Editing Data envelopment analysis (section)

==Description==
DEA is used to [[empirically]] measure [[productive efficiency]] of decision-making units (DMUs). Although DEA has a strong link to [[Production (economics)|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.<ref>Mahmoudi et al (2021)</ref> In benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a “production frontier”, but rather lead to a “best-practice frontier.”<ref name=":0"/><ref>Sickles et al (2019)</ref>{{rp|243–285}}

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.<ref name=cooper2007>Cooper et al (2007)</ref> 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. 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 ratio]]s.<ref name=ease>Cooper et al (2011)</ref>