ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number

• Published in: Computers & industrial engineering 2017-06, Vol.108, p.100-110
• Main Authors: Certa, Antonella, Enea, Mario, Galante, Giacomo Maria, La Fata, Concetta Manuela
• Format: Article
• Language: English
• Subjects: ELECTRE TRI; Failure modes classification; FMECA
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2017.04.018

•Proposing an ELECTRE TRI-based approach for the failure modes classification into predefined and ordered risk classes.•Direct identification of failure modes on which performing the corrective actions with priority.•Easy definition of risk classes on the basis of DMs expertise and perception of the industrial context considered.•Application to dairy manufacturing processes. Failure Mode and Effects Analysis (FMEA) is an engineering technique aimed at the detection of potential failures, their causes and consequences on the system/process under investigation. When used for the failure modes prioritization, FMEA is also referred to as Failure Mode, Effects and Criticality Analysis (FMECA). In traditional FMECA, risk priorities of failure modes are determined through the Risk Priority Number (RPN), which is a function of the three risk parameters Occurrence (O), Severity (S), and Detection (D). In the present paper, an alternative approach to the RPN is proposed for the criticality assessment of process/system failure modes. Particularly, the Multi-Criteria Decision Making (MCDM) method ELECTRE TRI is employed to assign failure modes to predefined and ordered risk classes, from the highest to the lowest risky one. Contrarily to the traditional RPN, the method allows the Decision Maker (DM) at taking into account the relative importance of risk parameters as well as his/her uncertainty in assigning each failure mode to a specific risk class. The ELECTRE TRI-based approach is implemented on the applicative case proposed by Kurt and Özilgen (2013) with reference to Turkish dairy manufacturing industries. A sensitivity analysis is finally performed in order to test the influence of the input parameters on the classification results.