Recently, deloitte touche tohmatsu released the "China smart manufacturing, stability and prosperity -- 2018 China smart manufacturing report", which surveyed more than 100 large and medium-sized enterprises from the automotive, equipment manufacturing, electronics and electrical appliances industries.The survey found that Chinese industrial enterprises have significantly improved their digital capabilities, and intelligent manufacturing has significantly increased its contribution to the generation of benefits. In addition, the demand for industrial robots in China is growing strongly, and China has become the largest consumer country.

The maturity of emerging technologies and the high acceptance of new technologies in China have obviously greatly promoted the pace of their application. Intelligent terminal products such as smart speakers are familiar to consumers, but the impact of intelligent manufacturing in industrial enterprises is still relatively unfamiliar to most people.In this article, euromonitor analyzed the deployment strategy of intelligent manufacturing in industrial enterprises by interpreting the deloitte report.

First of all, deloitte pointed out in the report that "intelligent manufacturing is based on the new generation of information technology, which runs through the design, production, management, service and other manufacturing activities. It is an advanced manufacturing process, system and mode with functions of self-perception of information depth, intelligent optimization and self-decision-making, and precise control and self-execution".

That is to say, intelligent manufacturing covers a series of processes from manufacturing intelligent products and providing services with intelligent technologies to utilizing intelligent technologies in the production process.Now, the reconstruction of global value chain and the adjustment of division of labor have forced enterprises to layout intelligent manufacturing to protect their position.As the forerunner, traditional manufacturing enterprises have upgraded their intelligent transformation to a strategic position. Strategic cooperation has been carried out among major technology companies to provide systematic solutions.There is no doubt that China's intelligent manufacturing has entered a period of high-speed growth, and industrial enterprises have begun to make efforts in intelligent manufacturing.

Deloitte surveyed companies on their priorities for smart manufacturing deployment and identified five priorities: digital factories, deep excavation of equipment and user value, industrial Internet of things, business model restructuring, and artificial intelligence.

digitalized factory

Intelligent manufacturing takes digital as the core driving force, and data collection undoubtedly plays an important role in various manufacturing processes.Therefore, whether it is production data, product data or the most upstream supplier data accumulation is the main task of the enterprise.Through the accumulation of data and the support of information technology, the realization of the series of each link in the manufacturing process can quickly establish the appropriate model and provide guidance for the production and manufacturing of enterprises.Through the establishment of this digital factory, enterprises can improve the accuracy of decision-making.

In the construction of digital factories, it is of great significance to get through the guidance of data flow and real-time observation of data changes: through data changes, the production process can be optimized, the collaboration of business, process and capital flow can be realized, and resources can be rationally allocated.At present, up to 62% of the surveyed enterprises have got through the data flow from production planning to execution and then to field equipment.In the field of aerospace, due to the precision manufacturing and strong quality control, the construction capacity of digital factories of enterprises in this field is generally high.

Equipment and user value in-depth mining

Equipment value mining refers to the improvement of product and equipment related service benefits in each link.For example, to develop and design more intelligent products, provide financial services related to equipment in the sales stage, collect and monitor data based on products in the after-sales stage, so as to conduct performance analysis and explore more service opportunities.User value mining is to meet the customer's personalized needs as the core, to further reduce costs and improve efficiency.A typical example is the C2M model, which is different from the previous enterprise manufacturing before sales. Before the enterprise r&d and design, the C2M model receives the scattered and personalized needs of users. Through targeted orders and manufacturing, it not only better meets the needs of users, but also greatly reduces the waste of intermediate links.

Now, in industries where competition is more intense and pricing is more transparent, digging deep can generate new sources of value.According to the report, 62% of enterprises are actively deploying equipment and user value in-depth mining.

Industrial Internet of things

In fact, faced with the high cost of cloud deployment, enterprises are generally not enthusiastic about cloud deployment before they fail to create core value.The same is true of domestic industrial manufacturing.At present, the perception, analysis, decision-making and other capabilities to be realized in the system of intelligent manufacturing all involve relevant technologies of the Internet of things, forcing enterprises to start collecting data by using sensors and the platform of the Internet of things, and making further use of big data capability analysis.Its application scenarios range from device monitoring and management to understanding how products are used to innovative services.

At present, 47% of enterprises are deploying industrial cloud, among which sensor and platform applications are most popular in the electronic and electrical industry, which we guess is mainly due to the need to establish closer contact with consumers in this field.

Restructuring future business models

In addition to realizing intelligent processes to help enterprises reduce costs and increase efficiency, intelligent manufacturing can also be applied to the innovation of enterprise products and services.Moreover, the constant influx of highly innovative startups is also challenging the status of traditional enterprises.Therefore, enterprises need to reconstruct their business models and realize value.

At present, the business model of industrial enterprises mainly focuses on platform type, large-scale customization, "product + service" and intellectual property.Platform business model is based on vertical industry, provide a variety of software services, build ecosystem;Scale customization mode refers to the horizontal expansion of the business field;"Product + service" refers to the exploration of new solutions based on customer needs;The intellectual property model refers to the application of patents to build technical barriers and occupy the market.Among them, the number of enterprises with the first three strategies is relatively large, reaching 30%, 26% and 24% respectively.But the challenges vary.

Artificial intelligence (ai)

The impact of artificial intelligence on industry mainly comes from two aspects: through the application of artificial intelligence in the production process, the intelligent control of quality, improve production efficiency;Second, the research and development of innovative products and services.

At present, the degree of automation in the domestic manufacturing industry is getting higher and higher, and the number of robot applications is the highest in the world.By further realizing machine learning and combining with big data, the coordination degree of production line can be improved and production problems can be reduced.Such as process automation, quality monitoring.By completely subverting its own products and services, and deeply combining with artificial intelligence technology, it is more competitive to design products with perception and judgment ability.Typical examples are autonomous driving.

According to the survey, more than half of the enterprises surveyed have used AI in manufacturing and management processes, and 46% have plans to manufacture AI products and services. In the industry distribution, the proportion of AI deployment in the field of automotive and automotive parts is much higher than other areas.

In fact, we can also see that more and more AI applications are being explored, and the market acceptance is optimistic. In recent years, AI has become a high-frequency term for entrepreneurship and capital market, and many unicorns have appeared in the field of AI. Moreover, the revenue of AI enterprises is increasing year by year. If we can jump out of the limitation of automated robots and boldly try more AI products and application scenarios, the development of AI will maintain this high-speed growth probability.