The future of CBM within the aerospace is promising but currently there are still numerous obstacles that need to be addressed. Most research focusses most of its efforts on improving the accuracy and flexibility of prognostic models. Making them increasingly capable of forecasting different sets of data with high precision. However there is a lack of integration of these newly developed models within a practical maintenance architecture. This research will aim to fill this gap in the state-of-the-art. It will do so by implementing a data-driven Grey model (GM) in a CBM framework and evaluate its financial benefits over a run-to-failure maintenance paradigm. Not only will this research focus on this alone, but a comparison will also be performed on different optimization strategies of such an integrated CBM system. Simulated turbofan run-to-failure data from the NASA PHM-08 data repository was used for this study as it provides an unique insight on degradation propagation until failure of a vital and expensive aircraft sub-system. This study shows that an integrated CBM model can lead to greater maintenance cost savings than a more traditional segmented approach. Furthermore it shows that the system configurations that focus on reaching local optima on the individual components of the CBM system does not lead to overall optimal system performance.