Banker-Charnes-Cooper Research Articles

Evaluation of integrated transport efficiency and equity at the county level——taking the counties in ningbo city as an example

The evaluation research on integrated transport has mainly focused on urban agglomeration or cities. Yet the integration of various modes of transportation is increasingly being seen at smaller scales, such as counties, due to population concentration and rising affluence levels. Consequently, it is becoming necessary to evaluate the efficiency and convenience of transportation services for people at each location in the county space. This study has evaluated the efficiency and equity of integrated transport from a county perspective. With respect to efficiency indexes, in addition to transportation infrastructure investment, transportation energy consumption, transportation turnover, and other general indicators, we also take into account the accessibility of people to various types of hubs in the county. We use the BCC (Banker, Charnes, Cooper) model in DEA (Data Envelopment Analysis) for efficiency evaluation. Besides, we evaluate transport equity by using the Gini coefficient to reflect the population distribution of the county and GIS-based spatial accessibility of each hub site. Finally, we establish an evaluation matrix of efficiency and equity; we employ a spatial overlay method to classify research counties into different quadrants. By comprehensively considering the relationship between efficiency and equity in integrated transport, this study can identify areas for improvement in transportation development. The case study evaluation in Ningbo counties, using data from 2017 to 2020, provides practical questions and constructive suggestions for integrated transport development in these areas. Overall, this research has important implications for local transportation development and could inform policy decisions related to integrated transport.

Does Supply Chain Finance Improve the Corporate Investment Efficiency of New Energy Firms? Evidence from China

ABSTRACT This research investigates how supply chain finance (SCF) affects the investment efficiency of new energy firms by adopting a sample of Chinese listed new energy enterprises from 2011 to 2019. With the help of data envelopment analysis (DEA) combined with the Banker-Charnes-Cooper (BCC) model, we calculate efficiency values and analyze the regression results of the Tobit model, and find that SCF significantly boosts the investment efficiency of new energy corporations by improving their cash-holding position. The results of mechanism analysis also suggest that both supply chain concentration and digital finance positively magnify the relation between SCF and corporate efficiency. After comparing the impact in the new energy firms with different characteristics, we conclude that the SCF effect in promoting corporate investment efficiency is more prominent for non-state-owned new energy enterprises located in the western region that have low equity concentration. The conclusions of this paper shed new light on the fusion of the SCF business into building a new energy industry cluster.

Assessment of Bridge Resilience Efficiency Using Data Envelopment Analysis

The assessment of resilience for bridges draws ample attention from bridge engineering and management community in recent years. Both qualitative and quantitative approaches are used to assess the resilience of the bridge. However, these approaches can be biased if they obtain bridge resilience scores without considering some essential variables such as age, design high flood level (HFL), and finish road level (FRL) of the bridge. Therefore, this study presents a methodology to measure the resilience efficiency of bridges using the data envelopment analysis (DEA). It computes the resilience efficiency of 12 bridges that are located across the Tapi river in Surat (India). For input and output variables of DEA model, variables like age, design high flood level (HFL), finish road level (FRL), and resilience score of the bridges are selected. Efficiency score was measured using a variable return to scale with an output-orientated Banker-Charnes-Cooper (BCC) formulation. A total of seven bridges out of twelve were observed inefficient as per this study. A sensitivity analysis is carried out to check the reliability of DEA model. Eventually, the finding of the study can be helpful to bridge owners for prioritizing the inefficient bridges to improve their resilience score.

Analyzing the accuracy of variable returns to scale data envelopment analysis models

The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA model that delivers the most precise estimates. To address this issue, we advance the Monte Carlo simulation-based data generation process proposed by Kohl and Brunner (2020). The developed process generates an artificial dataset using the Translog production function (instead of the commonly used Cobb Douglas) to construct well-behaved scenarios under variable returns to scale (VRS). Using different VRS DEA models, we compute DEA efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed by a benchmark value and ranking as well as statistical hypothesis tests to evaluate the quality of the efficiency estimates. The procedure allows us to determine which parameters negatively or positively influence the quality of the DEA estimates. It also enables us to identify which DEA model performs the most efficiently over a wide range of scenarios. In contrast to the widely applied BCC (Banker-Charnes-Cooper) model, we find that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better. Thus, we endorse the use of AR and SBM models for DEA applications under the VRS regime.

Effect of High-Tech Manufacturing Co-Agglomeration and Producer Service Industry on Regional Innovation Efficiency

The study discusses the effect of co-agglomeration between the producer service industry and the high-tech manufacturing industry on regional innovation efficiency. Based on data from public companies of three urban agglomerations from 2011 to 2019, we used the Data Envelopment Approach (DEA)- Banker, Charnes, Cooper (BCC) model to estimate real innovation efficiency. Results found that the industrial co-agglomeration and regional innovation efficiency have an “inverted U-shaped” relationship. The industrial co-agglomeration in regions with a low level of co-agglomeration plays an important role in expediting regional innovation efficiency than that in high-level areas of co-agglomeration. Moreover, it is confirmed that the prefecture-level cities of the three urban agglomerations have low innovation efficiency types and low collaborative agglomeration types. Yangtze and Pearl river delta urban agglomeration can promote innovation efficiency through industrial co-agglomeration. While for the industrial co-agglomeration of Beijing, Tianjin, and Hebei, the urban agglomeration has not become the main way to promote innovation efficiency. The regression results of different industry collaborative agglomeration found that the co-agglomeration of information transmission, computer services, software industries, and the high-tech manufacturing industry plays a significant role to improve innovation efficiency. Moreover, the co-agglomeration of the transportation service industry and high-tech manufacturing industry plays a relatively weak role in regional innovation efficiency. Therefore, it is suggested to formulate more adaptive and heterogeneous market policies. The paper provides an important idea for improving innovation efficiency by optimizing industrial spatial layout.

Does Efficiency of Oral and Dental Health Centers Change by the Development Level of Regions?

ObjectivesThis study aimed to analyze the efficiency levels of oral and dental health centers (ODHCs) affiliated to the Ministry of Health from the perspective of the development levels in the regions. MethodsWithin the framework of this study, the efficiencies of 127 ODHCs are evaluated with the data envelopment analysis based on Charnes, Cooper, Rhodes (CCR); Banker, Charnes, Cooper (BCC); and scale efficiency methods. ResultsIt is found that CCR, BCC, and scale efficiencies of the 42 ODHCs located in the first region, the highest development region, are efficient by 31%, 50%, and 38% whereas 13%, 26%, and 26% of other 85 ODHCs located in other regions are efficient, respectively. Additionally, the average efficiency scores of 42 ODHCs in the first region by CCR (0.82), BCC (0.89), and score efficiency (0.93) are much higher than the average efficiency scores of 85 ODHCs in other regions by CCR (0.73), BCC (0.80), and scale efficiency (0.92). ConclusionsAccording to the findings, it is considered that the individuals from the first region use the oral and dental healthcare more than the individuals do in other regions because the access to oral and dental healthcare is higher in the first region and the individuals living in this region have better predisposing and enabling factors. The study results are thought to present evidence-based information to health policy makers regarding the regional planning of oral and dental healthcare.

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

Estimating Performance Efficiency of Mining and Extracting Sectors Using DEA Models: The Case of Jordan

In this study, we estimated the performance efficiency of the Jordanian mining and extracting sector based on Data Envelopment Analysis (DEA). The utilized dataset includes 6 out of 15 corporations that reflect around 90% of the total market capitalization under the mining and extracting sector in the Amman Stock Exchange (ASE). The sample consists of 126 observations from 2000 to 2020. It should be noted that estimating the efficiency of the sector based on time series for each company is not mentioned in the literature review. Therefore, we applied BCC (Banker–Charnes–Cooper) models to estimate performance efficiency and compared between input and output models under DEA. We also estimated the average performance efficiency of the sector to detect weaknesses/strengths among companies. The market capitalization and the operating revenue are used to evaluate the companies’ performance. In addition to the performance variables as output to the DEA models, the current assets, non‐current assets, operating expenses, and general administrative expenses are also used as input variables under the DEA models. This study also examined the effect of Gross Domestic Product (GDP) growth and Return on Assets (ROA) on performance efficiency scores for BCC models. In the results, we found that there are differences in performance efficiency across time series in each company based on dynamic BCC models. It is observed that the performance efficiency of NAST Company is better than the other companies based on BCC (Input/output). The GDP growth and ROA reveal the effect on efficiency performance under BCC models. The proposed model can be used to improve the performance efficiency of companies in stock exchange markets.

Examples of the use of Data Envelopment Analysis (DEA) to assess the financial effectiveness of insurance companies

This paper surveys and critically evaluates the literature on the use of advanced methods to assess the financial effectiveness of insurance entities. The paper presents methodological assumptions for Data Envelopment Analysis (DEA) using the CCR (Charnes-Cooper-Rhodes) and BCC (Banker-Charnes-Cooper) models. The usefulness of these methods for assessing the financial efficiency of insurance companies was also shown on the example of entities conducting insurance activities in Nigeria, Malaysia, Singapore, Indonesia, Serbia, Poland and Slovakia.

The Evaluation of Financial Performances of Logistics Companies in Turkey: A Data Envelopment Analysis Approach

In this study, the financial performance of 10 logistics companies operating in the storage, transport and logistics services sector in Turkey is evaluated for years from 2011 to 2017 by using the Data Envelopment Analysis (DEA) method. In accordance with the purpose of this study, the input variables are selected as total assets, equity and number of employees whereas the output variables are net sales, profit before interest and tax and amount of exports. In measuring the financial performances of firms listed in the data set, the Charnes, Cooper, Rhodes (CCR) model based on the assumption of constant returns to scale and the Banker, Charnes, Cooper (BCC) model based on the assumption of variable return to scale are employed in the input-oriented DEA model. As a result of our study, efficient and ineffective logistics companies are identified for years from 2011 to 2017. The reference sets for ineffective companies are determined, and the extent to which ineffective companies should improve according to the companies they refer to is determined. The results of DEA CCR-I model suggests that 2015 has the highest number of active companies (7). The year in which the average efficiency scores of the companies is the highest (89.2%) is 2016. According to the DEA BCC-I model, 2015 is associated with the largest number of active firms (9) and the average productivity scores of these firms are the highest (99.1%) in that particular year.

Energy auditing and data envelopment analysis (DEA) based optimization for increased energy use efficiency in wheat cultivation (Triticum aestium L.) in north-western India

Wheat (Triticum aestivum L.) cultivation in north-western India is highly intensive in terms of resources used and associated energy input. We therefore, studied the resource use efficiency (RUE) for reduced energy footprints in wheat production using non-parametric measures viz. Cobb-Douglas Production Function (CDPF) and Data Envelopment Analysis (DEA) approaches. The return to scale (∑i=1nαi)value of 0.363 for CDPF revealed that 1% increase in all resources used, the wheat grain yield increased by ~ 0.363%. The adjusted coefficient of determination (R2Adj) of 0.29 therefore, suggested that CDPF does not explain a great deal of relationship between the dependent and independent variables. DEA considering the CCR (Charnes-Cooper-Rhodes) and BCC (Banker-Charnes-Cooper) models illustrates that ~ 38 (based on CCR) and ~ 55% (based on BCC) decision making units (DMUs), respectively were efficient and had technical efficiency (TE) score = 1.00. These results revealed that 10.1, 10.9, 20.5, 17.2, 50.0, 24.4 and 34.8% seed, diesel fuel, human labor, chemical fertilizers, biocides, irrigation water and electricity, respectively could be saved without compromising wheat productivity. The average TE score of 0.92 ± 0.01 therefore, suggests possibilities of ~ 8% saving in total resource use in wheat cultivation. DEA based benchmarking helped in reducing energy input in wheat by 1953.4 ± 46.9 MJ ha−1 (~ 7.2%).

NATURE AND SALIENT FEATURES OF PAKISTAN’S MANUFACTURING SECTOR: A COMPREHENSIVE INSIGHT

The study investigates the nature and salient features of Pakistan’s manufacturing sector by focusing on the structure, conduct and performance aspects for 2004-2018. In structural analysis, Pakistan State Oil Company Ltd has turned out to be the largest firm according to gross sales criterion while Oil & Gas Development. Co. Ltd is classified as the largest firm according to the total assets criterion. Six industries are categorized as most heavily concentrated industries, five industries are in moderately concentrated industries while four industries are placed in the least heavily concentrated industries according to four-firm concentration criterion while according to Herfindahl-Hirschman Index principle, six industries are characterized as highly concentrated industries, two industries are in moderately concentrated industries while seven industries are placed in low concentrated industries. Demand and market conditions point out the disparity between the own-price elasticity of market demand and the own-price elasticity of a firm’s product. In conduct analysis, Learner’s index shows that there is imperfect competition in Pakistan’s manufacturing sector. The chemicals & Pharmaceuticals industry has come out with high advertising while the Petroleum Products industry has the lowest advisement intensity. In performance analysis, Spinning, Weaving has more gross profit to sales ratio while Electrical Machinery & Apparatus has the lowest gross profit to sales ratio. The Motor Vehicles industry is at the top for Charnes-Cooper-Rhodes (CCR), BankerCharnes-Cooper (BCC) criteria while for scale efficiency, the cement industry stands at the apex

EVALUATION OF THE EFFICIENCY OF THE HEALTHCARE SYSTEMS OF THE RUSSIAN FEDERATION WITH DATA ENVELOPMENT ANALYSIS: AN EXAMPLE OF REPUBLICS

The purpose of this study is to measure the effectiveness of the health sector in the 22 republics of the Russian Federation using data for 2005, 2010 and 2013–2017. The data used in the study were obtained from the site of the Federal State Statistics Service of the Russian Federation. Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models of Data Envelopment Analysis (DEA) were chosen as the analysis method, and the super-efficiency scores were determined. After the analysis, potential improvement proposals for inefficient republics were developed, and Tobit analysis was conducted to determine the factors affecting efficiency. As input variables was used; The number of doctors of all specialties, nursing staff and hospital beds per 10,000 people of the population, as the output variable was used; life expectancy at birth, the incidence per 1000 population, and infant mortality rates. As a result of a study in the performance analysis conducted by the CCR method, in 2005, 2010, 2013 and 2014 6, in 2015 and 2017 5 and in 2016 4, in the performance analysis conducted by the BCC method, in 2005 9, in 2010 8, in 2014 and 2016 7, in 2013 6 and in 2015 and 2017 5 republics were rated as full efficiency scores. According to the results of the analysis of super-efficiency carried out by the CCR method, the Republic of Ingushetia and Chechnya, according to the BCC method, the republics of Chechnya and Tatarstan have the highest super-efficiency score.

Chinese liquor company management efficiency from a social responsibility perspective: a two stage dynamic directional distance function

Because the liquor industry is an important industry in China, alcohol companies have greater social responsibilities when upgrading their production processes to improve efficiencies. Two main data envelopment analysis (DEA) methods have been used for efficiency analyses: radial models, such as CCR (Charnes, Cooper, Rhodes) and BCC (Banker, Charnes, Cooper) and non-radial methods such as SBM (slacks-based model); however, both have disadvantages. The radial DEA model ignores the non-radial slacks and the non-radial DEA model ignores the characteristics of the same proportion in the radial DEA model. Therefore, this research used a dynamic two-stage directional distance function (DDF) model to analyse the production efficiencies of Chinese listed liquor companies from 2016 to 2018 by evaluating each company’s poverty alleviation input efficiencies, business operations efficiencies, and their social responsibility efficiencies, which was based on their wastewater treatment efficiencies. To overcome the disadvantages in traditional data envelopment models, the DDF model consisted of both input and output direction vectors, the indicator values for which indicated their relative importance or priorities. The analyses found that the indicator efficiencies at most liquor companies had declined, with the poverty alleviation indicator efficiencies being generally lower than the wastewater treatment indicator efficiencies. Several policy and management recommendations are given to improve the overall corporate efficiency and social responsibility in the Chinese liquor industry.

Ranking multiple-input and multiple-output units: A comparative study of data envelopment analysis and rank aggregation

Ranking multiple-input and multiple-output units is a critical problem that arises in a broad range of disciplines. While various methods have been proposed and applied, their comparative strengths and weaknesses are not well understood. In this paper, we assess and compare two popular methods, data envelopment analysis (DEA) and heuristic rank aggregation approach (i.e., the Borda method), in the context of ranking multiple-input and multiple-output units. Both methods exploit the output-input ratios, but in different ways. The Borda method sorts the units by taking the arithmetic average of the ranks in terms of individual output-input ratios, whereas DEA ranks the units based on composite output-input ratios. We use simulations to compare Borda rank aggregation and six DEA models, including CCR (Charnes, Cooper, Rhodes), super-efficiency CCR, BCC (Banker, Charnes, Cooper), super-efficiency BCC, SBM (slacks-based measure), and super-efficiency SBM. The simulations are based on Cobb-Douglas and translog production functions with both single output and multiple outputs. We show that the heuristic Borda rank aggregation, though simple to implement, performs better than DEA models for Cobb-Douglas production function under three situations: small sample size, relatively balanced weights for production factors, and presence of multiple outputs. For translog production function, the Borda method generally performs better than the CCR model, but cannot match up to other DEA models. We also demonstrate the performance of different methods via application to the well-known problem of ranking countries by human development. Our research sheds light on the potential of rank aggregation to complement or even supplant DEA under certain conditions.

THE EVALUATION OF THE FINANCIAL EFFICIENCIES OF HOSPITALS THROUGH DATA ENVELOPMENT ANALYSIS METHOD IN TERMS OF THEIR TYPE AND GROUP - HASTANELERİN TÜRÜ VE GRUBU AÇISINDAN FİNANSAL ETKİNLİKLERİNİN VZA YÖNTEMİYLE DEĞERLENDİRİLMESİ

This study has been done with the aim of examining the financial operations of 825 hospitals affiliated to the Ministry of Health.For this purpose,the efficiencies of hospitals have been assessed based on their financial data according to their group and type.Selected 6 input and 3 output variables have been analysed through Charnes – Cooper – Rhodes (CCR) Input Oriented Primal Model, Banker – Charnes – Cooper (BCC) Input Oriented Primal Model and Super Efficiency Model.In the light of the results obtained, 73 hospitals in CCR Model and 143 hospitals in BCC Model have been determined financially efficient.The results of both CCR and BCC Model have been found to be the same according to hospital type and group;however,efficiency levels of hospitals according to their groups have shown differences.Furthermore;although it appears at the end of the study that all input should be reduced and output should be increased according to hospital group,it has been seen that the number of inputs and outputs to be reduced and increased are not the same in every hospital group.It is thought that the results obtained will be a guide to operate hospitals with more efficient financial policies throughout the country.

Data envelopment analysis in measuring the efficiency of volleyball teams in Primorsko-Goranska County

Every sport organisation strives to evaluate its performance: its weaknesses and strengths. Measuring efficiency and sports are two interrelated concepts and it is not surprising that most of the research on sports is focused on analysing the efficiency of teams according to player techniques, attack and defence efficiency. However, there are very few studies based on the analysis of financial factors such as teams’ revenue and costs. In this paper two Data Envelopment Analysis (DEA) models were used to evaluate 16 young cadet volleyball teams in Primorsko-Goranska County based on two economic inputs. The paper aims to explain the importance of teams’ financial resources in achieving sports efficiency. To analyse the relative efficiency of teams, two frequently used models are employed, the Banker Charnes Cooper (BCC) and the Charnes Cooper Rhodes (CCR) model. In the end, a super efficiency analysis was conducted to make a distinction in efficiency scores between efficient units. Analyses showed that financial factors are not crucial factors for efficiency score and gave possibility to use obtained results and improve the performance of inefficient volleyball teams. The study was conducted on a sample of 16 teams through 4 inputs and 1 output collected during 2017/2018 season.

Environmental efficiency evaluation of China's iron and steel industry: A process-level data envelopment analysis

To resolve the increasingly higher energy and environmental pressures, the evaluation of environmental efficiency in China's iron and steel industry is essential for identifying a precise energy conservation and emission reduction path. However, current studies have only focused on the efficiency evaluation in national, regional, or enterprise level, lacking the analysis of different processes. Therefore, the objective of this research is to conduct a process-level data envelopment analysis (DEA) to evaluate the environmental efficiency of China's iron and steel industry. Totally, 54 enterprises are contained, as the input-output structure of 5 processes: sintering, coking, ironmaking, steelmaking, and steel rolling are set specifically in this study. In addition, to compare the effects to the efficiency results of different DEA methods, Banker, Charnes & Cooper (BCC) model, Slack-based Measure (SBM) model, and Bootstrap-DEA methods are adopted. Finally, a regression model is used to investigate the key environmental protection strategies influencing the environmental efficiency. The results show that: (1) Within different methods, the average efficiency scores from SBM model are lower than the ones from BCC model, and the Bootstrap-DEA method also has a negative modification. (2) Regional efficiency difference exists, as the enterprises in South China perform best in sintering and coking processes but have the lowest overall efficiency scores. (3) Most enterprises have one or more short board processes. 12 enterprises are the enterprises with individual low environmental efficiency process, while other 25 are the enterprises with imbalanced environmental performances. (4) The coefficient factor between environmental protection investment and the efficiency scores are positive, but the factors of proportion of environmental protection staffs, and whether the enterprise has environmental protection research are negative. In sum, this study is hoped to contribute to formulating more precise environmental management measures in China's iron and steel industry.

Evaluating the technical efficiency of care among long-term care facilities in Xiamen, China: based on data envelopment analysis and Tobit model

BackgroundThe technical efficiency (TE) of care among the elderly in long-term care facilities (LTCF) have become increasingly crucial policy concerns faced by developing countries and Asia, especially China. The purpose of this study was to evaluate the TE and the quality of care and identify its influencing factors among LTCF.MethodsA total of 32 registered LTCF in Xiamen of China were surveyed in 2016. The Banker-Charnes-Cooper (BCC) model and Slacks-Based Measure (SBM) model of Data Envelopment Analysis (DEA) were used to evaluate the TE of LTCF. The TE has been decomposed into pure technical efficiency and scale efficiency. Utilization of DEA with human, financial, and material resources as inputs and quantity, quality of nursing care as outputs allowed estimation of the relative TE of care in LTCF. In addition, this study applied SBM to measuring the efficiencies and slacks. Furthermore, Tobit model was performed to explore factors associated with TE.ResultsThere were 7 public and 25 private LTCF respectively, with a total of 6729 beds and 3154 elderly people. 17 LTCF were technically efficient (53.1%). In the BCC model, the average TE was 0.963. The average pure technical efficiency and scale efficiency of LTCF were 0.979, 0.984, respectively. There were 5 LTCF with increasing returns to scale, 8 LTCF with decreasing returns to scale. In the SBM model, the average TE was 0.813, and it had the same effective decision-making unit with SBM model. Depending on TE score from high to low, the top eight are private LTCF, and the last four were public LTCF. The slack analysis showed that they can be reduced in 8 LTCF with decreasing returns to scale such as 53.31% administrative staffs, 67.73% medical staffs, 33.1% caregivers, 51.66% paramedical staffs and 4.1% beds on average. The TE of private LTCF was higher than that of public LTCF. The LTCF in urban were more effective than rural. The TE of LTCF raised by increasing of working hours, training frequency and institutional occupancy.ConclusionsThe overall TE of LTCF in Xiamen of China was relatively high, especially in private institutions. However, LTCF still needs to further improve the utilization of physical resources and the management and training of human resources. The TE of LTCF was associated to their location, institutional nature, allocation of human resources and occupancy rate. It was needed to focus on promoting the efficiency and quality of LTCF in order to achieve sustainability.

Efisiensi dan produktivitas industri perbankan pada sistem moneter ganda di Indonesia

This study aims to examine the level of efficiency of 115 commercial banks in Indonesia for the period between 2010 and 2016. We use basic models of Data Envelopment Analysis (DEA) consisting of CCR (Charnes Cooper Rhodes) and BCC (Banker Charnes Cooper) models. This study also investigate the value of productivity of each bank which is then compared. The study finds that commercial banks in Indonesia exhibit an increase in productivity although it is relatively small. The stagnation of the level of productivity is due to the low level of technological change (technological change) rather than a decrease in efficiency (Efficiency change). Another interesting finding is that around 70 percent of the hypothetically relative credit market share in Indonesia is controlled by only 29.56 percent of commercial banks. This also means that around 30 percent of the remaining credit market share is contested by the rest of other banks.