Measuring efficiency has become the core objective of a structured research agenda in production economics and management sciences since at least the early 1980s. The methods used, however, might have a significant impact on results and need to be adapted to the data at hand. Using a synthetic general model to compare the various approaches and relying on key contributions of the literature, we show that each class of model implies specific assumptions on the nature of the data, and that in some cases, the models are inconsistent. Most studies meet the basic requirements proposed by Cowing and Stevenson in 1983, as they rely on the solid theoretical foundations of production economics. Yet, many methods were nevertheless developed in the fields of statistics, operations research, or accounting. These methods often fail to include all relevant theoretical considerations. For example, authors relying on economic theory have applied empirical methods with stochastic error terms that are sometimes at odds with certain properties of their models.