Managing warranty claims for defective products or components is a challenge for organizations, especially in industries where quality and reliability are key factors in customer perception and regulatory compliance. Warranty cost decision and reliability analysis depend on the product configuration and its components. Typically, warranty cost analyses have relied on the use of classical probability theory to treat failure uncertainty and associated costs. Therefore, we propose a methodology for component warranty cost analysis using Fuzzy Cognitive Maps (FCM), which consists of four stages: defining the warranty cost variables of the product components, performing a multiple correlation analysis, running the inference process of the FCM, and analysing the relevance of the variables. It was applied in the analysis of warranty claims of a motorcycle assembler and made it possible to identify and sort the components with the highest impact on the total warranty costs.