The fuel station sector is crucial to the global economy, particularly in Latin America and Peru, where it supports transportation, agriculture, and manufacturing. However, fuel stations face significant challenges, including poor inventory management and inaccurate sales forecasting, leading to inefficiencies and customer dissatisfaction.

This study proposed a Lean Warehouse Service Model for fuel stations, incorporating demand forecasting and Lean principles. The model addressed service level issues using the Winters Method for demand forecasting, the Harris Wilson Method for optimizing order quantities, and the 5S methodology for improving organizational efficiency. This comprehensive approach aimed to reduce costs, prevent stockouts, and enhance service levels.

Key findings showed a service level increase from 90% to 97.32%, on-time deliveries from 61.34% to 99%, loss-making sales reduction from 240,970 to 2,498 gallons, and 5S compliance improvement from 34% to 89%. These results confirmed the model's effectiveness.

Academically, the study adds to Lean applications in services, and socioeconomically, it provides a practical solution for enhancing fuel station efficiency. Future research should explore new technologies like AI and machine learning to further improve forecasting and operations in service industries.

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Mr. Martín Ivan Guevara-Cardozo