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Efficiency measurement using independent component analysis and data envelopment analysis

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

Abstract

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.

Suggested Citation

  • 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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    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. Katarzyna Wierzbicka, 2019. "The Impact of Venture Capital Funds on Innovative Activities: The Case of European Union Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 518-532.
    6. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    7. Muhammed Ordu & Hediye Kirli Akin & Eren Demir, 2021. "Healthcare systems and Covid19: Lessons to be learnt from efficient countries," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(5), pages 1476-1485, September.
    8. 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.
    9. 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.
    10. Tien-Hsiang Chang & Ling-Jing Kao & Tsung-Yin Ou & Hsin-Pin Fu, 2018. "A Hybrid Method to Measure the Operational Performance of Fast Food Chain Stores," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1269-1298, July.
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