Next-generation intelligent manufacturing is imposing more specific requirements on manufacturing enterprises.
On October 20, at a press conference held by the State Council Information Office, Wang Weiming, Chief Engineer of the Ministry of Industry and Information Technology (MIIT), stated that the key to developing next-generation intelligent manufacturing lies in promoting the deep integration of AI technology with the entire manufacturing chain. MIIT is currently researching and drafting relevant policy measures to accelerate this integration.
This deployment involves the tiered cultivation of smart factories, innovation in smart equipment, promotion and application of smart technologies, and international cooperation. Notably, AI applications are not limited to R&D design and production manufacturing but also extend to operations management and after-sales service. Industrial agents must undergo application validation before being gradually promoted.
From a policy perspective, AI in manufacturing is moving from isolated applications toward deeper business integration. However, for enterprises, the real question is no longer "whether to use AI," but where to start, how to effectively implement it, and whether it can create actual value.
01 From Technology Application to Full-Chain Integration: What Hurdles Must AI Overcome?
With the rapid development of large model technologies, capabilities such as intelligent Q&A, document generation, and knowledge retrieval are increasingly integrated into daily enterprise operations. However, moving from general office scenarios to specialized manufacturing environments presents more complex challenges for AI.
Take equipment maintenance as an example: identical fault phenomena may require different handling methods depending on the equipment model, operating environment, and process conditions. The same applies to R&D design; determining whether a technical parameter is applicable often requires comprehensive judgment based on product models, material standards, and historical solutions.
The fact that AI can quickly provide answers does not mean it truly understands manufacturing operations.
If professional knowledge support is lacking, even if a large model retrieves relevant content, it may provide inaccurate suggestions due to issues such as mismatched document versions or unclear applicability conditions.
For engineers, finding an answer is only the first step. More importantly, they need to verify the reliability of information sources, determine if the content is applicable to the current scenario, and ensure that related bases are traceable.
Therefore, promoting the deep integration of AI with manufacturing operations cannot stop at merely connecting models or building intelligent Q&A systems. It requires enabling AI to understand enterprise-specific professional knowledge and apply it to concrete business tasks.
Meanwhile, scaling from isolated applications to widespread adoption also faces challenges. Different product lines, equipment models, and job roles have varying requirements for knowledge content, access permissions, and business processes.
Success in one scenario does not guarantee that a single methodology can be directly replicated across others.
This aligns with the progressive approach of tiered cultivation and gradual promotion inherent in next-generation intelligent manufacturing. For enterprises, rather than pursuing immediate coverage of all business areas, it is better to first solidify the foundation of professional knowledge, validate effectiveness in specific scenarios, and then gradually expand the scope of application.
To address these challenges, 360 Yifang Intelligent has launched the "AI Knowledge Management Solution for the Manufacturing Industry." This solution focuses on typical scenarios such as R&D design, equipment maintenance, and pre-/after-sales services, outlining a six-step implementation method to provide reference for enterprises exploring intelligent upgrades.
02 From Data Accumulation to Knowledge Empowerment: Smart Manufacturing Requires a Stronger Foundation
Promoting the full-chain integration of AI and manufacturing relies on high-quality industrial data and the long-term accumulation of professional knowledge within enterprises.
In this deployment, MIIT proposed building high-quality industrial datasets around core segments like R&D design and production manufacturing, and developing advanced, applicable vertical-domain large models and industrial-specific models.
For manufacturing enterprises, data records equipment operation and production processes, while product specifications, process documents, technical manuals, and expert experience provide the professional basis for analyzing problems, determining causes, and formulating solutions.
In reality, manufacturing enterprises do not lack knowledge accumulation. From product drawings and design specifications to maintenance work orders and quality records, extensive technical materials permeate the entire process of R&D, production, maintenance, and service.
However, the current situation is that although enterprises have accumulated vast amounts of knowledge, they may not utilize it efficiently.
R&D personnel switching between multiple systems repeatedly to find historical solutions; maintenance engineers relying on senior experts when encountering complex faults; and after-sales staff coordinating with multiple departments to resolve customer issues—these are common occurrences.
Behind these issues lies the long-term dispersion of professional knowledge across various systems, files, and employee experiences, making it difficult to retrieve quickly and reuse effectively.
In the past, traditional knowledge bases primarily addressed file storage, sharing, and retrieval. As intelligent agents begin to participate in manufacturing operations, simply making files "findable" is no longer sufficient; the priority is ensuring knowledge is "used correctly."
This requires enterprises to further connect knowledge associations, forming an organized, traceable, and continuously updated knowledge system that integrates equipment information, technical specifications, historical cases, and expert experience, thereby providing reliable business evidence for intelligent agents.
When dispersed professional knowledge can be accurately understood and retrieved on demand, expertise previously dependent on a few individuals can gradually transform into reusable organizational capabilities.
Moving from knowledge management to knowledge empowerment is the crucial foundation for manufacturing enterprises to transition AI from isolated applications to business collaboration.
03 Initial Investment Should Focus on Verifiable Tasks
Next-generation intelligent manufacturing covers a broad scope, and enterprises do not need to address all aspects simultaneously in their first project.
When choosing a starting point, prioritize business tasks where knowledge needs are concentrated, usage frequency is high, and results are easy to verify.
R&D Design: Accelerating Technical Documentation Support for Product Innovation.
Engineers need to search for current standards, design specifications, and historical solutions to confirm design constraints. AI can assist in locating relevant materials and citing sources, which are then reviewed by professionals. Pilot effectiveness can be assessed based on material retrieval time, version errors, and redundant searches.
Equipment Maintenance: Transforming Veteran Experience into Reusable Knowledge Assets.
By linking maintenance manuals, historical work orders, and fault cases to specific equipment, AI assists in organizing troubleshooting bases and handling recommendations, helping engineers locate issues faster. After maintenance results are audited, they can feed back into the knowledge base, promoting continuous experience accumulation.
Pre- and After-Sales Service: Supporting Efficient Customer Service with Professional Knowledge.
Pre-sales personnel need to organize product parameters and technical solutions, while after-sales staff query user manuals, service policies, and fault cases. AI-assisted retrieval and material organization reduce cross-departmental communication loops, improving technical support and service response efficiency.
The commonality among these three scenarios is that AI first provides reliable, verifiable professional evidence, leaving the final judgment to business personnel.
For manufacturing enterprises, evaluating AI applications should not focus solely on response speed or call volume. Attention must be paid to whether material retrieval time has shortened, problem resolution has become more efficient, and expert experience can be continuously reused.
Applications involving production scheduling, parameter tuning, or equipment control require stable advancement combined with specialized industrial models, business systems, and security verification mechanisms.
Only by starting from real business problems and achieving verifiable improvements through technology can smart manufacturing move from pilot exploration to scaled application.
04 Integrating Professional Knowledge Deeply into Manufacturing Operations
From R&D design to equipment maintenance, and then to pre- and after-sales service, although the AI application scenarios differ, all rely on the support of professional knowledge.
As the advancement of next-generation intelligent manufacturing accelerates, knowledge bases are evolving from traditional file storage and retrieval tools into knowledge infrastructure supporting intelligent agent applications.
In this direction, 360 Yifang Intelligent has already initiated application explorations in the manufacturing industry. Its AI Knowledge Management Solution for the Manufacturing Industry, based on the 360 AI Knowledge Base, integrates dispersed technical documents, equipment manuals, and historical cases. Through intelligent parsing, knowledge association, and continuous governance, it enables professional knowledge to be understood and retrieved more accurately.
Building on this, combined with role-specific intelligent agents, knowledge is further integrated into business segments such as R&D design, equipment maintenance, and customer service, driving enterprises from knowledge management toward business intelligence.
What are the actual results? Let us look at the practical outcomes of several manufacturing projects.
Shenzhen Airlines | Intelligent Assistance for Aircraft Maintenance
By integrating maintenance manuals, historical work orders, and maintenance records, an aircraft maintenance agent was built to assist engineers in locating faults and obtaining maintenance evidence. Project materials indicate that fault diagnosis accuracy improved from 45% to 90%, and diagnosis time decreased by 50%, shifting maintenance troubleshooting from experience dependency to intelligent assistance.
Shanghai Electric | R&D Design and Fault Diagnosis
An industrial knowledge hub was constructed, integrating design specifications, technical drawings, maintenance records, and project experience to support R&D design and equipment fault diagnosis. Project materials show that knowledge retrieval time was reduced by 70%, R&D design time by 30%, and the accuracy of knowledge service Q&A exceeded 85%.
A Certain Electronic Appliance Manufacturer | Quality Analysis and Experience Reuse
Linking product designs, test data, quality issues, and corrective measures allowed historical experience to enter the R&D and quality management processes. Application effects listed in project materials include: knowledge retrieval efficiency increased by over 70%, experience reuse efficiency by over 50%, and problem closure speed by over 30%.
A Certain New Energy Manufacturer | Cross-Regional Knowledge Collaboration
Facing four factory locations in Shanghai, Shangrao, Haining, and Malaysia, a unified knowledge hub was built. Through cross-domain knowledge retrieval, multi-center deployment, and permission management, it provided cross-regional knowledge sharing and collaborative support for over 15,000 group employees.
These practices demonstrate that for AI to truly deliver value in manufacturing, it requires not only model capabilities but also deep integration of professional knowledge with business scenarios. Only when knowledge can be continuously accumulated, accurately retrieved, and reused across scenarios can technological application evolve from isolated efficiency gains to organizational collaboration.
The implementation of next-generation intelligent manufacturing does not need to start with comprehensive transformation. Instead, it can begin with a verifiable business scenario, gradually solidifying the knowledge foundation and expanding the scope of application to let technology create real business value.
From super individuals to super organizations, the value of AI lies not just in improving personal efficiency, but in transforming the professional knowledge accumulated by enterprises into sustainable, reusable organizational capabilities.
For manufacturing enterprises planning AI pilots, the "AI Knowledge Management Solution for the Manufacturing Industry" launched by 360 Yifang Intelligent outlines typical application scenarios and a six-step implementation method, providing reference for exploring paths to intelligent upgrade.