Effective inventory management is crucial in manufacturing and wholesale businesses to reduce operation costs and meet service level guarantees. Due to the continuous increase in product catalogues and highly volatile demand, inventory management complexity continues to grow. This paper introduces a decision support tool designed to aid in inventory management through an indirect Q-value estimator technique. The proposed tool employs simulation, optimization and forecasting techniques to enable purchase actions evaluation for large horizons. By integrating both simulation and optimization into a supervised learning algorithm, the tool provides an easy to interpret cost estimation that can directly be used to make informed procurement decisions. A case study in the textile industry demonstrates its use and its performance in a single-echelon supply chain setting. This research presents a comprehensive step by step framework to support the creation of a decision support tool that can offer valuable aid for decision-making processes across different supply management contexts.