The current market is dynamic and, consequently, industries need to be able to meet unpredictable market changes in order to remain competitive. To address the change in paradigm, from mass production to mass customization, manufacturing flexibility is key. Moreover, current digitalization of the industry opens opportunities regarding real-time decision support systems allowing the companies to make strategic decisions, and gain competitive advantage and business value. The main focus of this paper is to demonstrate a proof of concept Prescriptive System applied to Reconfigurable Manufacturing Systems. This system is capable of suggesting sequences of machines throughputs that best balance productivity, and the impact of the proposed throughput in the degradation of the equipment. The proposed solution is mainly composed of two modules, namely manufacturing environment simulation and optimizer. The simulation module is modeled based on Directed Acyclic Graphs and the second one on Genetic Algorithms. The results were evaluated against two metrics, variation of pieces referred as differential and availability of the system. Analysis of the results show that productivity in all testing scenarios improves, and in some instances, availability slightly increases showing promising indicators. However, further research should be conducted to be able to generalize the obtained results.