This study explores how sensor-based technologies can support Lean replenishment in food retail environments. The main objective is to present the development and early implementation of the Stockout Detection and Monitoring System (SDMS), a prototype designed to detect stockouts in real time and support flow-based replenishment. The research follows an action research methodology. It involves five iterative stages: diagnosis, planning, action, observation, and reflection. Data were collected through direct observations, ergonomic assessments (REBA and NIOSH), technical monitoring, and collaboration with store teams and technology partners. Initial results show that the SDMS achieved 84% detection accuracy and 96% shelf coverage. It successfully identified key stockout zones and reduced reliance on manual inspection. Real-time dashboards were developed to visualize product availability and prioritize replenishment. Sensor calibration proved effective even in complex conditions, such as reflective packaging. The system contributes to both operational efficiency and Lean learning. It supports real-time decision-making, reduces physical strain on workers, and minimizes unnecessary product handling. These outcomes align with Lean principles of waste reduction, flow optimization, and respect for people. Although still in validation, the system shows strong potential for future integration with ERP and ESL platforms. Future research will focus on scaling the system, improving detection logic, and collecting user feedback from store personnel. The SDMS provides a concrete example of how digital tools can reinforce Lean practices in complex retail operations and offers new perspectives for smart retail transformation.