- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
We propose a new approach to estimate technical coefficients from a set of Decision Making Units (DMUs) under the assumption that their production plans are set by process engineers through Linear Programming (LP) techniques. The idea behind this approach is that most manufacturing and agricultural firms routinely resort to LP-based modeling in their decision making processes in order to plan output production and, therefore, this particularity should be taken into account when estimating their technical efficiency. A usual model of LP for these sectors is the so-called product-mix problem, which we relate to a standard Data Envelopment Analysis (DEA) model in terms of the Directional Distance Function. In this paper, we finally show how to estimate the technical coefficients of a sample of Andalusian farms in Spain and how this information can be seen as a complement to the usual byproducts associated with estimating technical efficiency by DEA.
We introduced a new methodology based on the product-mix problem to assess the production performance of firms, which is related to the well-known Data Envelopment Analysis approach, thereby complementing its results with information about the value of technical coefficients. As in DEA the new approach assumes a common technology, offering novel analytical possibilities to researchers interested in this field by introducing the ability of performing efficiency evaluation. An outstanding feature of the new approach is, therefore, the possibility of estimating the technical coefficients in the evaluation process, thereby determining the underlying relationship between each pair of inputs and outputs. The new approach can be readily implemented as it departs from the product-mix problem used by process engineers in many industrial and primary sectors, particularly manufacturing and agriculture. The derived piece-wise linear output production set, based on the Tshebishev metric, is compared with the Directional Distance Function in the context of Data Envelopment Analysis. As a result of this comparison we prove that the new approach yields a production possibility set representing an outer approximation of that corresponding to DEA (vice versa, DEA is an inner approximation of the former), which extends to their efficient subsets.