scholarly journals Efficiency and productivity measurement of rural township hospitals in China: a bootstrapping data envelopment analysis

BMJ Open ◽  
2016 ◽  
Vol 6 (11) ◽  
pp. e011911 ◽  
Author(s):  
Zhaohui Cheng ◽  
Miao Cai ◽  
Hongbing Tao ◽  
Zhifei He ◽  
Xiaojun Lin ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Productivity Measurement ◽  
Township Hospitals ◽  
Efficiency And Productivity ◽  
Rural Township

Application of a robust data envelopment analysis model for performance evaluation of electricity distribution companies

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alireza Fallahi ◽  
Fatemeh Fallahi ◽  
Hassan Sarhadi ◽  
S.F. Ghaderi ◽  
Reza Ebrahimi
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Stability Test ◽  
Productivity Change ◽  
Data Envelopment ◽  
Electricity Distribution ◽  
Productivity Measurement ◽  
Content Type ◽  
Informed Decisions ◽  
Efficiency And Productivity

Purpose This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran’s 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions. Design/methodology/approach The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model’s robustness. Findings The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels. Practical implications By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions. Originality/value The paper’s novel concept applies DEA to Iran’s electricity distribution companies and analyzes them by examining geographic classification, size and the type of the companies. In addition, a stability test is conducted and productivity changes are estimated.

Introduction to Performance Improvement Management Software (PIM-DEA)

2015 ◽  
pp. 256-275
Author(s):  
Ali Emrouznejad ◽  
Emmanuel Thanassoulis
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Improvement ◽  
Data Envelopment ◽  
Management Software ◽  
Large Sets ◽  
Extensive Range ◽  
Dea Models ◽  
Full Capacity ◽  
Efficiency And Productivity ◽  
Theoretical Developments

This chapter provides information on the use of Performance Improvement Management Software (PIM-DEA). This advanced DEA software enables users to make the best possible analysis of the data, using the latest theoretical developments in Data Envelopment Analysis (DEA). PIM-DEA software gives full capacity to assess efficiency and productivity, set targets, identify benchmarks, and much more, allowing users to truly manage the performance of organizational units. PIM-DEA is easy to use and powerful, and it has an extensive range of the most up-to-date DEA models and which can handle large sets of data.

EFFICIENCY AND PRODUCTIVITY CHANGE IN FIELD CROP AND MILK FARMS IN LUBLIN PROVINCE IN 2014-2016

2018 ◽  
Vol XX (1) ◽  
pp. 137-142
Author(s):  
Alina Syp ◽  
Dariusz Osuch
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Dairy Farms ◽  
Productivity Change ◽  
Data Envelopment ◽  
Field Crop ◽  
Dea Model ◽  
Malmquist Indices ◽  
Efficiency And Productivity ◽  
Efficiency Indicators

The aim of the study was assessment of efficiency and productivity of farms in the Lublin province in the years 2014-2016. The analysis was based on the Data Envelopment Analysis (DEA) model oriented on inputs and Malmquist indices with its components. The calculations were made for medium-sized field and dairy farms that continuously collected data for the FADN system during the period under consideration. In our research all efficiency indicators for dairy farms were larger than for field crop farms. In the years 2014-2016, the average technical efficiency of dairy farms was 0.752, which means that in those farms it is possible to reduce inputs on average by 25% and the value of production will remain at the same level. In the case of field crop farms, inputs should be limited by 33%. The applied decomposition of calculated Malmquist indices allowed to define what factors influenced changes in productivity.

scholarly journals Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA)

2018 ◽  
Vol 2 (3) ◽  
pp. 27 ◽  
Author(s):  
Shanta Mazumder ◽  
Golam Kabir ◽  
M. Hasin ◽  
Syed Ali
Keyword(s):  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Analytic Network Process ◽  
Data Envelopment ◽  
Contributing Factors ◽  
Productivity Measurement ◽  
Apparel Manufacturing ◽  
Network Process ◽  
Dea Approach ◽  
Constant Returns To Scale

Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an analytic network process (ANP) and data envelopment analysis (DEA) approach to tackling productivity measurement and benchmarking problems in a manufacturing environment. The ANP was used to capture the interdependency between the criteria taking into consideration the ambiguity and vagueness. The nonparametric DEA approach was utilized to determine the input-oriented constant returns to scale (CRS) efficiency of different value-adding production units and to benchmark them. The proposed framework was implemented to benchmark the productivity of an apparel manufacturing company. By applying the model, industrial managers can gain benefits by identifying the possible contributing factors that play an important role in increasing the productivity of manufacturing organizations.

Effects of cross-border acquisitions on efficiency and productivity of acquired companies: evidence from cement industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Slađana Savović ◽  
Predrag Mimović
Keyword(s):  
Data Envelopment Analysis ◽  
Transitional Economies ◽  
Cement Industry ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Data Envelopment ◽  
Content Type ◽  
Cross Border ◽  
Efficiency And Productivity ◽  
Practical Implications

PurposeThe purpose of this paper is to explore the effects of cross-border acquisitions on the efficiency and productivity of acquired companies in the cement industry in the context of a transitional economy.Design/methodology/approachThe Data Envelopment Analysis (DEA) and Malmquist Productivity Index were used to assess the efficiency and productivity of the acquired companies over the period 2000–2018. DEA and Malmquist index are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. The study assesses partial efficiency and productivity of three inputs: material, capital and labour, as well as the total factor efficiency and productivity of the acquired companies in the short and long term after the acquisitions.FindingsThe research results suggest that efficiency of material, efficiency of labour and the total factor efficiency of the acquired companies are higher after the acquisitions than before, while efficiency of capital is lower. In addition, the results show that the acquisitions had a positive impact on total factor productivity of the acquired companies.Practical implicationsThe results of this study have practical implications for managers, especially for policy-makers and industry analysts in deciding whether to encourage or discourage cross-border acquisitions in transitional economies.Originality/valueThe study contributes to a better understanding of the impact of cross-border acquisitions on efficiency and productivity of acquired companies in the manufacturing industry. Research in transitional economies related to subject matter is limited, and this study is the first empirical investigation of the effect of cross-border acquisitions on the efficiency and productivity in the cement industry in Serbia by applying the Data Envelopment Analysis.

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