2.1 Dealing with the disappearance of the demographic dividend

 

With the rise of labor costs, the demographic dividend has faded, and the manufacturing industry has seen phenomena such as "recruitment shortage" and "employment shortage". As a highly automated electronic assembly process, SMT requires a relatively small number of operators compared to traditional manufacturing. However, a certain number of personnel are still needed to guarantee the supply of materials, the operation of the machine, and the inspection of products. Intelligent manufacturing will minimize the number of employees in the workshop to reduce the risk of enterprise employment. At the same time, it can also liberate the boring, cumbersome, and low-value-added transactional work of some managers. Give full play to the subjectivity of employees and improve their work efficiency in order to give full play to their maximum life value.

 

 

2.2 Turn data into resources

 

With the progress of society, the development of science and technology. Enterprise data has become a new production factor in addition to the traditional three elements of labor, land and capital. In traditional manufacturing management, the effectiveness of enterprise data is only used for simple data analysis in the production management process, and it has not reached the height of becoming a resource. Therefore, it is necessary to rely on artificial intelligence equipment and big data related algorithms. Upgrading corporate data into production resources and commercial resources of the enterprise.

 

 

2.3  Guarantee the accuracy, timeliness and comprehensiveness of data

 

At present, the main problems of enterprises in data recording are KPI-oriented, and manual recording is the main method. Due to the subjectivity and limited energy of people in society. This has led to inaccurate, untimely and incomplete data in the company's existing data. Intelligent manufacturing will use smart sensors to collect data as needed and use the data transmission network to transmit the data to the data warehouse in a timely manner. To ensure the accuracy, timeliness and comprehensiveness of the data.

 

2.4 The decision-making reference is more comprehensive and more objective

 

The data in the enterprise includes management data, production data, industry data, policy data, etc. When making decisions, relevant enterprise managers can only rely on their own accumulated experience, because people's experience and industry-related knowledge are insignificant. Therefore, from this aspect, the personnel's decision-making is more objectively limited. Intelligent manufacturing uses the 5V characteristics of big data technology (large volume, value, diversified Variety, rapid Velocity, veracity) and artificial intelligence technology to conduct comprehensive analysis and decision-making.