Today's manufacturing industry has become more and more intelligent. Factories begin to use sensors, wireless networks and other technologies to capture all kinds of data in the product production process, and realize intelligent manufacturing through data application. Intelligent manufacturing can make production more efficient and achieve sustainable profitability. Risks, such as expected delivery delays, can be identified through computational simulations.

The new generation of information technology, such as Internet of things, big data, cloud computing, artificial intelligence and other technologies, is bringing profound changes to traditional industries. In this round of development wave, there are huge opportunities and challenges. How can enterprises break through this opportunity? New technology has brought infinite reverie, but it will lose everything if it is not used properly. Therefore, only by clearly understanding the mystery behind the new technology can we grasp the real opportunity.

The change of market demand often makes people unprepared. Modern manufacturing should make good use of the new generation of information technology and speed up its response to the market. This market-oriented economic model solves the problem of unstable orders. The vision of intelligent manufacturing is to improve the efficiency and flexibility of production and produce any product at any time and anywhere.

Intelligent manufacturing is a big project. In order to promote the transformation and upgrading of factory application of new technologies and transform technology into production capacity, all parties need to make joint efforts. Academia promotes the forefront of science and technology, from artificial intelligence to deep learning, but does not consider how to apply it. Manufacturers want to know what types of data should be collected and how to use these data to make a difference. For these reasons, the combination of academia and manufacturing can really apply knowledge to reality.

Intelligent manufacturing is a mode to improve the level of production process through some advanced technologies, so as to realize a higher overall value chain. In the process of intelligent manufacturing, each part or product has digital information to follow. Collect more data for inspecting the production process, tracking the supply chain, assembly and testing process, and finally give it to the user, which is a product or service bundled with physical and digital.

With the deterioration of the aging scene, the manufacturing industry is promoting cheap labor, and machine replacement is imperative. Therefore, one of the purposes of intelligent manufacturing is to save manpower and solve the problems of expensive employment cost and labor shortage. At the same time, the new production mode can ensure that manufacturers can shorten delivery time and reduce business risks, so as to create more economic benefits.

The ultimate goal of intelligent manufacturing is to improve production efficiency and reduce costs, so as to enable enterprises to obtain more value returns. However, if you only blindly invest in automation equipment, it is not enough. Enterprise managers also need to know how to operate your factory, otherwise, it may lead to more cost waste. At this stage, what should enterprises do to make the factory give full play to more benefits? Intelligent manufacturing design needs to abide by certain principles in order not to waste time and manpower in the digital process.

Converting the production process into information is the primary task to realize intelligence. This part should adopt the Internet of things program for automatic collection as much as possible to reduce manual data entry, because the efficiency of human operation is too low, and errors may occur.

Establish distributed nodes, which are used for machine, factory and enterprise level autonomous diagnosis and decision-making. The information is planned according to the needs of each machine and equipment, factory workshop and product unit. Allow universal use of data and information throughout the product value chain, including visibility of each product value chain, manufacturer to customer connections, and supplier networks. Automatic execution of daily tasks and decisions, but the process includes manual processing, manual adjustment and complex analysis to assist decision-making. Optimize the implementation scheme, use all the data of the obtained production process, obtain insight through advanced analysis and machine learning algorithms, and reasonably adjust the production plan. Adopt machine to machine (M2M), application to application (A2A) and business to business (B2B) integration standards, integrate the Internet integration platform of hardware and software from multiple factories, and learn to establish and promote data exchange standards among assets, production processes and supply chains. Establish learning and training mechanism to let automation personnel understand new skills and knowledge. The introduction of new technologies needs more high-end talents. Enterprises must help workers learn how to configure and maintain intelligent machines.

In the next few years, the connectivity of intelligent manufacturing will develop to a new level. The resulting comprehensive value chain process will bring revolutionary productivity growth, which is exciting for the manufacturing industry and make traditional equipment more efficient.