In this work we develop a novel modeling and global optimization-based planning formulation, which predicts product yields and properties for all of the production units within a highly integrated refinery-petrochemical complex. Distillation is modeled using swing-cut theory, while data-based nonlinear models are developed for other processing units. The parameters of the postulated models are globally optimized based on a large data set of daily production. Property indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables are introduced to denote unit and operation modes selection. The planning model is a large-scale non-convex mixed integer nonlinear optimization model, which is solved to e-global optimality. Computational results for multiple case studies indicate that we achieve a significant profit increase (37-65%) using the proposed data-driven global optimization framework. Finally, a user-friendly interface is presented which enables automated updating of demand, specification, and cost parameters. (C) 2016 American Institute of Chemical Engineers