Abstract Before looking at the picture, think about a question, why is Smart Manufacturing and Industry 4.0 the next direction? Beginning with the use of tools by the earliest craftsmen, the material form of technological advancement is basically embodied in the process of manufacturing development. The application of technology to the manufacturing process can best reflect human reason...
Before looking at the picture, think about a question, why is Smart Manufacturing and Industry 4.0 the next direction? Beginning with the use of tools by the earliest craftsmen, the material form of technological advancement is basically embodied in the process of manufacturing development. The process of applying technology to manufacturing can best reflect the great realm of profit maximization and efficiency by advanced means such as human rational application tools and technology. . Therefore, according to the logic of the development of science and technology, manufacturing must develop to the stage of informationization and intelligence. In short, it is likely that human wisdom has developed to this stage, and it does not show that it can no longer demonstrate the higher capabilities of human beings.
Picture 1: Smart Manufacturing Process Panorama
As a good pioneer, Siemens Germany Digital Factory has achieved 75% of production automation in 25 years. There are more than 1000 online monitoring nodes on the production line, and more than 50 million data are collected every day. It produces 3 billion components per year and can supply customers 24 hours a day. The production capacity has increased by 8 times compared with before digitization.
Looking at the numbers, I think that basically the core key indicators of intelligent manufacturing are mentioned, but I can't directly feel where the specific cows are. The HCR Huichen TMT Research Department has compiled a panoramic view of the intelligent manufacturing process (see below), hoping to sort out the relevant players and influencing factors of smart manufacturing from a global perspective.
It is not difficult to see from the above figure that intelligent manufacturing is a systematic project, even if single-point cutting requires systematic planning and implementation one by one.
Challenge 1: Fully connected
The lack of a connection to any of the nodes can affect the implementation of full automation. How many connections will it involve? For example, a product that is not too common now, such as a motorcycle, has more than 250 parts for the engine, and about 30,000 for the car. For the manufacturing process, one screw can't be less, and the smart manufacturing connection is the same. In addition to these, other relevant information including the amount of funds, management information flow, logistics information flow, service information flow and other related links need to be fully connected.
In the informationization stage, the biggest problem of the ERP system is the difficulty of implementing the reverse process. In the intelligent phase, in addition to the connection point, you need to set two-way monitoring points and production management connection points in the full connection point. Based on the requirements of time-to-time mass information transfer and multi-node control, separate connections and data flow channels are needed to ensure that the entire process is continuously and without packet loss, and the entire process is successfully completed. In addition, whether there is a smart product is a prerequisite for establishing a direct connection with the user.
Challenge 2: Full Control
Intelligent manufacturing takes data flow as the core and connects all manufacturing and related links. The whole process in the middle is like a “black boxâ€. It is very necessary to know what is happening at any time and to artificially correct and intervene. The interaction design and computing power of each node is the basis for full control. In addition to the control of the link, it is also necessary to monitor and control smart devices, including industrial robots. Smart manufacturing lines will be replaced by multiple smart devices to perform their work. The cooperation between man and machine and the control and management of people to the machine are also the problems that are more likely to occur in the intelligent manufacturing challenge.
Challenge 3: Resource Integration
The social environment and users in the picture are all factors influencing smart manufacturing. In the intelligent manufacturing stage, the main existing factory forms are large manufacturing platforms and small individualized studios. The large platform can meet the customization needs of small batches, and the small studio is more reflected in the more direct, shorter and faster connection with the users. As for the smart supply chain, there will also be a large supply chain integration platform to provide fast, “zero inventory†supply for different individual needs. Intelligent manufacturing system engineering needs to integrate supply chain, production, logistics, service platform, marketing resources, etc., in order to maximize the automation of intelligent manufacturing and maximize production capacity.
Since there are such high requirements in intelligent manufacturing, two implementation paths can be summarized from the above: one is to be a leading enterprise, try to copy the successful experience to other enterprises in the industry, and promote the overall progress of the industry, so as to achieve a wider range of intelligence. manufacture. The other is that major companies in the industry have integrated relevant resources and tried to focus all relevant links on this platform to operate as an independent OEM center. It is not difficult to infer that industry alliances and third parties will provide relevant solutions and data services that will become essential.
In summary, intelligent manufacturing, even if it has not been implemented, is in line with the laws of social and economic development. It is a long-term task. In addition, the so-called challenge is not the same for different stages of development and enterprises with different degrees of digitization, and cannot be generalized.
Figure 2: Smart Manufacturing Data Flow Diagram
The collection and integration of data inside and outside the enterprise is the basis of intelligent manufacturing efficiency. The acquisition of data related to smart products will also be the basic data for product upgrades. The ability to acquire and integrate data, especially external environmental data, industry data and user data, is the most costly and best reflects the strength of enterprise resource integration. The requirements of intelligent manufacturing for enterprise data capabilities include the number of data entry controls, data collection methods (new models after crowdsourcing), data center planning and implementation capabilities, data computing resources, and the ability to control intelligent algorithms.
Challenge 5: Data Transfer Channels Interact with Time
This involves the construction of network channels and multi-node protocol standards on the network. Multi-node interaction, monitoring and control, as well as cross-industry, cross-domain, cross-product and other multi-scenario requirements, need to establish new, systematic, unified protocol standards, in addition to the overall architecture and basic Internet of Things, at least from the same The industry (domain) began to refine and establish uniform standards. In addition, whether it is from the bandwidth (real-time data capacity) or network speed requirements, the current network resources obviously cannot support the development requirements of intelligent manufacturing. Now everyone is putting their hopes on 5G, and hopes for the new protocol standards of the Internet of Things. What else can you say? Let's look forward to it.
Challenge 6: Multi-scenario creation and opening of the data model
A statistical method eats all directions, and a data model that captures the happiness of the whole world will not happen again. Despite the big data and intelligent algorithms, the real test of intelligent manufacturing is based on data architecture and model application of different scenarios and conditions, as well as data and data models in multi-mode and scenario. Everything will be biased. Even if there is no deviation, it needs to be adjusted according to external changes. It is also impossible to rely on the machine to interpret and generalize the data. Therefore, professional analysts who have insight into industry development and business lines are required to adjust, optimize, upgrade and abolish the rules.
In the future, data will become the lifeblood of smart manufacturing, data collection, storage, rapid transfer, model building, rule creation and integration, computing and application, each link is connected to connectivity, control and automation. HCR Huichen TMT Research believes that data service capabilities will become an important development area and direction for third-party services in the future, and data experts and engineers will become hot talents.
Some experts say that even from the perspective of the three major industries, the Internet has been committed to affecting the tertiary industry in these years, and then it must be in the agricultural and industrial (manufacturing), and it is very promising.
But from the difficulty of the challenge, it is not enough to rely solely on the power of business. At present, the world's top two economies have acted in this area, and the US government announced on June 20 a bill to promote the revival of US manufacturing by stimulating a method known as "smart manufacturing." The Institute for Intelligent Manufacturing Innovation is the ninth manufacturing center issued by the Obama administration and will launch five regional manufacturing innovation centers within the United States, each of which will focus on relevant technology migration and labor development in the region. In May last year, the Chinese government also issued the “Made in China 2025†strategic plan for the medium and long-term development of the powerful countries. It is expected that the key areas of manufacturing will be fully intelligent in 2025, the operating costs of pilot demonstration projects will be reduced by 50%, and the production cycle will be shortened by 50%. The rate of defective products is reduced by 50%.
Policy-driven, talent pool, enterprise investment, and scientific research support are all necessary conditions. The development and application of new technologies will inevitably have to be fought in countless pits. How many heroes and sighs will come to life, and they will only be able to turn a blind eye to those who have grasped the best combination of technology and market, and continue to turn their backs. Take a mud pit. For the development of intelligent manufacturing, what is most needed is the warriors who dare to smash one after another, pat the soil, sum up lessons and continue the next round of trials. Without the determination to meet the challenge, there is no chance to win the victory. (Hui Chen TMT Internet Research Department analyst team, research intelligence +, focus on intelligent hardware, artificial intelligence, cloud computing, business intelligence and related fields. WeChat public number: Intelligent Institute.)
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