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Efficiency Ranking via Combining DEA Evaluation and Bayesian Prediction for Logistics Enterprises

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Intelligence Science and Big Data Engineering (IScIDE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11266))

Abstract

Data envelopment analysis (DEA) has been widely used in economic development evaluation and enterprise performance analysis. When a conventional DEA is used directly for ranking, there are some issues. It requires the investment and income data, and the latter are after-the-fact data, so DEA is ex post analysis. In addition, coarse granular DEA results may cause parallel ranking at high frequencies. Therefore, combining DEA and Bayes, we propose an efficiency prediction approach that does not require income data. The approach can predict efficiency levels under different investment combinations, and help logistics enterprises to rationally allocate limited resources in their decision-making. Furthermore, an efficiency ranking algorithm is designed by incorporating the overall probability distribution of data set and then is applied to evaluate fourteen A-level logistics enterprises in Anhui, China. Empirical results show that the DEA-Bayes approach has good discrimination for efficiency ranking. Unlike expert scoring, our evaluation process is based on logistics enterprise data and easy to operate.

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Acknowledgments

This work was supported by the Research Centre Project of Logistics Engineering in University of Science and Technology of China, the Anhui Provincial Major Research Project for Social Science Innovation and Development under Grant 2017ZD005, the Anhui Provincial Planned Text-book Project under Grant 2017ghjc384, and the Anhui Provincial Natural Science Foundation under Grant 1608085MF141.

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Authors and Affiliations

  1. Anhui Finance and Trade Vocational College, Hefei, 230601, China

    Chenglong Cao

  2. Research Center of Logistics Engineering, University of Science and Technology of China, Hefei, 230026, China

    Chenglong Cao

  3. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Xiaoling Zhu

Authors
  1. Chenglong Cao
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  2. Xiaoling Zhu
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Corresponding author

Correspondence to Chenglong Cao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Shanghai Jiao Tong University, Shanghai, China

    Kai Yu

  3. Tsinghua University, Beijing, China

    Jiwen Lu

  4. Central China Normal University, Wuhan, China

    Xingpeng Jiang

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Cao, C., Zhu, X. (2018). Efficiency Ranking via Combining DEA Evaluation and Bayesian Prediction for Logistics Enterprises. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02697-4

  • Online ISBN: 978-3-030-02698-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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