Maintenance providers in commercial aviation are looking into new strategies to improve maintenance operations and aircraft availability in order to reduce cost and increase customer satisfaction. Aircraft availability and thus profitability highly relies on adequate maintenance because an aircraft is only profitable when flying. One of the strategies that can be used to achieve higher aircraft availability is Predictive Maintenance (PdM). The new generation of aircraft produces a flood of data enabling PdM, a maintenance strategy in which failure scan be predicted and maintenance takes place proactively instead of reactively. PdM is enabled by prognostics, which is an engineering discipline that aims to estimate the Remaining Useful Life (RUL) of(aircraft)components. However, using prognostic information effectively and determining the benefits and impact on processes in the supply chain of these maintenance providers still proves to be very difficult in practice. This is amplified by the fact that this prognostic information is imperfect, not only can prognostic models produce false alarms or fail to predict coming failures, the exact timing of these events is subject to uncertainties. On top of that, little research into the benefits of prognostic information in supply chains is available. Especially, research considering global supply chains with pool processes such as in commercial aviation, is very limited. Efficiency of supply chains in commercial aviation is important in order to increase aircraft availability, reduce delays and cancellations, and reduce supply chain operating costs. This study provides a novel approach to using imperfect prognostic information to optimize a global supply chain considering a pool process and answers there search question: ’How to optimize a supply chain in commercial aviation using imperfect prognostic information?’ A discrete simulation model has been selected and developed for the simulation of a global supply chain of spare components for Boeing 787 aircraft at KLM Engineering and Maintenance(E&M). This discrete simulation model provides insight in processes in this supply chain while simultaneously considering uncertainties and maintaining a superior computational time. Model verification shows thatthe model is programmed correctly and model validation shows the programmed model accurately represents the real supply chain in a case study at KLME&M. Simulation and optimization shows that enabling the supply chain at KLM E&M with prognostics can reduce the total cost by 20% per year. Results show that the prediction horizon, which is the amount of time failures can be predicted in advance, is the main contributor to this reduction in cost. Not only does this prediction horizon provide the supply chain department with enough time to ship components in a cheap manner, replacing components long before the failure massively reduces damage and thus repair cost. However, a sensitivity analysis shows there is a clear trade-off between minimizing cost and a reduction of the Mean Time Between Removals(MTBR). Cost can be minimized by predicting failures12 days in advance, which simultaneously results in an MTBR reduction of 2.1%.This effect can be mitigated when the accuracy of prognostic models is increased. Furthermore, there duction in cost heavily relies on the percentage of aircraft in the pool for which failures can be predicted.  This research shows the effects and potential benefits of using imperfect prognostic information in a supply chain in commercial aviation. Recommendations for further research are provided in order to use prognostics to optimize the entire logistics supply chain of aircraft maintenance, including the phase-out, reuse, and recycling of aircraft and their components. This could have a huge impact on the environmental impact and sustainability of the global industry that is commercial aviation.