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The knowledge of loads acting on machines and components is crucial in many application fields. Input estimation though is a very challenging inverse problem with which researchers have struggled over the last decades. Several issues related to ill-conditioning and ill-posedness are not sufficiently understood nor solved. This paper proposes an in-depth numerical study covering some of the aspects that can make the difference between a reliable and an unreliable force estimation. A states-input estimation algorithm for multiple force estimation is implemented as a linear augmented Kalman filter/smoother coupled with a reduced order model of a complex ill-posed mechanical structure, namely a twistbeam rear suspension. The influence of different noise levels, measurements scaling and time horizon used for the estimation is thoroughly analyzed. Finally, an observability-based optimal sensors placement strategy is implemented showing robustness improved accuracy of the estimated quantities.