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Data analysis and real-time processing in Industry 4.0 are highly connected with the use of various product development tools. Information’s quality is of significant importance in this process, as heterogeneous data in raw form and different backgrounds of development agents might cause costs and time issues, related to divergent interpretations. In this context, Failure Mode &amp; Effect Analysis (FMEA) contributes to decision-making by processing data to improve design, manufacturing and maintainability of products. The plethora of criteria of FMEA, although standardised, concerns different perceptions of impacts and weights in different areas of organisations, which might result in subjectivity during evaluation. This research proposes a tool, here called FEMATO, based on MCDM methods to improve accuracy and reduce bias in the evaluation process of FMEA in different organisations. Main findings show that traditional methods using different evaluators result in difference in analysis and weighting on FMEA, resulting in divergent interpretations. FEMATO’s application ranked the importance of criteria and contributed to reduce bias of evaluators during Product Development in three different companies. The changing weighting system for criteria, based on AHP, has shown improved adherence to companies’ objectives, while the TOPSIS portion of the tool helped in improving the process of evaluation.

A multi-criteria decision tool for FMEA in the context of product development and industry 4.0