, and applicable to various typesof fleet in order to prevent recurrence of focused operational facts.Table 4 provides the suggested mechanisms as preventive actionproposals in accordance with their priorities.
The suggested mechanism includes Safe Ship SystemMechanism (SSSM), Safe Ship Operation Mechanism (SSOM) andSafe Ship Execution Mechanism (SSEM). The priorities of rootcauses derived from qualitative simulation application are usedto scheduling of preventive action proposals produced within thesuggested mechanism. Besides ensuring a systematic approach,the mechanisms also classify the root causes and correspondingsolutions into design, operation or management perspectives.
For instance, the function of SSSM is to ensure system operabil-ity, maintainability, equipment reliability via testing arrange-ments, design and installation monitoring. Especially, theergonomics of user interfaces are the main concern of SSSM. Inpractice, the SSSM provides invaluable feedbacks to the designand construction phases of fire control and safety system at newbuildings as well as to the reconstruction, conversion, repair andmaintenance processes of existing ships in fleet. Hence, SSSM pro-vides continuous response against defective elements in systemlevel, leading to firefighting vulnerabilities. To achieve that, theSSSM requires establishing and integrating the following subsys-tems: (i) safe design and installation feedback system, (ii) operabil-ity monitoring system, (iii) equipment reliability assessmentsystem.
On the other hand, SSOM mainly targets to improvesafety awareness in shipboard operations. For example, an advance
scheduling and responsibility allocation system should be
Table 1 (continued)
Cause odðiÞ idðiÞ IPI i odðiÞP 13:6 IPI i P 2 Potential root cause
C75 14.0 3.0 4.65 Yes Yes YesC76 10.9 4.7 2.33 No Yes NoC77 10.0 11.0 0.91 No No NoC78 11.3 4.6 2.45 No Yes No
Fig. 12. Influence-dependence chart for indirect relationships (dashed linerepresents IPI = 2, solid line represents xRod ¼ 11:9).
O. Soner et al./ Safety Science 77 (2015) 25–41 37
Results of the analysis of indirect relationships.
Cause Rod(i) Rid(i) IPI i RodðiÞP 11:9 IPI i P 2 Potential root cause
C1 3.4 29.4 0.12 No No NoC2 5.8 13.8 0.42 No No NoC3 5.9 21.2 0.28 No No NoC4 9.4 15.9 0.59 No No NoC5 14.6 11.2 1.30 Yes No No
C6 5.5 17.9 0.31 No No NoC7 5.4 6.1 0.89 No No NoC8 5.0 37.3 0.13 No No NoC9 16.6 14.5 1.14 Yes No NoC10 4.0 36.3 0.11 No No NoC11 8.1 32.8 0.25 No No NoC12 10.2 18.1 0.56 No No NoC13 10.4 12.1 0.86 No No NoC14 9.9 11.4 0.87 No No NoC15 8.9 7.5 1.19 No No NoC16 13.9 12.0 1.17 Yes No NoC17 9.2 28.9 0.32 No No NoC18 5.5 9.6 0.57 No No NoC19 5.5 11.8 0.47 No No NoC20 9.3 10.6 0.88 No No NoC21 20.5 11.4 1.80 Yes No NoC22 9.0 16.7 0.54 No No No
C23 15.7 21.2 0.74 Yes No NoC24 15.9 12.0 1.33 Yes No NoC25 14.0 14.7 0.96 Yes No NoC26 14.3 9.7 1.47 Yes No NoC27 4.6 10.7 0.43 No No NoC28 11.2 4.7 2.40 No Yes NoC29 8.0 4.0 2.02 No Yes NoC30 3.5 6.4 0.54 No No NoC31 1.6 27.8 0.06 No No NoC32 6.8 34.2 0.20 No No NoC33 8.9 18.8 0.47 No No NoC34 9.9 30.3 0.33 No No NoC35 7.5 12.8 0.59 No No NoC36 6.8 10.5 0.64 No No NoC37 2.6 4.3 0.61 No No NoC38 6.2 9.4 0.66 No No NoC39 9.0 7.9 1.14 No No NoC40 10.1 9.9 1.02 No No No
C41 19.9 22.4 0.89 Yes No NoC42 8.9 23.6 0.38 No No NoC43 28.2 23.1 1.22 Yes No NoC44 10.4 21.6 0.48 No No NoC45 10.0 24.1 0.42 No No NoC46 11.7 14.9 0.79 No No NoC47 19.9 25.4 0.78 Yes No NoC48 21.6 7.7 2.80 Yes Yes YesC49 12.5 6.2 2.00 Yes No NoC50 9.6 12.8 0.75 No No NoC51 14.2 18.1 0.78 Yes No NoC52 23.5 20.6 1.14 Yes No NoC53 15.0 25.8 0.58 Yes No NoC54 16.5 26.4 0.63 Yes No NoC55 23.0 28.2 0.82 Yes No NoC56 11.5 9.0 1.28 No No NoC57 26.1 25.9 1.01 Yes No No
established to control the time constraints on operational pro-cesses. Identification of suitable training items is another criticalpoint of interest which can be supported by problem-based train-ing including regulatory amendments rather than traditionalsafety trainings. The experienced operational safety cases shouldbe analysed and shared as critical lessons to be learned. Themotivating factors (i.e. resting hours’ compliance, fair promotionprocess, etc.) should be considered and systematically actualizedin order to increase the number of good practices on board. To
address the mentioned issues, the SSOM should be supported withthe following sub-systems: (i) safe operation verification system,(ii) crew improvement program, (iii) safety regulation compliancesystem.
At the organizational level, the SSEM deals with governing theoverall process of fire safety improvement at sea. The mechanismmight require organizational redesign to avoid the incorrectorganizational behaviour, ordinary policies and managementpractices. It is the most significant issue to be addressed. It might
Table 2 (continued)
Cause Rod(i) Rid(i) IPI i RodðiÞP 11:9 IPI i P 2 Potential root cause
C75 10.2 2.3 4.43 No Yes NoC76 8.7 4.4 1.99 No No NoC77 9.9 11.2 0.89 No No NoC78 8.1 4.3 1.87 No No No
Fig. 13. Results of the FCM simulation process for Scenario 66.
Table 3
Priorities of potential root causes.
Scenario Activated cause # of Activated concepts (>0.5) Rank order w.r.t. I1 Rank order w.r.t. I2 Priority
critically affect the management review decisions, utilization of methodological tools, even existing managerial skills, and specificpolicies. In this cycle, the mentioned attempts will improve theproactive fleet management capability. Since the majority of deficiencies are caused by a combination of root causes, strictsafety barriers at organizational level have the potential to preventvarious root causes to develop and interact. At organization level,the SSEM necessitates to adopt the following sub-systems: (i)safety performance measurement system, (ii) safety management
database, (iii) operational problem analysis and solution system.
5. Conclusion
Managing safety at sea is a complex problem which requiresgenius onsite solutions. In fact, the safety level on board shipscan be enhanced via two key approaches: (i) eliminate the poten-tial causes leading to event, (ii) strength the response and pre-paredness level. It can be assumed that if the operators (crew)and organizations (executives) can manage both targets, theprobability and severity of accidents (i.e. fire on board) would beminimized. This study proposes a novel proactive modellingapproach that intends to prevent reoccurrence of the fire relateddeficiencies or accidents. It utilizes HFACS and FCM model to
scientifically analyse the fire related deficiency database. In addi-tion, qualitative simulations are performed to verify and prioritizethe derived root causes. The integration phase of the proactivemodel substantially reveals three mechanisms such as SSSM,SSOM, SSEM. Various sub-systems (i.e. equipment reliabilityassessment system, crewimprovement program, operational prob-lem analysis and solution system, etc.) are suggested in detail. Themain issues addressed in this paper can be summarized as follows:(i) provide proactive safety modelling towards fire related deficien-cies on board ship, (ii) identify and prioritize the consistent rootcauses, (iii) apply HFACS–FCM to maritime safety literature, (iv)promote human element on board ships, and (v) encourage themaritime researchers to produce genius fire safety systems. Themain idea behind the paper is to analyse the operational data to
strengthen the organizational safety barriers. Besides active rea-sons, identification of the latent factors is recognized as an oneroustask. Hence, the methodological background in this researchaddresses the mentioned expectations. Consequently, the papercontributes to consistent prediction of the root causes while theproposed proactive model strengths the safety barriers and fire-fighting capability in ship fleets. Hence, this study providesreasonable contributions to safety improvements at sea.Furthermore, detailed projections of suggested safety mechanismsand sub-systems can be studied in order to manage the integrationin operational level.
Acknowledgements
This article is produced from MSc thesis research entitled ‘‘ Ahuman factor analysis approach to prevent fire safety related
deficiencies on board ships’’ which has been executed in MScProgram in Maritime Transportation Engineering of IstanbulTechnical University Graduate School of Science, Engineering andTechnology. The authors would like to express their gratitude tovarious expert professionals (i.e. Maritime researchers (Group#1), (ii) Maritime stakeholders (Group #2), (iii) Port state controlofficers (Group #3), (iv) Ship management executives (Group #4),
(v) Safety researchers (Group #5), (vi) Industrial engineers(Group #6), and (vii) Experienced seagoing officers/engineers(Group #7)) for providing technical knowledge support to theresearch.
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