Following the wide spread usage of Fuzzy Systems, Rule Reduction has emerged as one of the most important areas of research in the field of Fuzzy Control. Many rule reduction methods have been proposed in the literature and can be broadly classified into Lossless or Lossy with respect to the inference, based on whether the outputs of the original and the reduced rule bases are identical or not. In a typical Multi-Input-Single-Output fuzzy system the number of rules far exceeds the number of fuzzy sets defined on the output domain. This suggests that the rule base can be partitioned into sets of rules, each set being mapped to a single consequent fuzzy set. In this paper, we investigate the conditions on the inference operators employed in a fuzzy system that enable "lossless" merging of rules with identical consequents. After briefly surveying the many techniques that have been proposed towards reducing the number of rules, we propose a general framework for Inference in Fuzzy Systems and then propose some sufficiency conditions on this general framework that give us a class of Fuzzy Systems that allow lossless rule reduction of the type mentioned above. We then explore these conditions in the setting of Fuzzy Logic. We find that R- and S-implications play a very critical role. We give examples from the above class of Fuzzy Systems. In this study we apply the above technique only on rules whose antecedents and consequents are fuzzy sets.