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. Description ĭEA is used to empirically measure productive efficiency of decision-making units (DMUs). DEA has been applied in a large range of fields including international banking, economic sustainability, police department operations, and logistical applications Additionally, DEA has been used to assess the performance of natural language processing models, and it has found other applications within machine learning.
Data envelopment analysis ( DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers.
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