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Developing a new chance constrained NDEA model to measure performance of sustainable supply chains

  • S.I.: CLAIO 2018
  • Published:
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Abstract

Owing to the increasing importance of sustainable supply chain management (SSCM), it has received much attention from both corporate and academic over the past decade. SSCM performance evaluation plays a crucial role in organizations success. One of the practical techniques that can be used for SSCM performance assessment is network data envelopment analysis (NDEA). This paper develops a new NDEA for performance evaluation of SSCM in the presence of stochastic data. The proposed model can evaluate the efficiency of SSCM under uncertain conditions. A case study in the soft drinks industry is presented to demonstrate the efficacy of the proposed method.

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Acknowledgements

The authors would like to thank two anonymous Reviewers for their insightful and constructive comments and suggestions. Furthermore, the fifth author would like to appreciate Czech Science Foundation (GAČR 19-13946S) for the supports.

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Authors and Affiliations

  1. Department of Mathematics, College of Science, Arak Branch, Islamic Azad University, Arak, Iran

    Mohammad Izadikhah

  2. Department of Industrial Engineering, Faculty of Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Elnaz Azadi

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Majid Azadi

  4. Faculty of Business, Sohar University, Sohar, Oman

    Reza Farzipoor Saen

  5. Department of System Engineering, Faculty of Economics, Technical University of Ostrava, Ostrava, Czech Republic

    Mehdi Toloo

  6. Department of Operations Management and Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman

    Mehdi Toloo

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  1. Mohammad Izadikhah
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  2. Elnaz Azadi
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Correspondence to Reza Farzipoor Saen.

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Izadikhah, M., Azadi, E., Azadi, M. et al. Developing a new chance constrained NDEA model to measure performance of sustainable supply chains. Ann Oper Res 316, 1319–1347 (2022). https://doi.org/10.1007/s10479-020-03765-8

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