Mitigating operational costs for circular supply chain by leveraging big data analytics driven-dynamic capabilities: Insights and implications for the industry

Abstract

The circular supply chain plays a critical role in minimizing operational costs and enhancing eco-efficiency by strategically aligning diverse organizational processes. To effectively generate these circular supply chains, it is vital to comprehend the dynamic capabilities shaped by big data analytics within a comprehensive framework. In this context, the capabilities driven by big data analytics are analyzed by using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, which assists in identifying intricate cause-and-effect relationships among the various factors affecting the supply chain. This method enables a nuanced understanding of how different elements interact and influence one another. Moreover, based on their level of influence, the Interpretive Structural Modeling (ISM) method is employed to organize these capabilities hierarchically. The resulting hierarchical model categorizes the factors into four distinct levels. The results reveal a four-level hierarchy in which the Sensing DC (BDDC3) and Seizing DC (BDDC13, BDDC11) at Level IV act as core capabilities for the entire system. The findings specifically demonstrate that prioritizing, sensing, and other capabilities are the primary drivers of operational cost optimization in successful resource reconfiguration and circular operations. Organizations can better navigate the complexities of circular supply chains by establishing this structured approach. It ultimately leads to improved sustainability outcomes and enhanced economic performance.