Measuring congestion in sustainable supply chain based on data envelopment analysis

Maryam Shadab, Saber Saati*, Reza Farzipoor Saen, Mohammad Khoveyni, Amin Mostafaee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Sustainable Supply Chain Management (SSCM) involves the integrating of environmental, social and economic concerns into supply chain management activities with emphasis on the managers' efforts in the context of reducing the negative social and environmental impacts. Evaluating sustainable supply chain performance and efficiency is a significant topic for many researchers and scholars. Presence of input and intermediate product congestion is one of the key issues that results in lower efficiency and performance in a sustainable supply chain. Therefore, determination of congestion is of prime importance and removing it improves performance of the sustainable supply chain. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Some studies have been conducted to detect the intermediate product congestion via solving Network DEA (NDEA) models without considering the role of intermediate products. In this study, a sustainable supply chain with two-stage structure was considered. Then, the congestion status according to the role of intermediate products was found out for the first time. Towards this aim, different scenarios which congestion can occur in intermediate products were identified. Then, in each scenario, the dominant cone definition was developed in network structure and NDEA models were proposed. Finally 20 Iranian sustainable supply chains of Resin manufacturing companies have been used to demonstrate applicability of the proposed models.

Original languageEnglish
Pages (from-to)12477-12491
Number of pages15
JournalNeural Computing and Applications
Volume33
Issue number19
DOIs
Publication statusPublished - Oct 2021

Keywords

  • Congestion
  • Intermediate products role
  • Network Data Envelopment Analysis (NDEA)
  • Sustainable Supply Chain Management (SSCM)

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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