Manufacturing processes are characterized by an increasing complexity, making them susceptible to failures. An effective strategy to avoid such failures, or at least to minimize their impact, is data-driven failure management. However, for many small and medium sized manufacturers, this strategy is not feasible due to a paucity of relevant failure data, which can be explained by the severe limitations and shortcomings of available solutions: ranging from the error proneness and high efforts of manual solutions to the high costs and implementation efforts of automated solutions. Against this backdrop, our study follows a design science research approach to design, develop, and evaluate a novel ontology-based speech recognition app that addresses key shortcomings of currently available solutions. Main contributions of our study are the development of design requirements and principles, as well as their instantiation in an app prototype for collecting failure data in the context of manufacturing processes.