Studying the influence of the imperfect prognostics information on maintenance decisions is an underexplored area. We bridge this gap and propose a new comprehensive maintenance support system. First, a survival theory-based prognostics module employing the Weibull time-to-event recurrent neural network was deployed. In which, the prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction, new predictive maintenance (PdM) planning model was framed via a tradeoff between corrective maintenance and time lost due to PdM. This optimises the time for maintenance based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning of cutting tools within a manufacturing facility. We provide systematic sensitivity analysis and discuss the impact of the imperfect prognostics information on maintenance decisions. Results show that uncertainty, regarding prediction, declines as time goes on, and as the uncertainty declines, the maintenance timing gets closer to the remaining useful life. This is expected as the risk of making the wrong decision decreases.