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RESEARCH PRODUCT
Assessing supply chain risks in the automotive industry through a modified MCDM-based FMECA
Ilyas MzouguiAntonella CertaJoaquín Izquierdo SebastiánSilvia CarpitellaZoubir El Felsoufisubject
Risk analysisSupply chain risk managementcriticality and risk analysisComputer scienceAHPSupply chain09.- Desarrollar infraestructuras resilientes promover la industrialización inclusiva y sostenible y fomentar la innovaciónAutomotive industryAnalytic hierarchy processBioengineeringcriticality and risk analysi02 engineering and technologylcsh:Chemical technologySystems engineeringlcsh:Chemistry0502 economics and businessSettore ING-IND/17 - Impianti Industriali Meccanici0202 electrical engineering electronic engineering information engineeringChemical Engineering (miscellaneous)lcsh:TP1-1185systems engineeringbusiness.industryProcess Chemistry and Technology05 social sciencesCriticality and risk analysisFuzzy DEMATELSupply chainMultiple-criteria decision analysis12.- Garantizar las pautas de consumo y de producción sosteniblesFailure mode effects and criticality analysislcsh:QD1-999Risk analysis (engineering)020201 artificial intelligence & image processingMATEMATICA APLICADAbusinessFailure mode and effects analysis050203 business & managementFMECAdescription
Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of &ldquo
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-05-01 |