Unlocking IoT Potential: A Holistic Analysis of Implementation Success in Automotive Supply Chains

The automotive industry is experiencing a profound shift with the integration of Internet of Things (IoT), encompassing predictive maintenance and autonomous driving. However, IoT implementation faces hurdles such as security, privacy, and interoperability concerns. This study identifies IoT adoption enablers in the automotive supply chain through literature review and expert insights. Methodology combines Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Interpretive Structural Modelling (ISM), and Matrice d’Impacts Croisés Multiplication Appliquée an un Classement (MICMAC) analysis. AHP-TOPSIS prioritizes alternatives based on importance levels, while ISM-MICMAC identifies causal relationships among enablers. Findings highlight Data Analytics, Predictive Maintenance, and Real-time Monitoring as key adoption drivers. Insights aid automotive stakeholders in developing strategies to overcome IoT deployment challenges and leverage its benefits.