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JINGDONG Logistics (JD Logistics or JDL) is JD.com’s technology-driven logistics and supply-chain solutions business. It operates warehouses, transportation, delivery, bulky-goods, cold-chain and cross-border networks, then coordinates them with forecasting, operations research, digital twins, cloud software, mapping systems and robots. Its “JD Logistics Super Brain” is the company’s name for this integrated intelligence layer, not a single chatbot or model.
JD Logistics began inside JD.com in 2007, opened its services to outside customers in 2017 and listed in Hong Kong in May 2021. The company’s own disclosures show a network of more than 1,600 warehouses and approximately 19,000 delivery stations and service outlets at the end of 2024, while JD.com’s 2025 filings describe more than 34 million square meters of nationwide fulfillment space and 642,940 warehouse and delivery employees. Those figures come from different reporting periods and scopes, so they should not be treated as a like-for-like headcount comparison.
What JINGDONG Logistics is
JD Logistics is the logistics arm that JD.com built first for its own retail operation and later turned into a third-party supply-chain service. Rather than offering only parcel transportation, it manages multiple stages of fulfillment: inventory placement, storage, picking, packing, sorting, line-haul movement, delivery-station operations and final delivery. It also provides specialized capabilities for large or heavy products, temperature-controlled goods and international shipments.
The external business is called an integrated supply-chain service because a customer can use a connected set of facilities and software instead of contracting separately for warehousing, transport and delivery. In 2024, JD Logistics reported 80,703 external integrated-supply-chain customers and RMB182.8 billion in total revenue. Revenue from those external integrated-supply-chain customers was RMB32.3 billion for the year, according to the JD Logistics 2024 Annual Report.
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How the business developed
- 2007: JD.com established the logistics operation to support its own orders.
- 2017: JD Logistics began serving outside businesses.
- May 2021: JD Logistics listed in Hong Kong.
This history explains why JDL emphasizes network control and technology integration. It was designed around a large retail order flow, then adapted into a service for manufacturers, brands and other merchants that need fulfillment capabilities.
What “JD Logistics Super Brain” means
Super Brain is JD Logistics’ 2024 name for an integrated digital and intelligent supply-chain technology ecosystem. The company says it combines artificial intelligence, big-data analytics, operations research, digital twins, software and physical equipment across planning, warehousing, transportation and delivery.
That description matters because “AI” is not one standalone system making every decision. Different tools handle different jobs: demand forecasts influence where inventory is stored; optimization algorithms assign space and routes; digital-twin models represent warehouse or network conditions; sensors and warehouse robots execute physical movements; and workflow software gives instructions to employees and monitors exceptions.
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- Collect signals: orders, sales patterns, inventory levels, regional conditions, delivery-team availability and facility status provide the operating data.
- Predict and simulate: forecasting models estimate demand, while optimization and digital-twin tools test inventory, capacity and routing choices.
- Assign work: the system turns those choices into warehouse tasks, transportation plans, station coverage and delivery routes.
- Observe results: scans, sensor data and operational exceptions feed back into the software so plans can be adjusted.
The stated operating objective is to place products as close as possible to consumers, reduce handling frequency and minimize fulfillment distance. In practice, that means using data to decide not only how to move an order, but where the item should be before the order arrives.
How AI supports inventory and planning
Planning is the first layer of JDL’s AI-enabled supply chain. The company says Super Brain dynamically optimizes warehouse slot layouts using sales forecasts. Fast-moving products can be positioned in locations that reduce walking, travel or robot movement, while slower items can occupy less convenient space.
Network and inventory placement
At the network level, the software supports warehouse-network deployment and inventory-positioning decisions. A business can place stock in facilities nearer to likely demand instead of waiting for every order to travel from a central site. The potential benefits are shorter fulfillment distance and fewer transfers, but the trade-off is that inventory is spread across more locations and must be replenished intelligently.
Prepackaging and quick-ship operations
JDL also describes prepackaging in quick-ship warehouses. Products or product combinations can be prepared before an individual customer order is received when forecasts indicate repeat demand. This can reduce the number of handling steps during peak periods, although forecast errors can leave prepackaged inventory needing rework or a different destination.
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Why this is more than a chatbot
A conversational interface could help an employee query information, but the documented planning functions are operational: forecasting, constraint-based allocation, layout optimization and network deployment. Those systems must account for physical capacity, labor, delivery cutoffs and inventory availability. Calling the entire process “a chatbot” would misrepresent how the supply chain operates.
Warehouse automation and robots
JD Logistics uses Super Brain to coordinate warehouse software with automated storage, picking, packing and sorting equipment. Employees receive system-generated instructions and work alongside logistics robots. Automation is therefore a combination of software decisions and machines that move or present goods; it is not necessarily a lights-out warehouse with no people.
Zhilang goods-to-person systems
The 2024 JD Logistics report describes a Zhilang goods-to-person solution that combines Zhilang handling automated guided vehicles, lifting robots and three-dimensional shelving. In a goods-to-person design, robots bring inventory to a workstation or present it in an accessible position, reducing the distance a picker must walk through storage aisles. Lifting robots and vertical shelving can use warehouse height as well as floor area.
Software determines which item should be presented, in what sequence and at which station. The practical objective is to synchronize inventory retrieval with packing and outbound schedules. Performance still depends on accurate item identification, charging and maintaining robots, replenishing stock and handling exceptions such as damaged or misplaced goods.
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, equipped with hundreds of robots and intended to improve picking and outbound efficiency.
These announcements show continued deployment, but they do not establish one universal automation level for every JDL facility. Warehouse designs vary by product type, order volume, building layout and local labor conditions. A robotics rollout in selected sites should not be read as a claim that all more than 1,600 warehouses use identical equipment.
Transportation and last-mile delivery decisions
Super Brain also integrates mapping and route-planning technology. JD Logistics says its inputs include regional conditions, order distribution and delivery-team availability. The outputs include decisions about station locations, delivery coverage and routes for delivery teams.
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Station and coverage planning
Before a driver or courier receives a route, the network must decide which delivery station serves an address and how much territory each team can cover. Those decisions affect handoffs, travel distance and promised delivery windows. A station may need additional capacity during a sales event, while a low-density area may require a different coverage pattern.
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Daily route optimization
Route planning has to balance geography with operational constraints such as order density, vehicle or courier availability and service commitments. An algorithm can propose a sequence, but real-world conditions—road restrictions, weather, failed delivery attempts or an overloaded station—require continuous adjustment. The company’s description points to a decision-support system connected to operations rather than a one-time route calculation.
Large models and exception handling
JD Logistics says it has applied logistics large models to dozens of scenarios involving abnormal-control, process automation and intelligent interaction. In this context, an abnormal-control task is about identifying or preventing irregularities in an operating process, such as a shipment or workflow that does not follow the expected pattern.
These models can help surface anomalies, automate routine process steps and provide frontline workers with information in a more usable form. They complement, rather than replace, the forecasting, optimization, mapping and robotics systems that move physical goods. The public disclosures do not provide a single accuracy rate or a quantified labor-saving figure for all of these scenarios.
How a JD Logistics order can flow through the system
The following simplified example shows how the documented components fit together without implying that every customer uses the same configuration.
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- Forecasting: sales data indicates that a product is likely to be needed in a particular region.
- Inventory positioning: the network-planning system assigns stock to a nearby warehouse or recommends replenishment.
- Slotting: the warehouse system places the item in a location suited to its expected movement rate and physical characteristics.
- Order release: when a customer orders, software selects a fulfillment site and creates picking tasks.
- Robot and worker execution: an automated guided vehicle, lifting robot or other equipment presents the item, while a worker or automated station verifies and packs it.
- Sortation and transport: the parcel is sorted and assigned to a line-haul plan based on destination and network capacity.
- Last mile: mapping and route tools assign the delivery station, coverage area and delivery sequence.
- Exception response: scans, status events or anomaly models identify a problem and trigger a revised workflow.
How large JD Logistics is
| Measure | Reported figure | Period and source qualification |
|---|---|---|
| Warehouses | More than 1,600 | At December 31, 2024, in JD Logistics’ annual-report disclosure |
| Delivery stations and service outlets | Approximately 19,000 | At December 31, 2024, in JD Logistics’ annual-report disclosure |
| In-house delivery and operations personnel | More than 370,000 | At December 31, 2024, in JD Logistics’ annual-report disclosure |
| International warehouse footprint | More than 1 million square meters in over 100 bonded, international direct-distribution and overseas warehouses | Across 19 countries and regions at the end of 2024 |
| Nationwide fulfillment-network warehouse area | More than 34 million square meters | JD.com 2025 Form 20-F disclosure |
| Warehouse and delivery employees | 642,940 | JD.com 2025 Form 20-F disclosure; 2025 reporting scope |
| LangzuTech automated warehouses | More than 20 in nearly 20 Chinese cities | By December 31, 2025, according to JD.com’s 2025 results release |
The 2024 and 2025 figures should be read with their reporting labels. “In-house delivery and operations personnel” in the JD Logistics disclosure is not necessarily identical to the “warehouse and delivery employees” category in JD.com’s Form 20-F. The later number also may reflect a broader company reporting scope, so subtracting one from the other would produce a misleading trend.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.External and international supply-chain reach
JDL’s external service is not limited to parcel delivery. Its 80,703 external integrated-supply-chain customers in 2024 indicate a business serving organizations that need warehousing, transportation, inventory and fulfillment coordination. The RMB32.3 billion external integrated-supply-chain revenue figure is a company-reported financial measure for that year, not a promise of savings or service quality for every customer.
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Internationally, the company reported more than 100 bonded, international direct-distribution and overseas warehouses totaling more than 1 million square meters across 19 countries and regions at the end of 2024. Cross-border operations add customs, tax, documentation, carrier and regional service constraints, so the domestic network model cannot simply be assumed to work identically in every country.
What JD.com says its technology foundation includes
JD.com’s 2025 Form 20-F identifies three core areas of supply-chain technology: AI, big-data analytics and cloud computing. It also describes AI scientists and engineers and a smart supply-chain platform with application-level products for JD’s business and ecosystem.
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How to assess JD Logistics’ AI claims
Company disclosures establish the types of systems deployed and the scale of the network, but they do not independently validate every performance outcome. When comparing JDL with another logistics provider, examine the following evidence rather than treating the word “AI” as a performance guarantee:
- Network ownership and density: who controls the warehouses, stations and transportation links, and how close they are to demand.
- Automation coverage: which facilities use automated storage, goods-to-person systems, sorting equipment or robots, and what share of volume passes through them.
- Planning breadth: whether software covers inventory positioning, warehouse execution, transportation and final delivery as one workflow.
- Industry fit: support for bulky products, cold-chain goods, regulated items or other specialized handling requirements.
- Domestic versus cross-border capability: facilities, customs processes and service coverage in the actual markets a customer uses.
- Measured outcomes: independently verified cost, speed, inventory turns, delivery accuracy, capacity utilization and service-quality results.
JDL’s public material is strongest on network scale, named technology components and deployment examples. It provides less universal, independently verified evidence for a single cost reduction, delivery-speed improvement or accuracy rate across the whole network.
Bottom line for readers
JINGDONG Logistics is a full-stack supply-chain operator built around JD.com’s retail logistics infrastructure. Its AI strategy is an integrated operating system: forecasts and optimization decide where inventory and capacity should go; digital twins and analytics model the network; warehouse robots and software execute physical work; mapping tools plan delivery; and large models help automate exceptions and frontline interactions. The company’s 2024 and 2025 disclosures show substantial domestic and international scale and a growing number of automated warehouses, while the exact benefits depend on facility design, product category, geography and the quality of the data and processes connected to the system.
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