Will the large-scale models that logistics giants are rushing to deploy prove to be a powerful remedy for cutting costs and boosting efficiency?

For the practical application of large models, the first step should be to… “ Treat the illness with the right medicine. ”。

SF Express is once again stepping up its efforts in the large‑model arena!
Following Following the launch of its self-developed “Fengzhi” logistics decision‑making large model on August 18, SF Technology unveiled a domain‑specific large language model for the logistics industry—“Fengyu”—on September 8, and demonstrated its real‑world applications across more than twenty use cases in SF’s marketing, customer service, pickup and delivery, international customs clearance, and other business functions.
In fact, as With the advancement of AI technology, large-scale models are increasingly being deployed across the logistics industry. Dozens of logistics companies—including SF Express, JD Logistics, Cainiao, COSCO Shipping, G7 Yiliu, Huolala, and Fuyou Truck—are leveraging these large models in various specialized use cases.
How exactly are large models transforming the logistics industry, and what tangible benefits can they deliver in terms of cost reduction and efficiency gains?

How can large models help logistics reduce costs and improve efficiency?
The emergence of large models was once hailed as a revolutionary productivity tool that would usher in historic opportunities. Every industry is poised to be reshaped by these large models, an upheaval no less profound than a new industrial revolution.
According to data from the China Business Industry Research Institute, the market size of China’s large model industry has grown from… The market size grew from RMB 1.5 billion in 2020 to RMB 7.0 billion in 2022, posting a compound annual growth rate of 116.02%, and is projected to reach approximately RMB 14.7 billion in 2023. Furthermore, the Chinese AI large-model industry is expected to expand to a market size of RMB 21.6 billion by 2024.
“Large models” are “large” because they must process and learn from massive datasets and intricate parameter spaces. In the logistics industry, training high‑precision large models requires not only general‑purpose public data but, more importantly, high‑quality data tailored to specific use cases.
According to statistics, there are now over a hundred large-scale models tailored for logistics and supply-chain applications. With the support of related technologies and products, these large models are delivering tangible benefits across multiple stages of the logistics process, including transportation, warehousing, last-mile delivery, and customer service.

1) Optimize transportation routes.
Large-scale models can analyze historical data and real-time traffic conditions to recommend optimal transportation routes for logistics companies, reducing unnecessary mileage and thereby lowering fuel and time costs.
Such as The “Fengzhi” logistics decision‑making large model is primarily applied to intelligent analytics in the logistics supply chain, sales forecasting, transportation route optimization, and packaging optimization, among other decision‑making domains.
2) Improve warehouse efficiency.
In warehouse management, large-scale models can forecast inbound and outbound cargo flows, optimize inventory levels, mitigate overstocking or stockouts, and enhance the utilization of warehouse space.
For example, JD Logistics has leveraged large-scale model technology to launch Jinghui, a one-stop, intelligent supply chain data management platform. 3.0 offers features such as sales forecasting, inventory alerts, and intelligent replenishment, helping customers optimize their supply chain planning to reduce costs and improve efficiency.
3) Intelligent scheduling.
Large-scale models can intelligently match the most suitable modes of transport and routes based on multi-dimensional data such as cargo type, volume, and weight, thereby enhancing overall transportation efficiency.
In September 2023, Baidu Maps announced the launch of a beta version of its logistics large model, leveraging Baidu’s advanced large‑model capabilities and tailored to the specific needs of the logistics industry. The model is being piloted in two key areas: logistics address parsing and logistics dispatch decision‑making.

4) Empowering automation and robotics.
The intelligent decision-making capabilities of large models can enhance the operational efficiency of automated equipment, reduce manual intervention, and lower labor costs.
With AI-powered intelligent systems—such as trunk‑line autonomous driving, drones, and driverless vehicles—as well as a range of warehouse robots and automated forklifts, can, when integrated with large language models, enhance human–machine collaboration, optimize delivery efficiency, and expand service coverage.
5) Risk management.
Large-scale models can analyze a variety of potential risk factors, such as weather changes and traffic congestion, issue early warnings, and develop response strategies to mitigate operational risks.
This year In March, Huolala launched its self-developed “Worry-Free Freight” large language model, which emphasizes scenario-specific applications and lightweight design, positioning itself as “your personal logistics expert.” Currently, its accuracy in factual freight‑related Q&A exceeds 90%.

6) Enhance Customer service efficiency.
Large-scale models can analyze customer feedback and behavioral patterns to deliver more personalized services, enhance customer satisfaction, and thereby strengthen customer loyalty and the company’s market competitiveness.
For example, SF’s Fengyu large language model, when applied to customer service, not only helps agents quickly extract key information from customer conversations and generate service summaries, but also identifies opportunities in customer feedback, thereby further enhancing service quality. Currently, the summary accuracy of this large model has exceeded 95%, which has reduced the average handling time for customer service agents after customer interactions by 30%.
Last year In October, Fuyou Truck signed an agreement with Tencent to establish a comprehensive strategic partnership aimed at co‑creating the first large‑scale digital freight model. The two parties have initially launched joint training and application efforts in OCR‑based intelligent recognition, successfully developing an end-to-end OCR large model that is now being used for the smart recognition and automated processing of logistics and freight documents as well as various types of delivery receipts. In addition, leveraging the unique characteristics of freight services, the two sides plan to expand their collaboration to include areas such as intelligent customer service and operational analytics.
7) Supply Chain Collaboration 。
Large-scale models can help logistics enterprises and upstream and downstream partners in the supply chain collaborate more effectively, enabling information sharing and process optimization, thereby enhancing the overall efficiency of the supply chain.
Last year In June, Cainiao Supply Chain launched “Tianji π,” a digital supply-chain solution powered by large-scale models. By leveraging Cainiao’s proprietary algorithms and generative AI—also grounded in large models—to support decision-making, the platform has enhanced both quality and efficiency across key areas such as sales forecasting, replenishment planning, and inventory health management, thereby accelerating the logistics supply chain’s transition into the era of large-scale models.
For example, when building its logistics decision‑making large‑model technology stack, SF Technology adopted a hybrid approach that integrates a business knowledge base with specialized small models. This enabled the creation of business‑expert AI agents endowed with supply‑chain domain expertise, as well as algorithmic expert AI agents equipped with advanced algorithmic capabilities. Through the collaborative operation of these agents, the system supports specific business use cases—such as sales analytics and inventory optimization—thereby effectively addressing and mitigating the inherent limitations of large models in supply‑chain contexts.

It is worth noting that this year In March, the Logistics Intelligence Alliance—the logistics industry’s first consortium dedicated to the research and practical application of large-scale models—was established.
According to reports, under the guidance of the China Federation of Logistics and Purchasing, the alliance comprises Alibaba Cloud, Cainiao, Amap, COSCO Shipping, China Eastern Logistics, YTO Express, STO Express, ZTO Express, Debon Logistics, and… G7 Yiliu, Dishi Tie, the Intelligent Transportation Research Institute of Zhejiang University, and other organizations have jointly established the alliance. The formation of this alliance will accelerate the deployment of large-scale models in the logistics sector.

Challenges of Deploying Large Models in Logistics Scenarios
In fact, while the application of large models in the logistics industry has brought numerous benefits, it may also entail certain risks and potential pitfalls.
First, there are issues related to data quality and privacy. Training large-scale models requires vast amounts of high-quality data; however, in the logistics sector, the collection, processing, and utilization of such data pose significant challenges in terms of privacy protection and data security.
Second, in terms of integrating technology with existing systems: effectively blending large‑model technologies with current logistics and supply‑chain systems to enable data‑driven decision‑making and optimization poses a significant technical implementation challenge.
Third, balancing cost and benefit. While large‑model technologies hold the potential to boost efficiency, their substantial training and operational costs must also be taken into account, requiring the identification of an optimal cost‑benefit trade‑off.
Overall, the practical deployment of large-scale models in the logistics industry still requires gradual refinement and continuous optimization. Logistics enterprises should align their strategies with current needs and future prospects, setting short-term goals such as enhancing management and improving efficiency, while identifying areas within their own business value chains that can be effectively supported by these technologies. The AI‑powered processes have been validated through pilot projects and are being steadily advanced.
Today, logistics companies are eagerly poised to take the lead; whichever player ultimately steers the sector, large-scale models will serve as a powerful catalyst for reducing costs and boosting efficiency across the industry. Yet, to harness their full potential, the key remains identifying the use cases that best suit your specific needs and applying the right solutions to each challenge.