data envelopment analysis data envelopment analysis

These are the unknowns in the primal model that determine the importance attributed to each factor. Since the value assigned to each weight depends on the measurement scale of the factor itself, it is difficult to compare the weights from different factors.

As the CCR primal model places no restriction on the weights other than a lower bound of epsilon, it is not rare for a unit to be rated efficient at the expense of having a very uneven distribution of weights where some or most of the factors have been practically ignored. To remedy the situation, …

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The process of developing a set of visual techniques such as graphs and charts through which the DEA results can be better understood.

Another name for weights

Virtual input is obtained for each input by taking the product of the input’s value and its corresponding optimal weight as given by the solution to the primal model. Virtual outputs are obtained analogously. A virtual input or output describes the importance attached to the given factor. The virtual inputs always add up to the …

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If it is suspected that an increase in inputs does not result in a proportional change in the outputs, a model which allows variable returns to scale (VRS) such as the BCC model should be considered.

An input or output factor. Since these are always known beforehand, their values are actually constants.

The efficiency scores of the DEA ratio models are independent of the units in which the factors are measured. The input and output values can thus be scaled through multiplication by a constant as proven in Chames and Cooper (1985). [Charnes A, Cooper W W (1985) ‘Preface to topics in data envelopment analysis’, in Thompson …

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Yet another name for the efficiency frontier, this term emphasises the fact that each segment of the frontier represents the trade-off possibilities that can be made between the inputs or outputs of a given DMU on the isoquant segment while keeping the DMU efficient.

Short for decision making unit or DMU.