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The Singapore Regional Waters (SRW) is one of the more complex tidal regions in the world. This complexity is caused by various factors including the interaction of the Indian and Pacific oceans with their mainly semi-diurnal and diurnal tide, respectively, complicated coastline geometry, small islands and sharply varying bottom topography. Tidal data analysis is hampered by the lack of reliable coastal stations with long-term water level records while numerical tidal modelling studies suffer from lack of accurate high-resolution bathymetry data and uncertainty in the prescription of the tidal open boundary forcing. The present study combines numerical modelling with available along-track satellite altimetry data and a limited set of reliable coastal stations. It proposes a structured approach to study the sensitivity of tidal propagation and interactions to parameters like the prescription of tidal forcing at the open ocean boundaries, local depth information and seabed roughness. To guide and facilitate this analysis, the open-source software environment OpenDA for sensitivity analysis and simultaneous parameter optimisation is used. In a user-controlled way, the vector difference error in tidal representation could so effectively be reduced by ~50%. The results confirm the benefit of using OpenDA in guiding the systematic exploration of the modelled tide and reducing the parameter uncertainties in different parts of the SRW region. OpenDA is also shown to reduce the repetitive nature of simultaneous parameter variation. Finally, the behaviour of the tide in the region and its sensitivities to changes in tidal boundary forcing and to local depth and friction variation in the narrow regions of the Malacca Strait is now much better understood. With most of the systematic errors reduced in the numerical model as a result of the sensitivity analysis, it is expected that the model can be applied to study tide-surge interaction and is much better suited for later application in combination with data assimilation techniques such as Kalman filtering for which systematic model errors should be minimal.