Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average
