Most Multi-Criteria Decision Analysis (MCDA) methods encode a decision-maker’s preferences through criterion weights, yet even a well-weighted model can yield ties or virtually indistinguishable scores. We propose Frobenius SPOTIS (Fro-SPOTIS), which is a generalization of the classical SPOTIS method that incorporates ordering information to resolve such ambiguities. Each alternative’s attribute-based ranking of criteria is compared with a reference ranking derived from the criteria weights. This comparison is performed by converting both rankings into pairwise preference-score matrices and computing the Frobenius distance between them. This distance, modulated by a tolerance parameter \(\tau \in [0,1]\) , is used to modify to the native SPOTIS score: \(\tau = 0\) recovers the original SPOTIS results, while higher values increasingly favor alternatives whose performance ordering aligns with the reference. A three-alternative example shows how Fro-SPOTIS untangles an otherwise unresolved tie, and two sensitivity analysis studies trace how rankings shift with (i) changes in the underlying data and (ii) variations in \(\tau \) . The results confirm that Fro-SPOTIS retains the simplicity of SPOTIS while offering a more flexible and expressive approach to tie-breaking in MCDA.