[논문]Safety performance evaluation in a steel industry: A short-term time series approach

Nazaripour, Ebrahim; Halvani, Gholamhossein; Jahangiri, Mehdi; Fallahzadeh, Hossein; Mohammadzadeh, Morteza

doi:10.1016/j.ssci.2018.08.028

Safety performance evaluation in a steel industry: A short-term time series approach

Safety science, v.110 pt.A, 2018년, pp.285 - 290

Nazaripour, Ebrahim (Department of Occupational Health, School of Health, Bam University of Medical Sciences) , Halvani, Gholamhossein (Department of Ergonomics, Shahid Sadoughi University of Medical Sciences) , Jahangiri, Mehdi (Research Center for Health Sciences, Institute of Health, Department of Occupational Health Engineering, School of Health, Shiraz University of Medical Sciences) , Fallahzadeh, Hossein (Department of Biostatistics and Epidemiology, Shahid Sadoughi University of Medical Sciences) , Mohammadzadeh, Morteza (Department of Biostatistics, School of Medical Sciences, Tarbiat Modares University)

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Abstract Background The aim of the present study was to assess the safety performance in a steel industry and predicting the occurrence of incidents using Customized Predictive Risk Index (CPRI) technique. Materials and methods 350 unsafe observations were recorded and scored based on risk factors of probability of danger, frequency of work exposure, number of persons at risk, and severity of consequence. Risk Index (RI) of each observation was calculated through geometric average of risk factors. Optimum forecasting time series model of RI was determined using the smallest value of Akaike information criterion (AIC) to predict the CPRI trend and forecast the occurrence of incidents with a 95% confidence interval. Descriptive analysis was conducted using SPSS 22.00 and incident forecasting based on CPRI trend was conducted using forecast package for R 3.3.1. Results Autoregressive of 6 consecutive observations (AR-6) was chosen as the optimum model to fit time series data and define CPRI (AC = 1226.58). Most incidents occurred when the CPRI value was exceeds five and this range was determined as action zone and forecasting criteria for predicting the occurrence of incidents. Conclusion Customized Predictive Risk Index (CPRI), developed in this study could be used as a leading indicator for safety performance in steel industry. Ensuring the managers about the functionality of leading indicators to assess safety performance could be facilitated with comparing the results achieved by models with the real data. Highlights Risk Index (RI) of unsafe acts and conditions (UAC) defined for predicting incidents. A Customized Predictive Risk Index (CPRI) defined for safety performance evaluation. Three modes of incident occurrence were determined according to the CPRI trend. RI mean and number of UAC could be used to predict the number of incidents.

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Ahmad, R.K., 2000. Developing a proactive safety performance measurement tool (SPMT) for construction sites: ⓒ Kunju Ahmad R.
Occup. Environ. Med. Barreto 54 8 599 1997 10.1136/oem.54.8.599 A nested case-control study of fatal work related injuries among Brazilian steel workers

상세보기
Brain Res. Rev. Boksem 59 1 125 2008 10.1016/j.brainresrev.2008.07.001 Mental fatigue: costs and benefits

상세보기
Brauer 2016 Safety and Health for Engineers
J. Loss Prevent. Process Indust. Chen 17 3 233 2004 10.1016/j.jlp.2004.03.001 A predictive risk index for safety performance in process industries

상세보기
IFAC-PapersOnLine Fruggiero 49 12 1691 2016 10.1016/j.ifacol.2016.07.825 Incorporating the human factor within manufacturing dynamics

상세보기
Hyndman, R., 2016. forecast: Forecasting Functions for Time Series and Linear Model 2016.
J. Loss Prevent. Process Indust. Jones 12 1 59 1999 10.1016/S0950-4230(98)00038-2 The importance of near miss reporting to further improve safety performance

상세보기
Kjellen 2000 Prevention of Accidents Through Experience Feedback
Reliab. Eng. Syst. Safe. Leveson 136 17 2015 10.1016/j.ress.2014.10.008 A systems approach to risk management through leading safety indicators

상세보기
Safe. Sci. Nordlof 73 126 2015 10.1016/j.ssci.2014.11.020 Safety culture and reasons for risk-taking at a large steel-manufacturing company: investigating the worker perspective

상세보기
Reliab. Eng. Syst. Safe. Øien 74 2 147 2001 10.1016/S0951-8320(01)00068-0 A framework for the establishment of organizational risk indicators

상세보기
Safe. Sci. Øien 49 162 2011 10.1016/j.ssci.2010.05.015 Building safety indicators: Part 2 - Application, practices and results

상세보기
J. Loss Prevent. Process Indust. Pasman 30 197 2014 10.1016/j.jlp.2013.06.001 How can we use the information provided by process safety performance indicators? Possibilities and limitations

상세보기
R Core Team 2015 R: A Language and Environment for Statistical Computing
Safe. Sci. Reiman 50 10 1993 2012 10.1016/j.ssci.2011.07.015 Leading indicators of system safety-monitoring and driving the organizational safety potential

상세보기
J. Loss Prevent. Process Indust. Santos-Reyes 15 2 77 2002 10.1016/S0950-4230(01)00066-3 Assessing safety management systems

상세보기
Safe. Sci. Sinelnikov 72 240 2015 10.1016/j.ssci.2014.09.010 Using leading indicators to measure occupational health and safety performance

상세보기
Vulkan-Verlag GmbH Von Starck 2005 Handbook of thermoprocessing technologies: fundamentals, processes, components, safety

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Safety performance evaluation in a steel industry: A short-term time series approach

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