A FACTORIAL ANALYSIS OF INDUSTRIAL SAFETY

Year 2022, Volume: 4 Issue: 1, 33 - 43, 31.01.2022

Cordelia Omoyi Ayodeji Omotehinse

https://doi.org/10.47933/ijeir.1027304

Cited By: 1

Abstract

In spite of all the efforts in different industries to reduce the number of undesirable accidents, a lot of events always threaten industrial societies. These events often cause huge damages to the environment, facilities and even in some cases, fatalities and disabilities for people. Therefore, it is important to analyse the numerous variables associated with industrial accident considerations and the inter play among these variables. This paper adopts a novel combination of two statistical methods to analyse the various hazards factors in the most important sector of the economy. In this analysis, critical hazards variables were identified and classified based on HSE standards OSHA. Kendall’s Coefficient of Concordance (KCC ) and Principal Component Analysis (PCA) was employed to analyse the set of data generated from respondents and summarize them into a number of factors that would promote occupational safety and health in industries. KCC was used to analyze data matrix generated by thirteen Judges who were requested to rank the thirty two identified hazards variables in merit order of sequentiality scaled with 5-point Rensis Likert’s attitudinal scale and administered to 22 respondents where only 13 were retrieved. The PCA aided by StatistiXL software package was proficient in achieving parsimony in factor reduction from thirty-two variables to mere five factors. The results obtained by KCC suggested that the judges ranking of the thirty two variables were consistent with index of concordance computed as W = 0.958. The result by PCA indicates that five factors creatively labelled: Work World Culture, Ground Rule Matters, Safety Considerations, Work Condition and Perception of Safety represent the principal factors that influence the industrial safety. It is hoped that this would help to unwrapped the deeper meanings associated with multi-dimensional factors in industrial safety.

Keywords

Principal Component Analysis, Hazards, Industrial accident, Concordance, Hazards, Industrial accident, Concordance

References

• [1] Igboanugo, A.C (2010). Markov Chain Analysis of Accident Data: The case of an oil and gas Firm in Niger Delta Area of Nigeria. International Journal of Engineering Research in Africa Vol.1(2010) pp. 29-38.
• [2] Brameret, P.A., Roussel, J.M., Rauzy, A. (2013). Preliminary System Safety Analysis With Limited Markov Chain Generation 4th IFAC Workshop on dependable Control of Discrete Systems (DCDS 2013), Paper n3, hal-00859173.
• [3] Nikbakht, M. (2011). Hazard Identification and Accident Analysis on City Gate Station in Natural Gas Industry. Proceedings of 2011 International conference on Industrial Engineering and Operations, pp.3-4.
• [4] Okwu, M. O., Nwaoha, T., Garrick, O. (2016). Application of Markov theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in Oil and Gas Sector. International Journal of Mechanical Engineering and Applications Vol. 4 No 3, pp.103-108.
• [5] Tam, C. M and Fung, I.W.H. (1998). Effectiveness of Safety Management Strategies on Safety Performance in Hong Kong. Journal of construction Management and Economics, vol.16, No.1, PP. 49-55.
• [6] Ghaffer, A., Hyder, A., Mastoor, M., and Sheikh, I. (1999). Injuries in Pakistan: Directions for future health policy. Journal of health policy and planning, vol.14, No.1, pp.11-17(7).
• [7] Koch, K and Salter, N. (1999). The Health and Safety System in the Fedral Republic of Germany. Journal of Industrial Relations, vol.30, No.1, pp.61-71(11). Blackwell Publishing.
• [8] Inoue, Y., Hiroe, Yu, Nishida, M. and Shirakawa, S. (2000). Sleep Problems in Japanese Industrial workers. Journal of psychiatry and Clinical Neurosciences,vol.54, No. 3, PP.294-295.
• [9] Sneddon, A., Mearns, K and Flin, R. (2006). Situation awareness and safety in offshore drill crews. Journal of Cognition, Technology and Work, vol.8, No. 4, pp. 255-267.
• [10] Denis-Remis, C. (2006). How can insurance benefit from more effective training programmes. The case of behavioural mitigation. International Journal of Emergency Management, vol.3, No.1, pp.73-82.
• [11] Zhong, M., Liu, T., Geng, F., Wei, X and Chen, X. (2006). The current construction status of China emergency systems against major industrial accidents. International Journal of Emergency Management, vol.3, No.2-3, pp.101-113.
• [12] Monday, O. A. (2013). Occupational health and safety in the oil and gas industry, Journal of Research in National Development JORIND.vol.2 pp. 11.
• [13] Achojakimoni, I. (2004). Hazards control measurement. Journal of Engineering and Emerging Trend. vol. 26, pp. (2004). 34-47.
• [14] Amol, P. (2011). Hazard identification and risk analysis in mining industry, Master’s Degree Thesis in Mining Engineering, National Institute of Technology, Rourkela.
• [15] Jeong, K., Lee, D., Lee, K. and Lim H, (2007). A qualitative identification and analysis of hazards, risks and operating procedures for a decommissioning safety assessment of a nuclear research reactor, Annals of Nuclear Energy, 35, 1954-1962.
• [16] Jelemenesky, L., Harisova, J. and Markos, J., (2003). Reliable risk estimation in the risk analysis of chemical industry case study: Ammonia storage pressurized spherical tank. 30th International Conference of the Slovak Society of Chemical Engineering, 58, 48-54.
• [17] Dziubinski, M., Fratczak, M. and Markowski, A. (2006), S.Pipeline failure and its possible consequences. Journal of Loss Prevention in the Process Industries, vol.19, pp.399-408.
• [18] Ramin, S. (2012). Forecasting time and place of earthquakes using a Semi-Markov model. Journal of Industrial Engineering International, pp.1-7.
• [19] Peng, K and Nisbett, R.E. (1997). Cross-Cultural similarities and differences in the understanding of physical causality. In M. Shield (ed.), Proceedings of conference on culture and science. Frankfort: Kentucky state university Press, pp.45-55.