On the way to Industry 4.0, what do we expect for China's manufacturing industry in 2020?
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The National Bureau of Statistics announced that China's Manufacturing Purchasing Managers' Index (PMI) was 50.2% in December, which followed the same path as the previous month.
There are two main characteristics that stand out in December's PMI: first, production continues to accelerate, and demand continues to expand. Affected by factors such as the approach of traditional holidays, the supply and demand ends of the manufacturing industry are relatively active.
The chart below shows the investment activities of the smart manufacturing industry in 2019. Sub-sectors include robotics, 3D printing, sensors, drones, semiconductors, commercial space and other hardware.
Among all those primary market investments, about 56% of activities in 2019 were before (and include) Series A, but this proportion had decreased by 12% compared to 2018, and more startups went to Series B and C.
Known as the 'the world's factory,' China has surpassed the US since 2011 to become the top nation in terms of manufacturing output, and the manufacturing value-added takes about 30% of China's GDP. As this industry has a special hold of the domestic and global economy, most manufacturing activities are highly repetitive and involve predictable physical work, with less technological leverage. The regulator has published policies to push the transformation, industry insiders are more eager to build a 'digital factory' and achieve smart manufacturing.
China's smart manufacturing equipment industry started late. International manufacturers have occupied most of the market share based on their technology and first-mover advantages. The industry's dependence on external development is still high. In recent years, with the strong support of the national strategy for the development of this industry and the continuous R&D investment of domestic enterprises, a few outstanding intelligent manufacturing equipment manufacturers have emerged in China.
The profit margins of low technology-based producing activities are relatively low, which means the manufacturers may not able to reinvest in transformation and system upgrades.
The average ROE of China' s listed manufacturing companies in 2018 was about 4.25%, and the median figure was 7.38%. In addition, due to the project implementation has a certain period of time, smart manufacturing equipment suppliers need to pay more costs in the early stages of the project. If multiple large-scale projects are jointly promoted, funding pressure will be greater.
At the same time, the scale of enterprises in China's intelligent manufacturing equipment industry is generally small, brand awareness is low, and the ability to resist risks is relatively weak. The accumulation of technological research，and the promotion of strategic R&D projects require a large amount of financial support. If they rely mainly on their own accumulation to invest, it is not conducive to the rapid growth of companies with technological competitive advantages.
Machine vision is a great example of how AI is being leveraged in the manufacturing industry. Acquiring, processing, analyzing and understanding digital images through optics and sensors and using it in industrial environments elevates the automation and flexible manufacturing system. In 2016, the market size of the machine vision sector in China was CNY 303 million, which contributed 9.6% of the global market share.
Generally speaking, machine vision replaces the human eye with a machine, but its functions are not only the reception of information but also the processing and judgment of information, like our brain. The machine vision technology mainly uses a multi-angle light source (visible light, infrared light, X-ray, etc.) suitable for the object to be measured and a sensor to acquire an image of the detected object, and extracts information from the image through a computer for analysis and processing. A typical machine vision system includes a light source, lens, camera, image capture card, image processing software, and so on.
To better empower the manufacturing, the requirements for the cameras and optic elements are high. It is necessary to screen the frame rate, resolution and other indicators of industrial cameras according to requirements.
Meanwhile, smart manufacturing system solutions will promote the overall development of China's manufacturing industry. China's smart manufacturing solution market benefits from the country's vigorous efforts to promote the development of smart manufacturing and the Industrial Internet, and manufacturing core process technologies are set to accelerate breakthroughs and keep a high growth rate in 2019. It is expected that the scale of China's smart manufacturing solutions market will exceed CNY 230 billion by 2020. At the same time, the steady advancement of the smart manufacturing system solution market has also driven the booming development of suppliers. Many different types of suppliers have emerged in the fields of industrial automation, industrial software, smart equipment, and integrated solutions.
The Internet of Things (IoT) touches every business in the world today and manufacturing is no exception. McKinsey estimates that by 2025 the total economic impact of the IoT within factories will be up to $3.7 trillion per year. The Industry 4.0, Smart manufacturing and the Industrial Internet of Things (IIoT) are all ways of describing the intersection of operational technology and the information technology to monitor physical processes within manufacturing and use data to make predictive, corrective and adaptive decisions to improve operational cost.
Industry 4.0 refers to the 4th Industrial Revolution, specifically the move toward smart manufacturing. The National Institution of Standards and Technology defines smart manufacturing as fully integrated, collaborative manufacturing systems that respond in real-time to meet changing demands and conditions in the factory, in the supply network, and in customer needs.
In a smart factory, there is no paper anymore, people only interface with equipment and equipment interfaces with other equipment. Data is connected with SCADA (supervisory control and data acquisition, a computer system for gathering and analyzing real-time industrial data), MES, ERP2, and cloud-based analytics.
In the meantime, there will be no more supervisors making decisions based on data yesterday, they are making decisions based on real-time metrics, and applying better Big data analytics, such as predictive maintenance and much bigger things, like machine learning.
Both of the above require a high level of digitalization. According to Accenture's research, based on a sample of 170 listed companies in the six major manufacturing industries in China, the chart below has shown that the overall level of digitalization of Chinese companies is currently low (full score is 4), and there is still much room for improvement in the four major areas of digitalization.
Compared to process manufacturing, discrete manufacturing in China has a relatively higher digitalization level. Process manufacturing relies on creating formulas or recipes to produce a product, whereas discrete manufacturing assembles parts in a prescribed process to produce a distinct item. Leaders in process manufacturing are the players with the most potential to have smart factories in the future.
Compared with the traditional manufacturing industry, the demand for high-quality talents in the intelligent manufacturing industry is more obvious, and the need for compound talents who need to understand a lot of knowledge and skills is even more urgent. Relevant professionals are required to have a combined knowledge background in mechanical, electrical, optical, and informatization, a deep understanding of downstream industries, and rich implementation experience. Further transformation brings a huge demand for high-end professional talents. There is a serious shortage of talents and it is difficult to meet the expansion needs of the intelligent manufacturing field.
Broadly, it is notable how essential but difficult it is for manufacturers to transform. But undoubtedly, smart factories encapsulate a range of transformational digital technologies that can help organizations improve efficiency, reduce cost or, in their widest application, create new business models that can drive competitive advantage.
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