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Data envelopment analysis efficiency in two-stage networks with feedback

Author

Listed:
  • Liang Liang
  • Zhao-Qiong Li
  • Wade Cook
  • Joe Zhu

Abstract

Conventional applications of data envelopment analysis generally treat the Decision-Making Unit (DMU) as a black box in that the internal processes are not examined in detail. In some situations, such as the measurement of performance of a set of supply chains, the DMU can be viewed as exhibiting a network structure. A significant body of recent literature has examined a particular form of network structure, namely, where the DMU is a two-stage serial process in which the outputs from the first stage are intermediate variables that serve as inputs to the second stage. The current article extends this idea to include those situations where outputs from the second stage can be fed back as inputs to the first stage. Such feedback variables thus serve a dual role. Models are developed for examining performance in this feedback setting and are illustrated using an application involving the measurement of performance of a set of Chinese universities.

Suggested Citation

  • Liang Liang & Zhao-Qiong Li & Wade Cook & Joe Zhu, 2011. "Data envelopment analysis efficiency in two-stage networks with feedback," IISE Transactions, Taylor & Francis Journals, vol. 43(5), pages 309-322.
  • Handle: RePEc:taf:uiiexx:v:43:y:2011:i:5:p:309-322
    DOI: 10.1080/0740817X.2010.509307
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    Citations

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    Cited by:

    1. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    2. Thuzar Linn & Broos Maenhout, 2019. "The impact of environmental uncertainty on the performance of the rice supply chain in the Ayeyarwaddy Region, Myanmar," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-29, December.
    3. Xianmei Wang & Hanhui Hu, 2017. "Sustainability in Chinese Higher Educational Institutions’ Social Science Research: A Performance Interface toward Efficiency," Sustainability, MDPI, Open Access Journal, vol. 9(11), pages 1-18, October.
    4. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    5. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
    6. Maryam Nematizadeh & Alireza Amirteimoori & Sohrab Kordrostami, 2019. "Performance analysis of two-stage network processes with feedback flows and undesirable factors," Operations Research and Decisions, Wroclaw University of Science Technology, Faculty of Management, vol. 3, pages 51-66.
    7. Ang, Sheng & Chen, Chien-Ming, 2016. "Pitfalls of decomposition weights in the additive multi-stage DEA model," Omega, Elsevier, vol. 58(C), pages 139-153.
    8. Chu, Junfei & Zhu, Joe, 2021. "Production scale-based two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 294(1), pages 283-294.
    9. Xianmei Wang & Hanhui Hu, 2017. "Sustainable Evaluation of Social Science Research in Higher Education Institutions Based on Data Envelopment Analysis," Sustainability, MDPI, Open Access Journal, vol. 9(4), pages 1-17, April.
    10. Sarah J.-Sharahi & Kaveh Khalili-Damghani & Amir-Reza Abtahi & Alireza Rashidi Komijan, 2021. "A new network data envelopment analysis models to measure the efficiency of natural gas supply chain," Operational Research, Springer, vol. 21(3), pages 1461-1486, September.
    11. Lartey, Theophilus & James, Gregory A. & Danso, Albert, 2021. "Interbank funding, bank risk exposure and performance in the UK: A three-stage network DEA approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
    12. Sungmin Park, 2015. "The R&D logic model: Does it really work? An empirical verification using successive binary logistic regression models," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1399-1439, December.
    13. Yu Shi & Anyu Yu & Huong Ngo Higgins & Joe Zhu, 2021. "Shared and unsplittable performance links in network DEA," Annals of Operations Research, Springer, vol. 303(1), pages 507-528, August.
    14. Meng, Fanyong & Xiong, Beibei, 2021. "Logical efficiency decomposition for general two-stage systems in view of cross efficiency," European Journal of Operational Research, Elsevier, vol. 294(2), pages 622-632.
    15. Kao, Chiang, 2019. "Inefficiency identification for closed series production systems," European Journal of Operational Research, Elsevier, vol. 275(2), pages 599-607.
    16. Sourour Ramzi, 2019. "Modeling the Education Supply Chain with Network DEA Model: The Case of Tunisia," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 525-540, September.
    17. Whittaker, Gerald & Färe, Rolf & Grosskopf, Shawna & Barnhart, Bradley & Bostian, Moriah & Mueller-Warrant, George & Griffith, Stephen, 2017. "Spatial targeting of agri-environmental policy using bilevel evolutionary optimization," Omega, Elsevier, vol. 66(PA), pages 15-27.
    18. Li, Wanghong & Li, Zhepeng & Liang, Liang & Cook, Wade D., 2017. "Evaluation of ecological systems and the recycling of undesirable outputs: An efficiency study of regions in China," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 77-86.

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