This chapter assesses the use of machine learning (ML) to estimate service process performance and outlines questions concerning the modeling process. In particular, this study focuses on experimentation with service networks using trained artificial neural networks (ANNs). The theory is based on proactive supply chain risk management studies, the core of which is the utilization of big data, ML, and predictive analytics concerning supply networks. This article presents the case of a rescue service assessment in residential areas, where societal changes and healthcare reforms drive the fire station network. Here, the managerial discussion connects to healthcare politics and safety risk management considering challenges emerging from decentralized home care plans. The empirical study contains a description of the datasets and process for ANN-based modeling and presents the empirical results. The data include emergency register data that are enriched with geospatial variables and are utilized to train the ANN. The trained model is then exploited in a scenario experiment process for response time estimates of rescue services in a specific region. The analysis revealed statistically significant differences between the scenarios at both the general level and the local means of response times. In conclusion, the applied analysis protocol has the potential to be expanded for use as an assessment tool to test the supply networks of other time-critical services.