We propose a new nonparametric approach for multi-process data analysis, in which each of the process is modeled as a combination of a fixed-effect and a random-effect Gaussian process (GP) regression model, namely, a mixed-effect Gaussian process (ME-GP) model. The ME-GP approach provides a flexible means to combine the common aspects of all processes and describe the heterogeneity among different processes. In particular, we model the mean and covariance structures of both the fixed- and random-effects simultaneously, and predict a future input using probability density distributions. We apply the ME-GP model to predict the melt-flow-length for filling of different molds in injection molding processes. It is shown that the ME-GP model obtains an improved performance against GP model only. (C) 2017 Elsevier Ltd. All rights reserved.