Optimizing the order picking problem in e-commerce warehouse management - Using simulation data: Managerial implications for Vietnamese companies

Order picking remains one of the most labor-intensive and cost-dominant activities in warehouse management, particularly for e-commerce platforms coping with rapid growth in order volumes and service-level expectations. This study employs discrete-event simulation to evaluate the operational performance of four prevalent order-picking strategies Nearest-Neighbor, S-shape, Largest-gap, and Return within the context of Vietnamese e-commerce warehouses. Simulation outcomes indicate that the Nearest-Neighbor strategy consistently outperforms the others in small-to-medium warehouse configurations typical of Vietnam, owing to its adaptability and shorter travel distances. The research contributes both practically and theoretically. Practically, it offers data-driven insights for logistics managers at platforms such as Shopee, Tiki, and Lazada, facilitating more informed decisions regarding routing strategy selection, workforce allocation, and investment in automation technologies. Theoretically, it extends warehouse optimization literature by situating the analysis in an emerging market context characterized by limited automation and highly variable order structures. While the model assumes a static layout and uniform SKU distribution, it establishes a foundation for future studies incorporating real-time data, IoT-enabled tracking, and AI-driven dynamic routing. Overall, the findings reinforce the strategic role of simulation modeling as a decision-support mechanism in digital logistics transformation and e-commerce supply chain optimization.