Lowin, MaximilianMihale-Wilson, Cristina2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37758Maintenance is a significant cost driver in many industries with tangible assets. Aiming to predict damages before they occur, this paper focuses on predictive maintenance (PdM) for smart buildings and apartments – a multi-billion-dollar market with substantial cost savings potential. Based on stakeholder groups’ heterogeneity within the smart housing industry, PdM cannot be a one-fits-all solution. To be effective, practitioners can enrich PdM with Artificial Intelligence (AI). However, to match very heterogeneous environments and the various needs of the stakeholders, PdM must be modular and flexible. Motivated by the challenges and peculiarities for implementing Predictive Maintenance as a Service (PdMaaS) in the smart housing industry, we provide a concept to support managers to overview and optimize complex PdM needs in complex and heterogeneous environments.ensmart servicespredictive maintenancesmart housing industryvisualization10.18420/informatik2021-0891617-5468