Efficient DMUs Research Articles

Measuring The Efficiency of Pesantren Cooperatives: Evidence in Indonesia

The Cooperative (Koperasi) as a non-Bank financial institution has the purpose of improving the welfare of its members as Koperasi Hidmat and the staffs of Latifah Mubarokiyah Koperasi Ponses Suryalaya that have been since decades ago. Over time, the ideal cooperative can show a significant development and increase the welfare of its members. This study aims to determine the efficiency of cooperative as a benchmark, because by known the performance value of a cooperation, it will known the weeknesses and advantages so that it can be improved the weaknesses and maintain the advantages.The method used is apply Data Envelopment Analysis (DEA). Inputs used from principal savings, mandatory savings, and fixed assets while the output used from savings in the cooperative, savings in other cooperative and SHU. As for result of this research indicates there are 9 perfect efficient DMUs (100 %) and inefficient DMU is 11 DMUs, consisting of 7 (IRS conditions) and 4 (DRS condition). The most inefficient cooperative is Koperasi Hidmat (2014) of 30.66% efficiency level.Kopkar IAILM is able to maintain its grade efficiency level from 2009 to 2015 when compared to other DMUs cooperatives in the observation, except in 2014. The calculation of efficiency level in this research is relative and it is not absolute, so that it is possible when the cooperative sample is added or the observation year is expanded, so it will get different result. The necessity of any cooperative or BMT based on Pondok Pesantren to make annual financial statements in order to increase accountability and transparency of fund management.

An Application of Goal Programming in Data Envelopment Analysis

This research paper describes the transformation of the CCR fractional Programming Problem in to the CCR linear programming problem and the dual of the CCR problem. Here the Dual of CCR problem is viewed as BCC [1] formulation Obtained by axiomatic considerations. A goal programming method for finding common weights in DEA with an improved discriminating power for efficiency was proposed by A. Makui et.al [2]. Hamid Reza Babaee Asil and Sara Fanati Rashdi [3] investigated the relationship between resource allocation problems with the problem of weight control and target selection. M. Izadikhah, R. Roostaee et.al [4] proposed a method in which DEA problem can be solved by transforming it into MOLP formulation. The present research study explains the method of finding extremely efficient DMUs and their marveric indexes, the procedure to increase the discriminating power of DEA and the Process of obtaining a set of multiplier weights common to all the DMUs which leads to Global efficiency. In the following discussion a procedure to improve the discriminative power of DEA with goal Programming is proposed.

A modified DDF-based super-efficiency model handling negative data

Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcoming in practice. In this paper, the radial super-efficiency model based on Directional Distance Function (DDF) is modified to provide a complete ranking order of the DMUs (including efficient and inefficient DMUs). This model shows more reliability on differentiating efficient DMUs from inefficient ones via a new super-efficiency measure. The properties of proposed model include feasibility, monotonicity and unit invariance. Moreover, the model can produce positive outputs when data are non-negative. An empirical study in bank sector demonstrates the superiority of the proposed model.

Efficiency analysis for nonprofit organizations using DEA

Purpose The purpose of this study is to provide models to analyze the efficiency of programs and efficiency of fundraising to apply the models to non-profit organizations (NPOs) in Korea and to draw out improvement points of inefficiency using data envelopment analysis (DEA). Design/methodology/approach Using DEA, this study analyzed the program efficiency and fundraising efficiency of 22 Korean NPOs in the field of humanitarian assistance. Findings Of 22 NPOs, 15 were identified as being efficient in the program efficiency and 7 of 15 NPOs were found efficient in the fundraising efficiency. In all, four organizations were found efficient in both the program and the fundraising efficiency. Using CCR and BCC model, this study proposed the cause of inefficiency and state of returns of scale. Practical implications This study presents non-profit efficiency evaluation models regarding program efficiency and fundraising efficiency. This study provides the inefficient DMUs with their reference set of efficient DMUs to improve efficiency and the cause of inefficiency, whether the inefficiency is because of the pure technical inefficiency or the scale inefficiency. This study also indicates the state of variable returns to scale to propose the way of improving inefficiency by controlling the scale of inputs. The methods and the results of this study can serve as a model for researchers and practitioners to follow when evaluating efficiency in the NPOs. Originality/value This study has the value of performing the empirical studies of efficiency analysis of Korean NPOs and providing non-profits with the model of efficiency analysis in programs and fundraising activities and basis for establishing strategies to improve both efficiencies.

Improving discriminating power in data envelopment models based on deviation variables framework

Abstract Lack of discriminating power in efficiency values remain a major contention in the literature of data envelopment analysis (DEA). To overcome this problem, a well-known procedure for ranking efficient units; that is, the super-efficiency model was proposed. The method enables an extreme efficient DMU to achieve an efficiency value greater than one by excluding the DMU under evaluation from the reference set of the DEA model. However, infeasibility problems may persist while applying the super-efficiency DEA model under the constant returns-to-scale (CRS), and this problem tends to be compounded under the variable returns-to-scale (VRS). In order to address this drawback sufficiently, we extend the deviation variable form of classical VRS technique and propose a procedure for ranking efficient units based on the deviation variables values framework in both forms – CRS and VRS. With our proposed method, scholars who wish to prescribe theories based on a set of contextual factors need not remove large number of DMUs that are infeasible, thus avoiding problems in generalizability of their findings. We illustrate the performance and validate the efficacy of our proposed method against alternative methods with two established numerical examples.

Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry

Supply chain performance evaluation problems are evaluated using data envelopment analysis. This paper proposes a fuzzy network epsilon-based data envelopment analysis for supply chain performance evaluation. In the common data envelopment analysis models which are used for evaluation of decision-maker units efficiency, there are several inputs and outputs. One of the bugs of such models is that the intermediate products and linking activities are overlooked. Considering these intermediate activities and products, the current study evaluates the performance of decision-maker units in an automotive supply chain. There are ten decision-maker units in the supply chain in which there are three suppliers, two manufacturers, two distributors, and four customers. Moreover, the overall efficiency of input-oriented (input-based) model and input-oriented divisional efficiency are calculated. In order to improve the efficiencies, the projections onto the frontiers are obtained by using the outputs of the solved model and Lingo software. In order to show the applicability of the proposed model, it is applied on automotive industry, as a case study, to evaluate supply chain performance. Then, the overall efficiencies of DMUs and each sections (divisions) of DMUs were calculated separately. Therefore, every organization can apply this evaluation method for improving the performance of alternative factors.

Direct targeting of efficient DMUs for benchmarking

We propose a two-stage procedure for finding realistic benchmarks for nonparametric efficiency analysis. First, the efficient units are identified by a free disposal hull approach. These benchmarks are directly targeted by directional distance functions and the extent of inefficiency is measured along the direction towards an existing efficient unit. Two variants for finding the closest or the farthest benchmark are proposed. With this approach there is no need to use linear combinations of efficient units as benchmarks which may not be achievable in reality and also no need to accept slacks which are not reflected by the efficiency measure.

The Study of Regional R&D Innovation Efficiency Based on Improved Two-Stage DEA Model: Evidence from 30 Provinces of China

The improved two-stage DEA model removed constrains that efficiency values of decision making units are not greater than one by means of the handling method in super-BCC model. In this way, those efficient DMUs were separated from the efficient frontier and therefore the problem that they were unable to sort in the traditional two-stage DEA model. In the meantime, this improved model gave full consideration to the dual role of intermediate outputs and the influence of scale effect， which gave the evaluation results larger reference value. Lastly, the case study of 30 provinces demonstrated the feasibility and rationality of the improved model. It is found that the level of research and innovation efficiency in east China is the highest; the comprehensive efficiency and stage efficiency of mid-south and north China are high; the comprehensive efficiency level in southwest and northwest China is high while the efficiency of scientific research and development is low; the efficiency of research and innovation in northeast China is lowest.

Developing a Strategy of Environmental Management for Electric Generating Companies Using DEA-Methodology

This paper investigates the possibility of utilization of the Data Envelopment Analysis (DEA), which is a non-parametric method of optimization, to solving problems of environmental management in electric generating companies. An advantage of DEA is the possibility to work with DMUs without any knowledge of the actual functional relation between inputs and outputs. We analyze methods of incorporating the negative ecologic effects into a model and propose an algorithm for applying the basic DEA CCR input-oriented model twice in succession for the purpose of developing an optimal (ecologically and economically) strategy for environmental management in electricity energy generating companies. The developed method consists of sequentially solving several DEA models: the first-stage model determines the effectiveness of DMUs from an ecologic perspective and calculates target values for decreasing negative ecologic effects of non-effective DMUs. The second stage requires solving one input-oriented CCR model for each non-effective object, using economic and social characteristics of projects meant to reduce negative environmental influence, and using the target values calculated in the first stage as outputs. Besides the problem of evaluation the comparative efficiency of DMUs, ecologically oriented studies also often needs to evaluate the changes in a DMU’s efficiency dynamically. For this, the Malmquist productivity index (MPI) is used. MPI is a non-parametric method for analyzing time series that allows to track changes in DMU efficiency over time by means of DEA models. We test this algorithm on the statistical data provided by Russian electric companies for the period 2009-2011, and discuss methods for its practical application. The statistical data used in our calculations is averaged, and the results do not reflect the entire picture and should not be used to judge the quality of ecologic management in these companies. Nevertheless, the calculations can be used to evaluate the progress of completion and the practicality of investment projects of companies, from an environmental viewpoint. They also may be used to help develop state programs for support of modernization in electric energy industry, ecologic standards or energy-saving programs.

Selection of corporate spare parts inventory for Brazilian refineries: a smoothed data envelopment analysis frontier function using calculus of variations

This paper intends to give a small contribution to data envelopment analysis (DEA) development, among the huge research carried out in the field, by developing a model for materials selection in a corporately managed inventory that includes high-value MRO spare parts. The goal of the model is to reduce the asset value in the stocks without compromising the reliability of the operating units for Brazilian refineries. We implemented a standard DEA model and defined a smoothed DEA frontier using the efficient DMU from the model. This is achieved through the calculus of variations, generating a non-linear polynomial frontier that is continuously differentiable and contains all DEA efficient DMUs, providing a unique tangent hyperplane for each target on the efficient frontier. The main reason for smoothing the DEA frontier is to create an agile assessment of the radial efficiency of new materials without running the entire DEA mathematical programme.

Measuring Efficiency of Life Insurance Instution in Indonesia: Data Envelopment Analysis Approach

This study aims to measure the level of efficiency of the life insurance industry in Indonesia. The calculation of the efficiency level in this study is relative, not absolute. The approach used is Data Envelopment Analysis (DEA). There are 8 research objects: Prudential, BNI Life, PaninDai-IchiLife, Asuransi Jiwasraya and Life Insurance Adisaranan Wanaartha, Takaful Takaful Insurance, Amanahjiwa Giri sharia insurance and Al-Amin sharia life insurance. This study consists of three input variables (cost of Commissive (X1), Operational Cost (X2), Total Equity (X3) and 2 output variables (Premium) (Y1) and Investment Revenue (Y2)). The results explain that there are 15 perfectly efficient DMUs (100%). And an inefficient of 24 DMU, consisting of 7 DMU conditions IRS and 17 DMU with DRS conditions. Of all the DMU observed, Prudential insurance is a life insurance company that is able to maintain its gradual efficiency level from 2013 to 2016 when compared to other life insurance in this observation. In general, the main factor inefficiency of life insurance industry in Indonesia (in observation) from 2012 to 2016 is from the output side. To be more efficient then life insurance companies should increase the value of premiums by 91% and investment income of 8%.

A directional distance based super-efficiency DEA model handling negative data

This paper develops a new radial super-efficiency data envelopment analysis (DEA) model, which allows input–output variables to take both negative and positive values. Compared with existing DEA models capable of dealing with negative data, the proposed model can rank the efficient DMUs and is feasible no matter whether the input–output data are non-negative or not. It successfully addresses the infeasibility issue of both the conventional radial super-efficiency DEA model and the Nerlove–Luenberger super-efficiency DEA model under the assumption of variable returns to scale. Moreover, it can project each DMU onto the super-efficiency frontier along a suitable direction and never leads to worse target inputs or outputs than the original ones for inefficient DMUs. Additional advantages of the proposed model include monotonicity, units invariance and output translation invariance. Two numerical examples demonstrate the practicality and superiority of the new model.

A Novel Weighting Method for Finding Common Weights in DEA

In the traditional DEA model, each DMU maximizes its efficiency with the most favorable weights. This leads to flexibility and unreality of input and output weights. Subsequently, it is unfair to compare and rank the efficiencies of different DMUs obtained on the basis of these weights. In this paper, we propose a novel approach to determine a common set of weights with more consensus to evaluate and rank the performance of all DMUs by weighting the rescaled weights based on the degree of consensus, where the weights obtained from DEA are rescaled for comparison among DMUs. Moreover, to overcome the non-uniqueness of the weights, a novel secondary goal is developed based on the agreement between self-evaluation and peer-evaluation. In addition, the restriction of weights is taken into account to avoid trivial weights. Finally, an example of 14 international passenger airlines is used to illustrate the performance and credibility of our proposed method.

A novel Data Envelopment Analysis model for evaluating industrial production and environmental management system

Industrial production and environmental management systems should be simultaneously considered for sustainable development. This paper evaluated the performance of an integrated two-stage system using a proposed type-2 fuzzy bi-objective two-stage slacks-based measurement Data Envelopment Analysis model with super efficiency. A Step Method was applied to solve for the Pareto optimal solution to ensure no implicit priority was given to one stage over the other, and a CV-based reduction method and generalized credibility based chance constrained programming were used to cope with the type-2 fuzzy variables. A case study in China was then developed from a time-perspective and a region-perspective, the results from which indicated that the overall performance of China's integrated system improved from 2005 to 2014, and the efficiency gap between the industrial production system and the environmental management system reduced, however, there was significant disparity shown across the different economic regions. Three comparative analyses were then conducted to highlight the superiority of the proposed model. The developed model was able to: measure efficiency scores and find proportionate ratios and disproportionate slacks for each DMU to decrease inputs for performance improvement, distinguish the DMU from DMUs with same efficiency value and indicate the maximum change scope for the inputs and outputs to maintain the DMU efficiency. In addition, type-2 fuzzy sets were incorporated to describe the fuzziness with greater flexibility, which can assist decision makers and produce more accurate, robust results.

Efficiency analysis of non-homogeneous parallel sub-unit systems for the performance measurement of higher education

Conventional Data Envelopment Analysis (DEA) models focus only on initial inputs and final outputs for efficiency evaluation. Thus, these models treat the production process as a ‘black box’, i.e., they do not take into account how exactly inputs are related to outputs. Various models that came later take care of internal processes of DMU. The existing models of internal processes for parallel sub-units consist of three stages: the first stage calculates the relative weights of sub-units, the second stage calculates the efficiencies of sub-units, and in the third stage efficiencies of sub-units are aggregated as the efficiency of DMU. It is observed that when existing models of internal processes are applied to non-homogeneous parallel sub-units, in the first stage, the weight assigned to the maximum efficient sub-unit is one and to other sub-units is zero. This implies that the efficiency of a DMU is equal to the maximum of efficiencies of its sub-units indicating that the efficiency of a DMU is not sensitive to the efficiencies of sub-units other than the sub-unit with maximum efficiency. This paper proposes a single stage DEA approach where the efficiency of a DMU and its sub-units can be measured simultaneously. The advantage of the proposed approach is that the efficiency of a DMU is sensitive to the changes in the efficiency of its sub-units, and weights of sub-units can be assigned a priori by the decision maker. The development of the proposed approach is inspired from the growing interest in evaluating efficiency of higher education system in India. In the proposed application, states are considered as DMUs and universities, colleges and stand-alone institutions are taken as three non-homogeneous parallel sub-units of DMUs.

PENGUKURAN EFISIENSI AGROINDUSTRI PANGAN LOKAL ENBAL DENGAN PENDEKATAN DATA ENVELOPMENT ANALYSIS

The current research aimed to analyze the relative efficiency level of enbal (sago starch) agro-industries. The relative efficiency analysis on 32 DMUs of enbal agro-industries showed that 40,63% of the industries were efficient and 59.38% were inefficient. Every efficient DMU became the reference for the inefficient DMUs based on the suggested quality. Each DMU of the enbal agro-industries has not reached a good efficiency level, which was indicated by the average relative efficiency scale of 0.886. This was a relatively low value, and improvements on the use of production input were needed. The analysis result on the DMUs of the enbal agro-industries which were on constant return to scale position were 40,62%. This showed that enbal agro-industries actors have applied production input efficiently, for the production increase was equal to the use of input. In other words, the use of input was more proportional. The DMUs of enbal agro-industries which were on decreasing return to scale position were 15,63%. This showed that the use of production input had been unsuitable so that the output decreases and the production cost increased. Meanwhile, the DMUs that were on increasing return to scale position were 43,75%. This showed that the industry actors who used certain production input would create efficient DMUs. On the other hand, the input excess would possibly decrease the output. As a result, the industry actors should be concerned about the use of production input in order to establish business efficiency.

Mining crime instance records of Philippine National Police District Vi âA ¸S˘ Province of Cavite, Philippines: An Exploratory Study to Enhance Crime Prevention Programs

Aim: This study aimed to measure the efficiency, performance, and organizational management in one of the countryâA˘ Zs national police ´forces. Crime management as the prime function of the police requires resources that were considered as decision-making units in this study, particularly in the data envelopment analysis.Methodology: The research study applied an input-oriented radial measure of efficiency and a Rate-to-Scale (RTS) using scale efficiency to determine the efficient DMUs. A structured approach to planning data mining activities (CRISP-DM) was used in the analytical tasks using the clustering technique.Findings: Based on the 3-year scale efficiency result, Municipality C is the efficient DMU.Implications/Novelty: This study is timely and relevant as to the current drive of the Philippines government and the Philippines National Police to outcast criminalities and unlawful activities. This study helped the crime detectives, law enforcement agencies with crime pattern detection, and geospatial analysis.

Development of FDEA Models to Measure the Performance Efficiencies of DMUs

Data envelopment analysis (DEA) is a linear programming-based methodology to measure the relative performance efficiencies of DMUs which produce multiple outputs by consuming multiple inputs. The input data and output data can be considered as a linguistic variables characterized by fuzzy numbers. So, in the present study, we extend DEA to fuzzy DEA (FDEA) in which the input and output data are taken as fuzzy numbers (FNs), in particular triangular FNs. In this paper, we develop two FDEA models to measure the left hand and right hand relative performance efficiencies of each DMU using $$\alpha$$ -cut approach. Further, we propose a ranking method to rank the DMUs based on left hand and right hand efficiencies. Finally, the developed FDEA models and proposed ranking models are illustrated with an example and then compared the results with geometric average efficiency index ranking method. The proposed model is also experimented with a real-life problem.

Two Methods for Measuring the Environmental Returns to Scale Using Data Envelopment Analysis Approach

Nowadays, the assessment of environmental performance has received considerable attention by environmental strategy advocators and decision makers. In recent years, increased emission of CO2 into the air, water pollution, and global warming are universal problems all over the world. Therefore, development of firms with less CO2 emission is an important issue of attention in every area of production. This paper applies data envelopment analysis (DEA) as a management technique for assessing the environmental performance of the firms. We then explore the measurement of scale economies (SE) and returns to scale (RTS) for environmental issues which have not been given the attention they deserve in the last few decades. Associated with SE and RTS for desirable outputs, the new concepts of ESE (Environmental Scale Economies) and ERTS (Environmental Returns to Scale) are proposed for both desirable and undesirable outputs. This paper presents two methods for determining the type of ERTS of efficient DMUs, and demonstrates their equivalency within a theorem. Finally, the offered models are employed to study the CO2 emission of Japanese electric power companies. Afterwards, the type of ERTS is determined for efficient companies and based on their type of ERTS, the optimal size is suggested for them.

On the Consistency of Slacks-based Measure-max Model and Super-slacks-based Measure Model

Slacks-based measure (SBM) (Tone [1]), Pastor et al. [2]) has been widely utilized as a representative non-radial DEA model. However, this model, called SBM-Min here, evaluates the efficiency of an inefficient DMU referring to the furthest frontier point within a range. In contrast, the SBM-Max model looks for the nearest frontier point and hence its score is generally greater than the SBM-Min score. The Super-SBM model (Tone [3]) evaluates the efficiency of an efficient DMU referring to the nearest point on the frontier except itself. We can foresee a close connection between SBM-Max and Super-SBM models, because the motivations behind the two models are same. In this paper we demonstrate this consistency using a real dataset.