震坤行出席第七届国有企业数智采购与供应链大会:打造工业用品AI基础设施 助力产业协同

This article shares Zhenkun Hang's latest practices and industry insights on AI-powered supply chain upgrading in the industrial MRO space, delivered at the 7th State-owned Enterprise Digital-Intelligent Procurement and Supply Chain Conference. Key takeaways are as follows:

1. Core industry judgment: AI competition has now entered the deep-water zone of industrial application. The industry focus has shifted from competing on model size to solving real-world industrial problems. The core determinant of AI value is completing the "last mile" deployment for industrial use cases, rather than the scale of model parameters.

2. Corporate实践成果: After over two decades of development, Zhenkun Hang has built a supply network covering 32 product categories and more than 2.7 million SKUs, serving over 180,000 manufacturing enterprises. It has self-developed a three-tier industrial AI infrastructure, with multiple scenario-specific agents already deployed at scale across multiple industries.

3. Future industry direction: China's industrial goods industry is transitioning toward deep supply chain collaboration, with competition shifting to the building of comprehensive infrastructure. Open collaboration and co-construction of ecosystems will be the mainstream direction going forward.

This article outlines the latest development trends and brand-building directions in the industrial goods sector, offering actionable insights for industrial goods brands:

1. Industry and consumer trends: AI has now entered the stage of large-scale industrial implementation. The entire industrial goods industry is shifting toward deep supply chain collaboration, and competition now centers on comprehensive supply chain infrastructure capabilities rather than products alone. Central state-owned enterprises and advanced manufacturing have clear demands for more transparent, efficient, and cost-effective procurement, making digital-intelligent transformation a major industry trend.

2. Guidance for product and channel development: Brands can learn from Zhenkun Hang's model to restructure supply chains with AI capabilities, build a global supply network to meet customers' diversified demands, and open up their own capabilities to connect upstream and downstream partners and expand service coverage.

3. Guidance for brand building: Brands can share practical achievements at authoritative industry summits, build dedicated industry knowledge exchange platforms to connect with target customers, build industry influence, and strengthen brand recognition.

This article sorts out new changes and opportunities in the industrial MRO sector, providing multiple insights for industrial goods sellers:

1. Market opportunity assessment: AI implementation in industry has now entered the stage of large-scale deployment. Central state-owned enterprises and manufacturing enterprises of all types have clear demand for digital transformation of procurement and supply chains, leaving large room for industry growth. Competition is increasingly focused on comprehensive supply chain capabilities, and players that invest early in digitalization will gain a competitive edge.

2. Guidance for business models and partnerships: Sellers can leverage the open AI infrastructure and ecosystem resources of mature platforms to cut the cost of their own digital transformation. They can also join industry exchange platforms such as "MRO Experts" to connect with high-intent customers and build industry reputation.

3. Risk warning: Do not blindly follow the large model hype. The core value of AI lies in solving real business problems. Sellers need to focus on their own business scenarios and deliver on the last mile of implementation to actually gain returns and avoid losses from concept-driven overinvestment.

This article offers multiple actionable insights for manufacturing factories pursuing digital transformation and supply chain cooperation:

1. Product and supply chain demand direction: The pain points in procurement and supply chains are particularly prominent as factories advance digital transformation, with strong demand for intelligent solutions in material governance, intelligent selection, inventory optimization, and procurement collaboration. AI can effectively address these pain points, helping factories achieve more transparent, efficient, and cost-effective procurement.

2. Business cooperation opportunities: Industrial goods service platforms like Zhenkun Hang have built mature industrial AI infrastructure and open up their capabilities to external partners. Factories can access these mature vertical industry AI services instead of building their own digital systems from scratch, reducing transformation costs.

3. Insights for digital transformation: Factories do not need to blindly pursue general-purpose large models when implementing AI. Prioritizing access to mature industrial AI infrastructure built for vertical sectors, and rolling out applications tailored to your own production and procurement scenarios, will deliver results faster and help avoid common transformation missteps.

This article shares the latest trends, pain points and solutions in the industrial goods supply chain service space, providing valuable insights for relevant service providers:

1. Industry development trends: AI competition has entered the deep-water zone of industrial implementation, and the industrial goods industry is transitioning toward deep supply chain collaboration. Unlocking the value of industrial AI cannot be achieved by a single company; it requires open collaboration across the industrial chain. There is clear market demand for AI infrastructure specifically adapted to industrial scenarios.

2. Core customer pain points: General-purpose large models have obvious shortcomings in industrial scenarios, including problems such as missing data, cognitive bias, and "industry hallucinations". They cannot meet the complex, multi-scenario demands of industrial goods supply chains, and customers need implementable solutions that solve real problems.

3. Guidance for solution development: Providers can learn from Zhenkun Hang's three-tier architecture: a dedicated underlying data engine, a middle-tier vertical large model, and upper-layer scenario-specific agents, to build a full-stack closed-loop solution. In addition, opening up your own capabilities to collaborate with upstream and downstream partners on ecosystem building, and constructing knowledge exchange platforms to accumulate industry expertise and connect with customers are also recommended paths.

This article shares the latest innovation practices of industrial goods service platforms, providing multiple insights for peer platform operators:

1. Market demand overview: Enterprise customers across sectors now have strong demand for digital and intelligent upgrading of procurement and supply chains. Customers are no longer satisfied with basic product supply; they expect platforms to provide complete intelligent procurement and supply chain solutions that help them cut costs and improve efficiency.

2. Guidance for platform operation and innovation: Platforms can learn from Zhenkun Hang's approach: focus on pain points in industrial scenarios, build full-stack AI infrastructure covering data governance, model training and scenario application, incubate intelligent products adapted to different core business scenarios, and achieve large-scale deployment. In parallel, platforms can open up their AI capabilities to collaborate with upstream and downstream on ecosystem building, and construct industry knowledge exchange platforms to accumulate industry resources and expand ecosystem influence.

3. Risk mitigation: Platforms should avoid falling into the trap of "parameter size obsession". Focusing on real industrial problems and delivering on the last mile of implementation is the only way to unlock real commercial value and avoid risks from concept-driven investment.

This article shares the latest industry trends and innovation practices of AI in the industrial goods supply chain sector, providing rich research materials for industry researchers:

1. New industry trends: The landscape of AI industry competition has shifted, from early competition over model technology to competition over industrial implementation, marking AI's official entry into the deep-water zone of industrial application. China's industrial goods industry is transitioning toward deep supply chain collaboration, with competition shifting to the construction of comprehensive infrastructure. China is now home to the first vertical AI large model for industrial goods that has completed official registration with the Cyberspace Administration of China, which has already achieved large-scale commercial deployment of multi-scenario intelligent agents.

2. Unresolved industry issues: While AI technology efficiency has improved dramatically, industry benefits have not grown in lockstep. General-purpose large models cannot adapt to the demands of industrial scenarios, and unlocking the value of industrial AI cannot be achieved by a single firm—it requires collaboration across the industrial chain. All these are new problems that the industry currently needs to solve.

3. Innovative business model: Zhenkun Hang has pioneered a new "physical + digital dual base + open AI infrastructure + ecosystem collaboration" model, which exports replicable integrated supply chain solutions and provides a researchable sample for digital transformation of the broader industrial goods industry.