Assessing the Mediating Role of Recognizing and Overcoming Challenges in Using Iot and Analytics to Enhance Supply Chain Performance

Objective: This study examines the impact of integrating Internet of Things (IoT) technologies and sophisticated analytics on supply chain efficiency within the automobile sector. It examines the alleviation of issues including data security, difficulties of system integration, and deficiencies in workforce skills to attain best operational results. Through this research we aim to achieve the sustainable development goals (SDGs) of 8 and 9, which are Decent Work and Economic Growth and Industry, Innovation and Infrastructure. It builds resilient infrastructure for the industry using innovative technologies like IoT and analytics and as a result achieving decent work and economic growth. Design/methodology/approach: The study used structural equation modeling (SEM) to examine data gathered from 150 automotive OEMs and their tier one supplier companies in and around Chennai, India. The research utilizes frameworks such as the Technology Acceptance Model (TAM) and the Theory of Constraints (TOC) to elucidate the connections among IoT problems, predictive analytics, and supply chain performance. Results and Discussion: The results show that predictive analytics enhances supply chain transparency, reducing inefficiencies, and thereby achieving accurate demand forecasting. Addressing the issues of IoT-related risks and operational inefficiencies significantly enhances supply chain performance metrics. Real-time data monitoring and strategic problem resolution were found to be essential for the successful integration of IoT and analytics. Practical implications: The research offers practical techniques for the use of IoT and analytics, highlighting real-time monitoring and data-driven decision-making to enhance supply chain responsiveness and efficiency. This research addresses IoT-related difficulties, enhancing academic debate on digital transformation in supply chains and providing a practical framework for the successful utilization of IoT and analytics. This study offers an effective framework for organizations in the automotive industry to successfully implement IoT and analytics by addressing key challenges. Originality/value: By focusing on the mediating role of recognizing and mitigating challenges in IoT and analytics adoption, this research contributes to the academic discourse on digital transformation in supply chains. It provides actionable insights for practitioners aiming to optimize supply chain operations through advanced technological solutions. The findings point out the importance of implementing proactive, data-driven strategies and fostering real-time visibility to achieve substantial gains in supply chain efficiency and responsiveness.