0545784 20220322093721.320210923235959.9 85114143180 000702351700087 10.1016/j.ress.2021.108022 16 s. cav_un_epca*02548910951-8320216Elsevier Complex systems DEMATEL ELECTRE TRI FMECA Maintenance management Predictive maintenance Risk management cav_un_auth*0414171 Ahmed U. IT cav_un_auth*0398866 Carpitella Silvia UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR IT Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0399068 Certa A. IT http://library.utia.cas.cz/separaty/2021/MTR/carpitella-0545784.pdf https://www.sciencedirect.com/science/article/pii/S0951832021005317 The present paper addresses the relevant topic of maintenance management, widely recognised as a fundamental issue involving complex engineering systems and leading companies towards the optimisation of their assets while pursuing cost efficiency. With this regard, our research aims to provide companies with a hybrid methodological approach based on Multi-Criteria Decision-Making (MCDM) capable to deal with the main failures potentially involving complex systems subjected to predictive maintenance. Such an approach is going to be integrated within the framework of traditional Failure Mode Effects and Criticality Analysis (FMECA), whose strengths and weaknesses are considered. In particular, the ELimination Et Choix Traduisant la REalité (ELECTRE) TRI is suggested to sort failure modes into risk priority classes while the Decision Making Trial and Evaluation Laboratory (DEMATEL) is proposed to highlight the most influencing failures within each risk class. The approach is applied to a real service system, whose critical components are monitored by sensors and subjected to predictive maintenance. Final results clearly demonstrate as highlighting the elements impacting the occurrence of other failures within specific risk classes is a significant driver towards the implementation of effective maintenance, maximising the whole level of performance of the analysed system over its lifecycle. SCOPUS AQ 30000 30300 30305 2022 3 RVO:67985556 http://hdl.handle.net/11104/0322773 1 U 108022 1 Article Engineering Industrial|Operations Research Management Science C ENGINEERING.INDUSTRIAL|OPERATIONSRESEARCH&MANAGEMENTSCIENCE 7.310 2.016 5.6 0.01646 1.04 26227 196 1.824 50.42 7.03 8765 Q1 4.659 735 Q1 83.5 Q3 Q1 1.4 Q1 87.931 2021 85114143180 SCOPUS PUBMED 000702351700087 WOS cav_un_epca*0254891 Reliability Engineering & System Safety 0951-8320 1879-0836 Roč. 216 č. 1 2021 Elsevier