On January 7, 2015, He You, an academician of the Chinese Academy of Engineering, led a six-member delegation to Shandong Linglong Co., Ltd. to discuss how to apply big data to tire manufacturing.
Linglong Co., Ltd. Exchange site
Experts said that the application of industrial big data will become a key factor in the future improvement of manufacturing productivity, competitiveness, and innovation capability, and it is also an important issue that the global industrial transformation must face.
Linglong Tire said in its official website that this time through the exchange with industry experts, the company aims to explore innovation and practice of big data research in the tire industry, so as to realize effective management, display and analysis of large business data, product development, and processes. Various aspects such as design, quality management, and production and operation are seeking more effective and new methods, which ultimately increase the quality and efficiency, and break the bottleneck of development.
During the inspection process, the two parties discussed the key technology research and application of manufacturing big data, and jointly explored relevant big data applications in terms of consumer insights, tire brand performance improvement, and reached preliminary consensus on joint construction of joint laboratories.
Linglong Tire said that the two parties will work together to promote data analysis of the tire industry, improve understanding of consumers, and achieve accurate subdivision of the population, so as to provide better products and services for the market.
He You Academician of the Chinese Academy of Engineering has long been engaged in information fusion technology research, engineering construction and personnel training, and is one of the pioneers in the field of military information fusion in China. Exquisite tires have also accumulated some experience in the fields of informatization and automation. At present, they have completed the deployment of data systems such as ERP, MES, and SAP.
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