This paper addresses the problem of achieving tight product consistency and enabling automated process changes to deliver user-selected criterion based product in a complex industrial batch process such as uni-axial rotational molding. To this end, a data driven state-space model is first identified. For a given trajectory of input moves (heater and compressed air profiles), this dynamic model is able to predict the evolution of the measured variable (internal product temperature). The dynamic model is augmented with a quality model, which relates its own terminal predictions to a selection of key quality variables (sinkhole area, ultrasonic spectra amplitude, impact test metric and viscosity). The dynamic and quality models are in turn utilized within a model predictive control (MPC) framework that enables specifying product quality requirements explicitly. Experimental results demonstrate the ability of the MPC not only in achieving tight quality control but also providing on-spec product for a new specification. (C) 2018 Elsevier Ltd. All rights reserved.