scholarly journals Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model

2018 ◽  
Vol 10 (9) ◽  
pp. 3168 ◽  
Author(s):  
Haoran Zhao ◽  
Huiru Zhao ◽  
Sen Guo
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Variables ◽  
Market Competition ◽  
Environmental Values ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Electricity Grid ◽  
Frontier Analysis ◽  
Dea Model ◽  
Statistical Noise

With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.

scholarly journals Measuring Operational Efficiency of Foreign Branches of Bangladeshi Banks as Financial Intermediary

2020 ◽  
Vol 15 (2) ◽  
pp. 165
Author(s):  
Asmina Akter
Keyword(s):  
Data Envelopment Analysis ◽  
Operational Efficiency ◽  
Parent Company ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Financial Intermediary ◽  
Intermediary Organization ◽  
Dea Model

In this study an Output-oriented DEA (Data Envelopment Analysis) model is used to measure operational efficiency of foreign branches of Bangladeshi banks as financial intermediary organization for borrowing funds from savers and lending those funds to others for making profit. Among 58 Bangladeshi banks there are only three Bangladeshi banks which have in total seven foreign branches in different foreign locations. A branch of bank can’t be separated legally from its parent company and supervised by its home authorities as part of supervision of the banking group as a whole. By employing DEA model and using “Financial Intermediary Approach” this study found that as a financial intermediary organization between savers and borrowers these foreign branches of Bangladeshi banks are performing efficiently over the years. Among three banks Janata Bank Limited and AB Bank limited are performing most efficiently and Sonali Bank Limited is performing less efficiently relative to two other banks in operating their foreign branches as a financial intermediary organization for borrowing funds from savers and lending those funds to others for making profit.

scholarly journals Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics

Inge CUC ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 137-146
Author(s):  
César David Ardila Gamboa ◽  
Frank Alexander Ballesteros Riveros
Keyword(s):  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Relative Performance ◽  
Data Envelopment ◽  
Supply Network ◽  
Distribution Centers ◽  
Frontier Analysis ◽  
Dea Model ◽  
Key Indicators ◽  
A Company

Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia. Objective: The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics. Methodology: A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system were analyzed to find options for improving the system. Conclusions: Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network.

scholarly journals The Operational Efficiency of Public Higher Vocational Schools in the Lubelskie Voivodeship

2020 ◽  
Vol 13 (1) ◽  
pp. 115-125
Author(s):  
Mariusz Pyra
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Assessment ◽  
Parametric Method ◽  
Operational Efficiency ◽  
Vocational Schools ◽  
Data Envelopment ◽  
Dea Model ◽  
The Public ◽  
Source Data

SummarySubject and purpose of work: This paper aims to assess the operational efficiency of public higher vocational schools in the Lublin Region.Materials and methods: The assessment was based on the non-parametric method of data envelopment analysis (DEA) using the standard CCR-O model.Results: In most of the analysed models (E, N, O series), the public higher vocational schools in the Lublin Region were found to have improved their efficiency in 2019 relative to 2017.Conclusions: E-series models are very susceptible to changes, both in terms of inputs and effects. This gives the possibility of a significant impact on the increase in the assessment of the effectiveness of investigated units DMUs. N-series models demonstrate the importance of aggregation and quality of source data for the results of performance assessment. Class O models justify the need to look for and compare the use of other DEA model variants in the study of the effectiveness of public higher vocational schools.

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 A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

2021 ◽  
Vol 9 (4) ◽  
pp. 378-398
Author(s):  
Chunhua Chen ◽  
Haohua Liu ◽  
Lijun Tang ◽  
Jianwei Ren
Keyword(s):  
Data Envelopment Analysis ◽  
Transportation Systems ◽  
Decision Makers ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Super Efficiency ◽  
Dea Model ◽  
Dea Models ◽  
Analysis Models ◽  
Transport Accidents

Abstract DEA (data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure (RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model, especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA model with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China’s regional transportation systems (RTSs) considering the number of transport accidents (an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

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