Consequently, Data Envelopment Analysis (DEA) has been a popular technique for bank ef-
ficiency studies; however, DEA requires that units operate in consistent ???cultures??? to produce
fair and comparable results .DEA has been appropriately applied to measure performance
in the service sector, as a means of measuring performance and possibly as a monitoring
tool for use in the longer term. In software processes also, DEA has been well applied. Data
Envelopment Analysis (DEA) methods are ideal for measuring the efficiency of software
production processes. DEA methods have proven to be very valuable, in practice, as a
management tool appropriate for both planning and control activities, and as new tools for
forecasting and trade-off analysis.
Another most interesting application of DEA has been in the context of dynamical systems,
be it historical, causal or closed (Vaneman & Triantis, 2003), which exhibit a high degree
of correlation between the variables at the initial time t0 with the variables at the final time
t. Here, DEA techniques have been defined and developed as the dynamic data envelopment
analysis (DDEA) by adding the element of time to the DEA model. This application
of DEA can be directly connected to the real-time data handling aspects of SLA monitoring
of services like VoIP, and so forth.
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