Smart Spare Parts Inventory Management: IoT and AI-Driven Integration to Improve Service Level in the Peruvian Automotive Sector

The article presents a proposal of an integral model to optimize the perfect order rate in an automotive dealership, in which this sector is important since it represented 2.53% of the national GDP in 2024. Currently, this sector faces significant challenges in inventory management due to the great variety of spare parts, which is affecting customer satisfaction and the company's profitability. In response to this problem, a solution was developed using demand forecasting models powered by artificial intelligence, combined with Lean Warehousing methodologies and traceability technologies such as IoT and RFID. This model aims to improve operational efficiency, minimize inventory errors and increase spare parts availability, which will raise the current POI of the case study from 65% to an indicator above 85.90%, where the leading companies in the industry are located. Simulation results using real company data showed an increase in inventory accuracy (ERI) from 85.00% to 93.30%, a decrease in out-of-stocks from 9.68% to 4.15%, and an improvement in warehouse location accuracy from 81.14% to 93.45%. Therefore, this model contributes to improving the competitiveness of the dealership under study, with a focus on digital transformation.