This paper identify, evaluate, and prioritize the most important smart supply chain technologies in the automotive sector, create a predictive decision-making model, and offer evidence-based recommendations on technology adoption. The study uses a mixed-methodology that combines SEM and MCDM to collectively integrate expert opinions, model dependencies, and causal connections, and rank smart supply chain technologies and strategies in the Kerala automotive industry. Structured responses to ten evaluation criteria were obtained through a purposive sample of 35 domain experts, which allowed making strong decisions in times of uncertainty and complexity. This study has found that the professionals believe that Data Security and Privacy, Technical Feasibility, and Improvement in Decision-Making are the most important criteria for the successful adoption of smart supply chains in the Kerala automotive industry. Structural Equation Modeling has confirmed strong causal relationships, such as that Technical Feasibility is a strong predictor of Operational Impact, and Data Security and Privacy is a strong predictor of Supplier and Customer Satisfaction. The TOPSIS approach identified IoT-based Tracking & Visibility, AI-driven Demand Forecasting, and Blockchain-based Supplier Verification as the most feasible, impactful, and strategic smart supply chain technologies and strategies because they ranked higher than other strategies. These findings indicate a consistent expert opinion, which emphasizes that the adoption of smart supply chains should focus on technologies and strategies that increase real-time visibility, predictive analytics, and data security, which is consistent with the transformation of Industry 4.0 and local industrial capabilities.

Menu

Dr. S. Robin Divahar