Growing environmental concerns drive interest in sustainable solutions, with vehicle sharing systems addressing transportation needs. Existing studies focus on operational level challenges in one-way station-based bike sharing systems (BSSs), neglecting the added value of precise trip demand forecasting. This study assesses the worth of data collection and trip demand forecasting models. A simulation-optimization framework is created. Simulation module consists of a discrete-event simulator, representing a city BSS. Optimization module optimizes the relocation routes for rebalancing operations where clustering is used for computational efficiency. We experiment on extreme and intermediate scenarios using case studies from four city BSSs, different in location and size, that reveal varying impacts of trip demand forecasting on small- and large-scale sys- tems. Findings emphasize the importance of demand forecasting in large-scale systems, offering insights for operators to enhance service levels, to optimize resource allocation, and to identify the maximum budget to allocate for trip demand forecasting.