How ZKH Leveraged NebulaGraph to Achieve 15% Gains in Core Business Areas

Chunhui Huang is a Senior Development Engineer of ZKH Big Data Platform. Since 2024, he has been promoting the deep integration of NebulaGraph into multiple business scenarios at ZKH, including intelligent procurement decisions and supply chain risk control systems. This article introduces these application practices.

About ZKH

ZKH is an online industrial products service platform dedicated to digital transformation. We aim to provide customers with transparent, efficient, and low-cost one-stop industrial product solutions. Leveraging digital management tools and big data technologies, we collaborate with partners to optimize the entire supply chain, covering the entire process from procurement and manufacturing to delivery. Additionally, ZKH offers high-quality industrial product selection services. Combined with self-built warehousing and logistics system, we create an efficient and reliable industrial product procurement and delivery experience for online and offline customers.

Business Challenges

Before adopting NebulaGraph, our main business pain points were as follows:

• Data Scale and Complexity: Business expansion led to rapid growth of complex relational data. Traditional databases struggled with efficient storage, querying, and real-time analysis.
• Relational Database Limitations: Significant performance bottlenecks in multi - table joins made it impossible to support real - time analysis of complex relational data.
• Data Value Realization Difficulty: Applications of complex relational data faced challenges in data modeling, algorithm optimization, and effectiveness verification.

NebulaGraph Selection and Architecture

Why NebulaGraph?

• Efficient Relational Expression: Graph structures intuitively represent entities and their relationships. This is suitable for ZKH's complex supply chain network.
• Powerful Path Analysis: Supports efficient graph traversal algorithms for rapid path analysis.
• Real - time Queries and Responses: Capable of high - concurrency, low - latency queries, supporting real - time recommendations and risk warnings.
• Scalability and High Performance: Distributed and storage - computing separated architecture, supporting horizontal scalability.

Technical Architecture

• Data Collection: Data from ERP, MySQL, etc., is synchronized to Alibaba Cloud MaxCompute via DataX or Flink - CDC. Client behavior data is collected via SDK and uploaded to Alibaba Cloud DataHub. Unstructured data is stored via OSS.
• Data Processing: Offline computing uses MaxCompute and Spark for large - scale batch data processing. Real - time computing uses Flink for stream data processing.
• Data Storage: Two clusters are deployed. The online service cluster handles real - time queries, and the graph algorithm cluster deals with complex graph algorithms.
• Data Services: API gateway provides unified interfaces. Customized APIs meet different business needs.

Application Practices

Precision Recommendation

Previously, our precision recommendation in e - commerce faced challenges. We planned to introduce NebulaGraph to build a smarter recommendation system.

• Data Input and Modeling: User behavior data and product data are used as inputs, cleaned and transformed, and loaded into NebulaGraph for graph modeling.
• Graph Database Structure: A graph structure centered on products and users is constructed. Relationships between users and products are established through behavioral trajectories.
• Real - time Feature Extraction and Recommendation Optimization: Through NebulaGraph's nGQL query language, we can real - time extract user behavioral features. These features are used to optimize the recommendation process.

Outcomes

After introducing NebulaGraph, we achieved a 15% increase in search recommendation click - through rates and a 5% improvement in order conversion rates. Moving forward, we aim to expand into more relational data dimensions and incorporate AI real - time learning mechanisms.

Supply Chain

ZKH's supply chain is product - centered and driven by customer demand. With the support of NebulaGraph, we have built an efficient and visual supply chain network.

• Procurement Decisions: After a customer order is submitted, the system intelligently selects the most suitable supplier channels within NebulaGraph, which can help to optimize the procurement costs.
• Inventory Management: Products are managed in inventory based on various attributes, such as batch, quantity, and status. Real - time data sharing lets warehouse staff instantly check product status across warehouses, improving stocking operations, reducing stockpiling, and enabling joint inventory management and smart replenishment.
• Intelligent Fulfillment: The system optimizes the entire process from customer orders to logistics delivery. With NebulaGraph, intelligent management not only enhances the inventory turnover but also reduces warehousing costs and optimizes the overall supply chain efficiency.
• Accurate Delivery: We utilize real-time logistics sharing and NebulaGraph's shortest path algorithm to optimize delivery routes, ensuring that products can be delivered to customers on time and efficiently.

Outcomes

• Customer Satisfaction: Transparent logistics info, on - time deliveries, and efficient order fulfillment significantly enhanced customer satisfaction, with a 20% drop in after – sales work orders.
• Intelligence and Automation: Graph algorithms based on NebulaGraph, such as the shortest path algorithm, collaborative filtering algorithm, etc., can automatically optimize inventory, procurement, fulfillment and delivery, reducing manual work. The intelligent supply chain system via NebulaGraph quickly responds to market shifts, boosting competitiveness.
• Cost and Efficiency: Optimizing inventory, procurement, and delivery has reduced operational costs, raised stock availability by 15%, and cut stagnant inventory by 10%.

Future Plans

Knowledge Base Management System

We're working with the NebulaGraph GenAI team to build a knowledge base management system for ZKH. We aim to integrate structured and unstructured data efficiently. This will empower our internal teams, including sales, customer service, and operations staff, to use the knowledge base for intelligent Q&A. They can quickly access the info they need, which will make work more efficient.

Intelligent Risk Control System

Leveraging NebulaGraph, we are exploring to build a unified customer-supplier risk-control network that fuses business registration, credit, fulfillment, and news data. At transaction time, the system instantly analyzes behaviors and legal risks, extracts risk features via nGQL, and applies algorithms to flag fraud, cutting supply-chain exposure.

Summary

The implementation of NebulaGraph at ZKH has achieved significant results in business scenarios such as precision recommendation and supply chain optimization. In the future, ZKH and NebulaGraph will further explore the deep application of knowledge bases and intelligent risk control.