Digital Transformation White Paper: Digital Intelligence Technology Drives Intelligent Manufacturing

Release date:2021-07-10

(1) Definition and connotation of intelligent manufacturing

Intelligent manufacturing originates from the research and application of artificial intelligence. Its concept was first proposed by Wright Byrne of the United States in his book "Smart Manufacturing". "Intelligent manufacturing" is defined as "through the integration of knowledge engineering, manufacturing software systems, robot vision and Robot control is used to model manufacturing technicians and expert knowledge, so that intelligent machines can produce small batches without human intervention." In the 1990s, as major developed countries invested in attention and research, the concept of "smart manufacturing" was further developed, from the original single intelligence to the organic integration of smart machines and smart production activities.

Since the 21st century, with the rapid development and application of new-generation information technologies such as artificial intelligence, big data, cloud computing, and the Internet of Things, the concept of “intelligent manufacturing” has been further deepened. According to the definition in the "Intelligent Manufacturing Development Plan (2016-2020)" issued by the Ministry of Industry and Information Technology of China in 2016, "Intelligent manufacturing is based on the deep integration of new generation information technology and advanced manufacturing technology, and runs through manufacturing activities such as design, production, management, and service. Each link has the characteristics of self-perception, self-decision, self-execution, self-adaptation, self-learning, etc., and is an advanced production method aimed at improving the quality, efficiency and core competitiveness of the manufacturing industry." In 2014, the US Department of Energy will "intelligent manufacturing" Defined as, "Intelligent manufacturing is a combination of advanced sensing, instrumentation, monitoring, control, and process optimization technologies and practices. They integrate information and communication technology with the manufacturing environment to achieve energy, productivity, and cost reduction in factories and enterprises. Real-time management."

No matter from which perspective, the understanding of “smart manufacturing” in various countries today is no longer limited to the production process or individual intelligence, but extends to all links of the industrial value chain, including all aspects of corporate activities, and no longer unilaterally emphasizes The application value of digital intelligence technology itself, but pays more attention to the deep integration and practical innovation of cross-field technologies such as digital intelligence technology and advanced manufacturing.

Starting from a new definition, the application and penetration of intelligent manufacturing in practice will help enterprises achieve intelligent upgrades in the four major aspects of product, production, management and service.

Product intelligence: that is to embed sensors, processors, memory, communication modules, and transmission systems into products, so that the products have dynamic storage, perception and communication capabilities, and become the terminal connected by the Internet of Things, so as to realize the product’s "traceable, identifiable, Positionable" function. According to Transforma Insights research, the number of these IoT terminals will increase to 24.1 billion by 2030, with a compound annual growth rate of 11%.

Manufacturing intelligence: includes two levels: manufacturing carrier intelligence and manufacturing process intelligence: manufacturing carrier intelligence, including stand-alone intelligence, and the interconnection of stand-alone equipment to form smart manufacturing units, smart production lines, smart workshops, smart factories, etc. ; Intelligent manufacturing process is through the integration and application of digital intelligence technology and advanced manufacturing technology, so that the various processes involved in the manufacturing process, production factors and upstream and downstream enterprises, centered on user value, to achieve networked collaboration and flexibility produce.

Intelligent management: With the continuous deepening of technology integration, the real-time, completeness, and accuracy of data obtained by manufacturing companies continue to improve. Combining intelligent analysis technology can help companies improve resource management, energy management, supply chain management, order management, and equipment The efficiency of decision-making in management and other aspects has changed from passive management to active management and preventive management, making management more accurate, efficient and intelligent.

Service intelligence: On the basis of product intelligence, the interaction between enterprises and end users is more direct. Providing users with a better service experience will become an important component and value increase of intelligent manufacturing. More and more manufacturing companies will Production-oriented manufacturing is transforming to service-oriented manufacturing, and the boundary between manufacturing and service is gradually disappearing.

(2) Driving factors for the development of intelligent manufacturing

Manufacturing upgrading is a common issue faced by all major manufacturing countries. The main goal is to enhance the competitiveness of the national manufacturing industry through the innovation and application of digital intelligence technology, overcome the rising labor costs, and keep the manufacturing industry in the country while maintaining It has its own manufacturing advantages, but due to the different manufacturing bases and advantages of various countries, there are differences in the core demands and strategic focus of the development of intelligent manufacturing.

Since World War II, the United States has faced a serious problem of hollowing out manufacturing. Leading the revival of manufacturing through the development of intelligent manufacturing is the main demand of the United States. The United States leads the world in informationization of manufacturing industry, especially in industrial software and the Internet. Therefore, its strategic focus is mainly Pay attention to the value chain links such as production design and service, and emphasize the integration of smart devices and software and big data analysis. Germany is a global leader in the field of industrial automation, has strong precision manufacturing capabilities, and high-end equipment reliability. The national strategy focuses on promoting intelligent manufacturing through CPS (Cyber-Physical Systems), and hopes to consolidate it through the integrated development of digital innovation and industrial manufacturing. , Defend the national industrial technology sovereignty. The Japanese manufacturing industry focuses on improving product quality and technological innovation, and firmly occupies a high-end position in the industrial chain. As Japanese society is facing serious problems of aging and declining birthrates, the development of intelligent manufacturing is mainly oriented towards problem-solving, and the strategy focuses on guiding the intelligentization of the industry into all aspects of social life in order to support the structural transformation of Japanese society and create " Super Smart Society". In recent years, China has continuously issued various favorable policies from top-level planning to action plans to promote the development of smart manufacturing. The driving force behind it is mainly derived from two factors, supply-side issues and demand-side changes.

From the supply side, although Chinese manufacturing is large in size, it faces the reality of "big but not strong" in the long-term competition, which is embodied in the following four aspects:

First, the relative advantage of China's overall manufacturing cost is gradually becoming smaller. In addition to labor costs, energy use costs, land costs, and financing costs are all rising. The Boston Consulting Group has compared the manufacturing cost index of 25 exporting economies and shows that the comprehensive cost of manufacturing in China is basically the same as that in the United States.

Second, the problem of overcapacity in China is more serious. According to expert estimates, China's capacity utilization efficiency is below the normal range of 79% to 83%, reflecting the current situation that my country's supply and demand sides need to be improved, and the overall production efficiency is low.

The third is that my country’s manufacturing industry is mainly in the processing and manufacturing link with low profit margins, and the technical content and added value are not high, and it is urgent to upgrade to the high-end of the industrial chain; at the same time, due to the basic materials, key components, advanced basic technology and industrial technology upstream of the industrial chain The foundation is relatively lacking, and the industry lacks a top-down autonomous system. In an environment of complex international situations and increasing uncertainties, the stability of the industrial chain and supply chain is facing challenges.

Fourth, the development of my country's manufacturing industry is highly dependent on energy resources, and the extensive production in the past has been more destructive to the environment. According to statistics from the World Bank in 2017, my country’s energy consumption per unit of GDP is about 1.53 times the world average, of which industrial manufacturing accounts for more than 70% of the country’s total carbon emissions, facing the new situation of active control of carbon emissions and carbon peaks in 2030. The future development of the manufacturing industry will be increasingly constrained by energy and environmental factors.

From the demand side, the consumer market presents two irreversible trends: First, users are paying more and more attention to consumer experience and product services, emphasizing individual needs, and driving manufacturing companies to transform their production methods to customization; second, users are seeking new ideas and quickness. Changes in demand require manufacturing companies to shorten product innovation and manufacturing cycles, and quickly respond to market trends. On the whole, the various problems accumulated on the supply side and the changing trend on the demand side are the main driving forces for my country to vigorously develop smart manufacturing. This is fundamentally different from the core demands of smart manufacturing strategies in other countries.

(3) The core value of intelligent manufacturing

Starting from the driving factors, summarize the five core values of China's development of intelligent manufacturing:

One is to reduce the overall cost of manufacturing enterprises. For example, through the use of machine substitutes or human-machine collaboration to improve labor production efficiency and reduce labor costs; use visual algorithms and other methods to improve detection consistency and stability, reduce product defect rates, and reduce economic losses caused by quality problems; Internet of Things The application of technologies such as, big data, and blockchain accelerates the integration of industry and finance, accurately characterizes business operations, evaluates corporate asset conditions, and provides supply chain companies with lower-priced credit funds; rationally arrange factor inputs based on market data feedback to reduce material waste , Or implement smart inventory management to reduce storage costs.

The second is to improve quality and efficiency. For example, data-driven instead of empirical judgments, fully optimize production processes, improve manufacturing processes, and increase production efficiency; scientifically and efficiently schedule production and increase equipment utilization; integrated digital intelligence technology to improve production execution accuracy and ensure product quality.

The third is to reduce energy resource consumption. For example, connected devices through the Internet of Things can monitor and control the use of energy and resources online in real time, and improve the efficiency of energy resource utilization; use intelligent energy-saving and emission-reduction equipment or solutions to replace outdated production capacity and production processes to achieve green production.

Fourth is to improve user experience. For example, the application of digital intelligence technology opens up the upstream and downstream of the industrial chain, realizes the direct connection between the demand side, the design side, and the manufacturing side, analyzes and predicts complex market dynamics, accurately grasps market opportunities, rapidly carries out product innovation, and realizes agile manufacturing and lean Production, respond to market changes and user personalized needs; by adding user interaction nodes in all links of the value chain, encourage users to participate in the product production process throughout the process, continuously iterate products for the best experience of users, and enhance product added value; based on product intelligence , Through interacting with the environment and users, the product can automatically return operation and environmental data, and provide users with remote preventive operation and maintenance services through data monitoring and analysis.

Fifth is to reshape production methods. The integration and application of digital intelligence technology and advanced manufacturing technology will bring about innovation and transformation of production models, and promote the transformation of traditional manufacturing enterprises from large-scale production to customized production. Enterprises are derived from pure manufacturers to services, and the process of value creation It will also shift from the traditional one-way chain process to a networked collaborative co-creation model.

Chapter 2 Digital Intelligence Technology Leads the Upgrade of Manufacturing "Intelligence"

(1) Entering "Digital Intelligence Technology"

Information technology is a general term that includes all the technical elements of the five major links of information collection, transmission, storage, analysis, and feedback. The five links constitute a closed loop of the information industry, and the technological advancement in each link will promote the development of the entire industry application ecology in a spiral manner. .

Digital intelligence technology is a collection of technologies that promote the development of the information industry in the intelligent era, including lower-cost information collection equipment, high-bandwidth and low-latency 5G transmission technology, Internet of Everything IoT technology, large-capacity storage and high-performance computing cloud services , And artificial intelligence technology that efficiently analyzes massive amounts of information. They are integrated and applied with manufacturing technology to promote the digital transformation process of the manufacturing industry and lead the manufacturing industry to complete the strategic goal of "smart" upgrading. In other words, integrating digital intelligence technology to complete the closed-loop process of information collection, transmission, storage, analysis and feedback is one of the preconditions for the realization of intelligent manufacturing. After completing the closed loop of intelligent manufacturing information, there are three main stages-digitization, networking and intelligence: one is to use information collection technology, including MEMS sensors, smart cameras, smart terminals and other sensing devices to realize the digitization of the physical world; the second is to integrate 5G Communication and transmission technologies such as the Internet of Things complete low-cost and efficient connections and interactions between different nodes, accelerating the circulation and sharing of data; the third is based on cloud-side computing and artificial intelligence technology, low-cost storage, processing massive data resources, and through intelligence Chemical analysis forms a series of decision-making instructions to guide corporate activities in all links of the value chain. Among them, the degree of completion of each stage determines the application value of the next stage of technology. In other words, digitization and networking are the necessary prerequisites for enterprises to realize intelligentization.

Another prerequisite is the two-way integration of digital intelligence technology and manufacturing technology. "The essence of manufacturing is the process of discovering and understanding problems, acquiring information in the process, and abstracting it into knowledge, and then using knowledge to recognize, solve and avoid problems. The way of understanding and solving problems determines the knowledge acquired The process of abstracting and applying knowledge determines the form of knowledge inheritance. 1” From the above, we can see that intelligent manufacturing is the process of “obtaining information, abstracting knowledge, forming cognition and solving problems” driven by data. It can be seen that data is the basic element of knowledge acquisition, and insight into the internal relationship of key data is the prerequisite for decision-making. This requires companies to integrate their knowledge of manufacturing technology in the application of digital intelligence technology, deeply understand the characteristics of production processes, and master the manufacturing process. Only with change can we carry out high-quality and efficient data collection and accumulation, and on this basis can we truly complete the above-mentioned closed loop of information.

(2) Digital Intelligence Technology: Value and Challenge

It has become a consensus for the digital transformation of the industry to replace experience driven by data. If data is regarded as the "new oil" in the intelligent age, then digital intelligence technology is the "refining factory" that drills and refines the value of "oil". It uses digital intelligence technology to acquire data extensively, conduct deep learning, and process massive amounts of raw data. It is knowledge and transformed into decisions or actions to guide the operation of the enterprise.

Digital intelligence technology is an indispensable key technology to promote the digital transformation of the industry, and its application value is mainly reflected in three aspects:

More timely decision-making: real-time automatic feedback of scenario/business data, combined with intelligent analysis for dynamic prediction, instead of manual experience judgment, to improve the accuracy and timeliness of decision-making, such as fault prediction and health management based on real-time analysis of equipment status, or Demand forecasts based on online user data accelerate product innovation and iteration cycles.

More refined operations: With the acceleration of the digitalization of the industry, the data obtained has become more granular and richer in data dimensions. Data-driven enterprise operations and management will be more refined, such as precision marketing based on user portraits, or Real-time monitoring and control of energy use, etc.

Smarter applications: Intelligent equipment/applications assist or replace manual posts, and self-iteration and optimization of algorithms are carried out in the application process to continuously improve the level of decision-making, such as product defect monitoring based on machine vision.

Although digital intelligence technology is of great significance to the digital transformation of the industry, there are still certain challenges in the actual implementation process:

The degree of digitalization is low, and the closed loop of information is difficult to close: The accumulation of data assets is an important prerequisite for the digital transformation of the industry. How to continuously obtain data and integrate the data distributed in different systems and organizations is the primary proposition of the digital transformation of enterprises. At present, most enterprises (especially small and medium-sized enterprises) are limited by lack of funds and talents, and insufficient investment in digital intelligence technology, resulting in low levels of enterprise digitalization and lack of complete information network infrastructure; in addition, due to the lack of unified standards, interfaces and coding systems , Making "data islands" clustered inside and outside the enterprise, unable to achieve intercommunication and sharing, resulting in limited scale and types of data used by enterprises, difficult to close the information loop, and the asset value of massive data cannot be fully utilized.

Difficulties in cross-industry integration, lack of compound talents: Digital transformation is actually a process of reengineering business processes using digital intelligence technology. It requires compound talents who have good digital intelligence technology literacy and understand industrial technology and development laws. According to the statistics of the global ICT talent survey conducted by the Internet Development and Governance Research Center of Tsinghua University in 2020, the current digital intelligence technology talents in my country are mainly concentrated in the technology industry, lacking industrial experience and practical background, and the industrial IT personnel generally have no knowledge of digital intelligence technology. It is difficult to support the needs of industrial digital transformation. According to the data analysis of the Ministry of Human Resources and Social Security, the demand for talents in the field of smart manufacturing will be 9 million in 2025, and the talent gap is expected to reach 4.5 million.

Different industries are very different, and the scale effect is difficult to form overnight: due to the huge differences in technology and processes between different industries or different fields and different companies, the in-depth penetration of digital intelligence technology in the industry must be customized in combination with specific scenarios. , There is no one-size-fits-all solution, which makes it difficult for the application of digital intelligence technology in the industrial Internet as in the consumer Internet era. It is difficult to establish scale effects and obtain huge benefits in the short-term, but it needs to cooperate with the industry. Cooperate and advance together, and continue to accumulate general problem-solving capabilities in vertical fields.

Network security issues cannot be ignored: With the application and promotion of digital intelligence technology, network security issues will become an important challenge in the process of digital transformation. On the one hand, traditional network security systems cannot keep up with the pace of digital intelligence technology application and innovation; on the other hand, digital transformation has brought about an explosive growth of information nodes and the total amount of information, which makes the potential loss of cyber attacks "exponentially" magnify, and affect the network. Safety technology puts forward higher requirements.

(3) Intelligent Manufacturing Driven by Digital Intelligence Technology

3.1 Core Features of Intelligent Manufacturing

For the manufacturing industry, digital transformation is the use of digital intelligence technology to carry out an all-round, full-cycle, and full-chain transformation process. Focusing on intelligent manufacturing, through deepening the application of digital intelligence technology in many links such as products, production, management and services, and two-way integration with manufacturing technology to accelerate the pace of digitization, networking and intelligence at the enterprise and industrial level, and continue to release digital intelligence The application value of technology is an important way for modern manufacturing to achieve high-quality, high-efficiency, and green development.

Intelligent manufacturing driven by digital intelligence technology is mainly manifested in two core characteristics: one is the integration of virtual and real, and the other is networked collaboration.

Feature 1: Virtual and real integration, that is, the complete mapping of the physical space in the information space. Information is interacted and integrated in the two spaces. The unified "software" platform coordinates and arranges the optimal allocation of resources, energy, and time, and continuously feedbacks upgrade.

Looking back on the development process of the industrial revolution, in the period of mechanized production, information technology has not yet appeared, and all production factors are concentrated in the physical space; in the period of electrified production, the large-scale production of machines has expanded the physical space where the physical elements occur, and has become a small workshop. Big factory.

With the development of information technology and the in-depth application in the manufacturing field, in addition to the physical elements in the physical space, information/data, as a new production element, plays an increasingly important role in corporate activities. In the period of automated production, sensors, controllers (PLC) and actuators form a tightly coupled control information loop, which is systematically deployed on each mechanical component, thereby forming a "closed" information space attached to the equipment. The collection and calculation of information elements, and then control the automatic operation of the connected machine parts in the physical space. Entering the era of intelligent manufacturing, the application of digital intelligence technology maps and reconstructs the entity elements of different physical spaces in the same information space to form a digital twin with perception, analysis, decision-making, and execution capabilities, thereby realizing physical space and information The space is integrated in a broader and deeper level of interaction, creating a manufacturing system that integrates reality and virtuality, and dynamically configures element resources through a unified "software" platform.

What needs to be emphasized here is that due to the application of artificial intelligence technology, machine algorithms will replace the human decision-making process, forming a dynamic allocation of production factors such as resources, energy, and time, and continuously optimize the accuracy of the algorithm in the data feedback to improve the decision-making level. That is, the intelligent manufacturing system has the ability of self-perception, self-learning, self-decision, self-execution and self-adaptation compared with traditional manufacturing.

Feature 2: Networked collaboration, that is, through the establishment of a unified "dialogue" standard to open up "data islands" scattered at different levels, links, and organizations, allowing data to flow freely between different systems, so as to achieve all levels of enterprise manufacturing (vertical), And the interconnection and collaborative production of all links (horizontal) in the industrial chain.

Specifically, the first is to realize the real-time transfer and processing of R&D data and manufacturing data to the production site and manufacturing equipment by opening up the vertical data link of the enterprise layer, the execution layer and the equipment layer. The seamless connection between different system levels within the enterprise is promoted. The refined operation and flexible production of the enterprise; the second is to horizontally open up the business data sharing within the enterprise and between different enterprises in the upstream and downstream of the industrial chain, including R&D and design, material procurement, production and manufacturing, marketing and sales, logistics and warehousing, product services, etc. Each enterprise organization in China can allocate resources and optimize plans based on the shared information of the entire industry chain, flexibly organize production to cater to market changes, and shorten product manufacturing and innovation cycles.

Through vertical and horizontal data connection, the complete interconnection of equipment, workshops, factories, processes, materials, personnel, and the value chain of the industrial chain is finally realized, making the value transfer process from the traditional one-way chain of manufacturing to concurrent collaboration, through real-time data Perceive, transmit, analyze and process, and carry out dynamic resource allocation and network collaboration around user needs and the full life cycle of the product, so as to maximize personalized customization.

3.2 System Architecture of Intelligent Manufacturing

Based on the two core characteristics, let us understand the system architecture of enterprise intelligent manufacturing.

The integration of virtual and reality at the bottom layer is to digitize all production factors, supply chain links, technological processes, management activities, etc. of the physical space, including manufacturing carriers and manufacturing processes, through the construction of information infrastructure, and converge through network connection and transmission. On top of a unified data platform, combined with intelligent analysis technology to deeply explore the value of data, internally empower internal enterprise management platforms such as energy, resources, supply chain, orders, etc., and improve corporate management and operational efficiency; externally, industrial application development can be used The platform is open to third-party developers, and customized development of industrial applications is carried out in accordance with the needs of the application. It can also be used to deposit corporate capabilities/resources and open them to industrial chains such as financial institutions, logistics, e-commerce, etc. in the form of industrial service micro-component libraries. Used by upstream and downstream enterprises to improve the resource allocation efficiency of the overall industry through collaboration and cooperation, and respond to changes in the needs of end users.

Chapter 3 Intelligent manufacturing reconstructs the future of the industry

(1) Current Status of Intelligent Manufacturing Development

Due to the dual promotion of digital intelligence technology development and industrial policy dividends, China's intelligent manufacturing has entered a stage of rapid development.

The investment and financing market has grown steadily. Since the release of the "China Smart Manufacturing 2025" policy in 2015, smart manufacturing-related investments have begun to increase. In 2020, the investment and financing amount of my country's smart manufacturing industry is 25.261 billion U.S. dollars, and the overall financing amount has risen sharply compared with 2019. In 2020, although the number of financing is relatively Decrease, but the amount of a single financing has increased, reflecting that capital has begun to concentrate on leading outstanding companies. According to government reports and statistics, since the 13th Five-Year Plan, the digital, networked and intelligent level of my country's manufacturing industry has been significantly improved through multiple measures including pilot demonstration applications, system solution supplier cultivation, and standard system construction.

The supply capacity has been continuously improved, the domestic market satisfaction rate of smart manufacturing equipment has exceeded 50%, and there are 43 system solution suppliers with main business income exceeding 1 billion yuan. The supporting system has been gradually improved, an internationally advanced standard system has been established, 285 national standards have been issued, 28 international standards have been formulated, and more than 70 industrial Internet platforms with certain influence have been cultivated. The promotion and application have achieved obvious results. The production efficiency of pilot demonstration projects has increased by 45% on average, the product development cycle has been shortened by 35%, and the defective product rate has been reduced by 35% on average. Discrete intelligent manufacturing, process-based intelligent manufacturing, network collaborative manufacturing, and large-scale New models and new formats such as personalized customization and remote operation and maintenance services. 2 According to the statistics of the Foresight Research Institute, the output value of my country's smart manufacturing industry will reach 2.5 trillion yuan in 2020.

The development prospects of China's intelligent manufacturing are promising, but there is still a big gap between my country and developed countries, which is mainly reflected in the following aspects:

One is that key technologies, core components/equipment, and high-end industrial software are constrained by others. my country's nearly 90% of chips, 70% of industrial robots, 80% of high-end CNC machine tools, and more than 80% of core industrial software rely on imports3, resulting in high costs of intelligent transformation of domestic manufacturing enterprises and restricting the overall progress of my country's intelligent manufacturing . Taking industrial software as an example, my country’s aircraft, shipbuilding, metallurgy, chemical industry, biomedicine, electronic information manufacturing and other key areas have long relied on foreign industrial software. Among them, EDA is basically monopolized by Cadence, Mentor and Synopsys of the United States, and CAE/CAD is mainly used by American ANSYS, Germany SIMENS, France DS Simul, etc. control.

The second is the relative lack of system integration capabilities. my country's intelligent manufacturing system solution supply capacity is insufficient. The business form is mostly to purchase complete robots from abroad, and then formulate solutions according to different needs. There is a lack of competitive system integrators like Siemens and GE.

Third, the informatization foundation of small and medium-sized manufacturing enterprises is weak, and it is difficult to integrate into the wave of intelligence. Small and medium-sized enterprises constitute the main body of my country's industrial manufacturing. Due to various factors such as weak information technology, insufficient self-owned funds, and lack of relevant talents, digital transformation is facing great trial and error costs and uncontrollable risks. Large and medium-sized enterprises in the industry are relatively large. The "digital divide". If we take German Industry 4.0 as the frame of reference, my country's manufacturing industry as a whole is still in the industry 2.0 stage, and some companies are moving towards the 3.0 stage.

(2) Development Trend of Intelligent Manufacturing

2.1 Trend 1: Data-driven production flexibility

The essence of flexible production is to quickly restructure resource elements to respond to new manufacturing needs, while intelligent manufacturing systems convert resource elements and their process status into digital information, and efficiently configure these resource elements through algorithm optimization. Realize data-driven flexible production.

For example, in the product research and development process, companies obtain real-time end-user interaction data, and realize "production based on demand" through analysis and prediction; in the product manufacturing process, collect real-time data from the entire production process through the Internet of Things and sensors, and integrate the data from upstream and downstream The user's data information is transmitted to the industrial Internet data platform, and the artificial intelligence then relies on the data to perform intelligent analysis, and finally formulates the best production plan, and transmits the instructions to the manufacturing line to achieve flexible production.

The trend of flexible production driven by smart manufacturing is particularly obvious in the field of consumer goods manufacturing, because the field of consumer manufacturing is the closest to users. For manufacturing companies that have the characteristics of "small batches, multiple varieties, and customization" such as automobiles, 3C products, clothing, and food, One of the main goals of the intelligent upgrade is to achieve flexible production, which can quickly and accurately meet the individual needs of end users, and the intelligent wave led by the consumer product manufacturing field is gradually transmitted to the upstream links, and then driven The entire industry chain is based on the trend of data-driven flexible production.

2.2 Trend 2: Industrial interconnection supported by platforms

More and more industry leaders and Internet giants are increasing investment in the industrial Internet. In addition to accelerating their own digital transformation, these companies open their own practical experience and ability endowments on intelligent manufacturing to small and medium-sized enterprises in the same field through platform construction. Enterprises, as well as relevant entities upstream and downstream of the industrial chain, form an important support for the intelligent upgrade of the entire industry.

According to statistics from the Ministry of Industry and Information Technology, my country's industrial Internet has been widely used in many industries such as steel, construction machinery, aerospace, home appliances, electric power, ports, energy, etc. There are more than 70 industrial Internet platforms with certain industry influence, such as XCMG Information The Xrea platform of Haier, the COSMOPlat platform of Haier, the smart platform of Yonyou Software, the Tianyi Cloud Industrial Internet platform of China Telecom, the supET platform of Alibaba Cloud, etc.

These platforms converge and share common resources such as design, production, logistics, and effectively integrate data resources such as product development, manufacturing, operation management, and services, and provide “low-cost, fast deployment, easy operation, and maintenance” to small and medium-sized enterprises in vertical fields. The lightweight application of "strong security" greatly reduces the threshold of use and the cost of intelligent transformation, accelerates the digital transformation process of small and medium-sized enterprises, so as to realize the connection and collaboration and data sharing between enterprises on the platform, and promote the intelligent upgrading of the overall industry.

2.3 Trend 3: User-centric intelligent manufacturing service

The integration of manufacturing and service industries is one of the main trends in the development of intelligent manufacturing. From the perspective of intelligent manufacturing, intelligent products embedded with digital intelligence technology can perceive changes in the surrounding environment, and through continuous interaction with users and the environment, automatically return operating data and status information to the enterprise platform, combined with intelligent analysis, enterprises can Real-time grasp of product usage and user needs changes, and respond in a timely manner, proactively provide users with high value-added service experience, through the "hardware products + software system + value-added services" model to meet users' individualized and diversified needs, Create a new value space. Product remote operation and maintenance service is a typical intelligent service mode of manufacturing enterprises. Enterprises use digital intelligence technology to collect and return real-time multi-dimensional data such as equipment status, operation operation, and environmental conditions of intelligent products in use. Based on the above data The analysis results provide users with services such as daily operation and maintenance, predictive maintenance, fault warning, diagnosis and repair, and remote upgrade of the product.

(3) Intelligent manufacturing industry ecology

The promotion of intelligent manufacturing is a long-term and gradual process. In addition to common issues such as talents, network security, and technical standards, my country is also facing poor reliability of intelligent manufacturing equipment, key technologies are restricted by people, and core components and industrial software mainly rely on imports. There are many challenges such as insufficient integration capabilities and weak overall manufacturing information infrastructure. Only to actively adapt to the development trend of intelligent manufacturing, actively play the guiding role of the government, and take enterprises as the main body to promote the "production, study, research and application" quadrilateral linkage to accelerate the cultivation of the intelligent manufacturing industry ecology In order to promote the high-quality development of intelligent manufacturing.

3.1 Create a new innovation carrier, strengthen "intelligence" to create new kinetic energy

Through the construction of a number of national and provincial manufacturing innovation centers and other carriers, we will carry out research and development of key common technologies, accelerate the construction of an intelligent manufacturing innovation system, and create new momentum for the "intelligence" of the ecological development of the intelligent manufacturing industry. The manufacturing innovation center is "a new type of innovation carrier established by enterprises, scientific research institutes, universities and other innovation entities voluntarily and independently, with enterprises as the main body and in the form of an independent legal person." Its purpose is to "complete the activities of all links in the innovation chain from technology development to transfer and diffusion and first commercial application, and create a cross-border collaborative innovation ecosystem." Since 2016, the Ministry of Industry and Information Technology has successively issued the "Manufacturing Innovation Center Construction Project Implementation" Guidelines (2016-2020)", "Guiding Opinions on Improving the Manufacturing Innovation System and Promoting the Construction of Manufacturing Innovation Centers", "Conditions for the Upgrade of Provincial Manufacturing Innovation Centers to National Manufacturing Innovation Centers", "National Manufacturing Innovation Guiding documents such as the Center Assessment and Evaluation Management Measures (Interim), the “General Layout of the National Manufacturing Innovation Center Construction Field (New in 2018)” and other guiding documents have gradually formed a policy system for the top-level design of the manufacturing innovation center. The standardization of construction put forward requirements. As of 2020, my country has established 15 national manufacturing innovation centers and 132 provincial manufacturing innovation centers, focusing on five key technical fields, including basic materials, core devices, key processes, major equipment, and software.

3.2 Carry out demonstration of smart manufacturing applications to help the industry to "smart" upgrade

Focus on the transformation and upgrading needs of enterprises, regions, and industries, carry out multi-scenario, full-chain, and multi-level application demonstrations around factories, enterprises, supply chains, and industrial chains, cultivate and promote new models of smart manufacturing, build a smart manufacturing industry ecology, and help the industry" Wisdom” upgrade.

The first is to focus on the key links of the manufacturing process. In regions and industries with good basic conditions and urgent needs, select leading companies in the industry to carry out demonstration projects of smart scenes, smart workshops, and smart factories, summarize and form effective experiences and models, and then focus on design and research and development. The whole life cycle of production, logistics, service, etc., select and determine a group of benchmark companies, transplant and promote the experience and models formed in related industries; at the same time guide the "chain master" companies to build a supply chain collaboration platform, and drive the simultaneous implementation of upstream and downstream companies Intelligent upgrade.

The second is the rolling selection of a cross-field and cross-industry comprehensive industrial Internet platform as a benchmark representative of industrial Internet technology breakthroughs and application empowerment, while supporting the development of industry/regional platforms, building a characteristic industrial Internet platform for key industries/regions, and driving changes. Multi-agents participate in the construction of the platform, accelerate the progress of the industrial Internet platform, give play to the empowerment role of the platform to small and medium-sized enterprises, and drive the overall intelligent upgrade of the industry.

Up to now, the Ministry of Industry and Information Technology has released a total of 15 "double-span" industrial interconnection platforms. In terms of application empowerment, the average number of registered users of the platform has reached 1.4 million, and a total of more than 80,000 industrial enterprises have been empowered, covering steel, petrochemical, energy, and electric power. There are more than 10 key industries; there are more than 70 platforms with certain industry and regional influence, the number of connected devices exceeds 40 million sets, the number of industrial apps exceeds 250,000, and the platform empowerment effect is further manifested.

The third is to cultivate a number of intelligent manufacturing demonstration bases, parks, and pilot areas, gather talents, scientific research, and industrial resources, gradually improve the intelligent manufacturing industry chain, promote industrial scale and agglomeration development, and center on the base, radiate and drive certain areas / Upgrade of the intelligent manufacturing industry within the scope.

3.3 Consolidate the basic support of intelligent manufacturing, and do a good job of new guarantee for "intelligent" manufacturing

Aiming at the development trend of smart manufacturing, we will improve the development foundation of standards, information infrastructure, security and other factors, and strengthen the support of factors such as finance, taxation and finance, and talent reserves to build a guarantee for the ecological development of the smart manufacturing industry.

"Intelligent manufacturing, standards first." Standardization work is an important technical foundation for intelligent manufacturing, including the construction of industry application standard systems in subdivisions, increasing the development of basic common and key technical standards, and promoting the promotion and application of standards. Since 2015, my country's intelligent manufacturing standard system has been continuously adjusted, improved and perfected in accordance with the development process of intelligent manufacturing. According to statistics from the Ministry of Industry and Information Technology, during the "13th Five-Year Plan" period, my country has issued 285 national standards for smart manufacturing and led the formulation of 47 international standards, covering the entire manufacturing process of enterprises. my country has entered the advanced global smart manufacturing standard system.

"Smart manufacturing, based on digital intelligence." Including the construction of network infrastructure represented by 5G, as well as the construction of computing power infrastructure such as data centers and smart computing centers, it is the basis for supporting the application of digital intelligence technology. However, due to the large investment and long construction period, the government needs to drive and organize social resources for early, large-scale deployment and construction. my country’s current 5G construction scale is currently leading the world. According to statistics from the Ministry of Industry and Information Technology, the cumulative number of terminal connections has exceeded 200 million, and a total of 718,000 5G base stations have been built and opened; the construction of computing power infrastructure has accelerated, but the current manufacturing utilization rate is relatively low. Accounted for 3%.

"Intelligent manufacturing, security is the soul." Intelligent manufacturing with "virtual and real integration" and "networked collaboration" as its core features will inevitably face information and network security challenges. In 2016, China's "Cyber Security Law" was issued, establishing industrial control security as an important prerequisite for the country to promote intelligent manufacturing; in 2018, the Ministry of Industry and Information Technology issued the "Industrial Control System Information Security Action Plan (2018-2020)", which proposed "one network, one database, three Platform” (i.e. online monitoring network, emergency resource library, simulation test, information sharing, information notification platform), focusing on situational awareness, security protection, and emergency response capability support system construction; in 2019, ten departments including the Ministry of Industry and Information Technology issued the “Regarding the Issue of Strengthening the Industrial Internet "Notice of Guiding Opinions on Security Work", proposed in the four aspects of "equipment and control security, improving network facility security, strengthening platform security, establishing and improving industrial APP pre-application security detection mechanism, and strengthening user information and data security protection in the application process" Construction requirements.

"Intelligent manufacturing, both human and financial." In terms of talent supply, it focuses on the training of cross-border talents in manufacturing and the digital intelligence industry, including promoting the vocational training system that integrates production and education, promoting the upgrading of the technology and knowledge structure of employees, and promoting the construction of new science and technology. , Strengthen the construction of related disciplines and curriculum systems, and improve the echelon training of talents in key areas of intelligent manufacturing. In terms of capital supply, in addition to special fund support and targeted tax incentives, the government encourages social capital to participate and increase investment in the field of smart manufacturing, and guide financial institutions to provide medium and long-term loan support for the intelligent transformation of enterprises, and the development is in line with the characteristics of smart manufacturing. Innovative financial products such as supply chain finance and financial leasing to broaden financing channels and reduce financing costs.

3.4 Give full play to the role of the main body of the enterprise, and build a new ecology of "smart"

Give full play to the main role of enterprises in the development of intelligent manufacturing, especially the leading and enabling role of leading enterprises in the promotion of intelligent manufacturing. Leading companies have strong technical, market and financial capabilities, and act as "chain masters" or system integrators in the industry chain. They are a key force in the ecological development of the smart manufacturing industry, highlighting the development of integrated innovation, engineering applications, and The main status of industrialization and pilot demonstrations, guiding and supporting them to grow and grow in practice is the key to building an intelligent manufacturing industry ecosystem.

At the same time, guided by market demand, with enterprises as the main body, through the combination of "production, study, research and application" and open platforms, the industry will maximize the concentration of advantageous resources in the industry, promote the incubation and transformation of innovation results, and promote the sustainable growth of "smart" manufacturing ecology . For example, one of the key tasks mentioned in the "Fourteenth Five-Year Plan for Intelligent Manufacturing Development" is to unite software companies, equipment manufacturers, users, and research institutes to jointly develop an integrated industrial software platform for sub-industries. Or system integrator and user interaction innovation, development of scenarios-oriented solutions, etc., is one of the initiatives around this direction.

Chapter 4 Digital Intelligence Technology X Intelligent Manufacturing Practice

(1) Analysis of typical application cases

1.1 Simulation technology drives design to improve R&D efficiency

The application of simulation technology in the manufacturing field is mainly in the R&D and design process. The entire manufacturing process is transferred to a virtual environment for "reproduction", and the best structure and configuration scheme are repeatedly tested in the virtual environment, so that all work can be made in decision-making. It is completed before the cost is determined, which greatly reduces the trial and error cost of the manufacturing enterprise's R&D link, shortens the R&D cycle, and greatly improves the product R&D efficiency. With the development of digital intelligence technology, the application scenarios of simulation technology are constantly enriched and expanded, especially in high-end manufacturing fields, including aerospace defense, aerospace, automotive, equipment manufacturing, electronic high-tech, etc. The application of simulation technology continues to deepen.

1.2 Intelligent vision liberates hands and releases manufacturing vitality

Intelligent vision is of great significance in the production process of industrial automation. Industrial robotic arms with embedded intelligent vision systems can complete positioning, grasping, sorting and assembly tasks faster, more accurately, and more flexibly. It is liberated from serious danger and heavy work, improves the efficiency of the production line, and can also greatly improve the degree of flexibility in production. At present, it is mainly used in the fields of manufacturing and logistics.

1.3 Intelligent visual inspection improves production line quality control efficiency

Quality is one of the core competitiveness of manufacturing companies, and companies have increasingly higher requirements for product quality. However, products sometimes have surface defects during the manufacturing process. How to conduct efficient quality control to avoid surface defects has always been one of the thorny issues facing manufacturing companies. Traditionally, manual inspection is mainly used for inspection. Due to the low sampling rate, poor real-time performance, and subjective factors such as the experience of the inspector, fatigue status, etc., the stability of the inspection results is often not high, the accuracy cannot be guaranteed, and defects and missed inspections are easy to occur. It is difficult to adapt to efficient production and quality requirements.

With the development of digital intelligence technology, the application of machine vision-based surface defect detection can be carried out in practice, which greatly improves the quality control efficiency of the production line and avoids factors such as operating conditions and subjective judgments that affect the accuracy and stability of the detection results. In the product manufacturing process, real-time detection of surface defects in each link and each product can be realized, and surface defects and defects of products can be identified more accurately and quickly. At the same time, it also saves the manual input of manufacturing enterprises in the quality inspection link and reduces Labor cost expenditure. Currently, it is widely used in electronics, packaging, printing, chemical, food, plastic, textile and other manufacturing fields.

1.4 Intelligent operation and maintenance help ensure the stable operation of industrial equipment

The intelligent manufacturing system is highly complex, and has high requirements on the reliability of the equipment, and the requirements for equipment maintenance will increase accordingly. At present, the industrial manufacturing field mainly relies on manual operation and maintenance, and there are two major problems: one is the shortage of professional operation and maintenance personnel, the cost is high, and the operation and maintenance mainly depends on the experience of personnel, and the reliability is difficult to guarantee; the second is that sudden equipment failures often occur, resulting in The temporary interruption of production caused economic losses. The intelligent operation and maintenance combined with digital intelligence technology can help the stable operation of industrial manufacturing equipment, improve the real-time monitoring of equipment, the accuracy of fault judgment, the timeliness of maintenance and management, and the realization of proactive preventive maintenance.

1.5 Industrial Internet platform empowers agile response to customized production

The industrial Internet platform directly connects the production process and consumers, can support low-cost flexible production, and carry out large-scale customization according to the individual needs of users, thereby changing the traditional standardized design, mass production, and homogeneous consumption mode, and realizing new The value creation model of customized design, more varieties and less batch production, and personalized consumption. The “new model” can quickly organize production and respond quickly to changes in the market, helping companies increase the added value of their products and enhance the level of differentiated competition. Haier's COSMOPlat platform is a typical representative of the customized production model.

(2) Digital Intelligence Technology X Practice Differences in Different Manufacturing Fields

Different manufacturing fields have different characteristics, so there will be certain differences in the application of digital intelligence technology in the process of practice.

For labor-intensive manufacturing industries, low labor costs have always been regarded as the core competitiveness of such enterprises. The most typical ones are the processing and assembly industries such as household appliances and electronics. Due to the rising labor costs in recent years, it has become more and more difficult to recruit workers. The overall profit of the industry is constantly squeezed. Therefore, the main goal of the intelligent upgrade of the industry is how to use digital intelligence technology to reduce the dependence of production on labor and increase profit margins, including reducing the deployment of production line personnel, and solving various additional problems caused by labor, such as low retention rates. High training costs, quality loss caused by instability of personnel, etc.

For the capital-intensive manufacturing industry, the initial investment in fixed assets is relatively large. The most typical one is the automobile manufacturing industry. With the advent of the era of personalized consumption, in order to cater to changes in demand, how to introduce digital intelligence technology to improve intelligent production lines and improve The degree of flexible manufacturing and low-cost customized production are the core goals of the intelligent upgrade of such enterprises.

For technology-intensive manufacturing industries, such as aerospace, biopharmaceuticals, etc., they mainly rely on technological innovation to build market competition barriers. Generally speaking, the early R&D costs are very high, and the R&D cycle is long, and R&D risks are uncontrollable. The core demands of such enterprises That is, by introducing digital intelligence technology to reduce R&D risks and costs, and shorten the R&D cycle.

In addition, there is also a type of market-sensitive manufacturing industries, such as clothing, food and other fast-moving consumer goods fields. The product life cycle is very short, and users have the strongest awareness of innovation and change in related products. Therefore, the use of digital intelligence technology can quickly understand users. Demand, and rapid product innovation and iteration to respond to market changes are the important goals of this type of enterprise's intelligent upgrade.

Report producer/author: Shangtang Intelligent Industry Research Institute