Assurance regions in tracking

2008 ◽  
Author(s):  
David D. Sworder ◽  
John E. Boyd ◽  
R. G. Hutchins
Keyword(s):  
Assurance Regions

Randomizing Efficiency Scores in DEA Using Beta Distribution

2014 ◽  
Vol 1 (4) ◽  
pp. 1-15
Author(s):  
Parakramaweera Sunil Dharmapala
Keyword(s):  
Beta Distribution ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Output Data ◽  
Uniform Sampling ◽  
The Past ◽  
Stochastic Dea ◽  
Dea Models ◽  
Assurance Regions ◽  
Fuzzy Dea

Data Envelopment Analysis (DEA) has come under criticism that it is capable of handling only the deterministic input/output data, and therefore, efficiency scores reported by DEA may not be realistic when the data contain random error. Several researchers in the past have addressed this issue by proposing Stochastic DEA models. Some others, citing imprecise data, have proposed Fuzzy DEA models. This paper proposes a method to randomize efficiency scores in DEA by treating each score as an ‘order statistic' that follows a Beta distribution, and it uses Thompson et al.'s (1996) DEA model appended with Assurance Regions (AR) randomized by our “uniform sampling”. In an application to a set of banks, the work demonstrates the randomization and derives some statistical results.

Fuzzy data envelopment analysis models with assurance regions: A note

2012 ◽  
Vol 39 (2) ◽  
pp. 2227-2231 ◽  
Author(s):  
Zhongbao Zhou ◽  
Siya Lui ◽  
Chaoqun Ma ◽  
Debin Liu ◽  
Wenbin Liu
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Fuzzy Data Envelopment Analysis ◽  
Assurance Regions ◽  
Analysis Models

scholarly journals A data envelopment analysis model for rank ordering suppliers in the oil industry

2002 ◽  
Vol 22 (2) ◽  
pp. 123-131 ◽  
Author(s):  
Rafael Brandão Rocha ◽  
Maria Aparecida Cavalcanti Netto
Keyword(s):  
Petroleum Industry ◽  
Oil Industry ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Consensus Panel ◽  
Two Phase ◽  
Dea Model ◽  
Assurance Regions

The benefits of integration companies-suppliers top the strategic agendas of managers. Developing a system showing which suppliers merit continuing and deepening the partnership is difficult because of the large quantity of variables to be analyzed. The internationalized petroleum industry, requiring a large variety of materials, is no different. In this context, the Brazilian company PETROBRAS S.A. has a system to evaluate its suppliers based on a consensus panel formed by its managers. This paper shows a two phase methodology for classifying and awarding suppliers using the DEA model. Firstly, the suppliers are classified according to their efficiency based on commercial transactions realized. Secondly they are classified according to the opinions of the managers, using a DEA model for calculating votes, with the assurance regions and superefficiency defining the best suppliers. The paper presents a case study in the E&P segment of PETROBRAS and the results obtained with the methodology.

Output-specific input-assurance regions in DEA

2011 ◽  
Vol 62 (10) ◽  
pp. 1881-1887 ◽  
Author(s):  
W D Cook ◽  
J Zhu
Keyword(s):  
Specific Input ◽  
Assurance Regions

Nonlinear Efficiency in DEA Relative to “Ideal Reference”

2014 ◽  
pp. 1637-1647
Author(s):  
P. Sunil Dharmapala
Keyword(s):  
Data Envelopment Analysis ◽  
Best Practice ◽  
Data Envelopment ◽  
Efficiency Measure ◽  
The Past ◽  
Efficiency Measures ◽  
Assurance Regions ◽  
Nonlinear Measure

Several researchers in the past have emphasized the importance of computing efficiency measures in Data Envelopment Analysis (DEA) relative to a best-practice benchmark. Thompson et al. (1995) introduced a nonlinear efficiency measure with linked-cone (LC) assurance-regions (AR) in DEA. In this paper, we compute Thompson-Thrall's measure vis-à-vis linear efficiency measures of CCR (Charnes et al., 1978), BCC (Banker et al., 1984), CCR/AR and BCC/AR (Thompson et al., 1992), relative to “ideal reference”- an industry average. We demonstrate the computations in an application to a set of banks and show that the nonlinear measure is stricter than the linear measures.

CAR-DEA: Context-Dependent Assurance Regions in DEA

2008 ◽  
Vol 56 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Wade D. Cook ◽  
Joe Zhu
Keyword(s):  
Assurance Regions ◽  
Context Dependent
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