What are the differences between "Industry 4.0" in different countries?

In 2015, after Premier Li Keqiang proposed the national strategy of "Made in China 2025", "Made in China 2025" was often mentioned on various occasions. At the same time, the "Industry 4.0" concepts of Japan, the United States, and Germany are often mentioned together with "Made in China 2025". Are they the same concept? With huge differences in industrial bases, what are the differences between them?

The relationship between big data and manufacturing

The relationship between big data and manufacturing can be represented by the following diagram, which has three important elements:

1. Problems: Linear or hidden problems in the manufacturing system, such as quality defects, lack of precision, equipment failure, processing failure, performance degradation, high cost, low efficiency, etc.

2. Data, obtained from the five elements of the manufacturing system, can reflect the process and cause of the problem. The acquisition of data should be problem-oriented, with the purpose of understanding, solving and avoiding problems.

3. Knowledge: The core of the manufacturing system, that is, know-how, including process, technology, design, flow and diagnosis. Knowledge comes from the process of solving manufacturing system problems, and big data analysis can be understood as a means of quickly acquiring and accumulating knowledge.

The relationship between big data and smart manufacturing can be summarized as follows: a large amount of data is generated during the process of problem generation and resolution in the manufacturing system. By analyzing and mining the data, we can understand the process of problem generation, the impact caused, and the solution method. When this information is abstracted and modeled and converted into knowledge, the knowledge is used to understand, resolve, and avoid problems. When this process can be carried out spontaneously and automatically, we call it smart manufacturing.

Problems and knowledge are the goals, while data is the means. In the above figure, if we replace data with people, it becomes "craftsmanship", "automated production lines and equipment" becomes "Industry 4.0", and "Internet+" becomes "Internet+".

The differences in the understanding, accumulation and inheritance of knowledge determine the different manufacturing cultures of different countries.

Japan: Continuously improving through organizational culture and human training, relying heavily on people to carry and pass on knowledge

The most important feature of Japanese manufacturing culture is to solve problems in the production system through continuous organizational optimization, cultural construction and human training. In the 1970s, Japan proposed a production management system with "TPM as the core", with the core idea of ​​full efficiency, full system and full participation, and the implementation method mainly improves in three aspects: improving work skills, improving team spirit and improving the working environment. After the 1990s, Japan chose "lean manufacturing" as its transformation direction.

The way Japanese companies solve problems is usually: when a problem occurs, people arrive quickly, check the scene, explore the reality, and solve the problem - analyze the cause of the problem and avoid it through improvement. Finally, knowledge falls on people, and when people's skills improve, their ability to solve and avoid problems also improves.

For Japanese companies, employees are the most important value. Trust in people is far greater than trust in equipment, data and systems. All automation and information construction is centered around helping people work, so Japanese companies never talk about replacing people with robots or unmanned factories. However, this culture has encountered huge challenges in recent years. Japan's aging population has led to a large shortage of young manufacturing talents, and no one can pass on knowledge. Japan is aware of its lack of data and information. In the structure and goals of Japan's industrial value chain industry alliance, 7 of the 19 work items are directly related to big data.

Japan's transformation strategy is a helpless move to deal with its demographic structure and social contradictions. It faces many challenges in the transformation process. The first is the lack of data accumulation. The second is the lack of software and IT technical talents caused by the conservative culture of Japanese industrial enterprises.

Germany: Through the continuous upgrading of equipment and production systems, knowledge is solidified in the equipment

Germany's advanced equipment and automated production lines are world-famous. At the same time, the Germans' rigorous style and unique "apprenticeship" higher education model make the German manufacturing style very pragmatic. However, Germany also faced the problem of labor shortage very early, and Germany had to make up for this deficiency by developing more advanced equipment and highly automated production lines.

Germany's logic for solving problems is: problems occur - people solve them - knowledge and processes for solving problems are solidified into equipment and production lines - similar problems are automatically solved or avoided. In addition to pursuing automatic problem solving on production lines, Germany also tries to reduce human influence as much as possible in terms of enterprise management. The best software suppliers for enterprise resource planning (ERP), manufacturing execution system (EMS), automatic scheduling system (APS) and other software are all from Germany. A large amount of information entry and plan generation and tracing are completed through software to minimize the uncertainty caused by human factors.

Germany also lacks accumulation of data collection, because the German manufacturing system has a zero-tolerance attitude towards faults and defects, and once a problem occurs, it will be solved once and for all through equipment improvements. Due to the high degree of automation and integration of the production line, the overall equipment efficiency is very stable, and there is relatively little room for data optimization.

Germany has gained huge economic returns from equipment exports, but most industrial products can only be sold once, and one less customer means one less customer. At the same time, the equipment manufacturing and industrial capabilities of developing countries have risen in the past two years, and Germany's market has been continuously squeezed. From 2008 to 2012, Germany's industrial exports barely grew. So Germany proposed the Industry 4.0 plan, behind which Germany has accumulated only the system foundation in the manufacturing system, and at the same time, provides German manufacturing knowledge to customers in the form of software or toolkits as a service to increase holdings, so as to achieve continuous profit from customers. The core element of the German Industry 4.0 design framework is integration.

The United States: Gaining new knowledge from data and immigration, and excelling at subverting and redefining issues

The United States pays the most attention to the role of data in solving problems. Customer demand analysis, customer relationship management, quality management, equipment health management, supply chain management, service life management and other aspects all rely heavily on data. American companies generally choose the 6-sigma system that relies heavily on data. The United States also proposed the concept of "product life cycle management (PLM)" in the early 21st century. The core is to manage all product-related data throughout the life cycle. The management object is product data, and the purpose is to provide value-added services throughout the life cycle and achieve a closed-loop data design at the design end.

In addition to using knowledge to solve problems, the United States is also good at using knowledge for disruptive innovation, thereby redefining problems. For example, in the aviation engine manufacturing industry, to reduce engine fuel consumption, most companies will solve the problem from the perspectives of design, materials, processes, and control optimization. However, General Electric found that aircraft fuel consumption is closely related to pilot driving habits and engine maintenance, so it jumped from the manufacturing end to the operation and maintenance end to solve the problem, and the effect was more obvious than the improvement on the manufacturing end.

China: Lean system chosen, lack of data accumulation

After 2000, most of the quality and management reforms in China's manufacturing industry have chosen the lean system. This is partly because of the similarities between Chinese and Japanese cultures, and more because Chinese companies generally lack data accumulation and information technology foundations. This problem still exists.

The position of the value chain in manufacturing is a decisive factor in competitiveness, and countries have different value chain layouts.

In production activities, the distribution of value elements from upstream to downstream is: idea innovation and demand creation, raw materials and basic enabling technologies, key equipment and core components, production process and production system, products and services. In the distribution of elements in the entire value chain, China has advantages in the production process and production system (mainly reflected in labor costs and production capacity), but is at a disadvantage in other links. Different countries have different distribution and layout in the value chain.

The United States: Firmly occupying the upstream of production factors and striving to extend to the downstream

In the distribution of production factors, the United States has obvious advantages in idea innovation and demand creation, raw materials and enabling technologies, and product value-added services. The core competitiveness of the U.S. industrial system comes from the 6S ecosystem: space/aerospace, semiconductors, shale gas, smart ICT service, Silicon Valley spirit, and sustainable talent pool.