JD Industrial (07618) announced interim results, profit of 619 million yuan increased 37.2% year-on-year, and continued to promote the intelligent upgrading of the industrial supply chain

According to the Zhitong Finance App, JD Industries (07618) announced results for the six months ended June 30, 2026. The group achieved revenue of 13.063 billion yuan during the period, up 27.4% year on year; profit for the period was 619 million yuan, up 37.2% year on year; and basic profit per share was 0.23 yuan.

The Group's product sales revenue, which mainly includes MRO products and production materials (BOM) products, increased by 28.2% from RMB 9.6 billion for the six months ended June 30, 2025 to RMB 12.3 billion for the six months ended June 30, 2026. This increase is mainly due to the Group's deepening cooperation with existing key corporate clients, which has prompted these customers to increase their expenses. During this period, the number of key enterprise customers served by the Group also increased from about 11,000 to about 13,000.

The customer business of key enterprises maintained rapid growth in the first half of 2026, mainly due to the continuous strengthening of customer service depth and leading the intelligent upgrading of industrial product procurement numbers. JD Industrial launched standardized solutions such as “SRM full link digital intelligence” and “full management of long-tail materials” to provide customers in different industries with cross-scenario and cross-category supply chain services.

In the first half of 2026, the “Taipu” industrial supply chain solution was upgraded with AI technology. The core modules of the Taipu Solution include procurement demand insight, material standardization, price compliance review, etc. Driven by AI technology, the efficiency of design and delivery has been greatly improved. Solutions no longer rely on the personal experience and efficiency of consultants, and are more standardized and easier to replicate and implement. As a result, the upgraded Taipu solution rapidly increased the number of customers and transaction volume covered by the Group. In the 12-month period ending June 30, 2026, the transaction value retention rate of key enterprise customers reached 119%, continuing to increase the share and stickiness of customers' wallets.

During the reporting period, the Group focused on improving industrial model capabilities, accelerating the integration of AI into business scenarios, empowering different customer groups with intelligent applications, and continuously promoting the intelligent upgrading of the industrial supply chain. At the level of the basic capabilities of the big model, the Group launched the “Baichuan Project” to jointly build a vertical model for the industry with leading brands to transform scattered product manuals and massive data into standardized data sets. The vertical model trained in this way can improve the accuracy of product identification, reduce manual dependency, and improve procurement efficiency. Relying on AI technology and industrial product data assets accumulated by JD Industrial over many years, the Group's standard product database achieved a key breakthrough in intelligent modeling: 30% of standard product data can be automatically generated by AI, increasing the efficiency of product creation by 14.5 times. This means that the industry problem of standardization of industrial products is gradually being overcome, providing a solid data base for accurate decision-making by downstream agents, and an important barrier for JD Industries to maintain its long-term competitive advantage.

Based on these model capabilities, the Group deeply embeds AI capabilities into business scenarios through intelligent devices to accelerate transformation into business value. In the first half of 2026, the company deployed more than 70 smart devices, covering core processes such as procurement, execution, and operation, contributing to the total AI incremental commodity transaction volume (GMV) accounting for 6.5% of the GMV of key enterprise customers, and improving labor efficiency in core positions by 16.6%.