At present, AI has become the core engine driving the high-quality transformation of China's manufacturing industry. Different from the traditional "product manufacturing" model, the greatest value of AI lies in opening up all links of the industrial chain
through data-driven and intelligent decision-making, realizing efficient resource allocation and ecological synergy.
"In practical applications, the first large-scale implementation of AI is mainly concentrated in intelligent quality inspection and predictive maintenance." Zhang Lili, researcher at the CIO Research Center of Renmin University of China and president of the
Shanghai Overseas Economic and Technical Promotion Association, said. Through machine vision and deep learning, AI can automatically identify product defects and greatly improve the yield rate; and based on the modeling and prediction of industrial
big data, enterprises can realize the status monitoring and fault warning of key equipment, significantly reducing operation and maintenance costs and downtime risks. In addition, AI-driven supply chain optimization has gradually realized the dynamic
coordination of orders, inventory, and logistics, providing intelligent decision-making support for upstream and downstream of the industrial chain. Zhang Lili gave an example that Haier's KAAS industrial Internet platform has achieved large-scale customization,
agile response of the supply chain, and full-process data closed loop through the integration of AI and IoT, becoming a typical model of "chain leader + ecology" in China's manufacturing industry. Tesla's smart factory shows an intelligent ecology of integrated
production, R&D, and logistics driven by AI. It is foreseeable that in the future, the value of AI in the industrial field will no longer be limited to single-point efficiency improvement, but will become a platform-based, ecological intelligent hub, fully enabling the
transformation of China's manufacturing to "intelligent manufacturing civilization".
The key to deep integration
Zhang Lili said that the deep integration of big models and manufacturing is in a strategic transition period. Although it has achieved remarkable results in R&D design, production and manufacturing, it still needs to face the following three challenges.
First, the value of big models depends not only on its ability to integrate massive industrial data, but more importantly on its deep logical reasoning and complex decision-making capabilities. At the current stage, only by solving the data barrier problem, realizing
the free flow of data elements, and promoting the model capability from "identification and induction" to "reasoning and creation" can the manufacturing industry be injected with continuous innovation power. This advancement marks the essential leap of industrial
intelligence from informatization to cognition.
Second, the efficient connection between basic general models and vertical industry models is becoming the core of industrial intelligence. It is difficult to overcome the application problems of the manufacturing industry that are changeable, complex and high barriers
by relying solely on the underlying big model. It is necessary to organically integrate the underlying AI capabilities with industry knowledge and expert experience to open up the full link of "technology-scenario-value", so that AI can truly move to the production line and
provide accurate and reliable intelligent support for industrial upgrading. This is the innovation responsibility that chain-leading enterprises and platform companies should share.
Third, the openness of AI infrastructure and the synergistic resonance of downstream application ecology determine the sustainable vitality of large model innovation. Only by opening up platform resources, computing power and data interfaces and stimulating the
extensive participation of industry enterprises can the manufacturing industry complete the fundamental transformation from "product-oriented" to "service ecosystem-oriented". This trend will reshape the industrial organization model, promote leading enterprises to
actively build an open ecosystem, and small and medium-sized enterprises will gain innovation opportunities and growth space in diversified collaboration.
Maximizing the release of industrial data dividends
High-quality data is an important cornerstone for the development of industrial large models. Industrial data collection and cleaning face multiple challenges. How should data standards and data security issues be solved? Zhang Lili believes that industrial data
standardization and security governance is not a "technical problem", but a profound change of "industry collaboration". Whoever can take the lead in completing the leap from "data barriers" to "data ecology" will have the opportunity to define the future pattern of
China's intelligent manufacturing. The particularity of industrial data determines the complexity of its governance. The variety of equipment types, different protocols, and inconsistent data formats and semantics make data collection, standardization and cleaning a
systematic problem. Not to mention, the real-time data of many core devices involve commercial secrets and production safety, and their owners are naturally concerned about their opening and sharing.
"The key to solving this dilemma is to reconstruct the data governance path from a systematic and ecological perspective." Zhang Lili concluded. First of all, it is necessary to promote cross-industry and cross-enterprise data standardization based on "industry consensus
+ government guidance". The leading chain-leading enterprises should be brave enough to take the lead, unite upstream and downstream, scientific research institutions and standard organizations, and jointly formulate unified standards for data collection, transmission,
interface and security. On this basis, promote data governance from "enterprises sweeping the snow in front of their own doors" to "full-chain collaboration and standard co-construction", so that every piece of data can flow safely and increase in value in a controllable
manner. Secondly, data security must not be achieved by "blocking" or "banning", but through "double insurance" of technology and mechanism. On the one hand, strengthen data desensitization, encryption and hierarchical management to ensure that the flow of
sensitive data is traceable, controllable and traceable. On the other hand, it is necessary to build a new paradigm of distributed data governance and "data does not leave the factory, models enter the factory", which not only meets data security protection, but also allows
AI capabilities to penetrate into front-line scenarios. Combined with innovative technologies such as blockchain and federated learning, model collaborative training can be achieved without sacrificing security, maximizing the release of industrial data dividends.
The key to competition in the manufacturing industry
Zhang Lili said that China's manufacturing industry urgently needs to shift from scale and cost competition to high value-added competition, and develop iconic products with global leadership such as smart terminals and high-end medical equipment. "This means that
we must use digital technology and industrial big models to empower product development, user experience and industrial chain collaboration, and promote the new ecological transition of "Made in China" to the integration of "intelligent manufacturing + high-end services".
"Co-building a new value network of "manufacturing + service" is the key path for China's intelligent manufacturing to take off in the future." Zhang Lili concluded. Through the continuous release of new quality productivity, manufacturing companies are no longer just
producers of physical products, but also organizers of digital services and industrial ecology. Digital technology and industrial Internet are becoming the core force to open up and reshape the supply chain and value chain. "The ultimate evolution of intelligent manufacturing
will be the trinity of 'high-end intelligent manufacturing + platform services + industrial ecology'." Zhang Lili predicted that whoever can first complete the transition from "manufacturing products" to "manufacturing ecology" will win the leading position in the new global
industrial landscape. This is the key to achieving high-quality development of China's manufacturing industry and promoting industrial upgrading and value transition.
