Data Envelopment Analysis
Chapter 12
Data Envelopment Analysis
Data Envelopment Analysis DEA is an increasingly popular management tool. This write-up is an introduction to Data Envelopment Analysis DEA for people unfamiliar with the technique.
For a more in-depth discussion of DEA, the interested reader is referred to Seiford and Thrall 1990 or the seminal work by Charnes, Cooper, and Rhodes 1978 .
DEA is commonly used to evaluate the e ciency of a number of producers. A typical statistical approach is characterized as a central tendency approach and it evaluates producers relative to an average producer In contrast, DEA compares each producer with only the "best" producers. By the way, in the DEA literature, a producer is usually referred to as a decision making unit or DMU.
DEA is not always the right tool for a problem but is appropriate in certain cases. See Strengths and Limitations of DEA.
In DEA, there are a number of producers. The production process for each producer is to take a set of inputs and produce a set of outputs. Each producer has a varying level of inputs and gives a varying level of outputs. For instance, consider a set of banks. Each bank has a certain number of tellers, a certain square footage of space, and a certain number of managers the inputs . There are a number of measures of the output of a bank, including number of checks cashed, number of loan applications processed, and so on the outputs . DEA attempts to determine which of the banks are most e cient, and to point out speci c ine ciencies of the other banks.
A fundamental assumption behind this method is that if a given producer, A, is capable of producing Y A units of output with X A inputs, then other producers should also be able to do the same if they were to operate e ciently. Similarly, if producer B is capable of producing
Y B units of output with X B inputs, then other producers should also be capable of the same production schedule. Producers A, B, and others
Data Envelopment Analysis
Data Envelopment Analysis DEA is an increasingly popular management tool. This write-up is an introduction to Data Envelopment Analysis DEA for people unfamiliar with the technique.
For a more in-depth discussion of DEA, the interested reader is referred to Seiford and Thrall 1990 or the seminal work by Charnes, Cooper, and Rhodes 1978 .
DEA is commonly used to evaluate the e ciency of a number of producers. A typical statistical approach is characterized as a central tendency approach and it evaluates producers relative to an average producer In contrast, DEA compares each producer with only the "best" producers. By the way, in the DEA literature, a producer is usually referred to as a decision making unit or DMU.
DEA is not always the right tool for a problem but is appropriate in certain cases. See Strengths and Limitations of DEA.
In DEA, there are a number of producers. The production process for each producer is to take a set of inputs and produce a set of outputs. Each producer has a varying level of inputs and gives a varying level of outputs. For instance, consider a set of banks. Each bank has a certain number of tellers, a certain square footage of space, and a certain number of managers the inputs . There are a number of measures of the output of a bank, including number of checks cashed, number of loan applications processed, and so on the outputs . DEA attempts to determine which of the banks are most e cient, and to point out speci c ine ciencies of the other banks.
A fundamental assumption behind this method is that if a given producer, A, is capable of producing Y A units of output with X A inputs, then other producers should also be able to do the same if they were to operate e ciently. Similarly, if producer B is capable of producing
Y B units of output with X B inputs, then other producers should also be capable of the same production schedule. Producers A, B, and others
References: A good source covering the eld of productivity analysis is The Measurement of Productive E ciency edited by Fried, Lovell, and Schmidt, 1993, from Oxford University Press