Analyzing occupational risks of pharmaceutical industry under uncertainty using a Bow-Tie analysis

Analyzing occupational risks of pharmaceutical industry under uncertainty using a Bow-Tie analysis

Risk analysis is a systematic and widespread methodology to analyze and evaluaterisks which are exposed in many working areas. One of the Quantitative RiskAnalysis (QRA) methods for risk assessment is Bow-Tie analysis which combinesfeatures of fault-tree analysis and event-tree analysis to identify the top event; itscauses and consequences (outcomes); and possible preventive and protectivecontrol measures or barriers. This study proposes an occupational risk assessmentapproach, which is known as Fuzzy Bow-Tie analysis, for pharmaceutical industryprocesses and work units. The aim is to evaluate critical risks and riskypharmaceutical work units and take safety precautions against accidents whichcaused by risky conditions. Thus, this methodology combines the concept ofuncertainty which comes from different (Decision Maker) DM’s evaluations andthe whole performance of the Bow-Tie analysis for hazard identification and riskassessment. To apply and validate the proposed method, a case study is performedfor pharmaceutical industry processes and work units. Based on the computed riskscore, which is calculated by multiplying probability ranking and impact rankingof criterion, the risks are prioritized and some measures are suggested formanagement to prevent accidents occur in the industry.

___

  • Attaianese, E. & Duca, G. (2012). The integrated assessment of occupational risks in a pharmaceutical manufacturing plant. Work, IOS press, 41, 1733- 1738. DOI: 10.3233/WOR-2012-0377-1733.
  • Keith, T. (1998). Encyclopaedia of Occupational Health and Safety. Fourth Edition Edited Jeanne Mager Stelman, International Labour Office, Chapter 79.
  • Naumann, B., Sargent, EV., Starkman, BS., Fraser, WJ . Becker, GT & Kirk, GD. (1996). Performance- based exposure control limits for pharmaceutical active ingredients. American Industrial Hyjgiene Association Journal, 57: 33-42.
  • Evelyn, N., Cristophe, R., Nathalie, B. & Alessandro, S. (2018). Chemical Risk Assessment Screening Tool of a Global Chemical Company. Safety and Health at Work, 9(1) 84-94.
  • Binks, S.P. (2003). Occupational toxicology and the control of exposure to pharmaceutical agents at work. Occupational Medicine, 53:363–370.
  • Shahriar , A., Sadiq, R. Tesfamariam, S. (2012). Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based Bow-Tie analysis. Journal of Loss Prevention in the Process Industries, 25(3), 505–523.
  • Khan, FI., Abbasi SA. (2001). Risk analysis of a typical chemical industry using ORA procedure. Journal of Loss Prevention in the Process Industries, 14, 43–59.
  • Sklet S. (2004). Comparison of some selected methods for accident investigation.Journal ofHazardous Materials.111 (1-3),29-37.
  • Couronneau, J.C., Tripathi, A. (2003). Implementationofthenewapproachofriskanalysis in France. Proceedings of the 41st InternationalPetroleum Conference, Bratislava,Slovakia.
  • Jacinto, C., Silva, C. (2010). A semi-quantitative assessment of occupational risks using Bow-Tie representation.Safety Science,48,973–979.
  • Khan FI, Abbasi SA. (1998). Techniques and methodologies for risk analysis in chemical process industries.JournalofLossPreventionintheProcessIndustries,11: 261–277.
  • Delvosalle, C., Fievez, C., Pipart, A., Fabreg, C.J., Debray, B. (2006). ARAMIS project: a comprehensivemethodologyfortheidentificationof reference accident scenarios in process industries.Journal of Hazardous Materials,130,200–219.
  • Montano, D. (2014). Chemical and biologicalwork- related risksacross occupations in Europe: a review. MontanoJournal of OccupationalMedicine and Toxicology2014,9:28.
  • Gowland, R. (2006). The accidental risk assessment methodology for industries (ARAMIS)/layer of protection analysis (LOPA) methodology: a step forward towards convergent practices in risk assessment?Journal of Hazardous Materials,130, 307–310.
  • Khan,F.(2001). Use maximum-credible accident scenario for realistic and reliable risk assessment.Chemical Engineering Progress, 11,56–64.
  • Chevreau,F.R.,Wybo,J.L.,&Cauchois,D.(2006). Organizing learning processes on risks by using the Bow-Tie representation.Journal of HazardousMaterials, 130(3), 276.doi:10.1016/j.jhazmat.2005.07.018.
  • Khakzad, N., Khan, F., Amyotte, P. (2013). Quantitative risk analysis of offshore drilling operations: a Bayesian approach.Safety Science,57, 108-117.
  • Deacon, T., Amyotte, P.R., Khan, F.I. (2013). A framework for human error analysis of offshore evacuations.Safety Science,51 (1),319-327.
  • Deacon, T., Amyotte, P.R., Khan, F.I. (2010). Human error risk analysis in offshore emergencies.Safety Science,48 (6),803-818.
  • Khakzad, N., Khan, F., Amyotte, P. (2012). Dynamic risk analysis using Bow-Tie approach.Reliability Engineering and System Safety,104,36- 44.
  • Cockshott, J.E. (2005). Probability Bow-Ties: a transparent risk management tool.Process Safetyand Environmental Protection,83 (4),307-316.
  • Duijm, N. J. (2009). Safety-barrier diagrams as a safety management tool.Reliability Engineering &System Safety, 94(2), 332-341. doi:10.1016/j.ress.2008.03.031.
  • Badreddine,A.,BenHajKacem,M.A.,BenAmor N. (2014). A three stages to implement barriers in Bayesian-based Bow-Tie diagram.27rdInternational Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE2014, 32-41.
  • Dianous, V., Fivez, C. (2006). ARAMIS project: a more explicit demonstration of risk control through the use of Bow-Tie diagrams and the evaluation of safety barrier performance.Journal of HazardousMaterials,130(3),220-233.
  • Mokhtari, K., Ren, J., Roberts, C., Wang, J. (2011). ApplicationofagenericBow-Tiebasedriskanalysis framework on risk management of sea ports and offshore terminals.Journal ofHazardous Materials,192(2),465-475.
  • Preeda S., Min A. (2015). Development of Risk Assessment and Occupational Safety Management Model for Building Construction Projects. World Academy of Science.Engineering and TechnologyInternational Journal of Civil and Environmental EngineeringVol.9,No.9.
  • Agarwal, H., Renaud, J. E., Preston, E. L., & Padmanabhan,D.(2004).Uncertaintyquantification using evidence theory in multidisciplinary design optimization.Reliability Engineering & SystemSafety, 85(1-3), 281-294. doi:10.1016/j.ress.2004.03.017.
  • Ayyub, B. M., & Klir, G. J. (2006).Uncertaintymodeling and analysis in engineering and the sciences.Boca Raton, FL 33487-2742, US: Chapman &Hall.
  • Wilcox, C.R., Ayyub, M.B. (2003). Uncertainty modeling of data and uncertainty propagation for risk studies.IEEE Proceedings on UncertaintyModeling and Analysis,184–191.
  • Bae, H., Grandhi, R. V., & Canfield, R. A. (2004). An approximation approach for uncertainty quantification using evidence theory.ReliabilityEngineering & System Safety,86(3), 215. doi:10.1016/j.ress.2004.01.011.
  • Boudraa, A., Bentabet, A., Salzenstein, F., & Guillon, L. (2004). Dempster-Shafer’s basic probability assignment based on fuzzy membership functions.Electronic Letters on Computer Visionand Image Analysis, 4(1),1-10.
  • Sentz, K., & Ferson, S. (2002). Combination of evidence in Dempster-Shafer theory.Technical USDepartment of Energy. Sandia National Laboratories.
  • Gentile,M.,Rogers,W.J.,&Mannan,M.S.(2003). Development of an inherent safety index based on fuzzy logic.Process Safety and EnvironmentalProtection,81(6),444-456.
  • Markowski, A. S. (2006). Layer of protection analysis for the process industry.Lodz: PAN, ISBN83-86-492-36-8.
  • Salzano, E., & Cozzani, V. (2006). A fuzzy set analysis to estimate loss intensity following blast waveinteractionwithprocessequipment.JournalofLoss Prevention in the Process Industries, 19,343-352.
  • Abrahamsson, M. (2002).Uncertainty inQuantitative Risk Analysis—Characterization and Methods of Treatment. Department of Fire Safety Engineering.Lund University,Sweden.
  • Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch, B. (2009). Methodology for computer aided fuzzy fault tree analysis.Process Safety andEnvironment Protection,87(4),217–226.
  • Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch, B. (2010). Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations.Risk Analysis: An InternationalJournal,31(1),86–107.
  • Markowski, A., Sam Mannan, M., Kotynia, A. (Bigoszewska)., Siuta, D (2010). Uncertainty aspects in process safety analysis.Journal of LossPrevention in the Process Industries,23,446-464.
  • Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch,B.(2011).Analyzingsystemsafetyandrisks under uncertainty using a Bow-Tie diagram: an innovative approach.Process Safety andEnvironmental Protection, doi:10.1016/j.psep.2011.08.010.
  • Tanaka, H., Fan, T.L., Lai, F.S., Toughi, K. (1983). Fault tree analysis by fuzzy probability.IEEETransactions on Reliability 32(5),455–457.
  • Singer,D.(1990).Afuzzyapproachtofaulttreeand reliability analysis.Fuzzy Sets and Systems 34,145–155.
  • Aqlan, F., Mustafa Ali, E. (2014). Integrating lean principles and fuzzy Bow-Tie analysis for risk assessment in chemical industry.Journal of LossPrevention in the Process Industries,29,39-48.
  • Markowski, A. S., Mannan, M. S., &Bigoszewska, A. (2009). Fuzzy logic for process safety analysis.JournalofLossPreventionintheProcessIndustries,22(6), 695-702.doi:10.1016/j.jlp.2008.11.011.
  • Bhusnure, O. G., Dongare, R. B., Gholve, S. B., Giram, P. S. (2018). Chemical hazards and safety management in pharmaceutical industry.Journal ofPharmacy Research,Vol. 12, Issue3.
  • Gathuru, I.M., Buchanich, J., Marsh, G., Dolan, D. (2015). Health Hazards in the Pharmaceutical Industry.Pharmaceutical Regulatory Affairs,4:3, DOI:10.4172/2167-7689.1000145.
  • Kumar,P.,Shukla,J.(2010).RıskAnalysisandRisk Management In Pharmaceutıcal Industry.Internatıonal Journal of Pharma World Research,Vol. 1, Issue 1.
  • Urushihara H, Kobashi G, Masuda H, Taneichi S, Yamamoto M, Nakayama T, Kawakami K,Matsuda T, Ohta K, Sugimori H. (2014).Pharmaceuticalcompany perspectives on current safety risk communications in Japan. SpringerPlus,3:51.
  • Bhowmik, D., Durai Vel, S., Rajalakshmi A.N, Sampath Kumar, K. (2014). Recent Trends in Hazards in the Pharmaceutical Industry and Safety Precaution.Elixir Pharmacy,69,23688-23691.
  • Patrick T.W., Guchelaar, H., Guchelaar, H. (2003). Risk assessment in clinical pharmacy.PharmacyWorld and Science; 25(3),98–103.
  • Anthony P. Acfield and Dr. Robert A. Weaver (2012). Integrating Safety Management through the Bow-Tie Concept A move away from the Safety Case focus.Australian System Safety Conferrence(ASSC2012).
  • Shuang, C., Jinqiu, H., Laibin, Z. (2016). Risk analysisofrefiningequipmentbasedonfuzzytheory andBow-Tiemodel.Proceedingsofthe35thChineseControl Conference July 27-29, 2016,Chengdu, China.
  • Zadeh, L.A. (1965). Fuzzy sets.Information andControl,8, 339-353,1965.
  • Zadeh,L.A.(1978).Fuzzysetsasabasisforatheory of possibility.Fuzzy sets and systems,1,3-28.
  • Lin, C., Wang, M.J. (1997). Hybrid fault tree analysis using fuzzy sets.Reliability Engineeringand System Safety,58,205–213.
  • Lee, K. (2005).First Course on Fuzzy Theory andApplications. Springer, ISBN978-3-540-32366-2.
  • L.X. Wang, (1997).A Course in Fuzzy Systems andControl.Prentice-Hall InternationalInc.
  • Cheliyan,A.S.,Bhattacharyya,S.K. (2018). Fuzzy fault tree analysis of oil and gas leakage in subsea production systems.Journal of Ocean Engineeringand Science,3(1),38-48.
  • Tyagi, S., Pandey D., Tyagi, R. (2010). Fuzzy set theoretic approach to fault tree analysis.International Journal of Engineering, Science andTechnology,2(5),276-283.
  • Sangaiah,A.,Kumar,A.,Thangavelu,A.(2013).An exploration of FMCDM approach for evaluating the outcome/success of GSD projects.CentralEuropean Journal of Engineering,3(3), 419-435.DOI:10.2478/s13531-012-0070-9.
  • Tsenga, T., Konadaa , U., Kwon Y. (2016). A novel approach to predict surface roughness in machining operations using fuzzy set theory.Journal ofComputational Design and Engineering,3,1–13.