Data Envelopment Analysis (DEA) is an approach based on linear programming to assess the relative efficiency of peer Decision Making Units (DMUs). Typically, each DMU is free to choose the weights of the factors used in its evaluation.However, the evaluator's preferences may not warrant so much freedom. Several approaches have been proposed to allow the incorporation of managerial preferences in DEA, but few address the Additive DEA model specifically. This paper presents additive DEA models that use Multi-Criteria Decision Analysis concepts to incorporate managerial preferences, and presents the corresponding preference elicitation protocols. The models developed allow the incorporation of preferences at different levels: on valuing performance improvements, on introducing weight restrictions, and on finding adequate targets.These were application-driven developments, resulting from discussing modelling options and preliminary results with the top-level management of a retail chain in the context of an assessment of stores' performance, also described in this paper.
The Portuguese natural gas sector is still growing, with a history of less than two decades. Consequently, the impact of regulatory reform is evidently recent. The aim of this paper is to assess the performance of the Portuguese gas distributors in order to define the efficiency targets for the regulatory period, 2010-2013. It also conducted further analysis in order to determine the best level of attainable operating costs by deriving the major costs driver of the Portuguese gas distributors. Therefore, the focus was on the deterministic frontier method, Data Envelopment Analysis (DEA) accounting for variable returns to scale (VRS). This technique allowed the computation of X-factors for each firm. The study suffered from data problems since the sector is very recent and the set of comparable companies is small. In order to avoid misspecifications or misinterpretations, firms were divided into three main groups with different scale factors and exogenous factors were also considered. The cross-section analysis was crossed with a dynamic one using panel data methodology.
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