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Estimation of true efficient frontier of organisational performance using data envelopment analysis and support vector machine learning

Estimation of true efficient frontier of organisational performance using data envelopment... Data envelopment analysis (DEA) and stochastic frontier functions (SFF) are two well-known tools for performance and efficiency analysis of profit and non-profit organisations, referred to as decision making units (DMUs). The challenge to traditional DEA is how to account for both managerial and observational errors if present in the analysis, so as to determine true efficient frontiers. This paper proposes a novel methodology to determine true frontiers in a non-parametric environment. Specifically, traditional DEA is integrated with SFF through support vector machine (SVM) learning to provide an adaptive way to estimate true frontiers considering managerial and observational errors. A statistical ratio is utilised to find the true frontiers, and the proposed methodology is applied to a real data set where frontiers are compared to ones obtained by other existing methods. The work in this paper can help organisations to plan a more realistic investment by providing reasonable sense of benchmarking. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

Estimation of true efficient frontier of organisational performance using data envelopment analysis and support vector machine learning

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2011.040421
Publisher site
See Article on Publisher Site

Abstract

Data envelopment analysis (DEA) and stochastic frontier functions (SFF) are two well-known tools for performance and efficiency analysis of profit and non-profit organisations, referred to as decision making units (DMUs). The challenge to traditional DEA is how to account for both managerial and observational errors if present in the analysis, so as to determine true efficient frontiers. This paper proposes a novel methodology to determine true frontiers in a non-parametric environment. Specifically, traditional DEA is integrated with SFF through support vector machine (SVM) learning to provide an adaptive way to estimate true frontiers considering managerial and observational errors. A statistical ratio is utilised to find the true frontiers, and the proposed methodology is applied to a real data set where frontiers are compared to ones obtained by other existing methods. The work in this paper can help organisations to plan a more realistic investment by providing reasonable sense of benchmarking.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2011

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