The Technical Efficiency of Engineering Industry Cluster at Hosur

2019 ◽  
Vol 46 (2) ◽  
pp. 100-116
Author(s):  
Ethirajan Bhaskaran
Keyword(s):  
Sustainable Development ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Value Chain ◽  
Value Added ◽  
Engineering Industry ◽  
Export Market ◽  
Data Envelopment ◽  
Inclusive Growth ◽  
Value Chain Analysis

For inclusive growth and sustainable development Engineering Industries (EIs) in Hosur have adopted the Cluster Development Approach (CDA). The objective is to study the value chain analysis, correlation analysis and data envelopment analysis by finding Technical Efficiency (Ø), Peer Weights (li), Input Slacks (S-) and Output Slacks (S+) of 50 EIs. The methodology adopted is data envelopment analysis of output-oriented Banker–Charnes–Cooper Model by taking the number of employment, plant and machinery as input and Gross Value Added (GVA) as output. The non-zero li’s represents the weights for efficient clusters. The S > 0 obtained reveals the excess machinery or number of employment (S-) and shortage in GVA (S+). To conclude, the variables are highly correlated, and for inclusive growth and sustainable development, the inefficient EI should increase their GVA or decrease the employment or machinery. Moreover, for sustainable development, the EI should strengthen infrastructure interrelationships, technology interrelationships, procurement interrelationships, production interrelationships and marketing interrelationships to decrease costs and to increase productivity and efficiency to compete in the indigenous and export market.

Data envelopment analysis methods in sustainable agricultural development research

2021 ◽  
pp. 179-205
Author(s):  
Katarzyna Smędzik-Ambroży ◽  
Agnieszka Sapa
Keyword(s):  
Sustainable Development ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Economies Of Scale ◽  
Scale Effects ◽  
Three Dimensions ◽  
Data Envelopment ◽  
Farm Accountancy Data Network ◽  
Business Entities ◽  
The Individual

Sustainable development of business entities can be analysed in terms of three dimensions, i.e., economic, social and environmental ones. The economic dimension of sustainable development can be assessed, inter alia, by entities’ technical efficiency defined as the relation of outputs to inputs. One of the methods that is used to assess the technical efficiency of business entities compared to other entities is the Data Envelopment Analysis (DEA) method. The aim of the chapter is to determine the relative technical efficiency of representative agricultural farms from the individual European Union countries in 2018. Moreover, the scale efficiency indexes and the area of scale effects (increasing or decreasing) of the analysed farms were also determined. In the study the data from the Farm Accountancy Data Network (FADN) for 2018 were applied. In order to achieve the assumed research goals, the input-oriented DEA model was used, and the technical efficiency indexes of farms were estimated with the assumption of constant return to scale (CRS) and variable return to scale (VRS). This allowed, among others, for indicating the countries with farms achieving the highest technical efficiency (Belgium, Spain, Italy, Malta and Netherlands assuming CRS, and Belgium, Spain, Italy, Malta and Netherlands, Greece, Ireland, Romania and Slovenia assuming VRS), the lowest technical efficiency (the Czech Republic and Slovakia) within surveyed group of farms. All relatively inefficient farms (except Slovakia) functioned in the area of increasing economies of scale.

scholarly journals Evaluating R&D and Transformation Functional Platforms’ Operational Performance Using a Data Envelopment Analysis Model: A Comparative Study

2019 ◽  
Vol 11 (18) ◽  
pp. 5023 ◽  
Author(s):  
Cao ◽  
You ◽  
Shi ◽  
Hu
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Performance Status ◽  
Value Added ◽  
Scale Efficiency ◽  
High Tech ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Operation Performance

The purpose of this paper is to provide a contribution to the development of R&D and transformation functional platforms by identifying key performance influencing factors in the use of data envelopment analysis (DEA) to analyze platform operation performance status and reasons. The DEA method is undertaken to calculate the comprehensive efficiency, pure technical efficiency and scale efficiency of R&D and transformation functional platforms in China’s 30 provinces within the period 2016–2018. Based on the 2018 pure technical efficiency and scale efficiency calculations, the K-means clustering method was used to classify the R&D and transformation functional platforms of 30 provinces. Finally, according to the clustering results, the corresponding clustering improvement scheme is given. The operational level of R&D and transformation functional platforms in many provinces of China still needs to be improved: the R&D and transformation capabilities are weak, the market share of leading products is low, the ability of new technology value-added is insufficient, and the development of R&D and transformation functional platforms has regional imbalance. This study is based solely on statistical data, these data alone obviously cannot fully describe and evaluate the real state of R&D and transformation functional platform due to the complexity and diversity of platforms. Further research is needed to generalize beyond the performance indicators constructed in this paper. For the problems of low overall operation efficiency, unbalanced regional development, redundancy of input resources and lack of professional management personnel in the operation of R&D and transformation functional platforms, policy suggestions can be put forward according to clustering results and input and output adjustment values calculated based on relaxation variables. The study presenting a methodology for analyzing R&D and transformation functional platforms’ operation performance, and the conclusions will provide reference for the development of platforms and high-tech industries.

scholarly journals Productivity Measuring In The Czech Banking Industry

2011 ◽  
Vol 4 (12) ◽  
Author(s):  
Dimitrios Angelidis ◽  
Katerina Lyroudi ◽  
Athanasios Koulakiotis
Keyword(s):  
Data Envelopment Analysis ◽  
Technological Change ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Financial Institutions ◽  
Banking Industry ◽  
Geometric Mean ◽  
Value Added ◽  
Data Envelopment ◽  
Factor Productivity

In this paper we investigate the productivity of the Czech banking industry for the period 1996-2002. The non-parametric frontier method of data envelopment analysis (DEA) is used in order to estimate the Malmquist total factor productivity (TFP) change indices for 134 year-firm observations. Using the value added approach and calculating the geometric mean of the TFP, we find that the level of productivity of financial institutions meets a decline of 0.7%. Moreover, the technical efficiency changes (TEC) is greater than unity, while the technological change (TC) is less than unity for the Czech banks during the period 1996 -2002.

A Data Envelopment Analysis Approach for Household Appliances and Automobile Recycling

2011 ◽  
pp. 378-387
Author(s):  
Elif Kongar ◽  
Surendra M. Gupta
Keyword(s):  
Data Envelopment Analysis ◽  
End Of Life ◽  
Technical Efficiency ◽  
Value Added ◽  
Performance Criteria ◽  
Environmental Damage ◽  
Data Envelopment ◽  
Household Appliances ◽  
Technology Products ◽  
Technological Developments

Rapid technological developments are leading to a significant decrease in the demand for old technology products. As a result, old technology products are rushed to their end-of-lives (EOLs) even though they still function properly and have the ability to satisfy stated needs. It is therefore important to find environmentally and economically benign ways to handle this accumulating waste to regain the value added to such products and to reduce the environmental damage. However, EOL recovery options are not always economically justifiable due to the complexity and uncertainty involved in the process. To reduce these setbacks, it is crucial to perform an analysis prior to taking any action and rank the products according to the importance of their EOL processing outcomes. To this end, this chapter proposes a data envelopment analysis (DEA) algorithm to determine the technical efficiency of end-of-life processing of household appliances and automobiles depending on various tangible and intangible performance criteria.

A Data Envelopment Analysis Approach for Household Appliances and Automobile Recycling

2010 ◽  
pp. 367-377
Author(s):  
Elif Kongar ◽  
Surendra M. Gupta
Keyword(s):  
Data Envelopment Analysis ◽  
End Of Life ◽  
Technical Efficiency ◽  
Value Added ◽  
Performance Criteria ◽  
Environmental Damage ◽  
Data Envelopment ◽  
Household Appliances ◽  
Technology Products ◽  
Technological Developments

Rapid technological developments are leading to a significant decrease in the demand for old technology products. As a result, old technology products are rushed to their end-of-lives (EOLs) even though they still function properly and have the ability to satisfy stated needs. It is therefore important to find environmentally and economically benign ways to handle this accumulating waste to regain the value added to such products and to reduce the environmental damage. However, EOL recovery options are not always economically justifiable due to the complexity and uncertainty involved in the process. To reduce these setbacks, it is crucial to perform an analysis prior to taking any action and rank the products according to the importance of their EOL processing outcomes. To this end, this chapter proposes a data envelopment analysis (DEA) algorithm to determine the technical efficiency of end-of-life processing of household appliances and automobiles depending on various tangible and intangible performance criteria.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

scholarly journals The Measurement of Innovation Efficiency of Chinese High-tech Industry Using Data Envelopment Analysis

2015 ◽  
Vol 65 (s2) ◽  
pp. 101-113 ◽  
Author(s):  
Ling Jiang ◽  
Yunyu Jiang ◽  
Zhijun Wu ◽  
Dongsheng Liao ◽  
Runfa Xu
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Environmental Variables ◽  
Scale Efficiency ◽  
High Tech ◽  
Data Envelopment ◽  
High Tech Industry ◽  
Economic Competitiveness ◽  
Development Level ◽  
Using Data

In the era of knowledge economy, a country’s economic competitiveness depends largely on the development level of high-tech industry. This paper evaluates the efficiency of China’s high-tech industry in 31 provinces in 2012 with data envelopment analysis. The empirical results are summarized as following. Firstly, when the effects of exogenous environmental variables are not controlled, the comprehensive technical efficiency of 31 provinces will be overestimated, the pure technical efficiency will be underestimated, and the scale efficiency value will be overestimated. Secondly, after eliminating the environmental impact, the comprehensive technical efficiency of 31 provinces with the average of 0.395 is rather low, due to the low scale efficiency.

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.

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