We present an approach based on queuing theory and stochastic simulation to help planning, managing, and controlling the project staffing and the resulting service level in distributed multiphase maintenance processes. Data from a Y2K massive maintenance intervention on a large COBOL/JCL financial software system were used to simulate and study different service center configurations for a geographically distributed software maintenance project. In particular, a monolithic configuration corresponding to the customer's point-of-view and more fine-grained configurations, accounting for different process phases as well as for rework, were studied. The queuing theory and stochastic simulation provided a means to assess staffing, evaluate service level, and assess the likelihood to meet the project deadline while executing the project. It turned out to be an effective staffing tool for managers, provided that it is complemented with other project-management tools, in order to prioritize activities, avoid conflicts, and check the availability of resources.