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System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model

Publié en ligne: 15 Jul 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 08 Mar 2022
Accepté: 06 May 2022
Détails du magazine
License
Format
Magazine
eISSN
2444-8656
Première parution
01 Jan 2016
Périodicité
2 fois par an
Langues
Anglais
Introduction

The safety culture of shipping enterprises is the sum of the spirits, ideas, behaviors, and physical states of safe production, safe life, and environmental protection created by all related industries in the shipping industry in the shipping production activities. At the same time, it is the embodiment of the integration of maritime safety values and maritime safety code of conduct [1]. Coastal, atomic energy, shipping, and other industries have publicly stated that they want to achieve a globalized safety culture atmosphere, which has gradually confirmed the importance of safety culture. Safety culture first appeared in the International Nuclear Safety Advisory Group report on the 1986 “Chernobyl disaster.” It describes the corporate climate or culture that safety should be understood, accepted, and the highest priority. Safety culture is a subsection of the corporate culture. It is a characteristic of an individual, job, organization, etc., that affects health and safety. In 1991, the 75-INSAG-4 report “Safety Culture” prepared by the International Safety Advisory Group was published, marking the official dissemination and practice of safety culture in countries worldwide. In March 1994, the Nuclear Emergency Response Office of the State Council and the China Nuclear Energy Society jointly held the “Symposium on Safety Culture” to advance the study of safety culture. Since then, China has gradually introduced and promoted this concept in nuclear power, transportation, construction, petrochemical, metallurgy, and other fields [2]. At present, the upsurge of research and construction of safety culture is rising in China.

Establishment of safety culture evaluation factors of shipping enterprises and establishment of fuzzy comprehensive evaluation model

Shipping companies cover a wide range of areas. The content includes shipowners, shipping operating companies, classification societies, chartering companies, shipbuilding companies, pilotage, marine product manufacturing, cargo owners, maritime courts, maritime arbitration institutions, etc. This is the main body of the safety culture of shipping enterprises. The decision-making management is the leader of the safety culture in the shipping enterprise [3]. Their safe behavior practices will lead to the effectiveness of the entire enterprise's safety culture construction. The company's working environment, daily operation management, reward and punishment system, safety assurance control, and the implementation of corporate safety activities all play a decisive role in the construction of an enterprise's safety culture—twenty main characteristics of safety culture in shipping companies. The content is shown in Table 1.

Elements of safety culture in shipping companies

Safety Commitment U1 Security commitment content u11
safety report u12
Safety integrated into management u13
Security Incentives u14
Security Agency U2 The role of the full-time security sector u21
Other regulatory roles u22
The role of amateur safety organizations u23
Awareness of safety management regulations u31
Security Management U3 Security system document implementation u32
Clarify the responsibilities of the safety management organization u33
Safe participation in U4 How security systems are formed u41
Safety Meetings and Safety Events u42
Security advice u43
Safety training and learning u44
safety feedback u45
Safe Environment U5 Safety Guidelines u51
Security u52
environmental perception u53
Security Information Management U6 Safety Information Repository u61
safety information system u62
Demonstration of safety performance u63
Safe Practice U7 Investigate Types of Incidents u71
Security Check Type Awareness u72
Safe exchange and cooperation u73
security emergency plan u74
Safety Incentive U8 Humanistic care u81
Treatment of safety performance u82
Incorporate safety performance into human resources assessment u83
Security System Persistence u84
Determine the evaluation factor strategy

U represents the evaluation factors: U1: safety commitment; U2: safety organization; U3: safety management; U4: safety participation; U5: safety environment; U6: safety information management; U7: safety practice; U8: safety incentive.

Object Evaluation

We use international conventions to select the appropriate set of comments A. A = {A0, A1, A2, A3} = {no risk, yellow risk, orange risk, red risk}

Determine the weight of evaluation factors

Factor weights reflect the internal relationship between the elements. The consequences reflect the importance of each factor in the whole aspect [4]. The weight vector is denoted as A ={a0, a1,..., a8}. At present, there are many methods to measure the weight, such as AHP, Delphi, scoring method, principal component analysis method, assignment method, and so on.

Establish a fuzzy relationship matrix

According to the evaluation results of the evaluators on the evaluation object, the proportion of each evaluation level of each factor is calculated. At this point we get the blur matrix R. R=[s1s2Ms8]=[r10r11r12r13r20r21r22r23MMMMr80r81r82r83] R = \left[{\matrix{{{s_1}} \cr {{s_2}} \cr {\rm{M}} \cr {{s_8}} \cr}} \right] = \left[{\matrix{{{r_{10}}} & {{r_{11}}} & {{r_{12}}} & {{r_{13}}} \cr {{r_{20}}} & {{r_{21}}} & {{r_{22}}} & {{r_{23}}} \cr {\rm{M}} & {\rm{M}} & {\rm{M}} & {\rm{M}} \cr {{r_{80}}} & {{r_{81}}} & {{r_{82}}} & {{r_{83}}} \cr}} \right]

Where j=03rij=1i=1,,8 \sum\nolimits_{j = 0}^3 {{r_{ij}} = 1i = 1, \cdots,8} .

Comprehensive evaluation of corporate culture

We can obtain the comprehensive evaluation matrix B according to the above weight vector A and fuzzy matrix R: B=AR B = A \circ R

The weighted average algorithm is used here: bj=i=18airij {b_j} = \sum\nolimits_{i = 1}^8 {{a_i}{r_{ij}}}

Quantify and analyze evaluation results

First, the comprehensive evaluation results are normalized. Use bj=bj/j=14bj b_j^{'} = {b_j}/\sum\nolimits_{j = 1}^4 {{b_j}} here to get the normalized matrix B. Secondly, the article aims to get a precise evaluation result [5]. Therefore, we assume that the range of the value of each grade variable is as follows: A (no risk): 90 to 100. B (yellow risk): 80–90. C (orange risk): 60–80. D (red risk) 0–60. If the group median is calculated to obtain the rank evaluation matrix p: [95, 85, 75, 65, 30], the quantified value of the comprehensive evaluation is S=B* P. Then, according to the size of S, compare with Table 2 to find out the corresponding grade comment. This comment is the final evaluation result of an enterprise.

Comparison of evaluation results and comment levels

Rating Comments Comprehensive evaluation value
A (No risk) 90–100
B (yellow risk) 80–90
C (orange risk) 60–80
D (red risk) 0–60
Case Analysis
Analysis of safety emergency plan

The probability P of the state si of different combinations of marine traffic safety risk hazard factors is as follows: P={p1,p2,p3,p4} P = \left\{{{p_1},{p_2},{p_3},{p_4}} \right\}

Then the fuzzy subset B of the marine traffic safety risk pre-assessment level at this moment can be expressed as B=PR={max1im(p1ri0),max1im(p2ri1),max1im(p3ri2),max1im(p4ri3),}={x0,x1,x2,x3} B = P \circ R = \left\{{\matrix{{\mathop {\max}\limits_{1 \le i \le m} \left({{p_1} \circ {r_{i0}}} \right),} \cr {\mathop {\max}\limits_{1 \le i \le m} \left({{p_2} \circ {r_{i1}}} \right),} \cr {\mathop {\max}\limits_{1 \le i \le m} \left({{p_3} \circ {r_{i2}}} \right),} \cr {\mathop {\max}\limits_{1 \le i \le m} \left({{p_4} \circ {r_{i3}}} \right),} \cr}} \right\} = \left\{{{x_0},{x_1},{x_2},{x_3}} \right\}

x0, x1, x2, x3 is the membership degree of the maritime traffic safety risk status to no risk, yellow risk, orange risk, and red risk level, respectively.

We use the fuzzy information allocation method to obtain the marine traffic safety risk matrix [6]. We take the 4–80,000-ton bulk carrier as an example to calculate the maritime traffic safety risk matrix when the visibility distance is 1/5<W≤2/5. Under this visibility distance, the possible value range U of ship traffic flow density is: U={u1,u2,,up,,u21}={0,1/4,1/2,,5} U = \left\{{{u_1},{u_2}, \cdots,{u_p}, \cdots,{u_{21}}} \right\} = \left\{{0,1/4,1/2, \cdots,5} \right\}

The domain V of the maritime traffic safety risk index is V={υ1,υ2,,υ11}={0,1/10,2/10,,1} V = \left\{{{\upsilon _1},{\upsilon _2}, \cdots,{\upsilon _{11}}} \right\} = \left\{{0,1/10,2/10, \cdots,1} \right\}

Then we use the two-dimensional information distribution formula to obtain the fuzzy relation matrix R from U to V. A total of 133 data were obtained in this group of sample point set H when the visibility distance of 40,000 to 80,000-ton bulk carriers was 1/5<W≤2/5. We denote it as h1, h2, ⋯, hg, ⋯, h134. Then, the average value of the sample points under each risk level is calculated as A¯0=0.04,A¯1,1,A¯2=2.06,A¯3=3.15 {\bar A_0} = 0.04,{\bar A_1},1,{\bar A_2} = 2.06,{\bar A_3} = 3.15

From the step size formula Δ=(A¯3A¯0)/3=1.04 \Delta = \left({{{\bar A}_3} - {{\bar A}_0}} \right)/3 = 1.04

At this point, we get the new discrete value universe of traffic flow density as Un={u1,u2,,u6}={0.00,0.04,1.08,2.12,3.15,4.19} {U_n} = \left\{{{u_1},{u_2}, \cdots,{u_6}} \right\} = \left\{{0.00,0.04,1.08,2.12,3.15,4.19} \right\}

Un is the control interval for H. We replace hg with up (p = 1,2, ⋯,6) as the input set of the information matrix.

We assume that there are β disaster data according to the natural disaster information distribution theory [7]. Then the e(e = 1, 2, ⋯, β) disaster data is De = (ge, Ae). ge is the e sample control point. According to the principle of two-dimensional information distribution, if upgeup+1, then the information amount qe (up), qe (up+1) that distributes ge to up and up+1 is expressed as follows {qe(up)=1(geup)/(up+1up)qe(up+1)=1(geup+1)/(up+1up) \left\{\matrix{{q_e}({u_p}) = 1 - \left({{g_e} - {u_p}} \right)/({u_{p + 1}} - {u_p}) \hfill \cr {q_e}({u_{p + 1}}) = 1 - \left({{g_e} - {u_{p + 1}}} \right)/({u_{p + 1}} - {u_p}) \hfill \cr} \right.

We use fuzzy subsets to define marine traffic safety risk levels. After normalization, the amount of information that each risk level A0 ~ A3 of marine traffic is assigned to each marine traffic risk index value υk (k = 1,2, ⋯,11) can be obtained [8]. Further, we can receive that the amount of information allocated by β De = (ge, Ae) to the control point (up, υk) is: qpk=e=1βqe(up)qe(υk) {q_{pk}} = \sum\limits_{e = 1}^\beta {{q_e}({u_p}){q_e}({\upsilon _k})} Q=(qpk)d×11 {Q^{'}} = {\left({{q_{pk}}} \right)_{d \times 11}}

d is the number of elements in U. Q′ is the required original information distribution matrix. We then normalize the rows and columns of the matrix Q′ respectively and perform the small operation. In this way, we get the fuzzy relationship R between visibility and traffic flow density [9]. The ambiguous relationship between visibility and ship traffic flow density under the visibility distance of 0.2 to 0.4 miles was calculated from actual data. The risk matrix is shown in Table 3.

Risk Matrix from Theoretical Calculations

up/vk 0 0.04 1.08 2.12 3.15 4.19
0 1 0.62 0 0 0 0
0.1 0.89 0.62 0.01 0 0 0
0.2 0.59 0.67 0.22 0.06 0 0
0.3 0.17 0.26 0.61 0.11 0.01 0
0.4 0 0.15 1 0.31 0.04 0.01
0.5 0 0.13 0.76 0.65 0.06 0.02
0.6 0 0.04 0.32 0.66 0.16 0.03
0.7 0 0 0.1 0.67 0.32 0.17
0.8 0 0 0.01 0.21 0.55 0.59
0.9 0 0 0 0.06 0.66 0.76
1 0 0 0 0 0.76 0.66

Academia organizes surveys and discussions with navigators or senior captains with rich sailing experience [10]. They directly give the degree of affiliation of the ship to different levels of risk when sailing in various severe weather according to the common practice and professional judgment of seafarers. Table 4 shows the risk matrix of 4–80,000-ton bulk carriers under the visibility distance of 0.2–0.4 mile weather obtained by using the expert survey method.

Risk matrix from expert survey method

Traffic density/[ship·(mile)−2] Risk level
A0 A1 A2 A3
0 0.9 0.1 0 0
(0,0.5) 0 0.8 0.2 0
[0.5, 1.0) 0 0.7 0.3 0
[1.0, 2.0) 0 0.5 0.4 0.1
[2.0, 3.0) 0 0.1 0.6 0.3
≥3.0 0 0 0.3 0.7

Table 4 shows the risk matrix of 4–80,000-ton bulk carriers in the weather with a 0.2–0.4 mile visibility distance from the expert survey method modified by the fuzzy information allocation method for the above results. It can be seen that the membership degrees of the revised risk levels are further concentrated [11]. The variability in the spatial distribution of risk increases. A change in up has an increased impact on the risk level. Therefore, the revised risk matrix reflects the sensitivity of changes in ship traffic flow density and the difference of spatial risk distribution and reflects the actual situation of maritime traffic safety risks.

Risk Matrix for Modified Expert Survey

Traffic density/[ship·(mile)−2] Risk level
A0 A1 A2 A3
0 0.9 0.1 0 0
(0,0.5) 0.2 0.8 0 0
[0.5, 1.0) 0.1 0.7 0.2 0
[1.0, 2.0) 0 0.4 0.5 0.1
[2.0, 3.0) 0 0.1 0.6 0.3
≥3.0 0 0 0.2 0.8
Example analysis of shipping enterprise safety culture system

We simulate fuzzy evaluation instances according to the above method. First, we determine the factor set A = {a0, a1,..., a8}. Second, we determine the comment set A = {A0, A1, A2, A3}. Then we define the evaluation factor weights. Here, the importance of each factor is measured as A = {0.163,0.125,0.048,0.013,0.014,0.133,0.104,0.240,0.08,0.08} using Analytic Hierarchy Process (AHP). Finally, we build the blur matrix. We invited several experts to evaluate the safety culture of a shipping company [12]. This paper summarizes the forms filled out by experts and writes the frequency of people who belong to the 5 grades in the corresponding grade column (see Table 6).

The proportion of evaluation factors in the comment set

A (No risk) B (yellow risk) C (orange risk) D (red risk)
Safety Commitment U1 0.8 0.12 0.06 0.02
Security Agency U2 0.75 0.15 0.1 0
Security Management U3 0.65 0.1 0.2 0.05
Safe participation in U4 0.6 0.2 0.1 0.1
Safe Environment U5 0.7 0.15 0.05 0.1
Security Information Management U6 0.8
Safe Practice U7 0.8 0.1 0.1 0
Safety Incentive U8 0.6 0.3 0.1 0

We calculate the comprehensive evaluation result B = AR, and then normalize bj=bj/j=14bj b_j^{'} = {b_j}/\sum\nolimits_{j = 1}^4 {{b_j}} to get B′=(0.730, 0.169, 0.080, 0.015, 0.004), so S=B′* P=90.931. Compare Table 2 to know that this corporate culture belongs to an outstanding grade.

Conclusion

The construction of safety culture mainly uses the penetration and influence of safety culture to assist safety management. This paper introduces the implementation mode of safety culture construction of the International Nuclear Safety Advisory Group to discuss and analyze the implementation direction of safety culture in shipping enterprises. Finally, an example is given to illustrate the evaluation method for evaluating the effectiveness of the construction of Shipping enterprise safety culture.

Risk Matrix from Theoretical Calculations

up/vk 0 0.04 1.08 2.12 3.15 4.19
0 1 0.62 0 0 0 0
0.1 0.89 0.62 0.01 0 0 0
0.2 0.59 0.67 0.22 0.06 0 0
0.3 0.17 0.26 0.61 0.11 0.01 0
0.4 0 0.15 1 0.31 0.04 0.01
0.5 0 0.13 0.76 0.65 0.06 0.02
0.6 0 0.04 0.32 0.66 0.16 0.03
0.7 0 0 0.1 0.67 0.32 0.17
0.8 0 0 0.01 0.21 0.55 0.59
0.9 0 0 0 0.06 0.66 0.76
1 0 0 0 0 0.76 0.66

Risk Matrix for Modified Expert Survey

Traffic density/[ship·(mile)−2] Risk level
A0 A1 A2 A3
0 0.9 0.1 0 0
(0,0.5) 0.2 0.8 0 0
[0.5, 1.0) 0.1 0.7 0.2 0
[1.0, 2.0) 0 0.4 0.5 0.1
[2.0, 3.0) 0 0.1 0.6 0.3
≥3.0 0 0 0.2 0.8

Comparison of evaluation results and comment levels

Rating Comments Comprehensive evaluation value
A (No risk) 90–100
B (yellow risk) 80–90
C (orange risk) 60–80
D (red risk) 0–60

The proportion of evaluation factors in the comment set

A (No risk) B (yellow risk) C (orange risk) D (red risk)
Safety Commitment U1 0.8 0.12 0.06 0.02
Security Agency U2 0.75 0.15 0.1 0
Security Management U3 0.65 0.1 0.2 0.05
Safe participation in U4 0.6 0.2 0.1 0.1
Safe Environment U5 0.7 0.15 0.05 0.1
Security Information Management U6 0.8
Safe Practice U7 0.8 0.1 0.1 0
Safety Incentive U8 0.6 0.3 0.1 0

Elements of safety culture in shipping companies

Safety Commitment U1 Security commitment content u11
safety report u12
Safety integrated into management u13
Security Incentives u14
Security Agency U2 The role of the full-time security sector u21
Other regulatory roles u22
The role of amateur safety organizations u23
Awareness of safety management regulations u31
Security Management U3 Security system document implementation u32
Clarify the responsibilities of the safety management organization u33
Safe participation in U4 How security systems are formed u41
Safety Meetings and Safety Events u42
Security advice u43
Safety training and learning u44
safety feedback u45
Safe Environment U5 Safety Guidelines u51
Security u52
environmental perception u53
Security Information Management U6 Safety Information Repository u61
safety information system u62
Demonstration of safety performance u63
Safe Practice U7 Investigate Types of Incidents u71
Security Check Type Awareness u72
Safe exchange and cooperation u73
security emergency plan u74
Safety Incentive U8 Humanistic care u81
Treatment of safety performance u82
Incorporate safety performance into human resources assessment u83
Security System Persistence u84

Risk matrix from expert survey method

Traffic density/[ship·(mile)−2] Risk level
A0 A1 A2 A3
0 0.9 0.1 0 0
(0,0.5) 0 0.8 0.2 0
[0.5, 1.0) 0 0.7 0.3 0
[1.0, 2.0) 0 0.5 0.4 0.1
[2.0, 3.0) 0 0.1 0.6 0.3
≥3.0 0 0 0.3 0.7

Fasoulis, I. Investigating the effectiveness of the maritime regulatory regime to address a socially responsible shipping industry: a content analysis study. International Journal of Sustainable Development and Planning., 2021; 16(4): 629–640 FasoulisI. Investigating the effectiveness of the maritime regulatory regime to address a socially responsible shipping industry: a content analysis study International Journal of Sustainable Development and Planning 2021 16 4 629 640 10.18280/ijsdp.160403 Search in Google Scholar

Xiao, J. Research on Supporting Safety of Gob Roadway in Coal Lane of Three Soft Strata. International Core Journal of Engineering., 2021; 7(1): 403–409 XiaoJ. Research on Supporting Safety of Gob Roadway in Coal Lane of Three Soft Strata International Core Journal of Engineering 2021 7 1 403 409 Search in Google Scholar

Qian, J., Gao, D., & Xu, L. A Probe into the Definition of Shipping Enterprise Safety Culture. International Core Journal of Engineering., 2021; 7(1): 250–255 QianJ. GaoD. XuL. A Probe into the Definition of Shipping Enterprise Safety Culture International Core Journal of Engineering 2021 7 1 250 255 Search in Google Scholar

Kim, Y. M., & Kim, J. H. The Relations between Safety Matters, Corporate Image and Performance in Logistics Company. Journal of Distribution Science., 2019;17(11): 35–45 KimY. M. KimJ. H. The Relations between Safety Matters, Corporate Image and Performance in Logistics Company Journal of Distribution Science 2019 17 11 35 45 10.15722/jds.17.11.201911.35 Search in Google Scholar

Wu, B., Gu, G., & Carter, C. J. The bond and retention of Chinese seafarers for international shipping companies: a survey report. Journal of Shipping and Trade., 2021; 6(1): 1–17 WuB. GuG. CarterC. J. The bond and retention of Chinese seafarers for international shipping companies: a survey report Journal of Shipping and Trade 2021 6 1 1 17 10.1186/s41072-021-00087-1 Search in Google Scholar

Xue, C., Tang, L., & Walters, D. Occupational health and safety indicators and under-reporting: Case studies in Chinese shipping. Relations industrielles/Industrial Relations., 2019; 74(1): 141–161 XueC. TangL. WaltersD. Occupational health and safety indicators and under-reporting: Case studies in Chinese shipping Relations industrielles/Industrial Relations 2019 74 1 141 161 10.7202/1059468ar Search in Google Scholar

Touchent, K. A., Hammouch, Z., & Mekkaoui, T. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives. Applied Mathematics and Nonlinear Sciences., 2020;5(2): 35–48 TouchentK. A. HammouchZ. MekkaouiT. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives Applied Mathematics and Nonlinear Sciences 2020 5 2 35 48 10.2478/amns.2020.2.00012 Search in Google Scholar

Xie, T., Liu, R., & Wei, Z. Improvement of the Fast Clustering Algorithm Improved by-Means in the Big Data. Applied Mathematics and Nonlinear Sciences., 2020;5(1): 1–10 XieT. LiuR. WeiZ. Improvement of the Fast Clustering Algorithm Improved by-Means in the Big Data Applied Mathematics and Nonlinear Sciences 2020 5 1 1 10 10.2478/amns.2020.1.00001 Search in Google Scholar

Mbong, T. S. S., & Bygvraa, D. A. Analysis of the implementation of the International Safety Management Code using motivation theory: the seafarer's views. International maritime health., 2021; 72(3): 172–178 MbongT. S. S. BygvraaD. A. Analysis of the implementation of the International Safety Management Code using motivation theory: the seafarer's views International maritime health 2021 72 3 172 178 10.5603/IMH.2021.0033 Search in Google Scholar

Pike, K., Wadsworth, E., Honebon, S., Broadhurst, E., Zhao, M., & Zhang, P. Gender in the maritime space: how can the experiences of women seafarers working in the UK shipping industry be improved?. The Journal of Navigation., 2021;74(6): 1238–1251 PikeK. WadsworthE. HonebonS. BroadhurstE. ZhaoM. ZhangP. Gender in the maritime space: how can the experiences of women seafarers working in the UK shipping industry be improved? The Journal of Navigation 2021 74 6 1238 1251 10.1017/S0373463321000473 Search in Google Scholar

Bhattacharya, Y. Hazard and Near-Miss Reporting–Safety Through Numbers?. Journal of Maritime Research., 2019; 16(3): 33–42 BhattacharyaY. Hazard and Near-Miss Reporting–Safety Through Numbers? Journal of Maritime Research 2019 16 3 33 42 Search in Google Scholar

Igwe, I. S., Ogbonnaya, E. A., Ajoko, T. J., & Ombe, T. M. Experience from ISM Code as Implementation Model for the Maritime Labour Convention, 2006. European Journal of Engineering and Technology Research., 219; 4(5): 50–57 IgweI. S. OgbonnayaE. A. AjokoT. J. OmbeT. M. Experience from ISM Code as Implementation Model for the Maritime Labour Convention, 2006 European Journal of Engineering and Technology Research 219 4 5 50 57 10.24018/ejeng.2019.4.5.1082 Search in Google Scholar

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