The integration of the optimization of the cost of the procurement of electric energy with production planning and scheduling is increasingly considered in various industries. The traditional approach to integrate production scheduling and energy procurement in industry is production driven, i.e. the production is scheduled first, after which the energy supply optimization is performed to find the best available energy portfolio. This will usually not lead to the optimum of the overall problem. The combined scheduling and energy procurement optimization can be formulated as an integrated monolithic optimization model, but the resulting problems are very hard to solve. Moreover, solutions to the two isolated problems may be available. In this paper, we propose to use Mean Value Cross Decomposition for solving the combined problem by iterating between energy-aware production scheduling and energy-cost optimization, possibly building on existing solutions. We apply the approach to two industrial use cases: a pulping process and a steel production process. MILP-based models are employed for the two scheduling problems and for the energy cost optimization a minimum-cost flow network model is used. Good quality solutions can be obtained within reasonable computation times for the two use cases.