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Efficiency measurement using independent component analysis and data envelopment analysis


  • Kao, Ling-Jing
  • Lu, Chi-Jie
  • Chiu, Chih-Chou


Efficiency measurement is an important issue for any firm or organization. Efficiency measurement allows organizations to compare their performance with their competitors' and then develop corresponding plans to improve performance. Various efficiency measurement tools, such as conventional statistical methods and non-parametric methods, have been successfully developed in the literature. Among these tools, the data envelopment analysis (DEA) approach is one of the most widely discussed. However, problems of discrimination between efficient and inefficient decision-making units also exist in the DEA context (Adler and Yazhemsky, 2010). In this paper, a two-stage approach of integrating independent component analysis (ICA) and data envelopment analysis (DEA) is proposed to overcome this issue. We suggest using ICA first to extract the input variables for generating independent components, then selecting the ICs representing the independent sources of input variables, and finally, inputting the selected ICs as new variables in the DEA model. A simulated dataset and a hospital dataset provided by the Office of Statistics in Taiwan's Department of Health are used to demonstrate the validity of the proposed two-stage approach. The results show that the proposed method can not only separate performance differences between the DMUs but also improve the discriminatory capability of the DEA's efficiency measurement.

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  • Kao, Ling-Jing & Lu, Chi-Jie & Chiu, Chih-Chou, 2011. "Efficiency measurement using independent component analysis and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 210(2), pages 310-317, April.
  • Handle: RePEc:eee:ejores:v:210:y:2011:i:2:p:310-317

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    References listed on IDEAS

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    1. repec:eee:ecofin:v:43:y:2018:i:c:p:71-86 is not listed on IDEAS
    2. Tuncay Ozcan & Fatih Tuysuz, 2016. "Modified Grey Relational Analysis Integrated with Grey Dematel Approach for the Performance Evaluation of Retail Stores," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 353-386, March.
    3. Lin, Tzu-Yu & Chiu, Sheng-Hsiung, 2013. "Using independent component analysis and network DEA to improve bank performance evaluation," Economic Modelling, Elsevier, vol. 32(C), pages 608-616.
    4. Andrejić, Milan & Bojović, Nebojša & Kilibarda, Milorad, 2016. "A framework for measuring transport efficiency in distribution centers," Transport Policy, Elsevier, vol. 45(C), pages 99-106.
    5. R. K. Jha & B. S. Sahay & P. Charan, 2016. "Healthcare operations management: a structured literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(3), pages 259-279, September.
    6. Aldanondo, Ana M. & Casasnovas, Valero L., 2016. "A note on the impact of multiple input aggregators in technical efficiency estimation," MPRA Paper 75290, University Library of Munich, Germany.
    7. Antônio Artur de Souza & Ewerton Alex Avelar & Alisson Maciel de Faria Marques & Douglas Rafael Moreira & Osmar Ferreira da Silva & Daniele Oliveira Xavier, 2014. "Performance Analysis Of Brazilian Public And Philanthropic Hospitals (Análise Do Desempenho De Hospitais Públicos E Filantrópicos Brasileiros)," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(2), pages 75-94.


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