Evaluation of Chemical Organizations According to The Root Causes of Industrial Accidents with Analytical Hierarchy Process

Abstract

In this study, it was aimed to evaluate the accident risk of chemical organizations according to the root causes of industrial accidents with the Analytical Hierarchy Process (AHP). First of all, the data of occupational accidents with death and loss of limb in the chemical industry between 2015 and 2020 were evaluated with Pareto analysis by using the statistics of the Social Security Institution (SSI). As a result of the analysis, the main criteria that make up 80% of the accidents in the sector were obtained. Then, the final main and sub-criteria that could be the root cause of industrial accident were determined, and these criteria were weighted over the opinions of the relevant experts using AHP method. Human errors, one of the main criteria, were determined as the most important criterion with 35%. By applying AHP for the second time, three sample high-level organizations were evaluated according to the root causes of industrial accidents. Organizations showed a distinctive ranking in terms of industrial accident risk (Organization B= 0.420> Organization A= 0.354 >Organization C= 0.226). With the proposed methodology, quantitative and qualitative evaluation criteria were included in the model simultaneously, and an objective result was obtained through expert opinions.

Keywords

• Industrial Accidents
• OHS
• Pareto Analysis
• MCDM
• AHP

References

1. Alekseeva, A., Volokhina, A., & Glebova, E. (2020). Identification of the root causes of accidents at hazardous production facilities of the fuel and energy complex. IOP Conference Series: Materials Science and Engineering, 2-5. https://doi.org/10.1088/1757-899X/976/1/012021
2. Andersan, B., & Fagerhaug, T. (2006). Root Cause Analysis, Simplified Tools and Techniques (Second Edition). Milwaukee, Wisconsin, ASQ Quality Press.
3. Aycin, E. (2019). Multi-Criteria Decision Making Computer Applied Solutions, Ankara, Nobel Publications, 333.
4. Botti, L., Melloni, R., Mosconi S., & Oliva M. (2020). A Detailed Investigation on Apparent and Root Causes of Accidents in Manufacturing, Advances in Manufacturing, Production Management and Process Control, 18-25.
5. Bragatto, P., Vairo, T., Maria, F. M., & Fabiano, B. (2021). The impact of the COVID-19 pandemic on the safety management in Italian Seveso industries. Journal of Loss Prevention in the Process Industries, 70. https://doi.org/10.1016/j.jlp.2021.104393
6. Ciftci, S. E., & Arikan, F. (2020). A Multiple Criteria Risk Analysis Model and a Case Study in Metal Industry, Open Journal of Business and Management, 8, 2048-2070. https://doi.org /10.4236/ojbm.2020.85125
7. Dabous, S. A., Ibrahim, F., Feroz, S., & Alsyouf, I. (2021). Integration of failure mode, effects, and criticality analysis with multi-criteria decision-making in engineering applications: Part I – Manufacturing industry. Engineering Failure Analysis, 122. https://doi.org/10.1016/j.engfailanal.2021.105264
8. Dincer, H., & Gorener, A. (2011). Dynamic performance analysis with analytical hierarchy process and VIKOR technique: An application in the banking sector. Istanbul Commerce University Journal of Social Sciences, 19, 10, 109-127.

9. Fata, C. M., Giallanza, A., Micale, R., & La Scalia G. (2021). Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR. Safety Science, 138. https://doi.org/10.1016/j.ssci.2021.105234.
10. Gal, T., Stewart, T.J., & Hanne, T.(1999). Multicriteria Decision Making Advances in MCDM Models, Algorithms, Theory, and Applications. Frederick S. Hillier, International Series in Operations Research & Management Science (ISOR, volume 21), Springer Science+ Business Media, LLC.
11. Ghatorha, K., Sharma, R., & Singh, G. (2020). Application of root cause analysis to increase material removal rate for productivity improvement: A case study of the press manufacturing industry. Materials Today: Proceedings, 26(2), 1780-1783. https://doi.org/10.1016/j.matpr.2020.02.374
12. Gonyora, M., & Ventura-Medina, E. (2024). Investigating the relationship between human and organisational factors, maintenance, and accidents. The case of chemical process industry in South Africa. Safety Science, 176, 106530. https://doi.org/10.1016/j.ssci.2024.106530
13. Gul, M. (2020). Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and coloring unit. International Journal of Occupational Safety and Ergonomics, 26(4), 705-718. https://doi.org/10.1080/10803548.2018.1492251
14. Gul, M. (2018). A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Human and Ecological Risk Assessment: An International Journal, 24, 1-38. https://doi.org/10.1080/10807039.2018.1424531
15. Heller, S. (2006). Managing industrial risk—Having a tested and proven system to prevent and assess risk. Journal of Hazardous Materials, 130, 1–2, 18-63. https://doi.org/10.1016/j.jhazmat.2005.07.067
16. Ilbahar, E., Karasan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124-136. https://doi.org/10.1016/j.ssci.2017.10.025
17. Koulinas, G.K., Marhavilas, P.K., Demesouka, O.E., Vavatsikos, A.P., & Koulouriotis, D.E. (2019). Risk analysis and assessment in the worksites using the fuzzy-analytical hierarchy process and a quantitative technique – A case study for the Greek construction sector. Safety Science, 112, 96-104. https://doi.org/10.1016/j.ssci.2018.10.017
18. Li, G., Joe Qin, S., & Yuan, T. (2016). Data-driven root cause diagnosis of faults in process industries. Chemometrics and Intelligent Laboratory Systems, 159, 1-11. https://doi.org/10.1016/j.chemolab.2016.09.006
19. Multiple Criteria Decision Making Applications in Management and Engineering, Constantin Zopounidis, Michael Doumpos, Springer International Publishing, Switzerland, 2017.
20. Ozdemir, U., Altinpinar, İ., & Demirel, F.B. (2018). A MCDM Approach with Fuzzy AHP Method for Occupational Accidents on Board, TransNav, The International Journal on Marine Navigation and Safety of Sea Transportation, 12(1), 93-98. https://doi.org /10.12716/1001.12.01.10
21. Oz, N. E., Mete, S., Serin, F., & Gul, M. (2019). Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards. Human and Ecological Risk Assessment: An International Journal, 25(6), 1615-1632. https://doi.org/10.1080/10807039.2018.1495057
22. Ozguvenc, D. (2011). Multi-criteria Pareto Analysis in the classification of quality problems, Master Thesis, Istanbul Technical University, Institute of Science and Technology, Istanbul.
23. Ouédraogo, A., Groso, A., & Meyer, T. (2011). Risk analysis in research environment–part II: weighting lab criticity index using the analytic hierarchy process. Safety Science, 49, 6, 785-793. https://doi.org/10.1016/j.ssci.2010.12.011
24. Prevention of major industrial accidents, An ILO contribution to the International Programme on Chemical Safety of UNEP, the ILO and the WHO (IPCS), International Labour Office, Geneva, 1991.
25. Regulation on Prevention of Major Industrial Accidents and Mitigation of Their Effects, Official Gazette dated 02.03.2019 and numbered 30702 (2019).
26. Reniers, G., & Cozzani, V. (2013). Domino Effects in the Process Industries Modelling. Prevention and Managing, Oxford: Elsevier.
27. Saaty, T.L. (1986). Axiomatic Foundations of The Analytic Hierarchy Process. Management Science, 32,7, 841-855. https://doi.org/10.1287/mnsc.32.7.841
28. Saaty, T.L. (1990). How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26. https://doi.org/10.1016/0377-2217(90)90057-I
29. Seker, S., & Zavadskas, E. (2017). Application of Fuzzy DEMATEL Method for Analyzing Occupational Risks on Construction Sites. Sustainability, 9, 2083. https://doi.org/10.3390/su9112083
30. Simsek, A.B., Kose, G., & Goktekin, Z. (2024). Evaluating country performance in preventing industrial accidents: A multi-criteria decision analysis approach. Journal of Loss Prevention in the Process Industries, 87, 105241. https://doi.org/10.1016/j.jlp.2023.105241
31. Singh, K., & Maiti J. (2020). A novel data mining approach for analysis of accident paths and performance assessment of risk control systems. Reliability Engineering & System Safety, 202, 107041. https://doi.org/10.1016/j.ress.2020.107041
32. Turskis, Z., Dzitac, S., Stankiuvienė, A., & Šukys, R. (2019). A Fuzzy Group Decision-making Model for Determining the Most Influential Persons in the Sustainable Prevention of Accidents in the Construction SMEs. International Journal of Computers Communications & Control, 14, 90-106.
33. Viegas, R. A., Mota, F. S., Costa, A. P., & Santos, F. F. (2020). A multi-criteria-based hazard and operability analysis for process safety. Process Safety and Environmental Protection, 144, 310-321. https://doi.org/10.1016/j.psep.2020.07.034
34. Wang, R., Gao, X., Gao, J., Gao, Z., & Kang, J. (2018). An information transfer based novel framework for fault root cause tracing of complex electromechanical systems in the processing industry. Mechanical Systems and Signal Processing, 101, 121-139. https://doi.org/10.1016/j.ymssp.2017.08.030
35. Wang, Y. F., Xie, M., Chin, K., & Fu, X. J. (2013). Accident analysis model based on Bayesian Network and Evidential Reasoning approach. Journal of Loss Prevention in the Process Industries, 26(1), 10-21. https://doi.org/10.1016/j.jlp.2012.08.001
36. Yildirim, B.F., & Onder, E. (2014). Multi-Criteria Decision Making Methods in the Solution of Operational, Managerial and Strategic Problems for Businesses, Engineers and Managers, Bursa: Dora Publications. Yilmaz, F., & Alp, S. (2016). Underlying Factors of Occupational Accidents: The Case of Turkey. Open Journal of Safety Science and Technology, 6(1), 4-8. https://doi.org /10.4236/ojsst.2016.61001
37. Yucesan M., & Gul M. (2021). Failure modes and effects analysis based on neutrosophic analytic hierarchy process: method and application. Soft Computing, 25, 11035–11052. https://doi.org/10.1007/s00500-021-05840-z
38. Zhang, J., Fu, J., Hao, H., Fu, G., Nie, F., & Zhang, W. (2020). Root causes of coal mine accidents: Characteristics of safety culture deficiencies based on accident statistics. Process Safety and Environmental Protection, 136, 78-91. https://doi.org/10.1016/j.psep.2020.01.024