How can data elements become industrial "craftsmen"?

Produced by | Tiger Sniff Think Tank

Edit | Huang Siyu

Big Whale AI Closed-door Meeting · The manufacturing session will be held in Suzhou on June 20. 30+ CEOs/CIOs/top experts from the field of AI and manufacturing, in a closed-door format and high-end roundtable dialogue, interpret the evolution trend and landing cases of AI in the manufacturing field.

Focusing on the Big Whale AI Closed-door Meeting and Manufacturing Session, Tiger Sniff Think Tank will launch a series of selected content in the field of AI large models and intelligent manufacturing, and this is the first article. This conference explores the feasibility and practical cases of AI large models in production scenarios in the manufacturing field, welcome to scan the QR code to register.

The following core points are from the "Data Elements × Industrial Manufacturing Case Study Report" written and published by Tiger Sniff Think Tank, click on the name of the report to get the content.

Key takeaways:

• At the industry level, data elements are not optimized for a single link or process, but for the whole world. In the field of industrial manufacturing, the operational efficiency and product cost performance of each link have been improved to a certain extent, realizing the multiplier effect on the industrial chain, and then bringing the possibility of changing the business model.

• Enterprises can use data decision-making to achieve the integration and end-to-end improvement of all factors of production within the scope of all production factors such as corporate governance structure and company management capabilities. For example, the improvement of management efficiency, the reduction of management costs, the refinement of work granularity, and the shortening of work processes.

• At this stage, enterprises are generally faced with insufficient accumulation of data assets. The reason for the lack of "raw materials" in the application of data elements lies in the different maturity levels of data collection and construction in different process scenarios.

• Data platform construction is not a one-time project or temporary solution, but a long-term strategic investment that should be kept in focus and continuously operated. "Temporary construction" that focuses only on short-term effects may lead to problems such as substandard data quality and untrustworthy and unavailable data assets after a period of time.

Enterprises' needs and pain points for data elements

There is a growing demand for data as it has great value and potential in areas such as breaking through bottlenecks, optimizing processes, integrating supply chains, and improving business decisions.

For example, in the manufacturing process, the analysis of data fluctuations can feed back the efficiency optimization of the process; In the supply link, data integration can be used for comprehensive analysis and solution suggestions; For business decisions, data cleaning and governance can improve data quality and support subsequent analysis and decision-making.

However, enterprises face the problem of lack of "raw materials" in the application of data elements. This is mainly due to the general lack of data accumulation in enterprises, and the maturity of data collection and construction in different process scenarios.

On the supply chain side, due to the early accumulation of data, the maturity of application and service improvement capabilities is high. In other scenarios, such as internal operations, some enterprises lack data collection equipment or rely heavily on manual labor, resulting in data traceability and insufficient data supply.

In this case, it is more difficult for enterprises to apply data, and more data assets and practical experience are needed to effectively improve the ability to mine the value of data elements.

Case: Data-driven product quality management of a power battery company

With the R&D, production and sales of lithium-ion batteries as its core business, the power battery company hopes to find production problems and provide corresponding solutions through the application of big data in the process of ensuring product quality stability, reliability and durability.

However, in the process of implementation, enterprises face problems such as data technology cannot be supported, data processing efficiency is low, and data quality is not high.

Under this premise, Singularity Cloud uses the DataSimba data cloud platform to optimize the data infrastructure of the enterprise, transform the communication protocol of the enterprise equipment, and add the data acquisition server to improve the real-time, consistency and integrity of the data.

At the same time, Singularity Cloud has established a quality closed-loop management mechanism based on the PDCA cycle with enterprises to ensure that quality problems are systematically improved.

In addition, the company also carried out real-time simulations and calculation reviews, and achieved real-time abnormal improvement in production quality. During the three-month trial operation, the abnormal improvement rate reached 100%, and the company's quality management was more refined, accurate and reliable.

The implementation of this solution not only solves the problems of insufficient data processing, low data quality, and low data application efficiency of battery enterprises, but also improves the efficiency of quality management, reducing the time of data processing by at least 75%, allowing more time for data analysis, optimizing scenario applications, and further enhancing the core competitiveness of enterprises.

Building a data platform is not a one-time project or a temporary solution, but a long-term strategic investment that should always be focused on and operated.

Enterprises should establish a standard and standardized data management system to support the integration and efficient processing and analysis of massive multi-source heterogeneous data, and improve data quality and management capabilities. At the same time, enterprises should build unified modeling and data applications based on the platform, reduce the dependence on customized development of technical personnel, and improve the data service capabilities of the platform.

epilogue

Look at and use data from a holistic and strategic perspective. The value of data elements is not only reflected in the optimization of a certain link, but also from the perspective of the whole industry chain, so as to realize the multiplier effect of the whole industry chain and promote the in-depth transformation of the business model.

Enterprises need to fully realize that data is not a silver bullet, but need to continue to accumulate effective data assets with the support of a reasonable data management system, and gradually improve the ability to mine the value of data.

If you want to know more about the application cases of data elements, you can sign up for the Big Whale A closed-door meeting hosted by Tiger Sniff Think Tank on June 20 · In the manufacturing session, there will be case studies of data elements and AI large models in the field of intelligent manufacturing.

About Big Whale AI Closed-door Meeting · In the manufacturing session, we invited representatives of a number of leading enterprises in the industry, and the guests who have been confirmed to attend include the Academy of Information and Communications Technology, Midea, TCL Zhonghuan, LONGi Green Energy, Schneider Electric, Goldwind, Qingzhan Artificial Intelligence Research Institute and other enterprises and institutions.

We sincerely invite you to participate in the conference to discuss the latest technical practices and challenges of AI large models and intelligent manufacturing!

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