Advancing Circular Supply Chains in Automotive Manufacturing with Digital Twins: a WASPAS-ISM-MICMAC Framework
摘要
This study integrates digital twins (DT) with circular supply chain (CSC) practices in the automotive manufacturing industry using the WASPAS-ISM-MICMAC framework. A multi-criteria decision-making approach was applied to identify key DT and CSC indicators, followed by normalization and ranking using the WASPAS method. The Interpretive Structural Modeling (ISM) and MICMAC analysis established interdependencies among indicators. Findings reveal that DT-driven practices such as demand forecasting and procurement visualization significantly optimize inventory management and reduce waste. Dynamic scheduling and automated guided vehicle (AGV) simulation enhance production efficiency and logistics, minimizing emissions. CSC practices like recycling and remanufacturing are more effective when integrated with DT’s real-time data. However, lower-ranked indicators, such as eco-design and employee training, indicate the need for greater emphasis on sustainability awareness. The study underscores DT’s role in improving CSC implementation, optimizing resource efficiency, and reducing environmental impact.