We consider the production/inventory control for a recoverable system with stochastic demand and returns where stock is replenished by manufacturing new items or remanufacturing returned items. The optimal control policy found by solving a Markov decision process (MDP) for a given problem instance can be represented by a list of the optimal manufacturing and remanufacturing decisions for each possible inventory state. This list does not provide any general structure or insight for the optimal control nor is it practical for implementation. We propose two heuristic methodologies which can be used together to provide intuitive, easy to implement, near-optimal to optimal policies. This allows the policy for new scenarios to be determined without requiring solution of the MDP model. Results from a numerical experimentation show that the characterisations provided by our proposed methodologies represent the optimal inventory policies well with small deviations from optimal cost.