Data envelopment analysis of urban efficiency—Interpretative methods to make DEA a heuristic tool

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

A game theoretic approach to modeling undesirable outputs and efficiency decomposition in data envelopment analysis

Data Envelopment Analysis (DEA) is a data-oriented performance evaluation method that has a very satisfactory performance when there are multiple inputs and outputs presented in the form of set of peer Decision Making Units (DMU). In the discussion of DEA, basically measures the efficiency of each DMU in specific performance based on DEA Efficiency Scores. In DEA, when there is a value of Non-Zero Input and Output Slacks then this often means inefficiency. This scalar measures directly with the input surplus and the output of the short decision of the decision making unit (DMU). Universitas Malikussaleh as one of the State Universities in Indonesia, it is necessary to follow the regulations of the Ministry of Research, Technology and Higher Education of the Republic of Indonesia (KEMENRISTEKDIKTI), one of which is the measurement of efficiency based on the number of lecturers research, the efficient use of resources included in the number of lecturers and employees, as well as students who can be accommodated based on efficiency measurements using DEA. This paper will measure the efficiency of each department in Malikussaleh University and if there is an inefficient department then it will be measured based on slack-based measures to advise aspects that need attention so that the department can be efficient.

Data Envelopment Analysis (DEA) has been recognized as an excellent method for analyzing performance and modeling organizations and operational processes, particularly when market prices are unavailable. In some situations (in real applications) of DEA applications, there are Decision Making Units (DMUs) which have two‐stage structure. In the previous two‐stage models have been assumed that all inputs and outputs of DMUs are known exactly and however this assumption may not be true (i.e. some or all of inputs and outputs may be bounded). This paper proposes a method to determine efficiency of two‐stage DMUs with bounded inputs and outputs and illustrates the proposed method using an example

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

This study attempts to exploit information from environmental variables together with data envelopment analysis (DEA) efficiency scores for efficiency predictions of groups with more limited information. Based on DEA efficiency scores, decision-making units (DMUs) are sorted into two sets containing efficient and inefficient units, respectively. Then they are reshuffled into homogeneous groups with respect to environmental factors. We assume that the efficiency of DMUs in such a homogeneous group would be correlated. However, efficiency of different groups would vary. A beta binomial logistic model is proposed to fit such phenomena and is applied to predict the performance of a new group of commercialization projects for given environmental characteristics.

Data envelopment analysis (DEA) provides a relative efficiency measure for peer decision making units (DMUs) with multiple inputs and outputs. While DEA has been proven an effective approach in identifying the best practice frontiers, its flexibility in weighting multiple inputs and outputs and its nature of self-evaluation have been criticized. The cross efficiency method was developed as a DEA extension to rank DMUs with the main idea being to use DEA to do peer evaluation, rather than in pure self-evaluation mode. However, cross efficiency scores obtained from the original DEA model are generally not unique, and depend on which of the alternate optimal solutions to the DEA linear programs is used. The current chapter discusses various cross efficiency approaches in dealing with non-unique solutions from DEA

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

This paper revises data envelopment analysis (DEA) estimates of efficiency for decision making units (DMU). This is done by smoothing the DEA efficiency frontier by training an artificial neural network (ANN) to fit a closed functional form for input or output prediction of multiple DMUs. By using neural network we overcome the regression model shortcoming of a single dependent variable. DEA is known to be sensitive to the operational efficiency of Decision Making Units (DMU). In the absence of high-performing DMUs, DEA reports falsely elevated efficiency levels. The problem of non-smooth, piece-wise linear efficiency frontier poses a handicap in the measurement of relative efficiency. In this paper we describe a procedure that uses two-stage DEA, a data generation procedure (DGP) and ANN training and prediction to overcome this data-based shortcoming. We also provide two empirical illustrations of this procedure and conclude by explaining the differences in the revised efficiencies of the DMUs. Though we have used constant returns to scale for the empirical illustrations, any other return-to-scale formulation would work just as well.

Conventional applications of data envelopment analysis generally treat the Decision-Making Unit (DMU) as a black box in that the internal processes are not examined in detail. In some situations, such as the measurement of performance of a set of supply chains, the DMU can be viewed as exhibiting a network structure. A significant body of recent literature has examined a particular form of network structure, namely, where the DMU is a two-stage serial process in which the outputs from the first stage are intermediate variables that serve as inputs to the second stage. The current article extends this idea to include those situations where outputs from the second stage can be fed back as inputs to the first stage. Such feedback variables thus serve a dual role. Models are developed for examining performance in this feedback setting and are illustrated using an application involving the measurement of performance of a set of Chinese universities.

Globally, insurance and takaful companies play a crucial role in economic and financial development of a country. Malaysia’s insurance sector has undergone substantial changes compared to two decades ago. Due to intense competition observed in Malaysian insurance industry, efficiency measurement and a complete efficiency ranking of insurance companies are very important for the decision makers so that necessary changes and improvement can be made. Data Envelopment Analysis (DEA) is a non-parametric method that has been acknowledged as an effective method to measure the efficiency of homogeneous decision-making units (DMUs). The main advantage of DEA is its ability to handle multiple inputs and outputs. However, standard DEA has poor discrimination power since it generates too many efficient units especially when the number of DMUs under study is insufficient in comparison to number of inputs and outputs. It cannot discriminate efficient units and therefore unable to give a complete ranking of DMUs. This study aimed to overcome the ranking problem found in standard DEA by integrating DEA and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II. The hybrid method was applied to evaluate the efficiency and ranking of 22 life insurance and takaful companies in Malaysia from the period of 2017 to 2018. Input variables used in this study comprised of fees and commission, and management expenses. Meanwhile, output variables were net premium and generated investment income. The proposed method involved two stages. In the first stage, DEA was applied to obtain the efficient scores for the DMUs. In the second stage, PROMETHEE II was implemented to rank the efficient units. This hybrid method has successfully obtained full ranking of all the insurance companies under study. The inefficient companies can learn strategies and practices from efficient companies to enhance their services. It is recommended for future research to integrate DEA with other Multi Criteria Decision Making (MCDM) methods such as TOPSIS and VIKOR to fully rank the DMUs.

Data envelopment analysis (DEA) has been commonly used in the energy saving of enterprise plants. Nevertheless, when the traditional DEA model analyzes the effectiveness of decision-making units (DMUs), over 1/3 of the DMUs’ efficiency values are 1, so the traditional DEA model cannot distinguish the cons and pros of the DMUs. And although the DEA cross-model(DEACM) is able to differentiate the cons as well as pros of the effective DMUs, it can’t obtain the improvement direction of the ineffective DMUs. Therefore, an energy saving and management method based on an improved DEACM, which can use the higher efficiency distinction to identify the efficiency state of the DMUs, is proposed in this paper. Meanwhile, the improvement direction of the ineffective DMU can be found by the self-evaluation of the improved DEACM. Finally, the improved DEACM is utilized to save and manage the energy configuration of the PTA solvent system in the industrial process. The experimental results reveal that the practicality and effectiveness of the proposed method are verified, and in addition, the efficiency discrimination is well. Moreover, the proposed model can find the direction of the quantitative targets of energy saving to improve the energy efficiency of PTA production.

Data envelopment analysis (DEA) has been widely applied in the performance evaluation of environment or economy recently. If economic and environmental systems exist simultaneously in a decision making unit (DMU), conventional DEA models generally treat the DMU as a black box, and the internal relationship of DMU is often ignored. Thus, the performance of DMUs may be overestimated. In order to solve this problem, a new two-stage multiplier DEA approach is proposed in this paper. The new model can not only obtain the efficiency score of each system, but also consider the intermediate outputs when assessing the overall efficiency of the two systems. In order to test the effectiveness of this approach, a numerical example of 30 provinces in China is illustrated.

Efficiency evaluation of homogenous decision making units (DMUs), is one of the primary objectives of data envelopment analysis (DEA), in which the large number of inputs or outputs compared with the number of DMUs leads to decreased evaluation precision of DEA models. Overcoming this problem is the main issue in two-level DEA. In the present study, we use the multi objective linear programming (MOLP) method proposed by Sumpsi et al., which is based on constructing the pay-off matrix and using goal programming (GP), to investigate this problem. Furthermore, we discuss the enhanced Russell's model for evaluating and benchmarking DMUs with two-level inputs and outputs. The proposed method is applied to 24 branches of an Iranian commercial bank.

Unoriented two-stage DEA: The case of the oscillating intermediate products