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A Big Data-Driven MCDM Model for Sustainable Supplier Selection
Abstract
In response to increasing environmental degradation and sustainability pressures, supply chain management (SCM) has expanded its focus to include sustainability alongside traditional performance goals. This study examines the use of Big Data Analytics (BDA) integrated with three Multi-Criteria Decision-Making (MCDM) methods—WASPAS, MAUT, and COPRAS—to support sustainable SCM decisions. Criterion weights are determined using the CRITIC method, enabling a more objective and data-driven evaluation framework. The study highlights how real-time data from Internet of Things (IoT), Enterprise Resource Planning (ERP) systems, and external sources enhance the accuracy and adaptability of sustainability assessments. A case study on supplier selection and logistics management demonstrates the effectiveness of the proposed approach. The results highlight a research gap in the comparative analysis of MCDM techniques in the context of improving decision quality, responding more quickly to change, and sustainability-focused supply chains.
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