Complaints about finished products are a major challenge for companies. Particularly for manufacturers of medical technology, where product quality is directly related to public health, defective products can have a significant impact. As part of the increasing digitalization of manufacturing companies (“Industry 4.0”), more process-related data is collected and stored. In this paper, we show how ... [Show full abstract] this data can be used to support the complaint management process in the medical technology industry. Working together with a large manufacturer of medical products, we obtained a large dataset containing textual descriptions and assigned error sources for past complaints. We use this dataset to design, implement, and evaluate a novel approach for automatically suggesting a likely error source for future complaints based on the customer-provided textual description. Our results show that deep learning technology holds an interesting potential for supporting complaint management processes, which can be leveraged in practice.