A cloud-based multi-criteria decision-making framework for green-resilient supplier selection of manufacturing...

The selection of suppliers is crucial for contemporary manufacturing firms to attain their supply chain goals. With the escalation of global environmental concerns, manufacturing companies are facing increasing pressure to assess their suppliers' environmental performance. To comply with international environmental regulations and align with environmental policies, companies must ensure that their chosen suppliers adhere to green principles. At the same time, given unforeseen circumstances such as natural disasters and political instability, companies increasingly recognize the importance of selecting resilient suppliers to maintain uninterrupted supply chain operations. In contrast, green suppliers are more susceptible to disruptions in the supply chain due to fluctuations in raw material costs and shifts in policy. Accordingly, this paper addresses the issue of selecting green-resilient suppliers. The selection of green-resilient suppliers is a multi-criteria decision-making (MCDM) problem. This study focuses on integrating environmentally sustainable practices and resilient strategies in supplier selection, particularly in the manufacturing industry. First, this paper considers the supplier's ability to resist risks and environmental protection and constructs a set of evaluation criteria system for selecting suppliers. Second, the C-DEMATEL (Cloud-Decision Making Experimentation and Evaluation Laboratory) method is utilized to ascertain the relative importance of each criterion and to identify the interrelationships among them. Next, the C-TODIM (Cloud-based Portuguese for Interactive Multi-criteria Decision Making) method is proposed to rank suppliers by calculating dominance degree of each potential supplier. Finally, a case study on selecting battery suppliers for a new energy vehicle company is presented to illustrate the proposed approach. Sensitivity analysis and comparative analysis demonstrate the stability and applicability of the proposed framework.