In the processing industries, operating conditions change to meet the requirements of the market and customers. Under different operating conditions, data-based process modeling must be repeated for the development of a new process model. Obviously, this is inefficient and uneconomical. Effective use and adaptation of the existing process model can reduce the number of experiments in the development of a new process model, resulting in savings of time, cost, and effort. In this paper, a particular process similarity, inclusive similarity, is discussed in detail. A model migration strategy for processes with this type of similarity is developed to model a new process by taking advantage of existing models and data from the new process. The new model is built by aggregating the existing models using a bagging algorithm. As an illustrated example, the development of a new soft-sensor model for online prediction of melt-flow length for new mold geometry for an injection molding process is demonstrated by taking advantage of existing models for different molds.