Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example. In addition during the warranty period, there may be a time called “clean point” where a defective component or subsystem is detected and replaced with a new more reliable unit for all products produced thereafter. This introduces a different statistical behavior before and after the detection of the “clean point.” We will present a modified GRP model for warranty forecasting of systems with or without a “clean point” and different production patterns. The capabilities of the proposed approach will be demonstrated using vehicle production data.