Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

Majid Azadi*, Reza Kazemi Matin, Ali Emrouznejad, William Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In era of reglobalization, sustainably resilient supply chains (SCs) are imperative in corporations to improve performance and meet stockholders’ expectations. However, sustainably resilient SCs could not be effective if are not assessed by using advanced frameworks, systems, and models. As such, developing a novel network data envelopment model (DEA) to appraise sustainably resilient SCs is our purpose in this article. To do so, we present a new double-frontier methodology to provide optimistic and pessimistic efficiency measures in network structures. Moreover, ideas of outputs weak disposability, chance-constrained programming, and discrete dominance are incorporated in a unified framework of modelling efficient and inefficient production technologies. The new network DEA model also can address dissimilar types of data, including undesirable and integer-valued and ratio outputs, stochastic intermediate products, and integer-valued inputs in a unified framework. Furthermore, an aggregated Farrell type efficiency measure is developed which allows to provide the complete ranking of units so that each decision-making unit (DMU) has its own rank in both overall and divisional point of view. We show the unique features of our developed model using a real case study in paint industry to evaluate the efficiency and reducing carbon dioxide (CO2) emissions. The results show that how well the proposed models can evaluate the sustainability and resilience of supply chains in the presence of uncertainty and with dissimilar types of data.

Original languageEnglish
Number of pages30
JournalAnnals of Operations Research
Early online date14 Jul 2022
Publication statusE-pub ahead of print - 14 Jul 2022

Bibliographical note

© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Funding Information:
Open Access funding enabled and organized by CAUL and its Member Institutions.


  • Chance-constrained programming
  • Double frontier
  • Network data envelopment analysis (DEA)
  • NetZero
  • Sustainable and resilient supply chains (SCs)
  • Weak disposability


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