Data Envelopment Analysis and Related Literature

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  • Topic: Output, Robust statistics, Estimation theory
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  • Published : January 26, 2011
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Data Envelopment Analysis and related literature
As already stated, a rich history of literature and research which is demonstrating the importance of processes in analyzing the performance of an organization exists (Chase, 1981; Chase et al., 1983; Levitt, 1972; Roth et al., 1995). Especially, Roth et al. (1995; here and in the following) showed that the key drivers are process capability and execution in an empirical way. It was described in their study that an inappropriate design of certain processes and also the poor execution of a process can lead to process inefficiency, and that both process capabilities and people as major factors affect business performance. When estimating the performance of processes usually a number of different outputs have to be taken into consideration. Data Envelopment Analysis, the estimation method described in this chapter and used as a basis for measuring the efficiency of business processes, deals with these multiple outputs by the use of frontier estimation. In this process, it is specifically determined which relative performance amongst multiple inputs and outputs are present. This in turn is achieved by calculating ratios of weighted outputs to weighted inputs, and the determination of the relative efficiency (which is seen as the distance from a peer object to the best practice frontier) compared with the efficiency of other so-called Decision Making Units (Charnes et al., 1978). Decision Making Units can be defined as firms or public-sector agencies, but also as single processes or process instances (Sengupta, 1995). Data Envelopment Analysis is therefore used in different areas of daily life, for example in education programs of schools, or the production and retail business (Metters et al., 2003). The Data Envelopment Analysis method was introduced into the operations research literature by Charnes, Cooper, and Rhodes in 1978 (see Charnes et al., 1978). They presented it as a new nonparametric (meaning it is...
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