  The explorations of models beyond the Standard Model (BSM) naturally involve
scans over the unknown BSM parameters. On the other hand, high precision
predictions require calculations at the loop-level and thus a renormalization
of (some of) the BSM parameters. Often many choices are possible for the
renormalization scheme (RS). This concerns the choice of the set of
to-be-renormalized parameters out of a larger set of BSM parameters, but can
also concern the type of renormalization condition which is chosen for a
specific parameter. A given RS can be well suited to yield "stable" and "well
behaved" higher-order corrections in one part of the BSM parameter space, but
can fail completely in other parts, which may not even be noticed numerically
if an isolated parameter point is investigated, or when the higher-order BSM
calculations are performed in an automated, not supervised set-up.
Consequently, the (automated) exploration of BSM models requires a choice of a
good RS {\em before} the calculation is performed. We propose a new method how
such a choice can be performed. We demonstrate the feasibility of our new
method in the chargino/neutralino sector of the Minimal Supersymmetric Standard
Model (MSSM), but stress the general applicability of our method to all types
of BSM models.
