This paper brings systematic methods for scenario tree generation to the attention of the Process Systems Engineering community. We focus on a general, data-driven optimization-based method for generating scenario trees that does not require strict assumptions on the probability distributions of the uncertain parameters. Using as a basis the Moment Matching Problem (MMP), originally proposed by Hoyland and Wallace (2001), we propose matching marginal (Empirical) Cumulative Distribution Function information of the uncertain parameters in order to cope with potentially under-specified MMP formulations. The new method gives rise to a Distribution Matching Problem (DMP) that is aided by predictive analytics. We present two approaches for generating multi-stage scenario trees by considering time series modeling and forecasting. The aforementioned techniques are illustrated with a production planning problem with uncertainty in production yield and correlated product demands. (C) 2014 Elsevier Ltd. All rights reserved.