From the "DeepSeek fever", we can see how AI is implemented in steel enterprises

Steel enterprises can adopt flexible billing models through negotiation, such as tiered pricing according to the call volume, to prevent cost overruns; Ensure the localized and secure storage of production data, and use advanced technologies such as federated learning to achieve "usable and invisible" data, which not only ensures the efficiency of data utilization, but also effectively prevents security risks such as supply chain interruptions, and ensures compliance and business continuity.

Iron and steel enterprises can focus on the collaborative optimization of the supply chain, and optimize the procurement plan and inventory management level by integrating procurement, inventory and logistics data to predict raw material price fluctuations and transportation risks. At the same time, it is committed to operational optimization, with the help of a data-driven decision-making mechanism, to greatly improve production efficiency and resource allocation efficiency. In terms of equipment prediction, steel enterprises should combine the original model of the enterprise, use the sensor data of production equipment, and accurately grasp the operation status of the equipment and predict the failure cycle through advanced large-scale model analysis technology, especially for key equipment such as blast furnaces and rolling mills.

In order to ensure that the large model can be seamlessly integrated with the existing system of the enterprise, Steel Companies must pay close attention to the continuity of data flow and avoid any links that may lead to data breakpoints. For some old or special systems, steel enterprises can flexibly use middleware or API gateway technology to achieve lightweight, low-cost integration and ensure a smooth transition.
　　While advancing technology, steel enterprises should also deeply understand the importance of employee empowerment and change management. To this end, steel companies can assist in carrying out a series of "AI + process" integration training to help engineers deeply understand the internal logic of the model output, such as why the model will recommend adjusting the rolling speed in a specific situation, so as to enhance their active decision-making ability, rather than just acting as a passive executor; A set of incentive mechanisms can also be established to actively encourage front-line employees to actively feedback the problems encountered in the practical application of the model, and effectively eliminate the possible resistance of employees through positive guidance and incentives, so as to ensure that advanced technology can truly take root and avoid idle waste of technical resources.

Our ultimate goal is to make the big model truly become the "master who knows the steel industry well", rather than just an "expensive decoration" placed there, so as to lead the enterprise towards a smarter and more efficient future.