A Hybrid IoT AI Model to Enhance Efficiency and Content Verification in Smart Logistics

Centeno B.; Romero C.; Pazce F.S. · Advances in Transdisciplinary Engineering (2026) · DOI: 10.3233/atde260739

Resumen

The research introduces a combined IoT and AI approach designed to boost efficiency in logistics processes where both variability and fast order-picking are prevalent. The integration of an RGB camera, millimeter-wave radar, and convolutional neural networks enables automated content verification, decision-support capabilities, and reduced variability in human judgment. The model generates real-time data that facilitates the calculation of standard processing times and contributes to stabilizing operational execution. Expected results, supported by recent evidence, project identification accuracies ranging from 95.6 % to 99.3 %, action-recognition rates above 95%, and an increase in picking efficiency from 68% to levels approaching 85%. These findings demonstrate the model's potential to achieve more consistent executions and more stable operational flows. The proposal offers a scalable and repeatable solution for supply chains aiming to automate verification using Industry 4.0 technologies, which helps enhance decision-making and boost overall logistics efficiency.

Instituciones: Universidad Peruana de Ciencias Aplicadas