DEA-CCR Model Research Articles

Stage interval ratio DEA models and applications

Data envelopment analysis (DEA) is a crucial method for assessing the efficiency of decision-making units (DMUs). In practice, it is common to encounter situations where the performance of a DMU at a certain stage is evaluated, especially in policy effect evaluation. The key to stage performance evaluation is to eliminate the influence of pre-stage inputs (outputs) on stage performance. This paper proposes stage ratio DEA models that consider two types of inputs and outputs: accurate ratio data and interval ratio data. The first form considers the evaluation of stage efficiency when accurate ratios of inputs and outputs are used at the beginning and end of the stage. The second form assesses stage efficiency using interval ratio data for interval inputs and outputs. To verify the validity of the proposed models, the numerical example validates the first form of stage ratio DEA models. And the second form is applied to evaluate the sustainable efficiency of 14 energy saving and environmental protection clean enterprises (ESEPCEs). The empirical results demonstrate that the proposed models provide a more accurate assessment of stage efficiency compared to the traditional DEA-CCR model.

Assessing the ICT development in Iranian cities: The strategy to accelerate digital advancement

Iran is one of the Middle East's developing countries, with a per capita GDP that ranks among the upper middle countries, according to international reports. On the other hand, according to research, the development of e-government in a country is directly tied to that country's economic status. Despite its economic strength, Iran is not well-positioned in the sphere of information and communication technology (ICT), with undesirable development of e-government and ICT infrastructure. The performance of Iranian provinces in the field of ICT development is analyzed in this study utilizing the DEA-CCR model, the Malmquist productivity index (MPI), reciprocal efficiency technique and k-means clustering. The main contribution of this study is that it aids policymakers and top managers by identifying poorly performing provinces, to make thorough preparations to strengthen them, and implement suitable policies and strategies. Indeed, by supporting and developing the weak provinces in the field of ICT, the overall ICT status of the country will improve.

Evaluation of Water Resources Utilization Efficiency Based on DEA and AHP under Climate Change

In the context of climate change, the problem of water scarcity is becoming increasingly serious, and improving the efficiency of water resources use is an important measure to alleviate this problem. The evaluation of water resources utilization efficiency has become the basis of water resource management. Data envelopment analysis (DEA) and analytic hierarchy process (AHP) are widely used in the evaluation of water resources utilization efficiency. However, one of these methods is mostly used for evaluation, which cannot reflect the influence of both objective and subjective factors. Therefore, in this study, we propose a water resources utilization efficiency index (WEI) to evaluate the water resources utilization efficiency of each region in the Tumen River Basin (TRB), combining both DEA and AHP methods. Firstly, the DEA-CCR model was used to quantify domestic, agricultural and industrial water use efficiency in the TRB. The DEA-BCC model was used to analyze the main influences on water use efficiency in each sector. Secondly, the WEI was constructed by assigning weights using the AHP model based on the importance of each water use sector. The results show that the WEI values for most areas within the TRB trended upwards between 2014 and 2019. In particular, domestic water use efficiency ranged from 0.294 to 0.775, while agricultural and industrial water use efficiency ranged from 0.039 to 0.054 and 0.031 to 0.375, respectively. Technical efficiency is the main factor influencing water use efficiency in TRB. This study could provide a basis for water resource management and mitigation of water scarcity in the context of climate change.

Measurement of DEA-Based ICT Development Efficiency Level with Modified CCR Method

Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that is useful for measuring the efficiency of the Decision Making Unit (DMU) of an organization. This study uses the Cooper and Rhodes (CCR) method known as the DEA-CCR multiplier which aims to determine the weight value of each input and output variable of the DMU being evaluated, but it is not sufficient to measure efficiency optimization. To get an efficient value of the weight value of each DMU as a reference to get updated DMU input and output values. So that the DMU efficiency value is obtained which is evaluated. The results of this study show how to modify the Multiplier Model-CCR into the Envelopment Model-CCR. Then displays the efficient level DMU which is evaluated as a result of the weight each DMU gets from the results of processing the LINDO application. Illustrations of changes in input variables and output variables are displayed in the form of tables and figures before and after the changes. The modified DEA-CCR model can also complete DMU super efficiency, effectiveness and productivity.

Industry-finance integration activities is beneficial to the improvement of business performance of fishery companies—Based on DEA-CCR Model and DEA-Malmquist Model

Fisheries are an essential component of the national economy. The evaluation of fishery enterprises’ business performance helps reveal the fishery industry’s operating efficiency and serve the fishery economy’s sustainable and high-quality development. This study used the data of fishery companies with industry-finance integration on the stock market from 2012-2021 and conducted a comparative study on the business performance of fishery companies with industry-finance integration on the stock market based on the DEA-CCR model and DEA- Malmquist index method. Two main results were revealed (i) the integration of industry and finance was an effective means to improve business performance, and the financial gains from the integration of industry and finance by listed fishery companies improve the overall efficiency and total factor productivity (TFP) of enterprises. (ii) The average TFP of listed fishery companies in China is declining, and technological decline is the main reason for the decline in TFP.

A Comprehensive Performance Evaluation of Chinese Energy Supply Chain under “Double-Carbon” Goals Based on AHP and Three-Stage DEA

In 2020, China put forward the goals of “peak carbon dioxide emissions” and “carbon neutrality” (“double-carbon”) and it is urgent for the energy industry to achieve green transformation. Aiming at the rigid requirements of the carbon-peaking and carbon-neutrality goals (“double-carbon”), this study established a performance evaluation index system for an energy supply chain of a four-tier structure based on the “double-carbon” goals, calculating its weight by the analytic hierarchy process (AHP). On this basis, a three-stage data envelopment analysis (DEA) evaluation model was established to evaluate the performance of the energy supply chain in 2010–2019. According to the three-stage DEA evaluation mode, the initial input–output efficiency value of the energy supply chain was calculated by the DEA-BCC (extended by Banker, Charnes and Cooper) model and DEA-CCR (proposed by Charnes, Cooper and Rhodes) model and the influence of environmental noise was eliminated by stochastic frontier analysis (SFA) regression; we then obtained the adjusted efficiency value for the energy supply chain. At the same time, taking 2015 as the dividing point, the advantages and disadvantages between the traditional energy supply chain and new energy supply chain were analyzed and summarized. Further analysis and suggestions are provided to consumers, enterprises and countries from four aspects: energy supply, energy production and processing, energy transmission and distribution and energy consumption.

Calculation of regional agricultural production efficiency and empirical analysis of its influencing factors-based on DEA-CCR model and Tobit model

The improvement of agricultural production efficiency and the transformation of production mode are the core of promoting agricultural modernization. Taking Chongqing as a sample case, this paper uses DEA-CCR Model, Malmquist Index and Tobit Model to calculate and analyze its agricultural production efficiency and its influencing factors, and accurately identifies the problems existing in its agricultural transformation and development. It has an important policy reference value for improving agricultural innovation and competitiveness, promoting the steady development of rural revitalization, and realizing agricultural modernization, which also provides some reference and enlightenment for countries or regions with similar characteristics of mountain agriculture development in the world to enhance regional agricultural production efficiency. Through empirical analysis and investigation, it is found that the overall agricultural production efficiency of Chongqing remains at the productivity level of 0.8 from 2009 to 2018, with an average annual growth rate of 12.8%, but there is a large gap in the level of regional development. Through Malmquist Index decomposition, it is found that agricultural technology progress has the greatest contribution to the improvement of production efficiency. Financial support for agriculture, urbanization level, regional economic development level and highway mileage have a significant positive impact on production efficiency, while the level of farmers’ disposable income has a negative impact on the increase of production efficiency, and the income gap between urban and rural residents fails to pass the significance test.

Evaluation of the Performance of E7 Countries in Terms of Women’s Health Status: DEA Analysis

The aim of this study is to compare the relative effectiveness of the health systems' performance on the health status of women according to the education level of women, their income level and the ratio of health expenditures to GDP of E7 countries. For this purpose, 4 inputs and 2 output variables were determined. As a result of the correlation analysis conducted before the analysis, the input variable "Education level (Population with at least secondary education, 15 years and over, female)", which is among the input variables and has a high correlation with other variables, was excluded from the analysis. The input oriented CCR-DEA model was used in the study. The analysis results in India, Indonesia and Turkey techniques fully effective; Russia was 93% efficient, China 87.49%, Mexico 84.93% and Brazil 82.3%. Unlike countries that enabled the analysis results with values below the average in Turkey while the input variables have values above the average in the output variables. Therefore, it is for example among the E7 countries with the highest number to have access to the technical activities to other countries in terms of the number of reference Turkey.

A Novel Hybrid Method Based on Three-Stage Extraction and DEA-CCR Models for Selecting the Optimal Conditions for Citronella Oil Extraction

In citronella oil extraction process by steam distillation, inefficient use of steam is the main cause of excessive energy consumption that affects energy cost and oil yield. This research is aimed to reduce the energy cost and increase the oil yield by studying the steam used in the process. The proposed method is the three-stage extraction model combined with the Data Envelopment Analysis developed by Charnes, Cooper and Rhodes (DEA-CCR model). Although the three-stage extraction model has been widely used, there is no research integrate this model with DEA-CCR model. It is well known that DEA-CCR model is an effective tool to evaluate efficiency of decision making units/alternatives. The advantages of this research were presented as the calculation of the optimum distillation conditions, including the steam flow rate and the distillation time, were achieved as discussed in this article. The study was comprised of 3 parts. Firstly, the three-stage extraction model for citronella oil was formulated. Secondly, the results of the proposed model were calculated under different conditions, classified by steam flow rates from 5,000 to 60,000 cm3/min for the distillation period of 15–180 min. Finally, the DEA-CCR model was utilized to evaluate and rank alternatives. The results expressed that the best condition for producing citronella oil was at the steam flow rate of 40,000 cm3/min and the distillation time of 60 min. The optimal energy cost and percentage of oil yield were equal to 0.440 kWh/mL and 0.7%, respectively. When comparing to the experimental results, the percentage error of optimal energy cost and oil yield were slightly different, with a value of 0.98% and 0.85%, respectively. Moreover, the energy consumption was also reduced by 34.6% compared to the traditional operating conditions.

ICT security in businesses - efficiency analysis

The purpose of this paper was to identify ICT security measures and to assess the level of ICT security in small, medium and large enterprises in spatial terms. The measures in the ICT security area were identified based on secondary data of European Union member states retrieved from the Eurostat database. The research used the CCR Date Envelopment Analysis (CCR-DEA) model to meet the research purpose.The research identifies countries where ICT security results were achieved with the optimum combination of expenditures, i.e. the so-called fully efficient countries. The authors demonstrate that the countries participating in the optimal shared technology are aligned to non-fully efficient countries and they can achieve their results at lower expenditures. In the optimal technologies of all non-fully efficient countries the volume of the achieved results of enterprises is slightly higher than the actual volume. Research conducted in the area of enterprise ICT security rarely focuses on the efficiency of actions undertaken. The authors of this paper examine the technical efficiency in the area of enterprise information security in spatial terms and formulate conclusions about enterprises in the EU member states. The application of the expenditure-oriented CCR-DEA model identifies countries that achieve their results fully utilising their expenditures and those that are able to achieve at least the same results as achieved by non-fully efficient countries but at lower expenditures. The technical efficiency analysis of actions undertaken represents the starting point for defining good practices and success factors in the area of ICT security, both at enterprise and country levels.

Is the Efficiency Score an Indicator for Incident Hypertension in the Community Population of Western China?

We aimed to explore the association between the efficiency score and the risk of hypertension. We conducted a prospective cohort study of 2412 adults aged 40 years or above without hypertension in Guizhou, China from 2010 to 2020. The data envelopment analysis input-oriented DEA-CCR model was used to calculate the efficiency scores. The Cox regression model was used to assess the relationship between the efficiency score and incident hypertension. The dose–response relationship was evaluated by restricted cubic spline. Quantile regression was used to analyze the effect of efficiency scores on SBP and DBP. A total of 857 new hypertension cases were identified with a mean follow-up of 6.88 years. The efficiency score was lower in the new hypertension cases than participants without hypertension (0.70 vs. 0.67). After adjusting for possible confounding factors, the HR of hypertension risk was 0.20 (95%CI: 0.09, 0.42) for per 0.1 increase in the efficiency score. The dose–response relationship showed a non-linear relationship between the efficiency score and hypertension risk. Our results showed that the efficiency score was a cost-effective tool to identify those at a high risk of hypertension, and suggested targeted preventive measures should be undertaken.

Does the Development of the Insurance Industry Promote the Purchase of Rural Commercial Health Insurance?

Deepening the reform of insurance companies and improving commercial insurance protection capabilities become issues important to national strategy. They involve improving China's multi-tiered social security system to analyze the deep-seated reasons impacting the purchasing behavior of commercial health insurance for rural residents in China. Using the DEA-CCR model, this paper evaluates the development of China's insurance industry, inspects the impact of insurance industry development on purchasing behavior of rural commercial health insurance based on the data of tracking survey projects from China's household, and carries out empirical analysis. The research result shows that the development of the insurance industry has obviously promoted the purchase behavior of commercial health insurance for rural residents. This research has significant practical value on protection and promotion of production and life quality of rural residents, which will also provide beneficial reference on the formulation and implementation of future operation strategy in China's commercial health insurance companies.

Research on Operating Efficiency of Security Companies Based on Super-Efficiency DEA-Malmquist Index

In China’s economic system, security companies are important participants, and their high operating efficiency can improve the country’s economy. Based on the traditional CCR-DEA model, this paper uses super-efficiency DEA model and MALMQUIST index model to analyze the operational efficiency of 10 listed security companies in China from 2016 to 2020, it is found that the operating efficiency of security companies as a whole has a slight upward trend, and the improvement of business efficiency depends on technical efficiency, and relates to pure technical efficiency and scale efficiency.

Performance Evaluation in Distance Education by Using Data Envelopment Analysis (DEA) and TOPSIS Methods

Depending on the global world conditions and technological developments, the education and job status of people are constantly changing. In the competitive environment of today, as in many other sectors, it has become necessary to compete with the competitors in the education sector and to open up to new markets. Universities are the most important education centers where qualified human resources are trained. Universities offer different types of education along with people’s lifestyles and technology. Distance education has become increasingly widespread in recent years as it can meet human needs. It has become necessary for universities to evaluate their performance to compete with their competitors in distance education. In recent years, data envelopment analysis (DEA) and technique for order preference by similarity to ideal solutions (TOPSIS) are widely used in performance evaluation. In this study, it has been aimed at performance evaluation of distance education departments of public universities in Turkey for the 2018–2019 academic year by using the DEA method and the TOPSIS method. In the study, it has been also aimed to compare and rank the efficient decision-making units as a result of efficiency measurement among themselves. The study has been carried out using 6 input and 4 output variables. As a result of the efficiency analysis using the CCR-DEA model, 7 universities were efficient and 49 universities not. Later, the efficient universities have been ranked using the super efficiency CCR-DEA model and TOPSIS method.

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.

Technical efficiency comparison of container ports in Asian and Middle East region using DEA

With the instigation of China Pakistan Economic Corridor (CPEC), the strategic significance of South Asian and Middle Eastern ports have been vitalized. The aim of this study is to compare the technical efficiency of less explored South Asian & Middle Eastern ports with the East Asian ports and determine ways for their efficiency enhancement and management optimization. The cross sectional data for the year 2018 was collected for 15 container ports each of South & Middle Eastern and East Asian region and arranged into input and output variables. The data was analyzed through the DEA-CCR and DEA-BCC model. Results indicate that only one port each from UAE and India among the Middle & South Asian ports were found efficient on CCR model with the number of efficient ports on BCC model increased by 47%. While, in East Asian region two ports of China and one of South Korea were found efficient on CCR model, with 33% increase on BCC model. Lianyungang port was the most prominent among the efficient ports being highly benchmarked. The average efficiency for East Asian region (CCR: 0.524, BCC: 0.901) remained similar to that of South Asian and Middle East Region (CCR: 0.517, BCC: 0.906).

The Theoretical Relationship between the CCR Model and the Two-Stage DEA Model with an Application in the Efficiency Analysis of the Financial Industry

Since Charnes, Cooper, and Rhodes introduced data envelopment analysis (DEA) in 1978, later called the DEA-CCR model, many studies applied this technique to different fields. Based on the original CCR model, many modified DEA models were developed by researchers. Since 1999, Seiford and Zhu presented a two-stage DEA model. Later, these models were widely used in many studies. However, the relationship between the efficiency scores that are obtained from the original CCR model and the two-stage DEA model remains unknown. To fill this gap, this study proposed a theoretical relationship between the efficiency scores that are calculated from the two-stage DEA model and those that are obtained from the original CCR model. How the sets of nonsymmetrical weights affected the efficiency scores were also investigated. Theorems regarding the relationship were developed, and then the model was utilized to evaluate the two-stage efficiency scores of the insurance companies (non-life) and bank branches. The results show that using a two-stage DEA model can get more information about operational efficiency than the traditional CCR model does. The findings from this study about the two-stage DEA technique can provide significant reasons for using this model to evaluate performance efficiency.

An Evaluation of the Technical Efficiency of the Selected Commercial Banks with the Usage of the CCR-DEA Model under the Conditions of Acquisitions in the Banking Sector

<p>The aim of this article is to evaluate the technical efficiency of the chosen commercial banks, which in the years 2014–2016 were participants in acquisitions in the banking sector, with the usage of the Data Envelopment Analysis (DEA) model. The DEA model was modified through reshaping the linear form using the Charnes, Cooper, and Rhodes (CCR) model, which is aimed at expenditures. Particular attention was paid to the impact of acquisitions in the banking sector on the improvement or deterioration of the technical efficiency of banks that act as acquiring banks.</p>

Merenje efikasnosti organizacionih performansi - radijalni DEA pristup

A radial measurement o f efficiency on a selected numerical example involving multiple decision units (DMUs) is presented in this paper. Original CCR and extended BCC radial DEA models are used for this purpose. It is demonstrated here that DMUs can be efficient or inefficient in the use o f resources in achieving work results. The results o f the efficiency measurement indicate that the evaluation by the CCR DEA model is more strict compared to the BCC DEA model for each inefficient DMU regardless o f whether the efficiency measurement focuses on inputs or outputs.

The Performance Analysis of Container Terminals in Vietnam using DEA-Malmquist

Seaports play a vital role in the economic development of countries, especially for countries having long coastlines such as Vietnam .Seaport industry in Vietnam has witnessed an impressive development in recent years. The national cargo throughput in the period 2013-2017 achieved a compounded annual growth rate (CAGR) of 11.8%/year, higher than that of the world (5.1%). However, the differences in planning policies and infrastructure systems has led to the differences in port performance efficiency of regional ports. Therefore, it is necessary to have a general and accurate view of the picture of Vietnam’s seaport. The objective of this study was to analyze the relative efficiencies of 26 Vietnam container terminals using traditional output-oriented CCR and BCC DEA model. Malmquist Productivity Index (MPI) was also applied to evaluate changes in container terminals productivity over time.