A systematic literature review of quantitative models for sustainable supply chain management

• Main Authors: Pablo Flores-Sigüenza, Jose Antonio Marmolejo-Saucedo, Joaquina Niembro-Garcia, Victor Manuel Lopez-Sanchez
• Format: Article
• Language: English
• Published: AIMS Press 2021-04-01
• Series: Mathematical Biosciences and Engineering
• Subjects: sustainable supply chain management; sustainability; systematic literature review; quantitative models supply chain; forward supply chain
• Online Access: http://www.aimspress.com/article/doi/10.3934/mbe.2021111?viewType=HTML

Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws and global agreements, have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers and practitioners. The main objective of this study is to provide a synthesis of the key elements of the quantitative model offerings that use sustainability indicators in the design and management of forward supply chains. To achieve this objective, we developed a systematic literature review that includes seventy articles published during the last decade in peer-reviewed journals in English language. In addition a 4 W's analysis (When, Who, What, and Where) is applied and three structural dimensions are defined and grouped by categories: Supply chain management, modeling and sustainability. As part of the results we evidenced a continuous growth in the scientific production of this type of articles, with a predominance of deterministic mathematical programming models with an environmental economic perspective. Finally, we identified research gaps, highlighting the lack of integral inclusion of a life cycle analysis in the design of supply chain networks.