In the era of industrial Internet, automobile enterprises build a new C2M manufacturing model.

In recent years, the pressure of automobile sales has been increasing, consumers' right to speak has been improved, and the demand for personalized and intelligent services has been increasing. The era of "fixing sales by production" in the automobile industry has passed, and "user-driven manufacturing" mode has become a "necessary option" for the transformation of automobile enterprises.

With the goal of collaborative flexibility and intelligent manufacturing, the automobile industry promotes the digitalization of production and realizes the integration of order, design and manufacturing and the integration of cloud, number and intelligence. In this process, how to get through the digital channels and realize the data transparency of the whole production process and the agile response of production decisions has become the same topic facing the industry.

Since 20 18, the automobile industry has entered the era of fierce stock competition, and the demand for reducing costs and increasing efficiency at the production end has become more prominent. Through the intelligent transformation of production lines, major car companies have improved production efficiency and avoided risks. But at present, the use of automation equipment, the improvement of manufacturing experience and the concept of lean manufacturing have greatly improved the production efficiency of the automobile industry, and the marginal effect is becoming more and more obvious.

In addition to lean production, it is difficult to go further. The more difficult problem is that mass-produced homogeneous automobile products have been difficult to meet the individual needs of users for automobiles; At the same time, in the face of changing market situation and diversified user needs, the traditional way of making production scheduling plan based on existing experience and market research has become more and more outdated, and the problems such as inventory backlog, mismatch of production resources and low implementation rate of production plan have become more and more prominent.

All the above drawbacks are forcing car companies to completely change the existing production process. A model called C2M (Consumer to Manufacturer) has been adopted and applied by the automobile industry to help automobile companies build up their ability to support information and flexibility, and provide small-scale personalized car purchase solutions for users.

In the past few years, the automobile industry has done a lot of practice in digital production. In the core production links such as stamping, welding, painting, final assembly and inspection, the industry automation rate has been constantly improving. Tesla Giga Shanghai, Chery Changshu and Geely Yuyao all announced that they have realized 100% automation in one or more links. However, while improving the automation rate, the data of each link has been in an island state for a long time, and the industry has not fully tapped the value of data assets precipitated in the production process.

In meeting the individual needs of users, some car companies have begun to try to customize products in small quantities. For example, SAIC Chase has introduced the Spider intelligent selection system, which allows users to choose the appearance, color, interior and configuration of their own vehicles. Lan Tu also announced that in the future, with the blessing of the industrial Internet, the number of people will change from 1,000 to 1,000.

Although the industry has made a lot of useful explorations, at present, the customization of vehicles by customers is still in the simple selection stage, lacking deeper participation channels. At the same time, scheduling difficulties, increased production costs, uncertainty of process flow and difficulties in coordinated response of supply chain brought by small batch production are all new problems faced by automobile enterprises in the process of production digitalization.

Previously, in the white paper "Digital Transformation of Automobile Industry" released by Tencent, the main challenges of automobile enterprises to promote digital transformation of production were summarized as two points. First of all, how to quickly convert multi-channel customer orders into factory production progress and deliver products to users in the shortest time; Secondly, how to ensure the quality of products in each link and finally off-line products through monitoring and intervention of the general welding workshop process in large-scale C2M production.

At the same time, the "White Paper" also proposes that based on the massive data processing capability of the cloud platform, it is an important means for car companies to meet the challenges of digital production by opening up digital channels that run through design, R&D and all production links, giving full play to the potential risks brought by the user-driven model, and minimizing the uncertainty in the production process and flow.

Specifically, it is the core goal of digital production to use digital factory to transform the traditional automobile manufacturing mode into flexible production mode, so as to quickly respond to consumers' individual needs and reduce all uncertainties and risks arising from meeting consumers' needs. To achieve these two points, it is necessary to achieve data transparency and agile decision-making.

As far as data transparency is concerned, through the effective connection, analysis and utilization of data generated in various production links such as inventory, manufacturing, logistics and quality inspection, the original extensive production mode can be changed, and the problems left over or undiscovered in the production links of automobile enterprises for a long time can be solved, so as to accurately control production efficiency and prevent potential risks.

The agility of decision-making can support automobile enterprises to truly realize the whole process of user-driven product design and manufacturing, support C2M flexible production, present materials, inventory, scheduling, logistics and other links in digital form and update them at any time, which will effectively solve the problems of demand fluctuation, material constraints, inventory constraints, capacity constraints and so on when making production plans.

For example, in August this year, Tencent and Geely's Wide Area Ming Dow jointly developed a large-scale flexible customization C2M suite to help manufacturing enterprises reconstruct the whole value chain from order to delivery, research and development to production. Through the high cooperation of market users, production enterprises and supply chain resources, the manufacturing enterprises will be promoted to realize the transformation from "product-centered" to "user-experience-centered", so as to seize the opportunity in the trend of increasingly personalized demand for products and services in the future.

Schematic diagram of Tencent Auto's direct connection user production integration solution

From the specific product architecture, Tencent C2M solution is based on leading cloud computing, big data, AI and other capabilities, and provides services for automobile and other manufacturing enterprises in the mode of "1+N". Among them, "1" stands for cloud service architecture, which can horizontally support the overall supply chain collaboration between upstream and downstream of automobile enterprises, vertically link intelligent and flexible factory production resources, get through all production links, and realize full closed-loop integration with supply chain. And "N" stands for freely combining services and "Lego" product modules according to different customers, different development stages and different business needs through n different product and service combinations.

In a word, under the new competitive situation, it is a new task to quickly respond to consumers' personalized needs, implement and transmit users' creativity, optimize user experience, and realize lean production and reduce costs and increase efficiency at the same time. Only by continuously digging deep into the value of data can we better realize "user-defined products" and make every order "customized" as scheduled.