Assessing resilience and sustainability of suppliers: an extension and application of data envelopment analytical hierarchy process

Majid Azadi, Zohreh Moghaddas, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In the globalization era, assessing the resilience and sustainability of suppliers is a key issue in supply chains (SCs). The literature survey shows data envelopment analysis (DEA) and analytic hierarchy process (AHP) are the two most effective approaches for evaluating suppliers. To take full advantage of these powerful approaches, we propose a novel data envelopment analytical hierarchy process (DEAHP) model. To this end, first, we combine the multiplier form of the slacks-based measure (SBM) model with goal programming (GP) and a common set of weights model to reduce the possibility of alternative optimal solutions. Then, we try to reduce the number of alternative optimal solutions of CSW. Next, to find the range of optimal weights, by setting the optimal objective functions to a constant value, minimum and maximum weights of inputs and outputs are calculated. Finally, to choose the weights, three strategies are suggested. Using the optimal weights, we calculate the pairwise comparison matrix and rank suppliers. To demonstrate the usefulness of the proposed approach, a case study in the resin industry is given.

Original languageEnglish
Number of pages42
JournalAnnals of Operations Research
Early online date15 Jun 2022
DOIs
Publication statusE-pub ahead of print - 15 Jun 2022

Bibliographical note

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

Keywords

  • Common set of weight (CSW)
  • Data envelopment analytical hierarchy process (DEAHP)
  • Goal programming (GP)
  • Performance measurement
  • Resilient-sustainable suppliers
  • Slacks-based measure (SBM)

Fingerprint

Dive into the research topics of 'Assessing resilience and sustainability of suppliers: an extension and application of data envelopment analytical hierarchy process'. Together they form a unique fingerprint.

Cite this