Currently, the global manufacturing sector is at a critical stage of deep integration between a new round of technological revolution and industrial transformation.
Artificial intelligence has evolved from a supplementary tool into an inclusive infrastructure that drives profound transformation in manufacturing.
As the backbone of China’s national economy, the manufacturing sector serves as the core battleground for smart manufacturing.
By 2025, the scale of China’s core AI industry is projected to exceed 1.2 trillion yuan, with more than 6,200 related enterprises.
Manufacturing enterprises above a certain scale will adopt AI technology at a rate exceeding 30%, and the country will have built 43,000 smart factories.
This solid industrial foundation strongly supports the integrated development of AI and manufacturing as it enters a new phase.
Value creation in manufacturing spans the entire process from product design to delivery and operations and maintenance.
This article focuses on five core areas—R&D and design, quality inspection, production scheduling, equipment maintenance, and warehousing and logistics—and analyzes typical AI application scenarios within each.
These five scenarios each fulfill distinct functions and integrate seamlessly, providing a comprehensive overview of how AI empowers the entire manufacturing value chain.
Five Key Areas Where AI Empowers the Manufacturing Industry
AI-Assisted R&D: A Paradigm Shift from Physical Trial and Error to Virtual Validation
Long R&D cycles and significant interdisciplinary collaboration challenges characterize the automotive manufacturing industry.
It typically takes 3 to 5 years for a new vehicle model to progress from design to final approval, and adjustments to the design can easily trigger a chain reaction of modifications.
To address this industry challenge, Alte Automotive Technology Co., Ltd. has established an “AI + Digital Intelligence Creation” product system, deeply integrating artificial intelligence into the entire automotive R&D process.
This system comprises three core modules:
First, the “Yuanfang” automotive R&D knowledge model, which leverages three core capabilities—drawing analysis, solution generation, and intelligent review—to shorten the design iteration cycle from several weeks to a matter of hours;
The second is the “Yufeng” aerodynamics AI system. It is trained using massive volumes of engineering data.
This system can predict drag in real time in the early design phase.
It can also generate compliant contours via reverse engineering. This shortens the drag development cycle by two months.
Third is the styling AI “Taiyi” 3.0, which has evolved from a single sketching tool into a comprehensive AI R&D design platform supporting 3D generation, video generation, and multimodal collaboration.
AI Visual Quality Inspection: Technological Evolution from Manual Visual Inspection to Intelligent Recognition
Traditional quality inspection technologies in screen manufacturing suffer from issues such as poor imaging quality, insufficient samples, and limited adaptability.
To address these bottlenecks, a certain company leveraged its technical expertise in the field of visual inspection to independently develop an intelligent visual defect detection system.
This product falls under the category of “intelligent software applications” and provides algorithm- and platform-based software services.
By integrating multimodal imaging, defect sample generation technology, and a lightweight neural network architecture—combined with a mutually supervised expert system—it significantly enhances the ability to identify complex defects.
This solution reduces equipment requirements for lighting and product appearance consistency.
Following its implementation, the company has cut its quality-inspection-related maintenance costs by more than 30% and greatly boosted overall inspection stability.
AI-Powered Production Scheduling: A Management Transformation from Experience-Based Scheduling to Algorithm-Driven Management
High product variety and small batch sizes characterize the electronics and electrical appliance industry, lowering efficiency and delaying exception responses under traditional manual production scheduling.
This problem needs to be solved. A certain enterprise has developed a collaborative technology.
This technology has multimodal, multi-configuration and multi-structural characteristics.
It upgrades the production scheduling model. The scheduling mode shifts from “experience-based scheduling” to “algorithm-driven” scheduling.
At the core of this technology is a scheduling engine built on multimodal data fusion and agent collaboration.
It breaks down data silos in systems such as ERP, MES, and WMS in real time, aggregating information on order priorities, equipment status, material inventory, and process routes into a unified digital production mirror.
This enables the system to automatically generate optimal production plans and respond rapidly to unexpected situations.
Since the company implemented the system, it has cut order response time from 30 minutes to 5 minutes, lifted equipment utilization from 65% to 92%, and shortened production debugging cycles by 60%.
Starting with the electronics and electrical appliances sector, practitioners have successfully extended this technology to 75 sub-industries, including shipbuilding and semiconductors.
AI-Powered Predictive Maintenance: A Paradigm Shift from Scheduled Maintenance to Predictive Operations
Production equipment in the steel industry operates under high-load conditions for extended periods, and the traditional model of scheduled maintenance and emergency repairs has consistently struggled to balance downtime losses with operational costs.
Baowu Smart Maintenance and Baowu Heavy Industry have combined decades of operational experience with Industrial Internet of Things (IIoT) technology to establish a three-tier intelligent operations and maintenance system.
By integrating large language models, they have achieved complementary micro-level diagnostics and macro-level decision-making.
The system uses multidimensional sensors to collect real-time equipment data, builds equipment health assessment models, and pushes failure risk alerts by severity level, thereby shifting the O&M model to on-demand maintenance.
Currently, the system can accurately identify 14 types of typical mechanical failures with diagnostic response times measured in seconds.
It has served 525,000 online pieces of equipment, achieving a failure warning accuracy rate exceeding 90%.
AI-Powered Smart Logistics: The Evolution from Manual Sorting to Automated Delivery
The home appliance manufacturing industry deals with a wide variety of materials and faces significant pressure regarding warehouse turnover.
Traditional manual warehousing models are labor-intensive, require a long training period for new employees, and often lead to issues such as production halts due to material shortages and inventory backlogs.
Midea’s Jingzhou refrigerator factory has established a five-tier cloud-based intelligent logistics and warehousing system, enabling unmanned delivery from the warehouse to the production line.
The facility uses a 5G network to dispatch AGVs for material transport and integrates MES barcode scanning, RFID, and visual recognition technologies to collect real-time production data, ensuring end-to-end product traceability and intelligent material scheduling.
The storage of finished products is automated through the use of unmanned forklifts paired with intelligent algorithms.
The factory has completed this intelligent upgrade. Its overall production efficiency has risen by 52%.
Production cycles have been cut by 25%. The production line can now manufacture one refrigerator every 1.5 seconds.
Meanwhile, warehouse logistics operations have undergone comprehensive optimization.
Real-World Challenges in the Practical Implementation of “AI+ Manufacturing”
National policy guidance and market demand drive artificial intelligence. Manufacturers have deployed it across a wide range of scenarios.
This technology offers solid support for manufacturing enterprises. It helps enterprises cut costs, boost efficiency and unlock greater growth potential.
However, to achieve widespread adoption of the technology and promote the deep integration of AI with manufacturing, there remain numerous practical challenges in the implementation process that urgently need to be addressed.
Computing Power Allocation and Transmission Systems Require Improvement
Computing power serves as the core foundation supporting the operation of industrial AI.
Currently, the industry is generally plagued by issues such as uneven allocation of computing resources and low utilization efficiency.
The computing power of intelligent devices at the factory edge is mostly below 10 TOPS, making it impossible to run highly complex AI models.
Although cloud computing power reserves are ample, overall utilization remains below 30% due to constraints such as network conditions and interface specifications.
Industrial scenarios impose stringent requirements on the real-time nature, integrity, and cost-effectiveness of data transmission, yet existing transmission systems cannot adequately address these multiple demands.
Remote data transmission suffers from high latency. The related operation costs are also high.
These problems may lead to the loss of critical production information.
It is therefore hard to build a complete closed loop. This closed loop is required for real-time operations.
Examples of such operations include production, quality inspection, and operations and maintenance.
Data Security Risks Hinder Data Sharing
Artificial intelligence (AI) cannot empower the manufacturing sector without the support of massive amounts of industrial data.
Production parameters, process formulas, equipment operation data, order information, and operating costs are all core commercial assets of manufacturing enterprises.
Since model training and operation rely on large volumes of data, companies that share their core data also face a high risk of data breaches.
The Beijing News’ Beike Finance released the “Survey on Entrepreneurs’ Use of Artificial Intelligence”. The survey shows relevant statistics of enterprises.
Among the surveyed companies, 57.81% cited “commercial data leaks” as the main pain point for AI adoption.
This makes data asset security the primary consideration. Enterprises must take this factor into account when deploying artificial intelligence.
This also reflects companies’ high level of vigilance regarding potential risks associated with AI data access and multi-model interactions.
Many factors impose constraints on industrial data sharing. These factors include trade secret protection, the definition of data ownership, and cross-border data flows.
For this reason, most companies hold a specific attitude toward industrial data.
We can summarize their attitude as “unwilling to share, afraid to share, and unable to share”.
This situation further widens industry data barriers. It also aggravates the problem of corporate data silos.
For security reasons, an increasing number of manufacturing companies are choosing to deploy AI systems in private environments, strictly adhering to the security principle that data “must not leave the factory.”
Shortcomings in the Industrial Data Governance System
High-quality, reusable industrial data is a prerequisite for the practical application of AI. However, systems within enterprises—such as ERP, MES, PLM, and WMS—are often built in phases by different vendors.
This results in inconsistent data standards, varying coding rules, and differing interface protocols, creating severe “data silos.”
Items such as material codes, equipment numbers, and process parameters cannot be effectively matched across systems, making it difficult for AI models to integrate data across systems.
Enterprises generally lack awareness of metadata management; they do not annotate production data with key information such as source, units of measurement, and process descriptions.
As a result, AI struggles to interpret the business context behind the data, ultimately causing algorithms to become disconnected from actual business scenarios.
On the production floor, issues such as insufficient sensor deployment, low equipment connectivity rates, and infrequent data collection persist, leaving vast amounts of industrial data idle.
Multiple problems have become increasingly prominent. These include data silos, insufficient model generalization, and inefficient scenario adaptation.
Meanwhile, data governance development lags behind demand. This lag forms a major bottleneck.
It prevents “AI+Manufacturing” from expanding from benchmark pilot projects to large-scale popular application.
The Industry Chain Collaboration Mechanism Is Not Yet Fully Developed
For artificial intelligence to be applied on a large scale in the manufacturing sector, collaboration and coordination across the entire industry chain are essential.
Currently, most enterprises upstream and downstream in the industry chain operate independently, resulting in poor information exchange and a lack of collaboration mechanisms.
These information barriers directly lead to a series of problems, including difficulties in supplier selection, obstacles to end-to-end product traceability, uncontrolled order delivery cycles, and inventory backlogs.
As a result, enterprises cannot realize parallel coordination across stages such as product design, smart manufacturing, material supply, and after-sales service.
The lack of industry-wide coordination not only reduces the efficiency of resource allocation across the entire chain but also severely hampers the large-scale expansion of AI technology from individual factories and specific scenarios to the entire industrial chain.
Optimal Pathways for the Integration of AI and Manufacturing
Coordinating Computing Power Deployment to Build a Cloud-Edge-Device Collaborative Computing System
To address the “triple challenge” of weak edge computing power, low cloud utilization, and data transmission latency, a coordinated approach is needed across three areas: hardware upgrades, network optimization, and scheduling mechanisms.
Hardware upgrading is a key optimization direction. Factories need to accelerate the computational power iteration of intelligent edge devices.
The goal is to boost edge computing performance from 10 TOPS to over 50 TOPS.
This upgrade targets the real-time inference requirements of lightweight AI models in manufacturing scenarios.
For existing equipment, users can deploy external AI acceleration modules to achieve low-cost computational power expansion and upgrades.
For network optimization, enterprises can build dedicated industrial 5G networks and Time-Sensitive Networks (TSN) and adopt network slicing technology to deliver low-latency, highly reliable transmission of core production data.
In terms of scheduling mechanisms, enterprises need to build a three-tier “cloud–edge–end” collaborative computing power scheduling platform.
This platform supports the implementation of a layered computing model.
The model follows the core logic of “local data processing, cloud-based model training, and edge-based inference of results”.
The cloud handles large-model training and complex inference, the edge handles real-time decision-making and data preprocessing, and end-point devices perform only lightweight inference tasks.
Through intelligent scheduling algorithms, cloud computing power utilization is increased from less than 30% to over 60%.
Improving Data Governance to Strengthen the Data Foundation and Security Safeguards
To address the dual challenges of inconsistent data standards, missing metadata, and enterprises’ “reluctance and unwillingness to share” data, a coordinated approach must be taken from the dual perspectives of “governance” and “security.”
1. Standardized Data Governance to Break Data Silos for AI Application
At the governance level, it is essential to streamline the entire process of transforming data silos into reusable AI data.
First, it is critical to advance the formulation and implementation of national standards for manufacturing data metadata.
Unified data coding and interface specifications should be applied to core industrial systems.
These core systems cover ERP, MES, and PLM platforms. Standardized rules help build stable data mapping relationships across different systems.
Second, enterprises need to build dedicated metadata management platforms.
They should label all types of production data with key attribute tags.
Common tags include data sources, measurement units, and procedural implications.
This constructs a unified enterprise-wide data dictionary. It enables AI systems to understand industrial data rather than processing it blindly.
2. Advanced Security Technologies to Eliminate Data Sharing Risks
On the security front, we must eliminate enterprises’ concerns about “daring not to share” data.
We should advance privacy-preserving computing technologies. Typical examples include federated learning and multi-party secure computation.
These technologies enable enterprises to conduct internal AI model training and inference and complete all processes without exporting internal data.
This approach technically safeguards overall enterprise data security.
3. Improved Rules and Pilots to Foster Active Data Sharing Ecosystem
Meanwhile, it is necessary to accelerate the release of industrial data classification and management guidelines.
These guidelines clarify targeted security requirements for data at different levels.
They also standardize the circulation rules for graded industrial data.
Establish a security assessment and filing system for AI applications, and implement mandatory security audits for high-risk scenarios such as quality determination and equipment control.
It is essential to support the development of industry-level pilot projects for trusted data spaces.
New data-sharing models are explored in this process. These models feature clear ownership, controllable circulation, and full traceability.
Such practices guide enterprise data usage behaviors.
They can transform enterprises from reluctant and cautious data-sharing participants into active, willing adopters.
Deepening Industrial Chain Synergy and Building a Differentiated Development Model Through Industrial Clusters
Currently, China has preliminarily formed three major artificial intelligence industrial clusters:
The Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area.
Different regions are charting distinctive development paths based on their unique strengths: cities rich in science and technology innovation resources focus on R&D;
Cities with a concentration of tech companies prioritize building industrial ecosystems; and cities with a strong manufacturing base emphasize the practical implementation of AI applications.
To promote the deep integration of AI and manufacturing, we must fully leverage the strengths of regional industrial clusters and strengthen collaboration across the entire industrial chain.
Leading enterprises shall take the lead in building industrial chain collaborative service platforms.
These platforms integrate information channels throughout the industrial chain.
They cover design, production, supply chain, and after-sales service links.
This initiative targets common industrial operational pain points.
It solves problems including difficult supplier matching, insufficient product traceability, poor inventory management, and lengthy delivery cycles.
We can learn from mature collaborative industrial models.
These models include “platform companies + small and medium-sized enterprises” and “large-model companies + end-user enterprises”.
This initiative promotes the sharing of industrial chain resources. It also realizes effective capability complementarity among enterprises.
We adhere to the development approach of “small entry points, deep penetration”.
We focus resources on high-value industrial scenarios and use typical benchmark scenarios to promote chain-wide application.
This breaks the fragmented operational state of individual enterprises.
It extends artificial intelligence applications from scattered cases to the whole industrial chain.
Lowering the Barrier to Development and Promoting the Intelligent Transformation of Enterprises in Phases
In line with domestic technology trends in China, multimodal interaction, model lightweighting, and efficient inference have become the mainstream in foundational model R&D.
Technologies such as AI agents, embodied intelligence, and intelligent robots are accelerating their move toward practical application, creating conditions for simplifying the implementation process.
Industry organizations can cooperate with research institutions and leading enterprises.
They can jointly launch universal industrial models and modular toolchains.
These efforts help standardize equipment compatibility and communication protocols.
They also reduce repetitive research and development on common technical issues.
Enterprises themselves can steadily advance their upgrades by following a “seven-in-one” implementation framework:
First, shift their development philosophy by incorporating data governance and computing power planning into their medium- to long-term digital strategies;
Second, strengthen their data foundation by establishing a comprehensive metadata management and data governance system;
Third, differentiated deployment of computing power resources should be implemented.
Customized cloud-edge-end collaborative architectures are designed for large enterprises.
Standardized and inclusive computing power services are selected for small and medium-sized enterprises.
Fourth, deploy hardware infrastructure to complete the integration and adaptation of industrial intelligence entities, development toolchains, and on-site production equipment;
Fifth, prioritize pilot implementations in high-value scenarios and gradually expand the scope of application from specific points to a broader scale;
Sixth, leverage industrial internet platforms to achieve the integration of all production factors and the entire technical system;
Seventh, jointly build an industrial ecosystem and actively participate in data sharing and business collaboration across the industrial chain.
We rely on the synergistic effect of multiple driving forces. These forces include government policy support, industry standard guidance, and enterprise-led implementation.
This mechanism steadily promotes the transformation and upgrading of the manufacturing industry.
It facilitates the intelligent and high-end development of the manufacturing sector.