Leveled-Data Envelopment Analysis (L-DEA) is an adaptation of traditional Data Envelopment Analysis (DEA) in that it affords a limited comparison of heterogeneous Decision-Making Units (DMUs). In other words, while traditional DEA is intended for comparison and benchmarking of homogeneous DMUs (e.g. comparing hospitals only against other hospitals), LDEA allows for a rather narrow-in-scope comparison of DMUs across industries (hospitals against banks). In theory, L-DEA affords technology managers the ability to build external context against which to evaluate internal efficiencies. The L-DEA method as originally proposed was computed using a parametric method that was computationally slow. This paper provides an applied computational alternative that remains true to mathematics undergirding L-DEA whilst speeding up its computation considerably. In addition, and importantly, L-DEA's ability to remain functional with adaptations and perform against another dataset are both evaluated, in order to explore its capacity for further use.