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In Industry 4.0, current trends on data analysis and real-time processing are intertwined with the use of development tools while conceiving products. The quality of information plays a pivotal role in this process, as the heterogeneity of unprocessed data may result in issues that increase the product’s cost and development time. In this context, the Failure Mode & Effect Analysis (FMEA) is a tool that aids decision-making by presenting pre-processed useful data that can improve product design, maintainability and manufacturing. FMEA criteria, although standardised, has ramifications that have different impacts and weight in an organisation, which might end up resulting in subjective evaluations. To cope with this issue and improve the quality of the information in product development, this research proposes a Multi-Criteria approach to define the importance of FMEA criteria and their impacts in organisations in a real industrial scenario. This research is applied using a case on a Brazilian electronics manufacturer, using AHP and TOPSIS Multi-Criteria Decision Making (MCDM) methods. Findings show that by traditional methods different evaluators end up generating different data and weights on FMEA, resulting in different results. In this sense, the application of the Multi-Criteria methods ends up ranking the importance of criteria and evaluating the inputs from all departments, returning more precise information. Furthermore, the weighting scale of FMEA has been shifted to a customised scale for the organisation in the study, based on AHP, more suitable to their needs and following their perception, to support the decision-makers most assertively.

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