Assurance regions in tracking

2008 ◽  
Author(s):  
David D. Sworder ◽  
John E. Boyd ◽  
R. G. Hutchins
Keyword(s):  
2014 ◽  
Vol 1 (4) ◽  
pp. 1-15
Author(s):  
Parakramaweera Sunil Dharmapala

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.


2012 ◽  
Vol 39 (2) ◽  
pp. 2227-2231 ◽  
Author(s):  
Zhongbao Zhou ◽  
Siya Lui ◽  
Chaoqun Ma ◽  
Debin Liu ◽  
Wenbin Liu

2002 ◽  
Vol 22 (2) ◽  
pp. 123-131 ◽  
Author(s):  
Rafael Brandão Rocha ◽  
Maria Aparecida Cavalcanti Netto

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.


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

Author(s):  
P. Sunil Dharmapala

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.


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

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