Evolution and modeling in sustainable supply chain management research

Abstract

Numerous researchers and practitioners are attracted to sustainability in supply chains (SCS) and it has become one of the favorite topics among academics and industries. The purpose of this paper is to review and analyze the research studies that have been published in the field of sustainable supply chain management (SSCM). A total 242 articles published between 2000 and 2019 reviewed. Content analysis and descriptive analysis were applied in order to analyze the modeling approaches, evolution of models and methodologies, as well as sustainability metrics. The content analysis in terms of modeling approaches is based on analytic categories that provided by Brandenburg et al. (2014). The present study identified gaps and potential opportunities in SSCM to develop knowledge. The results indicate that methods such as genetic algorithm, nonlinear programming, goal programming, dynamics programming are less used by researchers. The multi-criteria decision making (MCDM) technique has the most application in the field of SSCM research. Researchers are more inclined to use analytical and mathematical models. The sustainability metrics with the highest frequency include “energy consumption", “cost", “health & safety".