In the last ten years, new models based on Bayesian Net- works (BN) emerged to handle large and complex systems. These new models can be divided in two: the unification with First Order Logic and uncertainty (Markov Logic Networks, Bayesian Logic) and Knowledge Base Construction Mod- els (Probabilistic Relational Models, Multy-Entity Bayesian Networks, Relational Bayesian Networks). SKOOB, a consortium of researchers and engineers in risk management, focuses on Probabilistic Relational Models (PRM). Inference in such models is a much more difficult task than in BN. Structured Value Elimination (SVE) is the state-of-the-art algorithm for PRM models. In this paper, we propose an enhancement of SVE based on a well known complexity reduction technique from BN. We show how to integrate a d- separation analysis in SVE and how this leads to important improvements for the inference task.