Data envelopment analysis for estimating efficiency of intensive care units: a case study in Iran

2018 ◽  
Vol 31 (4) ◽  
pp. 276-282 ◽  
Author(s):  
Mohammad Amin Bahrami ◽  
Sima Rafiei ◽  
Mahdieh Abedi ◽  
Roohollah Askari
Keyword(s):  
Intensive Care ◽  
Data Envelopment Analysis ◽  
Intensive Care Units ◽  
Service Providers ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Comparative Performance ◽  
Content Type ◽  
Low Efficiency

Purpose As hospitals are the most costly service providers in every healthcare systems, special attention should be given to their performance in terms of resource allocation and consumption. The purpose of this paper is to evaluate technical, allocative and economic efficiency in intensive care units (ICUs) of hospitals affiliated by Yazd University of Medical Sciences (YUMS) in 2015. Design/methodology/approach This was a descriptive, analytical study conducted in ICUs of seven training hospitals affiliated by YUMS using data envelopment analysis (DEA) in 2015. The number of physicians, nurses, active beds and equipment were regarded as input variables and bed occupancy rate, the number of discharged patients, economic information such as bed price and physicians’ fees were mentioned as output variables of the study. Available data from study variables were retrospectively gathered and analyzed through the Deap 2.1 software using the variable returns to scale methodology. Findings The study findings revealed the average scores of allocative, economic, technical, managerial and scale efficiency to be relatively 0.956, 0.866, 0.883, 0.89 and 0.913. Regarding to latter three types of efficiency, five hospitals had desirable performance. Practical implications Given that additional costs due to an extra number of manpower or unnecessary capital resources impose economic pressure on hospitals also the fact that reduction of surplus production plays a major role in reducing such expenditures in hospitals, it is suggested that departments with low efficiency reduce their input surpluses to achieve the optimal level of performance. Originality/value The authors applied a DEA approach to measure allocative, economic, technical, managerial and scale efficiency of under-study hospitals. This is a helpful linear programming method which acts as a powerful and understandable approach for comparative performance assessment in healthcare settings and a guidance for healthcare managers to improve their departments’ performance.

Efficiency in the Indian iron and steel industry – an application of data envelopment analysis

2014 ◽  
Vol 11 (1) ◽  
pp. 4-19 ◽  
Author(s):  
Roma Mitra Debnath ◽  
V.J. Sebastian
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Steel Industry ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Annual Income ◽  
Content Type ◽  
Steel Manufacturing ◽  
Constant Returns To Scale

Purpose – The purpose of this paper applies to Indian steel manufacturing industries to evaluate the technical and scale efficiency (SE). Design/methodology/approach – Data envelopment analysis (DEA) has been employed to calculate the relative efficiency of the steel manufacturing units. The selection criteria for the inclusion of a steel manufacturing unit in the analysis has been annual income of more than 50 crores and units manufacturing pig iron, steel and sponge iron. Within the DEA framework, the output-oriented model with constant returns to scale and variable returns to scale were studied. Four input variables, namely, gross fixed assets, total energy cost, total number of employees and currents assets were considered. Among the output variables, the four variables considered are income, sales, PBIT and PAT. Findings – The result of the efficiency scores have been categorized into three parts. The pure technical efficiency represents local efficiency and the reason of inefficiency is due to inefficient operations. Technical efficiency indicates that the respective decision-making units are globally efficient in case the efficiency is 100 per cent. The SE explains that the inefficiency is caused by disadvantageous conditions. As the result shows, that public sector undertaking (PSUs) are operating under disadvantageous conditions as compared to private manufacturing units. One of the possible reasons of location disadvantage condition is manufacturing units for PSUs are scattered throughout India. Some of the units are located in such places where, the raw material, supply chain could be difficult. It has been found that 45 per cent of the private manufacturing units are technically as well as scale inefficient units. Practical implications – The result of the study would benefit the steel industry to develop a performance benchmarking as steel companies must be profitable in the long term to ensure sustainable achievements. Originality/value – This is an original study to apply DEA to get insights on productivity efficiency of the steel manufacturing units in India. Though the manufacturing units were selected on the basis of annual income, the analysis of productivity does not reflect any impact of income on the efficiency of the manufacturing firms.

scholarly journals Does Farm Size and Specialization Matter for Productive Efficiency? Results from Kansas

2011 ◽  
Vol 43 (4) ◽  
pp. 515-528 ◽  
Author(s):  
Amin W. Mugera ◽  
Michael R. Langemeier
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Production Technology ◽  
Returns To Scale ◽  
Productive Efficiency ◽  
Farm Size ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Sample Period ◽  
Analysis Techniques

In this article, we used bootstrap data envelopment analysis techniques to examine technical and scale efficiency scores for a balanced panel of 564 farms in Kansas for the period 1993–2007. The production technology is estimated under three different assumptions of returns to scale and the results are compared. Technical and scale efficiency is disaggregated by farm size and specialization. Our results suggest that farms are both scale and technically inefficient. On average, technical efficiency has deteriorated over the sample period. Technical efficiency varies directly by farm size and the differences are significant. Differences across farm specializations are not significant.

Efficiency evaluation of Indian oil and gas sector: data envelopment analysis

2019 ◽  
Vol 14 (2) ◽  
pp. 362-378 ◽  
Author(s):  
Vikas Vikas ◽  
Rohit Bansal
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Capital Investment ◽  
Oil And Gas ◽  
Stock Exchange ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Content Type ◽  
Indian Economy ◽  
Oil And Gas Sector

Purpose Data envelopment analysis (DEA), a non-parametric technique is used to assess the efficiency of decision-making units which are producing identical set of outputs using identical set of inputs. The purpose of this paper is to find the technical efficiency (TE), pure technical efficiency and scale efficiency (SE) levels of Indian oil and gas sector companies and to provide benchmark targets to the inefficient companies in order to achieve efficiency level. Design/methodology/approach In the present study, a group of 22 oil and gas companies which are listed on the National Stock Exchange for which the data were available for the period 2013–2017 has been considered. DEA has been performed to compare the efficiency levels of all companies. To measure efficiency, three input variables, namely, combined materials consumed and manufacturing expenses, employee benefit expenses and capital investment and two output variables – operating revenues and profit after tax (PAT) have been considered. On the basis of performance for the financial year ending 2017, benchmark targets based on DEA–CCR (Charnes, Cooper and Rhodes) model have been provided to the inefficient companies that should be focused upon by them to attain the efficiency level. The performance of the companies for the past five years has been examined to check the fluctuations in the various efficiency scores of the companies considered in the study over the years. Findings From the results obtained, it is observed that 59 percent, i.e. 13 out of 22 companies are technically efficient. By considering DEA BCC (Banker, Charnes and Cooper) model, 16 companies are observed to be pure technically efficient. In terms of SE, there are 14 such companies. The inefficient units need to improve in terms of input and output variables and for this motive, specified targets are assigned to them. Some of these companies need to upgrade significantly and the managers must take the concern earnestly. The study has also thrown light on the performance of the companies over last five years which shows Oil India Ltd, Gujarat State Petronet Ltd, Petronet LNG Ltd, IGL Ltd, Mahanagar Gas, Chennai Petroleum Corporation Ltd and BPCL Ltd as consistently efficient companies. Research limitations/implications The present study has made an attempt to evaluate the efficiency of Indian oil and gas sector. The results of the study have significant inferences for the policy makers and managers of the companies operating in the sector. The results of the study provide benchmark target level to the companies of Oil and Gas sector which can help the managers of the relatively less efficient companies to focus on the ways to improve efficiency. The improvement in efficiency of a company would not only benefit the shareholders, but also the investors and other stakeholders of the company. Originality/value In the context of Indian economy, very limited number of studies have focused to measure the efficiency of oil and gas sector in the context of Indian economy. The present study aims to provide the latest insight to the efficiency of the companies especially operating in the Indian oil and gas sector. Further, as per our knowledge, this study is distinctive in terms of analyzing the efficiency of Indian oil and gas sector for a period of five years. The longitudinal study of the sector efficiency provides a bird eye view of the average efficiency level and changes in the efficiency levels of the companies over the years.

scholarly journals Applying data envelopment analysis to operating performance in textile industry

2013 ◽  
Vol 13 (4) ◽  
pp. 99-103 ◽  
Author(s):  
Chia-Hui Ho
Keyword(s):  
Data Envelopment Analysis ◽  
Textile Industry ◽  
Market Competition ◽  
Operating Performance ◽  
Returns To Scale ◽  
Operating Efficiency ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Optimal Operating ◽  
Constant Returns To Scale

Abstract Operating performance could affect the survival and future development of a business that both businesses and business managers would devote to the enhancement of operating performance. Having developed for more than four decades, the consistent upstream, mid-stream and downstream system have been constructed in domestic textile industry. The output value of textiles in Taiwan has exceeded 480 billion NT dollars, which is not a sunset industry, as generally described. The impacts of high labour cost, environmental protection measures and changes of capital market as well as the competition of emerging countries, particularly Mainland China, have made textile industry in Taiwan face great market competition and pressure. Since textiles are regarded as one of the major products in Taiwan, the operating performance could affect the survival of the overall industry. In this case, operating performance survey of textile manufacturers in Taiwan during 2010–2012 is combined with Data Envelopment Analysis and Slack Variable Analysis to measure the total efficiency, pure technical efficiency and scale efficiency of top 12 textile manufacturers in Taiwan, tending to provide the reference of operating efficiency improvement for the manufacturers. The empirical results show that the overall efficiency in the 3 years appears 0.89 averagely. The relative efficiency (1) between two manufacturers, Far Eastern New Century and Ruentex Industries, achieves the optimal operating efficiency, whereas the remaining 10 are comparatively worse. Regarding the analysis of returns to scale, two textile manufacturers present constant returns to scale, with the optimal operating efficiency, whereas the remaining 10 show increasing returns to scale, revealing that expanding the scale could enhance the marginal return and further promote the efficiency.

Opportunity recognition efficiency of small Chinese firms: findings from data envelopment analysis

2019 ◽  
Vol 13 (4) ◽  
pp. 760-777
Author(s):  
Zisheng Guo ◽  
Jianqi Zhang ◽  
Heng Liu
Keyword(s):  
Data Envelopment Analysis ◽  
Small Firms ◽  
Opportunity Recognition ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Knowledge Stock ◽  
Chinese Firms ◽  
Content Type ◽  
Recognition Efficiency ◽  
Use Of Knowledge

Purpose Small firms in China anticipate entrepreneurial opportunities for continual growth. However, they may fail to recognize opportunities because of their inefficiency in managing their knowledge. Design/methodology/approach In this explorative paper, the authors assess the opportunity recognition efficiency of 168 small Chinese firms using data envelopment analysis (DEA). Supplementary Tobit regressions were conducted for further exploring the factors that influence the firms’ efficiency in opportunity recognition. Findings Results from the DEA suggest that most respondents recognize significantly fewer opportunities than those with equivalent knowledge stock. Moreover, many firms have low levels of pure technical efficiency but high levels of scale efficiency, indicating insufficient use of knowledge as a major reason for inefficiency in opportunity recognition. The Tobit regressions show that sales and research and development intensity are relevant to a firm’s opportunity recognition efficiency. Research limitations/implications This study calls for the investigation of efficiency issues in opportunity recognition and suggests that managers guard against unwarranted loss of opportunities owing to inefficient use of existing knowledge elements. Originality/value First, the authors introduce the concept of opportunity recognition efficiency within the entrepreneurial process. Second, they manifest the role of knowledge management in opportunity recognition. Third, they introduce DEA to investigate the relationship between knowledge stock and opportunity recognition. Fourth, this study reveals that inefficient use of knowledge is a disadvantage of small Chinese firms in terms of opportunity recognition.

Efficiency of glass firms in India: an application of data envelopment analysis

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Mini Kundi ◽  
Seema Sharma
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Design Methodology ◽  
Large Scale ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Efficiency Measures ◽  
Target Values ◽  
Practical Implications

Purpose The purpose of the present study is to evaluate the efficiency of glass firms in India. Design/methodology/approach Data envelopment analysis (DEA) has been employed to study the technical, scale and super efficiency measures of glass firms in India. Findings Major findings of DEA analysis show that 65 percent firms are found to be technically efficient. Returns to scale analysis indicate that five firms are operating at decreasing returns to scale and two firms are exhibiting increasing returns to scale. Further, results show that small– and medium–scale firms are more efficient than large–scale firms. Old firms are more efficient compared to the young firms and foreign-owned firms are technically more efficient compared to the domestic firms. Practical implications The results of this study would help the managers to assess their relative efficiency and take corrective measures to efficiently use their resources. Originality/value This seems to be the first study to apply DEA to analyze the efficiency of glass firms in India. No previous study on glass industry seems to have decomposed the measure of overall technical efficiency into its components, namely pure technical efficiency and scale efficiency and no study seems to have examined whether ownership, age and size of a firm are significant for its efficiency. In addition, no earlier study seems to have ranked the glass firms based on their efficiency values. Further, target values of inputs and outputs are demonstrated in this study. Stability of efficiency scores is also checked.

Assessing the Performance of Telecommunication Industry in India: A Data Envelopment Analysis

2010 ◽  
Vol 11 (2) ◽  
pp. 29-47
Author(s):  
Seema Sharma ◽  
Kirankumar Momaya ◽  
K. Manohar
Keyword(s):  
Data Envelopment Analysis ◽  
Rapid Growth ◽  
Service Providers ◽  
Efficiency Analysis ◽  
Scale Efficiency ◽  
Telecommunication Industry ◽  
Data Envelopment ◽  
Telecom Industry ◽  
Productivity And Competitiveness

Rapid growth of telecommunications in India has been creating opportunities for many players from Asia, Europe and other parts of world. Relative assessment of efficiencies can be used to enhance productivity and competitiveness. In this study an attempt is made to evaluate competitiveness of the telecom industry in India focusing on the efficiency. Input oriented data envelopment analysis is used to measure the relative technical and scale efficiencies of 10 service providers. Further, using output oriented model, the efficiency analysis is extended to 23 service circle areas. From the analysis performed on service providers the technically and scale efficient firms were identified. Technical and scale efficiency were assessed at circle level also. The findings confirm some assumptions and hint at several competitiveness implications for leadership in firms and government.

Efficiency of winemaking in developing countries

2017 ◽  
Vol 29 (1) ◽  
pp. 98-118 ◽  
Author(s):  
Anatoliy G. Goncharuk ◽  
Aleksandra Figurek
Keyword(s):  
Developing Countries ◽  
Returns To Scale ◽  
Wine Industry ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Potential Growth ◽  
Policy Makers ◽  
Content Type ◽  
Current State ◽  
Wine Market

Purpose This paper aims to the evaluation and comparison of the efficiency of winemaking in two developing countries (Ukraine and Bosnia and Herzegovina (B&H)) from the perspective of their development. Design/methodology/approach In this research study, four models of data envelopment analysis (DEA), correlation and other tools of the data analysis are used to analyze the efficiency of wineries in two developing countries. Returns to scale, scale efficiency, super-efficiency and some other indicators are examined. The research is based on the sample, including 33 wineries of Ukraine and B&H. Findings Characterized by the same average efficiency and number of leaders, in Ukraine, medium and large wineries are developing more efficiently than small ones, whereas the opposite is true for B&H. The authors found the high potential growth of efficiency on Ukrainian (up to 28.9 per cent) and Bosnian wineries (up to 28.3 per cent). The ways for its realization were suggested. Cross-country efficiency analysis enabled us to find inter-country leaders of wine industry. The authors grouped inefficient wineries, calculated the potential for inputs reduction and found the main directions for the improvement of efficiency for each group. Research limitations/implications The research is limited to a single industry in only two developing countries. Future studies can be devoted to the comparison of the efficiency of wineries in developed and developing countries. The results can determine which countries can be leaders in the global wine market in the future. Practical implications This study provides useful information for: researchers of wine market in developing countries enabling them to understand the current state, basic problems and efficiency levels of wineries in Ukraine and B&H; domestic policy-makers- to improve regulation of wine industry as to make it more competitive and efficient; wine producers in these countries- to find the benchmarks using the best practices to adapt them in own business and to increase an efficiency. Originality/value On the example of Ukraine and B&H, this study has shown that each respective country has its own conditions of doing wine business. This is the first paper that compares the efficiency of wine industry in Ukraine and B&H.

scholarly journals Assessment of transit transport corridor efficiency of landlocked African countries using data envelopment analysis

2018 ◽  
Vol 114 (1/2) ◽  
Author(s):  
Enagnon H. Fanou ◽  
Xuping Wang
Keyword(s):  
Data Envelopment Analysis ◽  
Transport Systems ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
African Countries ◽  
Comparative Performance ◽  
Data Set ◽  
Dea Models ◽  
And Performance ◽  
Using Data

We used a data envelopment analysis (DEA) to examine the efficiency and performance of transport systems of landlocked African countries (LLACs). We conducted a comparative performance efficiency analysis of transfer transport systems for LLACs’ corridors. Three different types of DEA models were proposed and used to measure the relative efficiencies of transit transport using a 6-year data set (2008–2013) of some selected LLACs. The results show that the average pure technical and scale efficiency scores are 90.89% and 37.13%, respectively. Two units (13.33%) are technically efficient (technical and scale efficiency) while four units (26.66%) are only purely technically efficient over the observed period. Swaziland was the most efficient corridor while the Central African Republic corridor was the least efficient throughout the monitored years. The results indicate the relevance of minimising trade costs to stimulate landlocked countries’ exports.

A data envelopment analysis (DEA) model for building energy benchmarking

2019 ◽  
Vol 17 (4) ◽  
pp. 747-768 ◽  
Author(s):  
Baabak Ashuri ◽  
Jun Wang ◽  
Mohsen Shahandashti ◽  
Minsoo Baek
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Building Energy ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Dea Model ◽  
Energy Benchmarking

Purpose Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking. Design/methodology/approach Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency. Findings The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers. Originality/value The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

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