Starting with the CCR model, named after Charnes, Cooper, and Rhodes, many extensions to DEA have been proposed in the literature. Since then, there have been a large number of books and journal articles written on DEA or about applying DEA in various sets of problems. In Germany, the procedure had earlier been used to estimate the marginal productivity of R&D and other factors of production. History īuilding on the ideas of Farrell, the 1978 work "Measuring the efficiency of decision making units" by Charnes, Cooper & Rhodes applied linear programming to estimate, for the first time, an empirical, production-technology frontier.
DEA's popularity stems from its relative lack of assumptions, the ability to benchmark multi-dimensional inputs and outputs as well as its computational ease owing to it being expressable as a linear program, despite its task to calculate efficiency ratios. DEA, one of the most commonly used non-parametric methods, owes its name to its enveloping property of the dataset's efficient DMUs, where the empirically observed, most efficient DMUs constitute the production frontier against which all DMUs are compared. In contrast to parametric methods that require the ex-ante specification of a production- or cost-function, non-parametric approaches compare feasible input and output combinations based on the available data only.
In benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a “production frontier”, but rather lead to a “best-practice frontier.” : 243–285 Although DEA has a strong link to production theory in economics, the method is also used for benchmarking in operations management, whereby a set of measures is selected to benchmark the performance of manufacturing and service operations. DEA is used to empirically measure productive efficiency of decision making units (DMUs).