Kellner, DomenicLowin, Maximilianvon Zahn, MoritzChen, Johannes2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37608In manufacturing, small and medium-sized enterprises (SMEs) face global competition. In the field of predictive maintenance (PdM), artificial intelligence (AI) helps to prevent machine failures and has the potential to significantly reduce costs and increase process efficiency. Even though PdM has several benefits, it also entails considerable challenges for SMEs, especially when it comes to user interactions. In this short paper, we harness the design science methodology and discuss several problems regarding user interactions with predictive maintenance applications. We incorporate two different literature streams, namely, predictive maintenance and decision support systems. Finally, we present necessary design requirements, principles, features, and propose a research design to further develop and evaluate a user-centric PdM decision support system. Thereby, we contribute to making AI tangible in SMEs.enpredictive maintenancemachine learningdecision support systemsdesign science researchexplainable artificial intelligence10.18420/informatik2021-1041617-5468