chemical tanker ship

Journal of Loss Prevention in the Process Industries 43 (2016) 424e431

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Journal of Loss Prevention in the Process Industries journal homepage: www.elsevier.com/locate/jlp

A hybrid human error probability determination approach: The case of cargo loading operation in oil/chemical tanker ship Emre Akyuz a, *, Metin Celik b a b

Department of Maritime Management, Bursa Technical University, Osmangazi 16190, Bursa, Turkey Department of Marine Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 May 2015 Received in revised form 16 May 2016 Accepted 27 June 2016 Available online 29 June 2016

HEART (human error assessment and reduction technique) is widely recognized a robust empirical tool in determination of HEP (human error probability) since prediction of human error is of paramount importance in marine industry (Williams, 1988). The method is successfully applied to a variety of domains such as aviation, railway, petrochemical, nuclear power plants, etc. The method has two fundamental parameters; generic error probability (GEP) and error-producing conditions (EPCs). The EPCs are the factors that affect the human performance negatively. The paper deals in principle with one of the methods used for human error probabilities estimation and it suggests its enhancement using analytic hierarchy process (AHP) (Saaty, 1980). Thus, quantification of the subjective judgements of experts during assessed proportion of affect (APOA) calculation is achieved in the course of HEP. To demonstrate the proposed approach, cargo loading operation in oil/chemical tanker ship is selected since the process is very critical to prevent loss of life and marine environment pollution. Consequently, besides its theoretical insight, the paper has practical outcomes to safety practitioners and maritime professionals for estimation of HEP in a specific task. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Maritime safety HEP Loss prevention Oil/chemical tanker

1. Introduction Human error prediction has become a critical issue in maritime transportation industry since it contributes to the majority marine causalities on-board ship (EMSA, 2015). Although numerous rules and regulations have been enforced by maritime regulatory bodies, it has not been achieved the desired effects in preventing maritime causalities due to human error (Noroozi et al., 2014; Gaonkar et al., 2011). The consequences of human error could potential harm to human life, marine environment and commodity on-board ship. Therefore, a high level of safety performance is required for effective shipping activities which have increased worldwide (UNCTAD, 2015). Within this context, safety and risk practitioners have been trying to enhance human error preventing procedures by introducing proactive approaches. At this point, this paper presents a novel approach to determine the probability of human error occurring during the completion of specific task in risk management. To achieve this purpose, the paper integrates the AHP (Analytic Hierarchy Process) technique with HEART (Human Error

* Corresponding author. E-mail addresses: [email protected], [email protected] (E. Akyuz). http://dx.doi.org/10.1016/j.jlp.2016.06.020 0950-4230/© 2016 Elsevier Ltd. All rights reserved.

Assessment and Reduction Technique). While the AHP technique quantifies the subjective judgement of experts and ensures expert elicitation verification, HEART provides quantification of HEP for specific task that is being assessed. Since human error and recovery are key attributes of a risk management in various domains, there are a wide range of approaches used to account for the human error contribution to risk management (Hameed et al., 2016; Akyuz, 2015, 2016; Prasad and Gaikwad, 2015; Sun et al., 2012; Arslan, 2009; Reniers, 2009). Most of studies discussed role of human error in different industries and addressed this issue to fatigue and insufficient training of operator. The recent researches show that risk management in maritime industry poses a significant role as it may help to minimize the probability of human error occurrence. Hence, safety practitioners and professionals have a tendency to seek proactive solutions by presenting proactive approaches to enhance safety and loss prevention. A number of risk-based approaches have been introduced in a recent times (Lavasani et al., 2015; Yang and Wang, 2015; Akyuz and Celik, 2015a; Wang et al., 2011; Jianhua and Zhenghua, 2012; Bubbico et al., 2009). Whilst risk management plays a critical role in various domains,

E. Akyuz, M. Celik / Journal of Loss Prevention in the Process Industries 43 (2016) 424e431

determining of HEP is of paramount importance to prevent loss of life. Therefore, this paper proposes a hybrid human error probability determination approach in risk management. To demonstrate the proposed approach, cargo operation process in oil/chemical tanker is selected. Thus, quantification of human error for each step of operation process is revealed. 1.1. Carriage of chemical substances in oil/chemical tankers Carriage of chemical cargo activities has increased around the world since most of product contain petrochemical compounds. The chemical substances are carried by oil/chemical tanker ships which are classified into three types; IMO type I, IMO type II and IMO type III (IBC Code, 2007). A large number of chemical substances and some edible cargo types in liquid form are transported by them. For instance, vegetable oils, chemical commodities and refined petroleum products are typically carried by oil/chemical tankers. The expectation from the crew on-board tankers is to complete each specific task without any failure. Therefore, it is necessary to perform prediction of HEP during critical shipboard operation in oil/chemical tanker ships. In light of the above, this paper introduces a novel approach to determine the probability of human error. In this context, the paper is organized as follows: this section provides motivation behind the study and a brief literature review. Section two describes methods and proposed approach. Section three illustrates the proposed approach application into cargo loading process in oil/chemical tankers. Final section provides conclusion and contributions of the study. Meantime, a list symbols and abbreviations is provided in Table 1 for reader’s easy perusal. 2. Methodology This paper prompts a hybrid approach to determine HEP by integrating HEART with AHP in risk management. Accordingly, the next section provides brief description about methodologies. 2.1. HEART approach HEART is an empirical tool to calculate HEP. The method is preliminary introduced by Williams (1988) in order to assess human error with defined values. There are a variety of HEART applications in the literature such as nuclear power plants (Kirwan, 1997; Kirwan et al., 2004), transportation (Kirwan and Gibson, 2008; Gibson et al., 2012; Castiglia and Giardina, 2013; Akyuz and Celik, 2016) and off-shore (Deacon et al., 2013; Noroozi et al., 2014) industries. The method is based on the expert judgement which relies on the experiences and knowledge. It is recognized as being flexible and applicable to different industries, although it was first developed for nuclear power plant. Accordingly, the method was successfully modified and extended to different industries such as aviation and railway to assess human reliability. The HEART has been empirically validated by Kirwan (1997). The technique provides some fundamental error reducing guidance to decrease HEP but this aspect of method is rarely utilised. Indeed, the HEART has been derived for long year’s researches and data-collection in human factors engineering. It comprises of two fundamental parameters; GEP (generic error probability) and EPC (error-producing condition). The first parameter gives generic human error probability values in accordance with generic task type (GTT) which allows user to determine appropriate task. There are eight qualitative descriptions (from A to M) and additional one (if none of eight descriptions appropriate for the task) GTT defined in the technique and those give the probability of human error which occurs in perfect condition.

425

The second parameter is EPC which defines the performance shaping factor of human in the course of task. There are thirty-eight EPCs which directly influence to HEP value for a specific task. The EPC can be defined as external or internal factors such as time shortage, age of operator, stress, familiarity, time of day, environmental factors, experiences, etc. that lead to increase GEP value in associated with GTT. Williams (1988) performed a comprehensive data mining to generate GEPs and EPCs from various domains such as petrochemical, off-shore, nuclear power plant, etc. The process of method begins with selecting GTT. Thereafter, relevant GEP value is determined according to relevant GTT. Then, related EPC is selected by assessor from the list of 38 possible statements. If there are more than one EPC, assessed proportion of affect is determined. In this context, HEP is calculated with following Equation (1).

HEP ¼ GEP 

nY i

ðEPCi  1ÞAPOApi þ 1

o

(1)

In the equation, EPCi is the ith (i ¼ 1,2,3, …...n; n  38) error producing condition and APOApi (0 < pi  1) is ith the assessed of proportion affect which is weighted the each EPC basis of its importance. Namely, EPCi is the ith error-producing condition of task and APOApi is the relative importance weight of ith EPC (Castiglia and Giardina, 2013). 2.2. AHP technique The AHP is a multi-criteria decision making method introduced by Saaty (1980). The method is a powerful tool to cope with complex decision making problems. It enables user to generate ratio scale from comparison pair-wise matrix. It may assist user to set weight priorities and provide the best decision. The technique fundamentally divides complex problems into smaller parts in decision making in order to rank the alternatives hierarchically. Thus, relative weights of alternatives can be calculated with respect to pairwise comparison of decision-maker. As a result, the AHP allows decision-maker to transform both qualitative and quantitative evaluation into a multicriteria ranking. It is quite reliable since consistency of the evaluations, performed by decision makers, can be calculated. The technique comprises of a couple of basic steps. The first one is to analyse decision making problem and divide into smaller parts. Then, a pair-wise comparison matrix is established for each criteria. The third step is to calculate relative weights of criteria. The final step is to check consistency of decision maker’s evaluations. In this study, relative weights of EPCs’ proportion effect is calculated by AHP technique to improve sensitiveness. 2.3. A hybrid human error probability determination approach proposal In this part, a hybrid HEP determination approach is introduced. Accordingly, a flow diagram of the proposed approach is depicted in Fig. 1. In this context, the main steps of the proposed approach are explained as follows. Step 1- Scenario definition: In the first step, a real-shipboard scenario is defined in accordance with the task being assessed. The scenario may involve current situation, weather condition, status of ship and terminal, duties of each crew, distribution of roles and so on. Since there are critical shipboard operations carried out on-board ship regularly, a real-shipboard scenario can be applicable instead of fictionalising. Step 2- Task analysis: The purpose of this step is to identify relevant tasks in accordance with the scenario. The task may consist of main tasks and sub-tasks on-board ship. The task or activity is the definition of steps which crew must complete during process.

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Table 1 Nomenclature. A aij A/B AHP APOA CI CR EPC GEP GTT HEP HEART HTA

Matrix Each criteria Able seaman Analytic hierarchy process Assessed proportion of affect Consistency index Consistency ratio Error-producing condition Generic error probability Generic task type Human error probability Human error assessment and reduction technique Hierarchical task analysis

i IBC code IMDG code IMO j MAHRA MSDS n P/V RI wi VRL

lmax.

The task analysis is carried out in accordance with hierarchical task analysis (HTA) where main tasks are divided into sub-tasks (Shepherd, 2001; Akyuz and Celik, 2015b). The HEP value can be calculated one by one for each sub-task. Step 3- Situation definition: In this step, a variety of instant conditions are defined according to the tasks. This involves a variety of situations such as working environment, noise level, experience, workforce morale, stress, time availability, etc. Decision-maker determines relevant EPC/s with respect to the situations. Step 4- Selecting generic task type: This step enables decisionmaker to select relevant GTT in conjunction with task analysis. Since there are nine qualitative descriptions of actions (from A to M) defined in the method, the most appropriate GTT is nominated by decision-maker. Accordingly, GEP value is ascertained for each

Constant in Eq. (2) Intr. code for the construction and equ. of ships carrying dangerous chemicals in bulk International maritime dangerous goods International maritime organization Constant in Eq. (2) Maritime human reliability analysis Material safety data sheet Constant in Eq. (4) Pressure/vacuum Random index Priority weight Vapor return line Maximum matrix eigenvalue

specific task according to the relevant GTT (Kirwan and Gibson, 2008). Step 5- Identifying EPC/s: The relevant EPC is selected by decision-maker in order to calculate HEP value since EPCs are factors that may increase the GEP value. The decision-maker may select relevant EPCs from the list of thirty-eight possible statements in traditional HEART. If the decision-maker selects more than one EPC, then sensitive APOA calculation is needed on the basis of scenario definition. Step 6- Calculation APOA: In this step, percentage of EPC effect is determined. In traditional HEART method, the APOA calculation is performed by decision-maker since it is the relative weight which EPC can has on the relevant sub-task. This paper presents a practical solution to deal with the APOA calculation by adopting

Scenario definition Task analysis Situation definition

Selecting GTT

Identifying EPC/s Composing a pair-wise comparison matrix

Calculating APOA

Determining HEP Calculating criteria weights

Determining CR values

No

CR acceptable?

Yes

Fig. 1. A Flow diagram of proposed approach.

E. Akyuz, M. Celik / Journal of Loss Prevention in the Process Industries 43 (2016) 424e431

AHP technique instead of traditional APOA calculation. Thus, effect of each EPC is calculated as the APOA value is between 0 and 1. Step 7- Composing a pair-wise comparison matrix: The first step is to establish a pair-wise comparison matrix as presented by Saaty (1986). The aim of that is to acquire relevant weight for each criteria. To achieve this purpose, Saaty’s 1e9 linguistic relative importance scale (Table 2) is used. The comparison matrix A is a n  n real matrix, where n denotes the number of evaluated criteria. Each criteria aij (i,j ¼ 1,2,3, …,n) inserted in the matrix A represents the relative importance of the ith against to the jth. It means that ith criteria is more important than jth, if the aij > 1. Otherwise, ith criteria is less important than jth in case aij < 1. In this context, if two criterias have same weight, then aij ¼ 1. The condition can be considered true if the pair-wise evaluations are completely consistent. In light of the above, the following Equation (2) is used to establish a comparison matrix A (Saaty, 1986).

aij  aji ¼ 1

n aij 1X Pn n j¼1 k¼1 akj

(3)

Step 9- Determining CR values: In this step, the consistency of data inserted in a pair-wise comparison matrix is checked to verify whether the matrix A is consistent or not. At this point, consistency index (CI) is calculated with following Equation (4).

CI ¼

lmax:  n

(4)

n1

In the equation, n denotes the order of matrix, lmax: states the maximum matrix eigenvalue which can be found with following Equation (5). (Vargas, 1982). n X

Table 3 Random index value. n

1

2

3

4

5

6

7

8

9

10

RI

0

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

Equation (1). 3. Illustrative example: the case of cargo loading operation in oil/chemical tanker ship The proposed approach is illustrated with cargo loading process in oil/chemical tanker as the task is being assessed in loading process can pose potential hazards to human life and marine environment. 3.1. Problem statement

(2)

Step 8- Calculating criteria weights: After established a pairwise comparison matrix, relative weight of the each criteria is calculated. The priority weight (w) can be found with following Equation (3) (Saaty, 1986, 1994).

wi ¼

427

aij wj ¼ lmax wi

(5)

j¼1

The consistency ratio (CR) should be calculated in order to check consistency. The CR value can be found by Equation (6). If the CR value is equal or smaller than 0.10, the judgments inserted in a pairwise comparison matrix are considered as consistent and reasonable. If the CR value is more than 0.10, the judgements are considered as inconsistent. Therefore, they should be revised. In the equation, random index (RI) value is provided in Table 3 (Saaty, 1994).

The chemical cargo loading process is one of the most critical shipboard operations on-board oil/chemical tankers due to the nature of work. It is therefore to exercise safety throughout all steps of loading process. The most of chemical substances carried by oil/ chemical tankers have hazardous content as they may be explosive, poisonous, corrosive or toxic (IMDG Code, 1996). Therefore, the transportation of the chemical commodities is considerably critical while it involves serious tasks for duty crew (Akyuz and Celik, 2015a). In this context, human error prediction is essential for safety professionals and master of ship. The expectation from the crew is to exercise each task without error. Therefore, it is necessary to determine probability of human error for each specific task during cargo loading process on-board oil/chemical tanker since the consequences could potential harm to human life and marine environment. 3.2. Analysis of respondents To demonstrate the proposed approach, a real-shipboard cargo loading operation is selected. It was contacted with a prestigious shipping company which has oil/chemical tanker ship fleets. The capacity of the oil/chemical tankers can reach up to 11 k dwt. A comprehensive survey has been carried out with ship’s master who has worked at high seas for a long year. In the survey, HTA of the cargo loading process was presented to master and requested to select the most appropriate generic task type for each specific subtask. Then, he was asked to select relevant EPC. The master of ship completed the comprehensive survey for each sub-task respectively at the time of cargo loading operation. The judgements of the survey were evaluated to determine HEP. 3.3. Task analysis

CR ¼ CI=RI

(6)

Step 10- Determining HEP: In this step, probability of human error is calculated for each specific sub-task according to the

Table 2 Saaty’s pair-wise comparison scale. Importance

Definition

1 3 5 7 9 2,4,6,8

Equal importance Moderate importance Strong importance Very strong importance Absolute extreme) importance Intermediate values

In light of the master assistance, task analysis is performed for cargo loading process on-board oil/chemical tanker. To demonstrate the proposed approach, loading process of propylene oxide cargo (Annex II, cat.Y) is selected as a real-shipboard process (IMDG Code, 1996). The propylene oxide cargo is one of the hazardous cargoes that is being transported by oil/chemical tankers. If it contacts with combustible materials or water, this may cause fire or explosion (ICS, 2014). Accordingly, Table 4 shows the HTA of propylene oxide cargo loading process at oil/chemical tanker (ICS, 2014). As it may be seen in the table, the loading process comprises of six main tasks: i) pre-arrival, ii)after arrival, iii)before commencement of loading, iv)during loading v) after loading and vi) before departure.

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Table 4 HTA of propylene oxide cargo loading process in oil/chemical tanker. Cargo loading process 1. Pre-arrival 1.1 Prepare cargo and manifold plan 1.2 Check if MSDS placed in mess room 1.3 Blown nitrogen into tank cofferdam and heating coils 1.4 Blind lines and remove heating coil valves 1.5 Make sure that 0.6 bar pressure trims mounted on PV 1.6 Prepare purging plan 1.7 Make sure that dopak sampling to be available and clean 1.8 Adjacent empty cargo and ballast tanks purged down to max. 2% oxygen 1.9 Check VRL line and make sure that no liquid 1.10Check if all VRL and cargo valves are in order 2. After arrival 2.1 Check if crew on deck use PPE 2.2 Make sure that all crew on deck carry oxygen analyser 2.3 Check if spill tanks are empty 2.4 Hoist necessary warning sign 2.5 Connect manifold to marine loading arm 3. Before commencement of loading 3.1 Make sure that initial and max. load rate agreed with terminal 3.2 Check if manifold pressure rate is acceptable 3.3 Verify that cargo temperature is available 3.4 Agree on max. nitrogen pressure during purging and padding 3.5 Agree on blowing line to ship or shore tank 4. During loading 4.1 Close stripping valves 4.2 Close drain valves and fit plugs in position 4.3 Mount the manifold pressure gauge and thermometers 4.4 Start loading process 4.5 Monitor empty ballast tank atmosphere 4.6 Open drains and plugs frequently during purging operation 4.7 Prepare bottles for manifold sample 4.8 Monitor cargo loading operation in every 30 min 4.9 Monitor cargo temperature and pressure regularly 4.10 Stop cargo loading 5.After loading 5.1 Close loading manifold valve 5.2 Sweep the line by pigging in order to drain all cargo residues 5.3 Check if agreed cargo quantity to be loaded 5.4 Disconnect manifold from marine loading arm 6.Before departure 6.1 Make sure that cargo samples are stored safely 6.2 Check if manifold’s blind flanges are bolted 6.3 Set P/V valves 6.4 Monitor cargo heating system frequently 6.5 Switch off all tank alarm systems 6.6 Make sure that all cargo tank manholes are properly closed

The cargo loading process was commenced at early morning hours. Chief officer, chief engineer, second officer, third officer, bosun, pumper and able seamen participated loading operation of propylene oxide cargo. The Master decided the most appropriate GTTs as well as EPCs for each sub-step in light of the environmental condition. To demonstrate the proposed approach, task 2 (after arrival) is selected as a sample of demo since there are excessive sub-tasks in whole procedure. 3.5. Selecting generic task type GTT is determined by the Master of vessel during the loading process of propylene oxide cargo. The first task is performed by the duty officer who has enough knowledge and experience about task. The second sub-task is carried out by the chief officer. He is responsible to control whether duty crew on-deck is fitted with oxygen analyser. The next sub-step is completed by the duty officer as well. Hoisting necessary warning sign such as flag B (refers handling of dangerous cargo on-board ship) is ignored or skipped since it takes a time. The last sub-task is to connect tanker ship manifold to marine loading arms. The task is performed by able seaman (A/B) and inspected by duty officer. In order to commence cargo loading operation, there would be no operational failures in the course of manifold connection. Typically, an A/B has good knowledge and experience about this job. In the context of above definition, the master of ship selected the most appropriate GTTs for task 2 accordingly. Table 5 shows the results. 3.6. Identifying EPC/s After selected the GTTs, the master of ship nominated relevant EPCs for each sub-task. The EPC is selected from the list of 38 possible statements in HEART. Table 5 provides selected EPCs for cargo loading process (task 2). 3.7. Calculating APOA The APOA calculation is performed to assess proportion effect of each EPC. The AHP technique is utilised to calculate APOA. According to Table 5, each sub-step has more than one EPC. Therefore, the APOA calculation is required to prioritise the EPCs. To demonstrate the calculation, sub-task 2.4 is illustrated.

3.4. Definition of scenario

3.8. Composing a pair-wise comparison matrix

Once the task analysis is completed, a variety of scenarios are defined for cargo loading process. To accomplish this, a realshipboard cargo operation process is illustrated. During the cargo loading process, the shipboard environment condition was almost perfect. The working environment, crew experiences, workforce moral, organization quality, crew collaboration, familiarity and time availability were in satisfactory level. The crew were rest enough. Noise level, stress on crew, mental workload were acceptable level. There was a windy weather and sea was moderate.

Since there are four EPCs selected by the master of ship for subtask 2.4, a pair-wise matrix is established for comparison. In this sense, Equation (2) is applied. The master is asked to compare each EPC in accordance with Saaty’s 1e9 linguistic importance scale. Table 6 illustrates the comparison matrix for sub-task 2.4. 3.9. Calculating criteria weight and CR value The priority weight of each EPC is calculated with respect to the

Table 5 Generic tasks and EPC/s for after arrival (task 2). Sub-task

Selected GTT

Nominated EPC

2. After arrival 2.1 Check if crew on deck use PPE 2.2 Make sure that all crew on deck carry oxygen analyser 2.3 Check if spill tanks are empty 2.4 Hoist necessary warning sign 2.5 Connect manifold to marine loading arm

G G G E F

EPC2, EPC17 EPC13, EPC17, EPC24 EPC8, EPC11, EPC22 EPC2, EPC17, EPC26, EPC28 EPC2, EPC17, EPC27

E. Akyuz, M. Celik / Journal of Loss Prevention in the Process Industries 43 (2016) 424e431 Table 6 Comparison matrix of EPCs for sub-task 2.4.

EPC2 EPC17 EPC26 EPC28

429

Table 9 HEP calculation for HTA of loading process of propylene oxide.

EPC2

EPC17

EPC26

EPC28

Sub-task

1 1/4 1/5 1/2

4 1 1/3 1/2

5 3 1 3

2 2 1/3 1

1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 2. 2.1 2.2 2.3 2.4 2.5 3. 3.1 3.2 3.3 3.4 3.5 4. 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5. 5.1 5.2 5.3 5.4 6. 6.1 6.2 6.3 6.4 6.5 6.6

Table 7 APOA calculation result for step 2.4. EPC

Priority weight

l max

CI

CR

EPC2 EPC17 EPC26 EPC33

0.498 0.231 0.076 0.195

4.208

0.069

0.077

Equation (3). Thereafter, CR ratio is determined by using Equations (4) (5) and (6) respectively. Since CR value is found 0.077 for subtask 1.4, the judgements inserted to matrix by master are considered as consistent. Table 7 shows the priority weights and CR value of the sub-task 2.4. 3.10. Determining HEP HEP value is calculated with respect to the Equation (1). Table 8 gives comprehensive outcome of HEP calculation for task 2 (after arrival). Accordingly, Table 9 illustrates the overall result for loading process of propylene oxide. 3.11. Findings and discussion In the view of calculated HEP values, the mean of HEP value through the cargo loading process of propylene oxide is found 5.11E-02. The highest mean of probability of human error appears in “before departure” stage. On the other hand, “before commencement of loading” stage seems the most reliable stage where mean of HEP value is the lowest. According to the sensitive HEP calculation, Fig. 2 shows HEP distribution graph throughout cargo loading process of propylene oxide. According to Fig. 2, most of specific tasks that are being completed during operation found satisfactory level. It means that performance of duty crew follows planned procedures as some provisional deviation is still possible. On the other hand, 8 of 39 sub-tasks’ HEP values are higher than mean. Particularly, HEP values show an increase at the end of operation where performance

GTT

EPC

HEP

G D G H G E G G G H

EPC14,EPC25 EPC11, EPC25 EPC2, EPC15, EPC36 EPC1, EPC22, EPC33, EPC36 EPC17, EPC18, EPC32 EPC2, EPC22, EPC26 EPC15. EPC17 EPC1, EPC2, EPC33 EPC6, EPC17 EPC15, EPC25, EPC, EPC32

1.34E-03 3.90E-01 1.62E-03 2.20E-04 6.85E-04 1.42E-01 1.29E-03 1.98E-03 1.15E-03 1.12E-04

G G G E F

EPC2,EPC17 EPC13, EPC17.EPC24 EPC8,EPC11,EPC22 EPC2,EPC17,EPC26,EPC28 EPC2,EPC17,EPC27

5.10E-03 1.25E-03 2.62E-03 1.94E-01 2.48E-02

F G G H F

EPC2, EPC8, EPC17 EPC11, EPC14 EPC17, EPC22, EPC26 EPC10, EPC22, EPC23 EPC17, EPC22

5.78E-02 2.46E-03 4.98E-04 1.02E-04 8.83E-03

H F G H E G G H G

EPC3, EPC8, EPC17, EPC25 EPC2, EPC15 EPC13, EPC17 EPC2, EPC33, EPC36 EPC17, EPC22 EPC1, EPC9, EPC15 EPC2, EPC 13, EPC25 EPC22, EPC33 EPC2, EPC28

3.48E-04 3.83E-02 4.26E-03 7.12E-04 5.17E-02 1.02E-03 2.99E-03 3.15E-05 2.74E-03

G E D G

EPC13. EPC22 EPC2, EPC10, EPC23 EPC14, EPC27 EPC11, EPC22, EPC23

1.51E-03 1.86E-01 2.09E-01 1.35E-03

G E G D G D

EPC13, EPC12, EPC15, EPC14, EPC11 EPC17,

9.71E-04 8.87E-02 7.29E-04 3.99E-01 2.00E-03 1.63E-01

EPC33 EPC17 EPC25, EPC29 EPC17 EPC35

of crew can be affected by fatigue and loss of concentration. Specifically, the highest probability of human error was related to subtask 6.4 (monitor cargo heating system frequently) which is due to the lack of inspection/monitoring and imperfect hand over deck-

Table 8 HEP calculation for task 2 (after arrival). Task 2 (after arrival)

GEP value

EPC

EPC effect

APOA weight

HEP value

2.1 Check if crew on deck use PPE

4.00E-04 4.00E-04

2.3 Check if spill tanks are empty

4.00E-04

2.4 Hoist necessary warning sign

2.00E-02

2.5 Connect manifold to marine loading arm

3.00E-03

11 3 4 3 1.6 6 5 1.8 11 3 1.4 1.4 11 3 1.4

0.647 0.353 0.142 0.334 0.525 0.123 0.557 0.320 0.498 0.231 0.076 0.195 0.285 0.485 0.230

5.10E-03

2.2 Make sure that all crew on deck carry oxygen analyser

EPC2 EPC17 EPC13 EPC17 EPC24 EPC8 EPC11 EPC22 EPC2 EPC17 EPC26 EPC28 EPC2 EPC17 EPC27

1.25E-03

2.62E-03

1.94E-01

2.48E-02

430

E. Akyuz, M. Celik / Journal of Loss Prevention in the Process Industries 43 (2016) 424e431

Fig. 2. HEP distribution graph throughout cargo loading process of propylene cargo.

watch. This error may induce to decrease the temperature of the cargo since it is necessary to keep the cargo well heated to avoid it going solid. The second highest HEP value is sub-task 1.2 (check if MSDS is placed in mess room) among all the sub-tasks in the cargo loading process. A MSDS (Material Safety Data Sheet) is kind of a guidance that contains necessary information about potential hazards and safety measures of chemical products. This documents should be properly placed in mess room since each crew, in particular deck rating, has to know that what hazards of the cargo are, how to take precautions, emergency procedures or what to do in case of incident occur. Therefore, the MSDS shall be placed mess rooms before arrival at berth. However, the duty officer who is responsible to place MSDS at mess rooms may skip or postpone the task due time limitation. Furthermore, sub-task 5.3 (check if agreed cargo quantity to be loaded) has also the highest HEP value among the all sub-tasks since it ranks on the third place. Incomplete feedback and physical capabilities are the main contributory factors that may cause high HEP. This error may cause commercial damage if there is cargo shortage. Another critical sub-task is 2.4 (hoist necessary warning sign) where HEP value is considerable high. An A/B is responsible person to hoist the necessary warning sign during cargo loading process (red flag - Bravo) but he may sometimes ignore or skip to perform task due to limited time. Inadequate inspection, which is carried out by duty officer, is the main contributory factor of this error. Sub-task 5.2 (sweep the line by pigging in order to drain all cargo residues) has also high HEP value among the all sub-tasks in the cargo loading process. Failure of sweeping the cargo line may cause cargo tank overflow due to the entry of compressed gas. This might seriously pollute the marine environment. Sub-tasks (6.6, 1.6, 3.1 and 6.2) have also high HEP values in the cargo loading process of propylene oxide since their values are higher than mean.

4. Conclusion This paper presents a hybrid human error probability

determination approach by integrating HEART and AHP methods. In the proposed approach, AHP technique is combined with HEART methodology to increase the consistency of APOA calculation. Since HEART is robust tool to determine HEP values systematically, it has some weaknesses in APOA calculation. To remedy this gap, this paper suggests an enhancement by using AHP. Hence, quantification of the subjective judgements of experts during assessed proportion of affect (APOA) calculation is achieved to enhance consistency of calculation. To demonstrate the model, a specific shipboard operation is selected: cargo loading process of propylene oxide on-board oil/chemical tanker as the consequences of human error are seriously damaging human life and marine environment. The proposed approach provides practical contributions to safety practitioners and maritime professionals during determining of HEP. The practical contributions of paper in the marine industry will be highly welcomed, in particular, before commencement of any critical shipboard operation such as cargo loading & discharging, ballasting, bunkering, manoeuvring, gas inerting, crude oil washing, tank cleaning, etc (Akyuz and Celik, 2015a,b,c). Since the proposed approach presents a practical tool, it can be applicable to other industries such as off-shore, aviation, petrochemical, production, nuclear, railway, construction and mining industries where human errors can have fatal consequences. The further study can offer designing a user-friendly software tool (a knowledge-based programming in the system to transform operational task scenarios in database into meaningful information for prediction HEP) on the basis of theoretical framework of this paper. The software will be presented as a practical application which allows assigning of the HEP value for the specific task.

Acknowledgement *This article is partially produced from PhD dissertation entitled “A decision-making model proposal on human reliability analysis on-board ships” which has been executed in Maritime Transportation Engineering Program of ITU Graduate School of Science

E. Akyuz, M. Celik / Journal of Loss Prevention in the Process Industries 43 (2016) 424e431

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