Zero-suppressed binary Decision Diagram (ZDD) is a notable alternative data structure of Reduced Ordered Binary Decision Diagram (ROBDD) that achieves a better size compression rate for Boolean functions that evaluate to zero almost everywhere. Deciding a priori which variant is more suitable to represent a given Boolean function is as hard as constructing the diagrams themselves. Moreover, converting a ZDD to a ROBDD (or vice versa) often has a prohibitive cost. This observation could be in fact stated about almost all existing BDD variants as it essentially stems from the non-compatibility of the reduction rules used to build such diagrams. Indeed, they are neither interchangeable nor composable. In this work, we investigate a novel functional framework, termed λDD, that ambitions to classify the already existing variants as implementations of special λDD models while suggesting, in a principled way, new models that exploit application-dependant properties to further reduce the diagram’s size. We show how the reduction rules we use locally capture the global impact of each variable on the output of the entire function. Such knowledge suggests a variable ordering that sharply contrasts with the static fixed global ordering in the already existing variants as well as the dynamic reordering techniques commonly used.