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3.3.3. AI in warehouse management
Within logistics centers and bonded warehouses, artificial intelligence is increasingly
used to develop smart warehouse systems. These systems automate various processes
including cargo classification, inventory monitoring, and storage space optimization.
AI technologies can analyze inventory data to forecast storage demand and
recommend optimal cargo placement strategies. This allows logistics operators to
minimize storage costs while maximizing warehouse capacity utilization.
Furthermore, AI-powered warehouse management systems can improve order
fulfillment efficiency by optimizing picking routes and automating inventory tracking
processes.
3.3.4. AI in customs clearance and trade facilitation
Another important application of artificial intelligence in import–export logistics
relates to customs procedures and cargo clearance processes.
AI technologies can analyze shipment data, evaluate potential risks, and support
customs authorities in identifying suspicious shipments that require further inspection.
This enhances both efficiency and security within international trade operations.
Vietnam’s electronic customs management system has already adopted various
data analytics technologies to streamline customs procedures. As a result, customs
clearance times for import–export goods have improved significantly in recent years.
The continued integration of AI into customs operations has the potential to further
enhance transparency, reduce administrative burdens, and facilitate cross-border trade.
4. Conclusion and recommendations
Although the application of artificial intelligence (AI) in Vietnam’s import–export
logistics sector presents significant potential, its implementation still faces several
challenges. First, most domestic logistics enterprises remain relatively small and have
limited financial capacity, making it difficult to invest in advanced technological systems.
Many firms primarily provide basic services such as transportation, freight forwarding,
and customs brokerage, while high-value integrated logistics services are largely
dominated by foreign enterprises. This limits the ability of domestic firms to adopt
advanced technologies such as AI in supply chain management.
Second, Vietnam’s logistics data infrastructure remains fragmented and
insufficiently standardized. AI systems require large volumes of high-quality,
interconnected data; however, logistics data are currently dispersed across multiple
stakeholders, including transport companies, port operators, customs authorities, and
service providers. This lack of integration reduces data utilization efficiency and hinders
the effective implementation of AI-based solutions.
Third, the shortage of skilled human resources in data science, artificial intelligence,
and supply chain management presents another major obstacle. The development and
operation of AI systems require specialized expertise, which remains limited in Vietnam’s
labor market.
Based on these challenges, several policy recommendations are proposed. First,
logistics enterprises should develop clear and long-term digital transformation strategies,
with AI identified as a core component for improving operational efficiency and service
quality. Second, firms should assess key factors influencing technology adoption,
including leadership commitment, workforce capabilities, operational processes, financial
resources, and technological infrastructure. Third, companies should adopt collaborative

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