Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. 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.

Description

DEA is used to empirically measure productive efficiency of decision-making units (DMUs). 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. In benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a “production frontier”, but rather lead to a “best-practice frontier.”

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. 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 ratios.

History

Building on the ideas of Farrell, the 1978 work "Measuring the efficiency of decision-making units" by Charnes, Cooper & Rhodes adding limited disposability

of inputs/outputs or varying returns-to-scale to techniques that utilize DEA results and extend them for more sophisticated analyses, such as stochastic DEA or cross-efficiency analysis. it found widespread application ever since Doyle and Green's 1994 publication. Cross-efficiency is based on the original DEA results, but implements a secondary objective where each DMU peer-appraises all other DMU's with its own factor weights. The average of these peer-appraisal scores is then used to calculate a DMU's cross-efficiency score. This approach avoids DEA's disadvantages of having multiple efficient DMUs and potentially non-unique weights. Another approach to remedy some of DEA's drawbacks is Stochastic DEA,

See also

  • Network Data Envelopment Analysis

Footnotes

References

  • Lovell, C.A.L., & P. Schmidt (1988) "A Comparison of Alternative Approaches to the Measurement of Productive Efficiency, in Dogramaci, A., & R. Färe (eds.) Applications of Modern Production Theory: Efficiency and Productivity, Kluwer: Boston.

Further reading

  • Data Envelopment Analysis official website
  • Journal of Productivity Analysis official website