The Leading Manufacturing Group has Built an Autonomous and Controllable AI Intelligent Agent Platform, Driving the Intelligent Upgrade of the Enterprise and the Automation Transformation of Core Business.
Issue
During the process of transitioning to an intelligent transformation, enterprises encounter two challenges:
On one hand, there are issues at the technical platform level:
1. High reliance on technology and weak autonomy: The AI platform overly relies on external technologies, has poor toolchain integration, lacks out-of-the-box intelligent agent capabilities, and is unable to support the large-scale development of the AI intelligent agent era.
2. Insufficient platform capabilities, restricting intelligent upgrade: The existing AI platform cannot meet the rapid business iteration requirements, severely limiting the improvement of office efficiency and the transformation of process automation.
On the other hand, there are also problems in the core business scenarios:
1. Low Process Design Efficiency: Thousands of component manufacturing processes must be manually created each year, relying on visual interpretation of complex CAD drawings. This process is time-consuming, labor-intensive, and prone to human errors and inconsistencies, leading to production delays.
2. Knowledge Transfer Crisis: The retirement of experienced engineers risks the loss of valuable tacit knowledge and expertise.
By building an independently controllable group-level AI platform and focusing on high-value business scenarios to implement the deployment of intelligent agents, we successfully connected the entire chain from AI infrastructure construction to the intelligent upgrade of core businesses.
Solution
We have adopted a "platform + Scenario" dual-drive strategy to create an integrated AI intelligent agent solution:
1. Build a new version of the generative AI platform for the leading manufacturing group to solidify the group-level AI technology foundation:
1) Adopt a multi-tenant architecture and unified permission management, integrate SSO and standardized APIs, to achieve secure and controllable, efficient collaborative AI capability management;
2) Pre-install intelligent digital advisors, automated data analysis, cross-language translation assistants, etc., supporting "out-of-the-box" use;
3) Merge low-code and high-code development models, build an "intelligent marketplace", promoting AI capability sharing and ecosystem collaboration;
4) Promote the phased migration of decentralized systems, unify access to new applications, and form a sustainable-evolving technical base.
2. Implement an end-to-end intelligent process planning system to achieve a breakthrough in the automation of key business operations:
1) Merge low-code and high-code development models to build an "intelligent marketplace", promoting the sharing of AI capabilities and ecological collaboration;
2) Utilize computer vision and multi-modal large models to analyze drawings, accurately identifying process features such as bending, drilling, and milling;
3) Automatically generate standardized process routes and directly connect them to the production workshop for execution, achieving a full-process closed-loop automation.
This has also played a crucial role in knowledge accumulation and talent release.
Impact
By building an autonomous and controllable group-level AI platform and focusing on high-value business scenarios to implement the deployment of intelligent agents, we have successfully enhanced the efficiency of technological autonomy and organizational scale, as well as the reconfiguration of business processes and the leap in quality.
1. Enhanced autonomy: The new version of the generative AI platform of the leading manufacturing group has significantly enhanced the group's level of AI autonomy and controllability, as well as the efficiency of technology reuse. It has also strengthened the flexible response and in-depth support capabilities for diverse business scenarios, promoting the wide popularization and deep integration of AI technology.
2. Accelerated large-scale application: Supported over a thousand employees to participate in the AI innovation competition, incubated dozens of proof-of-concept projects, covering core areas such as manufacturing optimization, intelligent logistics, and energy management, accelerating the exploration and implementation of AI in real-world scenarios, and injecting continuous impetus into digital transformation.
3. Research and development efficiency leap: A unified architecture and open ecosystem enhance the R&D efficiency and innovation capabilities at the organizational level, promoting cross-departmental collaboration and knowledge sharing. The processing time for process design is reduced by half, eliminating human errors and improving production quality and scheduling reliability.
4. Knowledge Assetization: Retaining and expanding expert experience, encoding valuable engineering knowledge into scalable digital systems, reducing the risks brought by personnel turnover.
5. Release of talent value: Freeing highly skilled personnel from repetitive tasks, creating higher business value.
