Data-based process models are usually developed by fitting input−output data collected on a particular process. The model built on one particular process becomes invalid with another similar process. Traditional data-based modeling methods have to completely rebuild a new process model on a similar process, leading to repetition of a large number of experiments, if process similarities between two similar processes are ignored. Effective use and extraction of these process similarities and migration of the existing process model to the new process can require a fewer number of experiments for the development of a new process model, resulting in savings of time, cost, and effort. In this paper, we present a model migration method that can quickly model a new process based on an existing base model and contrast information between the base model and the new process. The method developed involves a procedure of six steps: information extraction from the base model, initial design of experiments, slope/bias correction (SBC) to the base model, outlier detection and assessment, further design of experiments, and development of the new model by combining local difference models and the corrected base model. An example is provided to illustrate the new model development strategy for predicting injection molded part weight, taking advantage of an existing model.