Journal of Failure Analysis and Prevention

, Volume 16, Issue 6, pp 1024–1037 | Cite as

An Analytical Model to Measure the Effectiveness of Safety Management Systems: Global Safety Improve Risk Assessment (G-SIRA) Method

  • Gianpaolo Di Bona
  • Alessandro Silvestri
  • Fabio De Felice
  • Antonio Forcina
  • Antonella Petrillo
Technical Article---Peer-Reviewed


The ever-increasing complexity of production systems, together with the need to obtain efficient processes with limited costs, has led companies to develop custom tools for process control and management. Even for risk assessment, the traditional models often are overcome by methods that are best suited to specific needs. In this context, the aim of this paper was to propose a new model, which we call the global safety improve risk assessment (G-SIRA). This model can classify risks and identify corrective actions that allow the best risk reduction at the lowest cost. The proposed model, which is based on improvements to previous research, uses the analytic hierarchy process approach to develop a valid and simple tool for risk management. The G-SIRA method has been tested in a real-world application, i.e., it was applied to all of the processes of a textile company, and the results were compared with those obtained from the classical approach failure mode, effects, and criticality analysis. The comparison clearly showed the effectiveness of the proposed model.


Risk priority number Safety FMECA AHP/ANP 


  1. 1.
    M. Fera, R. Macchiaroli, Appraisal of a new risk assessment model for SME. Saf. Sci. 48(2010), 1361–1368 (2010)CrossRefGoogle Scholar
  2. 2.
    T. Aven, Risk assessment and risk management: review of recent advances on their foundation. Eur. J. Oper. Res. 253(2016), 1–13 (2016)CrossRefGoogle Scholar
  3. 3.
    M.H. Whittaker, Risk assessment and alternatives assessment: comparing two methodologies. Risk Anal. 35(2), 2129–2136 (2015)CrossRefGoogle Scholar
  4. 4.
    E. Garbolino, J.P. Chery, F. Guarnieri, A simplified approach to risk assessment based on system dynamics: an industrial case study. Risk Anal. 36(1), 16–29 (2016)CrossRefGoogle Scholar
  5. 5.
    G. Carmignani, An integrated structural framework to cost-based FMECA: the priority-cost FMECA. Reliab. Eng. Syst. Saf. 94(2009), 861–871 (2009)CrossRefGoogle Scholar
  6. 6.
    T.L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation (McGraw-Hill, New York, 1980)Google Scholar
  7. 7.
    F. De Felice, A. Petrillo, Proposal of a structured methodology for the measure of intangible criteria and for decision making. Int. J. Simul. Process Model. 9(3), 157–166 (2014)CrossRefGoogle Scholar
  8. 8.
    H.C. Liu, J.X. You, X.Y. You, M.M. Shan, A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method. Appl. Soft Comput. 28, 579–588 (2015)CrossRefGoogle Scholar
  9. 9.
    P.-S. Chen, M.-T. Wu, A modified failure mode and effects analysis method for supplier selection problems in the supply chain risk environment: a case study. Comput. Ind. Eng. 66, 634–642 (2013)CrossRefGoogle Scholar
  10. 10.
    M. Bevilacqua, M. Braglia, The analytic hierarchy process applied to maintenance strategy selection. Reliab. Eng. Syst. Saf. 70, 71–83 (2000)CrossRefGoogle Scholar
  11. 11.
    Y. Lin, D. Quan, P. Chen, The improved failure mode effects and criticality analysis method based on analytic hierarchy process. 65th International Astronautical Congress 2014: Our World Needs Space, vol. 9, IAC 2014, Toronto, Canada; 29 September–3 October, 2014, pp. 6169–6180Google Scholar
  12. 12.
    S. Zhang, Q. Zeng, G. Zhang, A new approach for prioritization of failure mode in FMECA using encouragement variable weight AHP. Appl. Mech. Mater. 289(2013), 93–98 (2013)Google Scholar
  13. 13.
    P. Trucco, M. Cavallin, F. Lorenzi, A standardised FMECA and risk factors monitoring method for clinical risk assessment: results from a multi centric application. 11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, vol. 7, ESREL 2012, pp. 5966–5975Google Scholar
  14. 14.
    F. Zammori, R. Gabbrielli, ANP/RPN: a multi criteria evaluation of the risk priority number. Qual. Reliab. Eng. Int. 28(1), 85–104 (2012)CrossRefGoogle Scholar
  15. 15.
    A. Silvestri, F. De Felice, A. Petrillo, Multi-criteria risk analysis to improve safety in manufacturing systems. Int. J. Prod. Res. 50(17), 4806–4821 (2012)CrossRefGoogle Scholar
  16. 16.
    C. Madu, Competing through maintenance strategies. Int. J. Qual. Reliab. Manag. 17(9), 937–948 (2000)CrossRefGoogle Scholar
  17. 17.
    P.C. Teoh, K. Case, Failure modes and effects analysis through knowledge modeling. J. Mater. Process. Technol. 153–154(2004), 253–260 (2004)CrossRefGoogle Scholar
  18. 18.
    P.N. Muchiri, L. Pintelon, H. Martinb, A.M. De Meyer, Empirical analysis of maintenance performance measurement in Belgian industries. Int. J. Prod. Res. 48, 5905–5924 (2010)CrossRefGoogle Scholar
  19. 19.
    D.C. Aguiar, H.J.C. de Souza, V.A.P. Salomon, An AHP application to evaluate scoring criteria for failure. Int. J. Anal. H. Process 2, 1936–6744 (2010)Google Scholar
  20. 20.
    M. Ben-Daya, A. Raouf, A revised failure mode and effect analysis model. Int. J. Qual. Reliab. Manag. 13(1), 43–47 (1996)CrossRefGoogle Scholar
  21. 21.
    W. Gilchrist, Modelling failure modes and effects analysis. Int. J. Qual. Reliab. Manag. 10(5), 16–23 (1993)CrossRefGoogle Scholar
  22. 22.
    C.E. Pelaez, J.B. Bowles, Using fuzzy logic for system criticality analysis, in Proceedings of the IEEE Annual Reliability and Maintainability Symposium, 1994, pp. 449–55Google Scholar
  23. 23.
    J.B. Bowles, An assessment of RPN prioritization in a failure modes effects and criticality analysis. Reliability and maintainability symposium, 2003, pp. 380–386Google Scholar
  24. 24.
    T.L. Saaty, Decision Making with Dependence and Feedback: The Analytic Network Process (RWS, Pittsburgh, 1996)Google Scholar
  25. 25.
    A. Silvestri, F. De Felice, D. Falcone, G. Di Bona, R.A.M.S. Analysis in a sintering plant by the employment of a new reliability allocation method modelling and simulation, 2004, Marina del ReyGoogle Scholar
  26. 26.
    J. Fleischer., U. Weismann, S. Niggeschmidt, Calculation and optimisation model for costs and effects of availability relevant service elements, Proceedings of 13th CIRP International Conference on Life Cycle Engineering (LCE2006), 31 May–2 June, 2006, Leuven, Belgium. Leuven: Acco, pp. 675–680.Google Scholar

Copyright information

© ASM International 2016

Authors and Affiliations

  1. 1.Department of Civil and Mechanical EngineeringUniversita degli Studi di Cassino e del Lazio Meridionale Ringgold Standard InstitutionCassinoItaly
  2. 2.University of Naples “Parthenope” NaplesNaplesItaly

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