Inventory Optimization in Global Automotive Manufacturing Supply Chains

Aim: With rapid growth in the automotive industry, especially in the manufacturing of electric vehicles (EVs), efficient inventory management has become an eminent tool to minimize production cost, increase supply chain resilience, achieve higher fill rates, and inventory turnovers. This study aims to examine and evaluate effective inventory optimization strategies in global automotive manufacturing supply chains, with a particular focus on electric vehicle production, where demand fluctuations, supply chain disruptions, and stiff competition pose unique challenges. Methods: The study employs an empirical analysis of inventory models including Reorder Point (ROP), Economic Order Quantity (EOQ), and service-level-based optimization, using real-world data and simulation to assess performance under varying supply chain conditions. Results: The findings indicate that incorporating storage space constraints significantly impacts inventory turnover and service levels, particularly in facilities supporting electric vehicle production. EOQ and ROP models demonstrated optimal performance under stable demand, while service-factor models were more resilient under high volatility.