Model predictive control (MPC) is currently the most widely implemented advanced process control technology for petroleum refineries and chemical plants. Based on the present state of the art in theory and practice, MPC works well for processes operating over a narrow range of conditions. However, processes frequently have to operate over a wide range of conditions, for reasons such as varying feedstocks, fluctuating markets for products and raw materials, large process disturbances, and equipment wear. Unsatisfactory MPC performance over widely ranging operating conditions may result in process downtime, environmental and safety risks, and waste of resources, with substantial economic losses. Therefore, there is a need for flexible MPC systems that perform well over a wide range of process operating conditions. While the inner complexity of such (next-generation) MPC systems may be high (to realize the sought improvements in control performance), the complexity of the design, operation, and maintenance of such systems by process engineers and operators should be low. The development and implementation of flexible MPC systems will almost certainly be facilitated by the future availability of predictably even more powerful computers and communication hardware.