Capital goods are systems that are used for providing services and products. Many operations cannot function when the required capital goods are down. The efficiency of maintenance operations is important to keep capital goods functioning. Maintenance service providers are responsible to provide maintenance services and spare parts to their customers according to service level agreements. Industry 4.0 technologies enable the storage of a large amount of data, continuous monitoring, and real-time data transfer. Internet of things (IoT) technologies such as sensors, databases, and systems that are connected to the internet or a local network create data-integrated environments for today’s production and service facilities. This dissertation aims to create knowledge on decision-making in data-integrated environments for maintenance optimization and spare parts management. This research also aims to provide insights to practitioners on the (cost) benefits of adopting these new technologies. The first part of this project is on maintenance optimization policies under the so-called time-to-failure model uncertainty and the second part of this project is on spare parts management by using advanced demand information.