Efficient DMUs Research Articles

Stability analysis and enhancement of super-efficiency model based on space distance

The traditional super-efficiency data Enveloping analysis (DEA) model can further distinguish the efficiency of efficient DMU. However, this distinction is unstable when there are perturbations in the efficient DMU inputs and outputs. The spatial distance can reflect the spatial variation of DMU on the envelope surface. We investigate the stability of the modified VRS super-efficiency model in the presence of data perturbations in efficiency DMUs and calculate its stability with spatial distances, providing a necessary and sufficient condition for such perturbations to affect the results of calculations of other efficient DMU super-efficiencies. A new super-efficiency model is proposed, which combines spatial distance to increase the constraint on projection point. Numerical examples are used to illustrate the model. On this basis, a spatial distance model for calculating inefficient DMU efficiency is further developed.

The decomposition of efficiency in parallel network production models

Kao (2012) proposed a method to decompose DMU efficiency into sub-unit efficiencies for parallel production systems. We provide a numerical example showing that the proposed method can yield negative sub-unit efficiency scores under variable returns to scale, against common sense and standard postulates requiring this score to be non-negative. As a solution, we propose a decomposition based on the directional distance function that does not suffer from this problem. In particular, we recognize that the overall inefficiency of the DMU is composed of the sub-units technical inefficiencies and a reallocation inefficiency component. The proposed method can be also applied to non-convex technologies, therefore providing a more general method to implement such a decomposition. Given the connection between the directional distance function and slack-based efficiency measurement, the method can easily be extended to this case as well.

Competition or cooperation? Research on the spatial game relationship of regional carbon emission efficiency

The gradient differences of economic development and industrial structure differences in Jing-Jin-Ji Region are significant. The ability of Jing-Jin-Ji Region to pay for environmental protection is imbalanced. The above two situations make it difficult to achieve coordinated pollution reduction and carbon reduction governance. Accurately measuring carbon emission efficiency and thoroughly analyzing the cooperation and competition relationship of regional carbon emission reduction based on fully considering stakeholders are of great significance for the joint construction, prevention and control of regional ecological environment. The research is also of great significance for promoting high-quality economic development and ensuring effective protection of the regional ecological environment. This study combines game theory and DEA theory to measure the carbon emission efficiency of the Jing-Jin-Ji Region, and then uses spatial game theory to analyze the average degree of being attacked, anti-attack ability, strongest attacker, and optimal partner for the carbon emission efficiency of different DMUs. The study concluded that: 1) Xicheng District and Hedong District have higher carbon emission efficiency, while Mentougou District, Fengtai District, Changping District, Fangshan District, Shijingshan District, Pinggu District and Yanqing County have the lowest carbon emission efficiency. 2) From an analysis of the degree of being attacked, it can be observed that economically developed areas experience a higher average degree of being attacked compared to economically underdeveloped areas. In regions with lower economic development, the influence of the local government on reducing carbon emissions is not significant, whether acting independently or in collaboration. Regions with contrasting industrial structures could be ideal partners, and areas with higher carbon emission efficiency could collaborate with regions with lower carbon emission efficiency to achieve greater reduction potential.

Efficiency analysis of productive waqf management at Jogokariyan Mosque Yogyakarta

Waqf plays an important role in promoting the welfare of society, especially in overcoming poverty and social inequality in Indonesia, a Muslim-majority country. This study aims to analyze the production efficiency of productive waqf management by Nazir in Jogokariyan Mosque Yogyakarta and identify problems in the management. The research method uses a quantitative approach with Data Envelopment Analysis (DEA) and Banxia Frontier Analyst software. Primary data comes from the Jogokariyan Mosque's finances managed by Nazhir waqf. The results showed that DMU 1 (lodging) achieved 100% efficiency in 2017, 2018, and 2019. DMU 2 (rice fields) achieved 100% efficiency from 2017 to 2023. Efforts to improve the efficiency of DMU 1 involve successively reducing the input variables of employee salaries (10.42%, 14.30%) in 2020-2021, and employee salary costs and operating costs in 2022 (14.19%, 4.35%). However, revenue and profit outputs need to be improved (27.94%, 32.45%) in 2020, (26.27%, 30.70%) in 2021, and (25.71%, 30.45%) in 2022. DMU 2 does not require efficiency improvement as it reached 100% from 2017 to 2022.

Facet analysis in data envelopment analysis: some pitfalls of the CRS models

The nonparametric estimator of the Extended Facet reference technology for the Constant Returns to Scale case has attracted some attention, because the associated production frontier by construction does only include strongly efficient faces of maximal dimension, or strongly efficient faces that are part of such strongly efficient faces of maximal dimension. The strongly efficient faces of maximal dimension are denoted Full Dimensional Efficient Facets (FDEFs). The identification of such strongly efficient facets is facilitated by removing all inefficient and all strongly efficient but not extreme efficient DMUs from the estimation procedure of the technology set. Any face that i) is passing through the origin and with (s + m − 1) linear independent extreme efficient observed DMUs positioned on it and ii) with a normal vector with strict positive (strict negative) output (input) components, is a FDEF, where s (m) is the number of outputs (inputs). It is, however, not correct that every face that satisfies only i) is a FDEF. We denote a face (a subface) that satisfies only condition i) but not condition ii) for an AP-face (an AP-subface). It is proved that a radial projection of any output input combination in the estimated EXFA technology set is positioned on the strongly efficient frontier if and only if i) no AP-(sub)faces exist, ii) a regulaty condition RC1 is satisfied and only dual multiplier constraints corresponding to extreme efficient DMUs are included in the estimation. A test for the fulfillment of the condition that no AP-(sub)faces exist is provided.

PERFORMANCE EVALUATION USING THE DEA METHOD: A CASE STUDY OF ELECTRICITY COMPANIES IN INDONESIA

Performance evaluation aims to continuously monitor the efficiency and economics of company operations and offers information for company decision-making. Adequate and comparable performance evaluation requires a method and measuring tool that can measure company performance in a complex manner. DEA is a method that allows the use of quantitative and qualitative characteristics. The State Electricity Company (PLN) is a company that focuses on the electric power sector for the benefit of society and the state. PLN's business sector consists of businesses such as: (i) production, transmission, and distribution of electric power; (ii) planning and development of electric power; (iii) exploitation and development of electric power; and (iv) business of services in the electricity sector. Indonesia's economic growth relies heavily on electricity. The aim of the research is to evaluate PLN's performance in Indonesia using the DEA method. DEA is a linear programming method that deals with performance measurement. Performance measurement consists of input and output parameters. Performance evaluation is one way to identify effective and ineffective DMU. In this way, PLN, as the driving force of the economy in Indonesia, can develop well in a sustainable manner. The research results indicated that efficient DMUs have a percentage of 94% and inefficient DMUs have a percentage of 6%.

УДОСКОНАЛЕННЯ ІСНУЮЧИХ МЕТОДІВ ОЦІНЮВАННЯ ФУНКЦІОНУВАННЯ АЕРОПОРТІВ ПРИ МІЖНАРОДНІЙ ІНТЕГРАЦІЇ

The article is devoted to the study of methods for assessing the operation of airports under international integration and their improvement. The recent scientific studies on assessing the operation of international airports were analyzed and it was found that the most actively used methods are the initial data envelopment analysis (DEA) and various variants of quantile regression, Tobit regression, assurance region methods, SBI and DMU, Malmquist index, classification and regression tree, bootstrapping tool and other tools. It was confirmed that the existing methods for assessing the airports’ operation have a significant number of indicators for calculations based on the evaluation of economic, technical or overall efficiency. The parametric methods for assessing the operation of airports, their advantages and disadvantages were characterized. The methodology of stochastic frontier analysis (SFA), the use of a non-parametric approach to evaluate airport performance, and the evaluation of the overall efficiency of two-stage DMUs with shared resources were described. The methodical approaches based on the use of the spatial regression model, the AAG model and the stochastic frontier model were characterized. The methodology for determining the target function Effi to assess the efficiency of each airport DMUi was described. The scheme for assessing the airports’ operation by input and output indicators was proposed, which includes a three-module assessment system for the passenger, cargo and joint modules. The cargo module is the authors’ own development and its creation is justified by the fact that a significant number of air cargo flows are served by passenger aircraft. The group of input indicators of the cargo module includes the length of the loading and unloading work front, the number of cargo receiving points, the working area of rack and stack storage, the number of electric forklifts and lift trucks, as well as the number of cargo warehouses, and the number of general and special categories of cargo are defined as output indicators.

Performance Evaluation Using the DEA-Stepwise Modeling Approach Method: Case Study of the Export-Import Sector in Indonesia

The export and import industry are now a significant determinant of a nation's economic progress in the age of globalization. A nation's ability to export helps it earn foreign cash that it can utilize to fund development and spur economic expansion. Conversely, import-related activities might assist in satisfying the need for products or services that cannot be effectively provided domestically. Entrepreneurs can increase the competitiveness of local products and the quality of their products through imports and exports, which will enhance both industrial progress and community welfare. To maximize the nation's economic progress, the government and business owners must continue to boost import-export activity. The objective of this study is to evaluate Indonesia's export-import sector's performance using the stepwise modeling approach and the DEA method. One technique to determine which export-import sector activities are effective and ineffective is performance evaluation. These two sectors can support the country's economy in this way and do so with a long-term approach. The research results indicate that efficient DMUs have the smallest presentation (20%) and inefficient DMUs have the largest percentage (80%). The efficient DMU category is C (1). Inefficient DMU categories, namely: SK (0.285), S (0.193), M-US (0.472), and T-J (0.11).

Efficiency evaluation of very large-scale samples: Data envelopment analysis with angle-index synthesis

In the era of big data, the rapid solution of large-scale data envelopment analysis model has attracted extensive attention of scholars. Finding efficient DMUs has become a key consideration in large-scale evaluation. The parallel building hull method and the framework method offer good performance in terms of finding efficient DMUs. However, with the surge in time series data and high-frequency data, the evaluation problem of large-scale samples has put forward the requirement for faster and larger scale solutions for traditional methods. In this study, we propose a prescoring method for DMUs by synthesizing the angle and the index, hereafter called the angle-index synthesis method. Meanwhile, we combine this proposed method with the framework method to obtain all efficient DMUs. Numerical experiments and a real application to the bankruptcy data of Polish companies show the significant advantages of our algorithm in terms of computational time in the large-scale sampling context, and optimal parameters can be ignored. Finally, in the Monte Carlo simulation of very large-scale samples, we show that our algorithm has linear increasing trend for the computational time and good validity even in 1 billion DMUs.

A novel algorithm for complete ranking of DMUs dealing with negative data using Data Envelopment Analysis and Principal Component Analysis: Pharmaceutical companies and another practical example.

When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity.

Eco-efficiency assessment of the sugarcane production system using a four-step integrated life cycle and data envelopment analysis

ABSTRACT Sugarcane is an energy-intensive crop that requires a large amount of input resources. The production and usage of all these resources are liberated huge amounts of GHGs (Greenhouse gases). Therefore, to address these issues, the objectives of the study were decided to assess the eco-efficiency of the sugarcane crop, quantify the GHG emissions from sugarcane farms, and investigate ways to reduce the GHG emissions from sugarcane farming. LCA+DEA approach was used to evaluate the eco-efficiency of the sugarcane production system using a four-step method. The output-oriented BCC model was selected for benchmarking and estimating the efficiency score of DMUs. The average sugarcane production and carbon emissions of study area were found to be 148.75 t ha−1 and 3234.83 kg CO2eq ha−1, respectively. Nitrogen fertilizers contributed highest share in carbon emissions (50.15%), followed by P2O5 (26.98%), diesel (11.72%), K2O5 (6.94%), micro nutrients (2.24%), total machinery (1.14%), and biocides (0.82%), respectively. The average current GHG emissions from the input resources were found to be 1590, 929, 267, 445, and 3231 Kg CO2eq ha−1 for N, P2O5, K2O, diesel, and total carbon footprint (CF), respectively, and targeted GHG emission from input resources for better performance were 211, 336, 621, 165, and 1333 Kg CO2eq ha−1, respectively. The GHG emission contribution of N, and P2O5 must be reduced by 86.72% and 63.83%, respectively, to meet the GHG emission target set by the efficient DMUs and additional K2O (57%) might be used for the farming operations. Furthermore, the diesel consumption of agricultural machinery is not possible to reduce because improved mechanization practices in sugarcane farming will increase the diesel consumption.

Crowding distance development for ranking efficient units in DEA

Data Envelopment Analysis models have proven ability to classify decision making units into two efficient and inefficient sets. Although inefficient ones can be sorted by the value of efficiency score, ranking the efficient units has always been a challenging subject in DEA. Various methods, in terms of implementation and nature, have been proposed to rank efficient DMUs. A novel method based on diversity and variety of units on the efficient frontier is presented in this study. Firstly, the crowding distance definition from the multi-objective evolutionary optimization is developed and then a linear programming model is introduced to detect and rank the efficient units at the same time. This means that by solving the proposed model for each DMU separately, ranking efficient units is fulfilled meaning there is no prior need to detect efficient ones. Furthermore, this model can be applied to rank inefficient units with the same logic of the efficient ones which leads to a homogenous ranking of all DMUs. To check the validity and compatibility of this method, it is analyzed through its application on a case study.

Preventive Risk Management of Resource Allocation in Romanian Higher Education by Assessing Relative Performance of Study Programs with DEA Method

Risk management is a key activity in every organization. The identification and evaluation of all risks in higher education institutions lead to the continuous monitoring of investments in people, high technology, and innovation. This paper evaluates the relative efficiency of study programs in Romanian higher education using the DEA method. This study is based on 38 study programs from a public university in Romania, using a traditional DEA approach: CRS-DEA and VRS-DEA models, with an output orientation for three academic years (2016–2019). To avoid distortions in the efficiency scores, we decided to implement the bootstrap method to correct DEA efficiencies. The results show that only four study programs were efficient during this period under the CRS-DEA approach, and eight study programs were efficient under the VRS-DEA model. According to scale efficiency and the bootstrap method, the results also showed that four study programs were efficient during the period analyzed. Finally, we observed that the inefficiency of study programs is relatively persistent (89%), compared with efficient DMUs (11%). Based on these findings, higher education institutions should consider the possibility of increasing the quality of study programs correlated with the degree of attractiveness of various programs in the current socio-economic environment.

Analysis of relative efficiency of vessels of passenger transportation in the Brazilian Amazon: an AHP-DEA approach

Waterway transport is of great importance for the population of the Amazon Region. It is responsible for moving, according to Agência Nacional de Transportes Aquaviários (Antaq, 2018), approximately 9.8 million passengers and 3.4 million tons of cargo distributed by state, interstate longitudinal transport interstate and crossing. However, despite the great importance, there are several blockages related to the conditions of the roads and vessels in certain routes. The study in question proposes the use of a mathematical tool to analyze vessels from the relative efficiency involving parameters of input and output for two analysis scenarios: Energy Efficiency Analysis and Modality Analysis, being analyzed variables such as power, consumption, total time of the trip, total distance of the line of action, passenger capacity and ticket price. For that, the data of 652 vessels used in the Amazon region registered in the Brazilian states (Pará, Amazonas, Amapá and Rondônia) were verified, and from these data, a sample space of 148 vessels from the same utilization level was selected. With these data, a model was developed using the Data Envelopment Analysis (DEA) methodology, with the aid of the DEA-Solver software. After the use of the solver, the relative efficiency indexes of each vessel were obtained, allowing to rank and measure the efficient and inefficient Decision Units (DMU'S). Lastly, analysis of the general characteristics of efficient vessels were made in order to stratify the parameters that may have transformed them into efficient DMU`s, making it possible to trace a profile of the type of vessel employed in each type of line, thus helping in decision making of new projects.

Emergency medical resource allocation among hospitals with non-regressive production technology: A DEA-based approach

This paper proposes an approach for medical resource allocation among hospitals under public health emergencies based on data envelopment analysis (DEA). First, the DEA non-regressive production technology is adopted to ensure that the DMU can always refer to the most advanced production technology throughout all production periods. Based on the non-regressive production technology, two efficiency evaluation models are presented to calculate the efficiencies of DMUs before and after resource allocation. Our theoretical analysis shows that all the DMUs can be efficient after medical resource allocation, and thus a novel resource allocation possibility set is developed. Further, two objectives are considered and a bi-objective resource allocation model is developed. One objective is to maximize the output target realizability of the DMUs, while the other is to ensure the allocated resource to each DMU fits with its operation size, preperformance, and operation practice (i.e., proportion of critically ill patients). Additionally, a trade-off model is proposed to solve the bi-objective model to obtain the final resource allocation results. The proposed approach contributes by ensuring that the medical resources are allocated in such a way that they can all be efficiently used as well as considering multiple objectives and practical constraints that make the approach more fitted with the practical application scenarios. Finally, a case study of 30 hospitals in Wuhan during the COVID-19 epidemic is applied to illustrate the proposed approach.

Efficiency evaluation of commercial banks in Pakistan: A slacks-based measure Super-SBM approach with bad output (Non-performing loans).

According to recent figures from the State Bank of Pakistan (SBP), since 2006, commercial banks’ non-performing loans (NPLs) have significantly risen. To this end, the primary objective of this research is to explore the impact of NPLs on the operational efficiency of commercial banks in Pakistan. NPLs were incorporated as bad output in the efficiency estimation of 24 CBs for the period 2006–2017. This study employs the data envelopment analysis (DEA) Super-SBM with the undesirable output for the efficiency evaluation of CBs. To test the robustness of our results, we used two different input-output bundles (model A and model B). The findings show a significant difference exists between the results estimated with and without undesirable output. Furthermore, the results of super-efficiency estimation rank the most efficient CB for the study period and distinguish it from other efficient DMUs. Models A and B show that foreign banks are always more efficient than domestic banks, while private CBs have higher efficiency scores than public CBs in domestic banking. In addition, the big five CBs show mixed findings, as in model A, they were more efficient than other domestic CBs, while in model B were less efficient. In the second stage of the empirical study, we use the system GMM to examine the impact of NPLs, bank size, and net interest margin on CBs efficiency. We discovered that NPLs have a negative and significant effect on banking efficiency, whereas bank size and net interest margin positively affect the efficiency of commercial banks in Pakistan.

TECHNICAL EFFICIENCY ESTIMATES OF COFFEE PRODUCTION IN DAVAO CITY, PHILIPPINES: A DATA ENVELOPMENT APPROACH

<p>The Philippine coffee industry is dominated by small-scaled farm households. This study was conducted to measure the technical efficiency of coffee production in Davao City using Data Envelopment Analysis. The study also determined the factors that contribute to the technical efficiency of coffee farms using Logistic Regression. Among the 166 respondents obtained, the study revealed that only 11 DMUs are technically efficient operating under constant return to scale while 154 and 1 were technically inefficient operating under increasing return to scale and decreasing return to scale, respectively. This means that input such as farm size, labor, number of coffee trees, age of coffee trees, fertilizers and pesticides has to be adjusted in order to reach the frontier to be technically efficient DMUs in which coffee farms are the potential to increase production if provided with appropriate support. Furthermore, to achieve bias-corrected DEA technical efficiency scores, Bootstrapping was done and later used for regression. Meanwhile, the logistic regression results revealed that there were significant relationships between technical efficiency and socioeconomic such as Age of Household Head, Sex, Access to Credit, Cropping System, Technology, and Coffee Variety. However, the logistic regression also revealed the factors that have no statistically significant relationship in the variables such as Education, Farm Experiences, Household Size, Extension Services, and Off-farm Activities.</p><p> </p><p><strong>JEL</strong>: L60; L66; O13</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0929/a.php" alt="Hit counter" /></p>

Benchmarking with nonconvex production possibility set through data envelopment analysis: An application to China’s transportation system

Convex production possibility set (PPS) is an implicit assumption in previous benchmarking studies. Some scholars point out that the implicit assumption is inconsistent with the classical economic theory although it facilitates operational analysis. Hence, the present study develops a new benchmarking approach on the basis of technology that assumes convexity on consumption correspondence instead of imposing convexity on PPS, in which the closest benchmark with the least effort is identified. First, the correlation between two classical technologies in benchmarking (convex PPS and free disposal hull technology), and two nonconvex PPS (production correspondence and consumption correspondence), are identified. Next, the extreme efficient DMUs are identified to describe Pareto-efficient points on the frontier of the consumption correspondence. Then, the closest benchmark is determined by selecting a Pareto-efficient point that minimizes the distance between the evaluated unit and the efficient frontier of consumption correspondence. In addition, this study employs the Manhattan distance for benchmarking, and the proposed approach is flexible to accommodate other distances to derive different economic interpretations. Besides, the proposed approach introduces a new non-radial efficiency measure and conceptualizes a benchmark index by relaxing the convexity assumption on PPS. Finally, the proposed approach is applied to select benchmarks and measure performance for China’s transportation system. The proposed approach contributes to relaxing convexity assumption on PPS in benchmarking using DEA and provides a powerful managerial instrument to improve the performance of China’s transportation system.

Development of FFDEA Models to Measure the Performance Efficiencies of DMUs

Development of FFDEA Models to Measure the Performance Efficiencies of DMUs

Investigating the Sustainability of Return to Scale Classification in a Two‐Stage Network Based on DEA Models

Purpose. The purpose of this study is to sensitivity analysis analyze the returns to scale in two‐stage network based on DEA models. The main focus of the firms has always been to obtain the maximum output with the least available resources, which points to the improvement of the firm’s performance and the importance of returns to scale and technical improvement. Design/Methodology/Approach. This study examines the sensitivity of returns to scale classifications in a two‐stage DEA network. A new input‐oriented model was progressed to identify the efficient decision‐making units in the two‐stage network, after which a new method of determining the returns to scale classifications in the efficient DMUs in two‐stage network (constant, increasing, or decreasing returns to scale) was established. Findings. The stability of the returns to scale classifications in the two‐stage network was analyzed. A stability region for changes in primary inputs and final outputs is only determined especially for DMUs that are efficient so that it maintains the classification of the returns to scale units. The results are shown by numerical examples. Practical Implications. The sensitivity analysis of returns to scale classifications is one of the most significant issues in data envelopment analysis (DEA), which plays an essential role in management decisions. Originality/Value. Using this model can help improve the performance of companies by using new tools and also improve the quality of work and increase acceptance competition.