供应链场景落地加生态构建 京东工业以大模型驱动产业数智化发展

This article highlights JD Industrial's latest developments and strategic布局 in industrial large models, and outlines the overall trends of digital and intelligent transformation in China's industrial sector. Key takeaways are as follows:

1. Industry trends: According to ebrun Research Institute's *2025 Industrial Internet Development Report*, industrial AI has entered the stage of large-scale application, covering the entire value chain from R&D and production to procurement. Vertical industrial platforms serve as the core carrier for AI implementation, and the industry is shifting from a traditional linear supply chain to a networked ecosystem.

2. Latest progress from JD Industrial: The company has launched nearly 40 AI agents serving more than 3,000 key enterprise clients. Having completed the layout of its 1.0 phase, which focused on single-point tool-enabled empowerment, JD Industrial is now transitioning to its 2.0 "AI expert" phase, where models can independently complete full-scenario tasks and proactively identify business demands. It has also launched Baichuan Program, China's first industrial large model ecosystem initiative, to facilitate joint ecosystem development across data, model and application layers.

3. Core implementation model: JD Industrial adopts a dual-driven approach combining "industrial large models + supply chain applications", which breaks down complex digital transformation challenges into actionable scenario-specific tasks, delivering tangible cost reduction and efficiency improvement across the industry.

This article outlines current trends in industrial digital and intelligent transformation, clarifying development directions and partnership opportunities for industrial brands. Key insights include:

1. Industry trends and structural shifts: Industrial AI has entered large-scale application across the entire industrial chain, and deep value chain restructuring is driving the shift from linear supply chains to a networked ecosystem. Brands can leverage the digital capabilities of vertical industrial platforms to break through bottlenecks in product standardization and brand building to achieve upgrading.

2. Accessible partnership ecosystem: JD Industrial has launched Baichuan Program, China's first industrial large model ecosystem initiative, which opens up its capabilities to upstream industry partners. It provides end-to-end support ranging from capability sharing and resource backing to business opportunity conversion, with a proven successful case of co-developing a practical electrical industry large model with Delixi Electric.

3. Core value proposition: By participating in the co-built ecosystem, brands can leverage large model capabilities to solve core pain points including non-standardized product parameters and mismatched supply-demand information, improve product standardization, optimize efficiency in scenarios such as product selection and purchasing guidance, and achieve end-to-end cost reduction and efficiency improvement.

This article shares the latest growth opportunities brought by digital transformation in the industrial supply chain, laying out clear development directions and accessible cooperation models for industrial product sellers. Key takeaways are:

1. Policy and industry winds: China is advancing national initiatives including "Industrial Data Foundation Building" and "Integration of Digital and Physical Industrial Systems". Industrial AI has entered the large-scale implementation stage, and deep industrial upgrading is generating substantial new growth opportunities, making digital transformation an inevitable path forward.

2. Clear opportunities and support policies: JD Industrial's Baichuan Program opens its ecosystem to upstream partners. Sellers that join the ecosystem can access large model technology, data resource support and business conversion assistance to address their core pain points: non-standard industrial product parameters, complex long-tail product categories and mismatched supply-demand information, ultimately reducing operating costs.

3. A proven transformation roadmap: JD Industrial breaks down complex digital transformation into actionable, scenario-level tasks. Sellers can follow this framework to start transformation from single-point scenarios and upgrade incrementally, lowering entry barriers and boosting labor and operational efficiency via AI.

This article summarizes the latest practical progress in industrial digital transformation, offering clear insights for factories seeking to advance digital upgrading and unlock new business opportunities. Key points include:

1. Industry demands and development direction: Industrial AI already covers the full chain from R&D, production to procurement, making digital transformation an inevitable industry trend. Large model technology can specifically solve core industrial pain points including fragmented and unstructured data and mismatched supply-demand information, helping factories cut costs and improve efficiency.

2. Accessible business opportunities: JD Industrial's Baichuan Program invites upstream factories to join its co-built large model ecosystem. Participating factories gain access to large model technology output, data capability support and business opportunity resources, which help them build standardized product libraries, improve product standardization and match downstream demand more accurately.

3. Insights for digital transformation: Factories can follow JD Industrial's phased transformation path: start with lightweight AI tool applications for single-point scenarios to address localized pain points, then gradually expand to end-to-end AI empowerment. Breaking down complex transformation into scenario-level tasks reduces implementation difficulty and enables steady upgrading.

This article clarifies current development trends, core customer pain points and proven implementation solutions for digital transformation in industrial supply chains, providing important reference for service providers growing their businesses. Key insights are as follows:

1. Industry development trends: According to ebrun Research Institute, industrial AI has entered large-scale application, with vertical industrial platforms emerging as the core carrier for AI implementation. The industry is shifting from traditional linear supply chains to a networked ecosystem, making ecosystem co-construction the core future development direction with substantial untapped service opportunities.

2. Core customer pain points: The industrial sector broadly struggles with non-standardized industrial product parameters, complex long-tail categories, and mismatched supply and demand information. Upstream industries also face the challenge of fragmented, unstructured data that is difficult to coordinate, which slows industry-wide large model development and reduces collaborative value. All these pain points represent core entry points for service providers.

3. A proven reference solution: JD Industrial has validated a dual-driven "industrial large model + supply chain application" model, building a systematic architecture of "industry large model empowerment, domain-specific small model execution, and application agent collaboration". It integrates resources via ecosystem co-construction and breaks down complex transformation tasks, making it a solid reference for service providers designing their own solutions.

This article shares JD Industrial's latest practice in large model implementation and ecosystem building as a vertical industrial platform, offering a reference for peer platforms advancing digital operations and ecosystem development. Key takeaways are:

1. Core enterprise demands from platforms: All types of industrial enterprises have strong demand for digital transformation to cut costs and improve efficiency, but most lack sufficient technical capabilities and data resources to transform at low difficulty. They require platforms to provide systematic solutions and ecosystem support to lower transformation barriers.

2. Reference for platform operation and partner recruitment: JD Industrial has developed a dual-driven "industrial large model + supply chain application" model, advancing AI capability development in phases from single-point tools to full-scenario AI experts. It also launched the Baichuan Program to build a co-created ecosystem across data, model and application dimensions, attracting partners via capability sharing and resource support, with proven successful cooperation paths in niche segments.

3. Risk mitigation guidance: When developing large model applications, platforms must avoid discussing technology in isolation from actual industrial scenarios. Large model capabilities must be integrated with deep, segment-specific industry scenarios, and transformation tasks must be broken down into implementable scenario-level tasks to deliver tangible value and avoid the risk of failed technology implementation.

This article discloses the latest industry developments in industrial supply chain large models, proposes an innovative business model and implementation path, and provides first-hand case material and research directions for industrial research. Key insights are as follows:

1. Latest industry developments: Industrial AI has entered the large-scale application stage. China's industrial large model sector has completed the 1.0 layout of single-point tool empowerment, and is now transitioning as an industry to the 2.0 "scenario-based AI expert" phase. The country has seen the launch of its first industrial large model ecosystem initiative, the Baichuan Program, opening a new phase of industry-wide collaborative large model ecosystem development.

2. Unresolved industry challenges: Research shows that upstream industrial sectors face a structural problem of fragmented, unstructured data that is difficult to coordinate, which slows large model development and weakens collaborative value. In addition, the industrial product sector continues to grapple with long-standing core pain points: non-standard parameters, complex long-tail categories, and mismatched supply-demand information, all of which require further exploration of solutions.

3. Sample for innovative business model research: JD Industrial has developed a new dual-driven cost reduction model combining "industrial large models + supply chain applications", building a systematic solution of "industry large model empowerment, domain-specific small model execution, and application agent collaboration". It drives industry-wide cost reduction and efficiency improvement via three-dimensional ecosystem co-construction, providing a high-quality first-hand research sample for research on industrial internet and industrial large models.