A computer-implemented method for predicting behavior of an industrial process, the method comprising: initializing an inferential model of a subject industrial process, the initializing: (i) selects process variables of the subject industrial process, (ii) configures one or more of the selected process variables as one or more input process variables that represents input of the inferential model; and (iii) configures one of the selected process variables as an output process variable that represents output of the inferential model; loading measurements for the selected process variables , wherein the loaded measurements for the configured output process variable are of a particular model output type; based on the particular model output type, determining a model structure representing the inferential model; using the loaded measurements, building and training the inferential model according to the determined model structure; deploying the built and trained inferential model online, the deployed inferential model continuously generating key performance indicators KPIs that estimate current behavior of the subject industrial process and predict further behavior of the subject industrial process; and displaying the generated KPIs on a user interface, characterized in that , based on the particular model output type being lab data with a reference variable : the loaded measurements for the configured output process variable are intermittent samples from lab analysis of the subject industrial process; the determined model structure is a hybrid FIR model and is built with a subspace identification technique and a PLS technique; and using continuous measurements for the reference variable , building and training the inferential model as the hybrid FIR model with the subspace identification technique and the PLS technique, wherein the reference variable is a process variable of the subject industrial process, the reference variable correlates to the configured output process variable in the subject industrial process. initializing an inferential model of a subject industrial process, the initializing: (i) selects process variables of the subject industrial process, (ii) configures one or more of the selected process variables as one or more input process variables that represents input of the inferential model; and (iii) configures one of the selected process variables as an output process variable that represents output of the inferential model; loading measurements for the selected process variables , wherein the loaded measurements for the configured output process variable are of a particular model output type; based on the particular model output type, determining a model structure representing the inferential model; using the loaded measurements, building and training the inferential model according to the determined model structure; deploying the built and trained inferential model online, the deployed inferential model continuously generating key performance indicators KPIs that estimate current behavior of the subject industrial process and predict further behavior of the subject industrial process; and displaying the generated KPIs on a user interface, characterized in that , based on the particular model output type being lab data with a reference variable : the loaded measurements for the configured output process variable are intermittent samples from lab analysis of the subject industrial process; the determined model structure is a hybrid FIR model and is built with a subspace identification technique and a PLS technique; and using continuous measurements for the reference variable , building and training the inferential model as the hybrid FIR model with the subspace identification technique and the PLS technique, wherein the reference variable is a process variable of the subject industrial process, the reference variable correlates to the configured output process variable in the subject industrial process. <EOS>