Theory and Methodology Estimating preferred target input-output levels using data envelopment analysis (original) (raw)
Related papers
Estimating preferred target input-output levels using data envelopment analysis
European Journal of Operational Research, 1992
This paper develops models which can be used to estimate alternative input-output target levels to render relatively inefficient organisational units efficient. The models can incorporate preferences over potential improvements to individual input output levels so that the resultant target levels reflect the user's preferences over alternative paths to efficiency. The paper illustrates the practical usefulness of the models developed and highlights the alternative measures of relative efficiency implicit in the models developed.
OPSEARCH, 2020
In managerial decisions, situations frequently arise when decision makers need to define their capabilities, desires, and limitations when trying to improve efficiency. In this paper, target setting models that accommodate predefined desired output targets or predefined available inputs during efficiency improvement in data envelopment analysis are proposed. The proposed approach guarantees efficient targets when inefficient or weak efficient units' desire expansion or reduction in outputs/ inputs, and cases of input/output redistribution, or nondiscretionary variables in a production system. The approach is applied to two empirical studies, first, on a poultry chain trying to improve efficiency of some branches, and second on water, energy, land and food nexus trying to attain future sustainability based on preexisting inputs. Results of the empirical studies supports the proposed models.
The general practice in performance and production efficiency measurement has been to ignore additional products of most transformation processes that can be classified as "undesirable outputs" -which are a subset of the output set. Without the inclusion of these factors, the efficiency evaluation becomes a purely technical measure of the system alone, and does not account for the interaction of the system with the surrounding environment and the impact of policy decisions on the system. In addition, there are also technological dependencies arising due to the relationships between the desirable and the undesirable outputs. One of the analytical tools normally used in efficiency evaluation is Data Envelopment Analysis, DEA.
A new bi-level data envelopment analysis model for efficiency measurement and target setting
Measurement, 2019
Data envelopment analysis (DEA) is a well-known and widely used method for performance evaluation in a set of homogeneous units. We propose a new bi-level DEA model for efficiency measurement and target setting. The fundamental novelty of the proposed model is threefold. We: (1) set both efficiency and profit concurrently as targets; (2) limit the amount of changes in the inputs and outputs to prevent unachievable targets; and (3) predict some targets for efficient units beyond the inefficient ones. We present a case study in the banking industry to demonstrate the efficacy of efficiency measurement and target setting in the proposed models.
Efficiency improvement of decision making units: a new data envelopment analysis model
International Journal of Mathematics in Operational Research, 2015
The main goal of this paper is to develop a new data envelopment analysis (DEA) model to use optimal weights of each decision making unit (DMU) to improve its relative efficiency aligned with other DMUs. In spite of the vast amount of studies in this area and related tools and techniques, current literature deploys the optimal weights of DMUs to calculate the DMUs' relative efficiency and benchmarking for each DMU is less investigated. In order to fill this gap, this paper proposes a model for obtaining benchmark(s) for each DMU. Furthermore, a numerical example is used to illustrate the capability of the proposed model.
A super-efficiency model for ranking efficient units in data envelopment analysis
Applied Mathematics and Computation, 2007
Data envelopment analysis (DEA) is a body of research methodologies to evaluate overall efficiencies and identify the sources and estimate the amounts of inefficiencies in inputs and outputs. In DEA, the best performers are called DEA efficient and the efficiency score of a DEA efficient unit is denoted by an unity. In the last decade, ranking DEA efficient units has become the interests of many DEA researchers and a variety of models (called super-efficiency models) were developed to rank DEA efficient units. While the models developed in the past are interesting and meaningful, they have the disadvantages of being infeasible or instable occasionally. In this research, we develop a super-efficiency model to overcome some deficiencies in the earlier models. Both theoretical results and numerical examples are provided.
A new model to Measuring efficiency and returns to scale on Data Envelopment Analysis
International Journal of Research, 2021
We extend the concept of returns to scale in Data Envelopment Analysis (DEA) to the weight restriction environments. By adding weight restrictions, the status of returns to scale, i.e. increasing, constant, and decreasing, may need a change. We first define "returns to scale" underweight restrictions and propose a method for identifying the status of returns to scale. Then, we demonstrated that this addition would usually narrow the region of the most productive scale size (MPSS). Finally, for an inefficient decision-making unit (DMU), we will present a simple rule for determining the status of returns to the scale of its projected DMU. Here, we carry out an empirical study to compare the proposed method's results with the BCC model. In addition, we demonstrate the change in the MPSS for both models. We have presented different models of DEA to determine returns to scale. Here, we suggested a model that determines the whole status to scale in decision-making units.Diff...
Data Envelopment Analysis - Basic Models and their Utilization
Organizacija, 2009
Data Envelopment Analysis - Basic Models and their Utilization Data Envelopment Analysis (DEA) is a decision making tool based on linear programming for measuring the relative efficiency of a set of comparable units. Besides the identification of relatively efficient and inefficient units, DEA identifies the sources and level of inefficiency for each of the inputs and outputs. This paper is a survey of the basic DEA models. A comparison of DEA models is given. The effect of model orientation (input or output) on the efficiency frontier and the effect of the convexity requirements on returns to scale are examined. The paper also explains how DEA models can be used to assess efficiency.