A continuous supply chain management approach using SPJS-Fuzzy DEMATEL and LPWBN for automotive electric vehicles in India

The rapid adoption of Electric Vehicles (EVs) is essential for reducing greenhouse gas emissions and promoting sustainable transportation. In India, the EV market faces significant challenges in supply chain management (SCM), particularly in integrating third-party recyclers to handle EV components and lithium-ion batteries (LIBs) efficiently. Existing studies lead to inefficiencies and potential environmental hazards. Addressing this critical gap, we propose a continuous SCM framework tailored for automotive EVs in India, incorporating EV component recyclers using Shannon's Probabilistic Jaccard Similarity-based Fuzzy Decision Making Trial and Evaluation Laboratory (SPJS-Fuzzy DEMATEL) and Lagrange Interpolation-based Probability Weighting Bayesian Network (LPWBN). The applicability of our method lies in its ability to enhance transparency, optimize resource utilization, and mitigate risks in the EV supply chain. Starting with the collection and transportation of raw materials to Indian manufacturing units, our framework employs JS-Fuzzy logic with sensor data to prevent manufacturing disruptions. The LPWBN enhances transparency between manufacturers and consumers, facilitating better demand forecasting and supply planning. Damaged or end-of-life products are routed to third-party recyclers, where JS-Fuzzy logic ensures the quality of recycled resources for remanufacturing. By clustering enablers and analyzing their correlations using SPJS-Fuzzy DEMATEL, we assess the impacts of economic, cultural, organizational, social, and environmental aspects on SCM. India, with its burgeoning EV market and unique socio-economic conditions, serves as an ideal context for applying our framework. Experimental results demonstrate that our proposed system achieves a 99.74% reliability score, outperforming existing methods and highlighting its potential to advance sustainable EV supply chains in India.