Decision Support Framework for Smart Implementation of Green Supply Chain Management Practices

Abstract

Sustainability has become a critical issue for both society and businesses globally. With the increase of natural disasters and global issues such as water shortages, acid rain and climate change companies have started focusing on reducing their carbon footprint to ensure that the world’s natural resources are sustained for the foreseeable future. Many international and local companies are now looking to incorporate green initiatives into their supply chain management. This has given rise to green supply chain management which is the incorporation of sustainable initiatives into the supply chain of a company. Designing green supply chains (GSCs) requires complex decision support models that can deal with multiple dimensions of sustainability and specific characteristics of products and supply chain. Multi-criteria decision making (MCDM) approaches can be used to quantify trade-offs between economic, social, and environmental criteria i.e. to identify green production options. This study presents a hybrid decision-making approach for group multi-criteria evaluation for green supply chain management (GSCM) implementation criteria, which clubs many green processes with order allocation for dynamic supply chains to cope market variations. More specifically, the developed approach imitates the knowledge acquisition and manipulation in a manner like the decision makers who have gathered considerable knowledge and expertise in this domain. Fuzzy DEMATEL is first applied to find the causal relationship between the criteria and to rank them. Fuzzy SWARA method is used for evaluation of GSCM implementation criteria weights, which are qualitatively meaningful. Thereafter, using fuzzy TOPSIS method, the criteria application is quantitatively evaluated again for order allocation of criteria. To illustrate the applicability of the proposed hybrid framework, a real-life case study is presented in the chapter, and the results are analysed accordingly.