In large and complicated datasets, a standard process could facilitate
performance assessment and help to (1) translate the aim of the
performance measurement to a series of small tasks, (2) select
homogeneous DMUs and suggest an appropriate input/output selection, (3)
detect a suitable model, (4) provide means for evaluating the
effectiveness of the results, and (5) suggest a proper solution to
improve the efficiency and productivity of entities (also called
Decision Making Units, DMUs).
We suggest a framework which involves six
interrelated phases: (1) Concepts and objectives, (2) On structuring
data, (3) Operational models, (4) a Performance comparison model, (5)
Evaluation, and (6) Results and deployment. Taking the first letter of
each phase, we obtain the COOPER-framework (in honour of and in
agreement with one of the founders of DEA). Figure 1 systemizes the six
phases.
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