Affine loops transformations have often bee used for program optimization. Usually their focus lies on single loops nests. A few recent approaches also handle global programs with multiple loop nests but they are not really scalable towards realistic applications with dozens of nests. To reduce complexity, we propose to split affine transformations into a linear transformation helps and a translation step. This translation step ca be used to perform multi-dimensional loops fusion. We show that loop fusion can be performed incrementally and provide a greedy algorithm, which we illustrate on a simple example. Finally we present a heuristic for data locality and provide some experimental results.