Abstract: The automotive spare parts industry plays a critical role in ensuring the continuity, efficiency, and resilience of global vehicle operations. As the industry faces increasing complexity due to fluctuating demand, global disruptions, and technological advancements, predictive analytics has emerged as a strategic tool for supply chain optimization. This study explores the impact of predictive analytics on key areas of the automotive supply chain, including demand forecasting, inventory management, risk mitigation, and cost efficiency. Using secondary data from the Interplex Inventory Sales Report (Oct 2024 – Mar 2025), the research applies analytical techniques such as time-series forecasting, treemaps, and box-and-whisker plots to uncover trends and performance insights. The findings reveal that predictive analytics enables more accurate demand forecasting, reduces inventory imbalances, and supports proactive decision-making in the face of potential disruptions. High-performing products like Battery Terminal – B show strong alignment between sales and inventory strategies, while underperforming items highlight opportunities for improvement. The study also discusses how companies like TI Fluid Systems can leverage predictive tools for better supplier coordination and strategic planning. Overall, the results affirm that predictive analytics is a valuable enabler of supply chain agility, operational efficiency, and competitive advantage in the automotive sector. Future implications suggest that greater integration of real-time data and AI-driven models can further enhance supply chain resilience and sustainability.

Keyword: Predictive Analytics, Automotive Supply Chain, Inventory Optimization, Demand Forecasting, Supply Chain Resilience

| DOI: 10.17148/IARJSET.2025.125139