In this paper, we present the design and implementation of a semi-automated warehouse system tailored for urban smart logistics. By integrating IoT-enabled sensor networks for localization, identification, and real-time monitoring, AI-based control modules, and cloud platforms, the system aims to enhance operational throughput and reduce manual interventions. To evaluate cost performance and inform optimization strategies, an activity-based costing (ABC) model was applied to analyze unit costs across product handling groups. Furthermore, multi-period simulations over a four-week span were conducted to assess predicted versus actual performance, supporting dynamic decision-making in logistics resource planning. The results highlight measurable improvements in cost efficiency, model accuracy, and profit optimization. Visual analytics and comparative ABC evaluation demonstrate the system’s scalability and practical value in smart city environments. This work illustrates a concrete application of IoT-based sensor technologies in warehousing, bridging the gap between theoretical sensor research and practical deployment in smart city logistics. Future directions include full automation, integration with reinforcement learning for adaptive control, and the deployment of digital twins for real-time logistics optimization.