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JINGDONG Logistics (JD Logistics, or JDL) is JD.com’s technology-driven logistics and supply-chain services business. It operates warehouses, transport, delivery, bulky-item, cold-chain and cross-border networks, then coordinates them with forecasting, optimization software, digital twins, cloud systems and robotics.
Its “JD Logistics Super Brain” is not one chatbot or one autonomous machine. It is a connected operating system for decisions from inventory placement and warehouse work through route planning, delivery-station coverage and exception management. The figures below come from JD Logistics and JD.com disclosures for 2024 and 2025, so they should be read as company-reported scale rather than an independent performance audit.
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What JINGDONG Logistics is
JINGDONG Logistics began as JD.com’s internal logistics department in 2007. It opened its network to external customers in 2017 and became a publicly listed company in Hong Kong in May 2021. The business now sells integrated supply-chain services to merchants and other organizations while continuing to support JD.com’s retail ecosystem.
The “integrated” description matters. JDL is not only a parcel carrier. Its service scope can include demand and inventory planning, warehouse design and operation, trunk transportation, delivery-station management, last-mile delivery, installation or handling of bulky goods, temperature-controlled logistics and international fulfillment. The company’s stated operating objective is to place products as close as possible to consumers, reduce handling frequency and minimize fulfillment distance.
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In 2024, JDL reported 80,703 external integrated-supply-chain customers. It reported RMB182.8 billion in total revenue, including RMB32.3 billion from external integrated-supply-chain customers. Those are annual company disclosures, not a claim that every customer uses every part of the network.
How the business grew and how large it is
JDL’s footprint combines facilities it operates for logistics services with the wider JD.com fulfillment network. The definitions and reporting periods differ, so the numbers should not be added together as if they measured one identical asset base.
| Measure | Company-reported figure | Period and scope |
|---|---|---|
| Warehouses | More than 1,600 | JDL disclosure at December 31, 2024 |
| Delivery stations and service outlets | Approximately 19,000 | JDL disclosure at December 31, 2024 |
| In-house delivery and operations personnel | More than 370,000 | JDL disclosure for 2024 |
| External integrated-supply-chain customers | 80,703 | JDL disclosure for 2024 |
| Overseas, bonded and international direct-distribution warehouses | More than 100 facilities and over 1 million square meters | Across 19 countries and regions at the end of 2024 |
| JD.com nationwide fulfillment warehouse area | More than 34 million square meters | JD.com 2025 Form 20-F, as of December 31, 2025 |
| Warehouse and delivery employees | 642,940 | JD.com 2025 Form 20-F, as of December 31, 2025 |
| LangzuTech automated warehouses | More than 20 in nearly 20 Chinese cities | JD.com 2025 results release, as of December 31, 2025 |
The 2025 employee figure is broader and comes from JD.com’s group filing, while the 2024 figure is JDL’s description of in-house delivery and operations personnel. Different definitions, entities and reporting dates explain why they should not be treated as a like-for-like headcount trend.
What “JD Logistics Super Brain” means
JDL introduced Super Brain as an upgrade to its digital and intelligent supply-chain technology ecosystem in 2024. The company describes it as an integration of artificial intelligence, big-data analytics, operations research, digital twins, software and physical equipment across planning, warehousing, transportation and delivery.
That wording describes a stack of specialized systems rather than a single general-purpose AI model:
- Prediction: demand and sales forecasts help determine where inventory should sit and how much capacity each node may need.
- Optimization: mathematical and operations-research methods choose warehouse layouts, network deployments, routes and labor assignments under changing constraints.
- Simulation: digital-twin tools can represent facilities and flows so proposed changes can be evaluated before they are applied to live operations.
- Execution software: warehouse, transportation and delivery applications turn decisions into tasks, scans, schedules and status updates.
- Hardware integration: sensors, automated guided vehicles, lifting robots, sorting equipment and other machines carry out physical movements while software coordinates them.
Calling this an AI supply chain does not mean that every decision is made by machine learning. Forecasting, routing, optimization, rules, maps, workflow automation and robotics each address different parts of the process.
How AI supports planning and inventory
Forecast-based slotting
In a conventional warehouse, a product may stay in a fixed location even as its sales rate changes. Super Brain is described as dynamically optimizing slot layouts from sales forecasts. Fast-moving items can be positioned where workers or robots travel less, while slower items can use less accessible capacity. The practical goal is to reduce walking, travel and congestion without changing the customer’s order.
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Warehouse-network deployment
Inventory placement is also a network decision. Systems can help determine which fulfillment node should hold stock, taking expected demand and regional coverage into account. Placing goods nearer likely buyers can shorten the final delivery distance, but it also creates trade-offs: more distributed inventory can increase duplicate stock, handling and transfer requirements. Optimization is therefore about balancing service, capacity and inventory cost, not simply putting everything everywhere.
Prepackaging and quick-ship preparation
JDL says Super Brain enables prepackaging in quick-ship warehouses. The idea is to perform suitable packing work before an order arrives when demand patterns make that safe. It can accelerate dispatch for predictable items, while products with uncertain configurations still need order-specific handling.
How warehouses use robots and automation
Yes, JD Logistics uses robots in its warehouses, but robotics is deployed as a coordinated system with people, software and conventional equipment.
Automated storage, picking, packing and sorting
Super Brain connects automated storage and retrieval, picking, packaging and sorting processes. Warehouse employees receive system instructions and work alongside logistics robots. Depending on the facility, automation may move inventory to a worker, carry containers between zones, lift goods, or divert parcels to a destination. The software decides which task should happen next and records the result.
Zhilang goods-to-person systems
JDL’s 2024 disclosure identifies the Zhilang goods-to-person solution. It combines Zhilang handling automated guided vehicles, lifting robots and three-dimensional shelving. In a goods-to-person design, the system brings a storage container or item toward a picking station instead of requiring a worker to walk to every shelf. That can reduce travel in high-throughput facilities, although the final process still depends on item dimensions, packaging, safety procedures and human verification where required.
LangzuTech expansion in 2025
JD.com’s 2025 results release reported more than 20 LangzuTech automated warehouses operating in nearly 20 Chinese cities by December 31, 2025. It also reported the first overseas LangzuTech warehouse in the United Kingdom. That site was equipped with hundreds of robots and was designed to improve picking and outbound efficiency.
The announcement establishes deployment scale and intended operational benefits; it does not provide a standardized, independently audited percentage improvement for every warehouse. Performance will vary with facility design, product mix, order volume and labor practices.
How AI plans transportation and last-mile delivery
Super Brain integrates mapping and route-planning technology with operational data. The annual-report description identifies regional conditions, order distribution and delivery-team availability as inputs. The resulting decisions can include where to locate stations, which addresses each station should cover and how delivery teams should sequence their routes.
Station and coverage decisions
A delivery station’s location affects travel distance, staffing and the time parcels spend waiting for dispatch. Coverage planning must account for geography, order density, road conditions and the available delivery workforce. A mathematically efficient location is not automatically practical if a building lacks loading access or local regulations restrict operations, so these systems support managers rather than removing every local judgment.
Daily route decisions
Routes change as orders arrive, promised delivery windows move, traffic or weather conditions shift and drivers become available. Mapping and optimization systems can recalculate assignments to limit unnecessary mileage while meeting service constraints. The quality of the result depends on the accuracy and timeliness of the underlying address, order and workforce data.
Exception handling and tools for frontline teams
JDL says it has applied logistics large models to dozens of scenarios, including abnormal-control, process-automation and intelligent-interaction tasks. In practical terms, these systems are aimed at identifying irregularities, automating routine workflow steps and helping employees obtain information or complete procedures more quickly.
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Examples of exception categories can include a process that deviates from its expected sequence, a shipment status that conflicts with other records, or a task that needs escalation. The public disclosures describe the purpose and breadth of these applications, but do not publish a single accuracy rate or a universal list of models used in every warehouse and station.
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JD.com’s 2025 Form 20-F says its supply-chain technology focuses on three key areas: AI, big-data analytics and cloud computing. The filing also describes AI scientists and engineers and a smart supply-chain platform with application-level products for JD.com’s business and ecosystem.
These layers solve different problems. Cloud systems provide shared computing and data services; big-data platforms collect and organize orders, inventory, scans, maps and equipment signals; AI models identify patterns or generate forecasts; and operations-research engines optimize decisions subject to capacity, time and service constraints. Sensors and robotics then provide the physical feedback needed to confirm whether a plan worked.
Domestic and international reach
At the end of 2024, JDL reported more than 100 bonded, international direct-distribution and overseas warehouses totaling over 1 million square meters across 19 countries and regions. This warehouse-centered international network supports cross-border inventory positioning and fulfillment rather than relying only on shipping each order from China.
International operations introduce extra variables: customs documentation, duties, local carrier capacity, language, labor rules, inventory ownership and different delivery standards. A platform that optimizes a domestic route cannot simply be copied overseas without adapting its data, constraints and operating procedures.
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What JD Logistics can and cannot prove from public disclosures
The disclosures establish substantial infrastructure, named technology programs and continued automation rollout. They do not, by themselves, prove that every customer receives the same service level or that AI alone caused a particular cost or speed result.
When evaluating JDL or another integrated provider, examine these evidence categories:
- Network ownership and density: Which warehouses, stations and transport assets are operated directly, and in which regions?
- Automation maturity: Are robots used for storage, movement, picking, sorting or only demonstrations? How are people and machines coordinated?
- Planning depth: Does the system cover forecasting, inventory positioning, warehouse layout, transportation and last-mile execution?
- Industry fit: Can it handle cold-chain, bulky goods, regulated products, high-value items or unusual packaging requirements?
- Geographic scope: Is the service domestic, cross-border, or supported by local facilities in the destination market?
- Measured outcomes: Ask for comparable figures on cost per order, delivery accuracy, inventory turns, throughput, labor productivity and service quality, with the measurement period and baseline.
A simple end-to-end example
Suppose a merchant expects a sales increase for a product in several provinces. Forecasting models estimate regional demand. Network software recommends how much stock to place in each fulfillment node. Within a warehouse, slotting logic positions the item near an efficient picking zone. A goods-to-person system brings the storage container to a station when an order arrives. Packing and sorting equipment prepares the parcel for dispatch. Route-planning software assigns it to a delivery station and team using address distribution, regional conditions and available workers. If a scan, route or process deviates from expectation, an exception workflow can flag it for correction.
This example shows why “AI in supply-chain management” is broader than a conversational assistant. The value comes from connecting many decisions and physical actions while keeping inventory and shipment data synchronized.
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Use the date and reporting entity attached to every figure. The 2024 JDL report’s more than 1,600 warehouses, approximately 19,000 stations and over 370,000 in-house delivery and operations personnel describe JDL’s disclosed operations at that time. JD.com’s 2025 filing’s more than 34 million square meters and 642,940 warehouse and delivery employees describe a later, broader group-level view. The 2025 LangzuTech count describes automated warehouses, not all warehouses in the network.
These distinctions prevent two common mistakes: treating a group-wide number as a pure JDL operating metric, or assuming that an automation count represents the entire warehouse estate. Company disclosures are useful evidence of scale and direction, while independent service-level validation would be needed to compare outcomes across providers.
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